Elimination Problem Research Articles

Estimates on front propagation for nonlinear higher-order parabolic equations: An algorithmic approach

We present an algorithm for the derivation of lower bounds on support propagation for a certain class of nonlinear parabolic equations. We proceed by combining the ideas in some recent papers by the author with the algorithmic construction of entropies due to Jüngel and Matthes, reducing the problem to a quantifier elimination problem. Due to its complexity, the quantifier elimination problem cannot be solved by present exact algorithms. However, by tackling the quantifier elimination problem numerically, in the case of the thin-film equation we are able to improve recent results by the author in the regime of strong slippage n \in (1,2) . For certain second-order doubly nonlinear parabolic equations, we are able to extend the known lower bounds on free boundary propagation to the case of irregular oscillatory initial data. Finally, we apply our method to a sixth-order quantum drift-diffusion equation, resulting in an upper bound on the time which it takes for the support to reach every point in the domain.

Application of Bacterial Foraging Algorithm and Genetic Algorithm for Selective Voltage Harmonic Elimination in PWM Inverter

Pulse Width Modulation (PWM) techniques are increasingly employed for PWM inverter fed induction motor drive. Among various popular PWM methods used, Selective Harmonic Elimination PWM (SHEPWM) has been widely accepted for its better harmonic elimination capability. In addition, using SHEPWM, it is also possible to maintain better voltage regulation. Hence, in this paper, an attempt has been made to apply Bacterial Foraging Algorithm (BFA) for solving selective harmonic elimination problem. The problem of voltage harmonic elimination together with output voltage regulation is drafted as an optimization task and the solution is sought through proposed method. For performance comparison of BFA, the results obtained are compared with other techniques such as derivative based Newton-Raphson method, and Genetic Algorithm. From the comparison, it can be observed that BFA based approach yields better results. Further, it provides superior convergence, reduced computational burden, and guaranteed global optima. The simulation results are validated through experimental findings.

Highly Expressive Query Languages for Unordered Data Trees

We study highly expressive query languages for unordered data trees, using as formal vehicles Active XML and extensions of languages in the while family. All languages may be seen as adding some form of control on top of a set of basic pattern queries. The results highlight the impact and interplay of different factors: the expressive power of basic queries, the embedding of computation into data (as in Active XML), and the use of deterministic vs. nondeterministic control. All languages are Turing complete, but not necessarily query complete in the sense of Chandra and Harel. Indeed, we show that some combinations of features yield serious limitations, analogous to FO k definability in the relational context. On the other hand, the limitations come with benefits such as the existence of powerful normal forms providing opportunities for optimization. Other languages are “almost” complete, but fall short because of subtle limitations reminiscent of the copy elimination problem in object databases.

Refining the complexity of the sports elimination problem

The sports elimination problem asks whether a team participating in a competition still has a chance to win, given the current standings and the remaining matches to be played among the teams. This problem can be viewed as a graph labelling problem, where arcs receive labels that contribute to the score of both endpoints of the arc, and the aim is to label the arcs in a way that each vertex obtains a score not exceeding its capacity. We investigate the complexity of this problem in detail, using a multivariate approach to examine how various parameters of the input graph (such as the maximum degree, the feedback vertex/edge number, and different width parameters) influence the computational tractability. We obtain several efficient algorithms, as well as certain hardness results.

DNA double-strand breaks and telomeres play important roles in trypanosoma brucei antigenic variation.

Human-infecting microbial pathogens all face a serious problem of elimination by the host immune response. Antigenic variation is an effective immune evasion mechanism where the pathogen regularly switches its major surface antigen. In many cases, the major surface antigen is encoded by genes from the same gene family, and its expression is strictly monoallelic. Among pathogens that undergo antigenic variation, Trypanosoma brucei (a kinetoplastid), which causes human African trypanosomiasis, Plasmodium falciparum (an apicomplexan), which causes malaria, Pneumocystis jirovecii (a fungus), which causes pneumonia, and Borrelia burgdorferi (a bacterium), which causes Lyme disease, also express their major surface antigens from loci next to the telomere. Except for Plasmodium, DNA recombination-mediated gene conversion is a major pathway for surface antigen switching in these pathogens. In the last decade, more sophisticated molecular and genetic tools have been developed in T. brucei, and our knowledge of functions of DNA recombination in antigenic variation has been greatly advanced. VSG is the major surface antigen in T. brucei. In subtelomeric VSG expression sites (ESs), VSG genes invariably are flanked by a long stretch of upstream 70-bp repeats. Recent studies have shown that DNA double-strand breaks (DSBs), particularly those in 70-bp repeats in the active ES, are a natural potent trigger for antigenic variation in T. brucei. In addition, telomere proteins can influence VSG switching by reducing the DSB amount at subtelomeric regions. These findings will be summarized and their implications will be discussed in this review.

Selective Harmonic Elimination of Cascaded Multilevel Inverter Using BAT Algorithm

Abstract There are several procedures to solve the selective harmonic elimination (SHE) problem. In this paper, the elimination of undesired harmonics in a multilevel inverter with equal DC sources by using bat evolutionary optimization method is presented. SHE is an efficient method for achieving the desired fundamental component and eliminating selection harmonics. The recently developed evolutionary optimization method named BAT algorithm is used to solve the nonlinear transcendental equations of SHE problem. To verify the presented method accuracy, simulation and experimental results are provided for a 7-level cascaded multi-level inverter. The feasibility and effectiveness of the proposed algorithm is evaluated with intensive simulation. The obtained results show that the BAT algorithm is more efficient than Bee algorithm (BA) and Genetic algorithm (GA) in eliminating the selective harmonics which cause the lower total harmonic distortion (THD) in the output voltage.

Characteristic operator functions for quantum input-plant-output models and coherent control

We introduce the characteristic operator as the generalization of the usual concept of a transfer function of linear input-plant-output systems to arbitrary quantum nonlinear Markovian input-output models. This is intended as a tool in the characterization of quantum feedback control systems that fits in with the general theory of networks. The definition exploits the linearity of noise differentials in both the plant Heisenberg equations of motion and the differential form of the input-output relations. Mathematically, the characteristic operator is a matrix of dimension equal to the number of outputs times the number of inputs (which must coincide), but with entries that are operators of the plant system. In this sense, the characteristic operator retains details of the effective plant dynamical structure and is an essentially quantum object. We illustrate the relevance to model reduction and simplification definition by showing that the convergence of the characteristic operator in adiabatic elimination limit models requires the same conditions and assumptions appearing in the work on limit quantum stochastic differential theorems of Bouten and Silberfarb [Commun. Math. Phys. 283, 491-505 (2008)]. This approach also shows in a natural way that the limit coefficients of the quantum stochastic differential equations in adiabatic elimination problems arise algebraically as Schur complements and amounts to a model reduction where the fast degrees of freedom are decoupled from the slow ones and eliminated.

Selective voltage harmonic elimination in PWM inverter using bacterial foraging algorithm

Pulse width modulation (PWM) techniques are increasingly employed in power electronic circuits. Among the various PWM methods used, selective harmonic elimination PWM (SHEPWM) method is popular and it is widely accepted for its better harmonic elimination capability and its ability to maintain output voltage regulation. However, in this method difficulty persists in the identification of switching instants as it involves non-linear transcendental equations for obtaining the desired solution. In this paper, bacterial foraging algorithm (BFA) method is proposed for switching angle selection in PWM inverter. The problem of voltage harmonic elimination together with output voltage regulation is drafted as an optimization task and the solution is sought through the proposed method. Extensive simulations are carried out using MATLAB/SIMULINK environment under various operating points with different switching pulses per half cycle. To demonstrate the superiority of the proposed method, BFA results are compared with other existing techniques such as Genetic Algorithm and Particle Swarm Optimization method. Further, the obtained simulation results are validated through experimental findings.

Context-preserving XQuery fusion

This paper solves the known problem of elimination of unnecessary internal element construction as well as variable elimination in XML processing with (a subset of) XQuery without ignoring the issues of document order. The semantics of XQuery is context sensitive and requires preservation of document order. In this paper, we propose, as far as we are aware, the first XQuery fusion that can deal with both the document order and the context of XQuery expressions. More specifically, we carefully design a context representation of XQuery expressions based on the Dewey order encoding, develop a context-preserving XQuery fusion for ordered trees by static emulation of the XML store, and prove that our fusion is correct. Our XQuery fusion has been implemented, and all the examples in this paper have passed through the system.

Физиотерапия при гипертонической болезни с позиций хаотической динамики параметров ссс у пациентов

The problem of one-type uncertainty is solved when cardiovascular system parameters in hypertensive patients undergoing physiotherapy aren’t differentiated by stochastics, but these differences are clearly revealed by the methods of neurocomputing and calculation of parameters of quasi-attractors. Simultaneously, the solution of system synthesis problem is possible, i.e. identification of more important diagnostic characters xi from the whole set of state vector of cardiovascular system x(t) in hypertensive patients. Efficiency of physiotherapy is estimated at two stages of course of treatment: in the initial state (initial physiotherapy) and after the termination of course of treatment. The dynamics of motion of quasi-attractors in phase space of states in hypertensive patients is shown. Thus, the problem of elimination of one-type uncertainty in studying the effi-ciency of curative measures is solved.

A Complexity Theory of Constructible Functions and Sheaves

In this paper we introduce constructible analogs of the discrete complexity classes $\mathbf{VP}$ and $\mathbf{VNP}$ of sequences of functions. The functions in the new definitions are constructible functions on $\mathbb{R}^n$ or $\mathbb{C}^n$. We define a class of sequences of constructible functions that play a role analogous to that of $\mathbf{VP}$ in the more classical theory. The class analogous to $\mathbf{VNP}$ is defined using Euler integration. We discuss several examples, develop a theory of completeness, and pose a conjecture analogous to the $\mathbf{VP}$ vs. $\mathbf{VNP}$ conjecture in the classical case. In the second part of the paper we extend the notions of complexity classes to sequences of constructible sheaves over $\mathbb{R}^n$ (or its one point compactification). We introduce a class of sequences of simple constructible sheaves, that could be seen as the sheaf-theoretic analog of the Blum-Shub-Smale class $\mathbf{P}_{\mathbb{R}}$. We also define a hierarchy of complexity classes of sheaves mirroring the polynomial hierarchy, $\mathbf{PH}_{\mathbb{R}}$, in the B-S-S theory. We prove a singly exponential upper bound on the topological complexity of the sheaves in this hierarchy mirroring a similar result in the B-S-S setting. We obtain as a result an algorithm with singly exponential complexity for a sheaf-theoretic variant of the real quantifier elimination problem. We pose the natural sheaf-theoretic analogs of the classical $\mathbf{P}$ vs. $\mathbf{NP}$ question, and also discuss a connection with Toda's theorem from discrete complexity theory in the context of constructible sheaves. We also discuss possible generalizations of the questions in complexity theory related to separation of complexity classes to more general categories via sequences of adjoint pairs of functors.

Degeneracy Loci and Polynomial Equation Solving

Let $$V$$V be a smooth, equidimensional, quasi-affine variety of dimension $$r$$r over $$\mathbb {C}$$C, and let $$F$$F be a $$(p\times s)$$(p×s) matrix of coordinate functions of $$\mathbb {C}[V]$$C[V], where $$s\ge p+r$$s?p+r. The pair $$(V,F)$$(V,F) determines a vector bundle $$E$$E of rank $$s-p$$s-p over $$W:=\{x\in V \mid \mathrm{rk }F(x)=p\}$$W:={x?V?rkF(x)=p}. We associate with $$(V,F)$$(V,F) a descending chain of degeneracy loci of $$E$$E (the generic polar varieties of $$V$$V represent a typical example of this situation). The maximal degree of these degeneracy loci constitutes the essential ingredient for the uniform, bounded-error probabilistic pseudo-polynomial-time algorithm that we will design and that solves a series of computational elimination problems that can be formulated in this framework. We describe applications to polynomial equation solving over the reals and to the computation of a generic fiber of a dominant endomorphism of an affine space.

State elimination for nonlinear neutral state-space systems

ak equation in the former case, even when one starts with a retarded system. In this paper the state elimination is therefore extended further to nonlinear neutral state-space systems, and it is shown that also in such a case an input-output representation, at least locally, always exists. In general, it represents a neutral system again. Computational aspects related to the state elimination problem are discussed as well.

DATA FILTERING METHODS FOR HYDROGRAPHIC SURVEY DATA

Current trends in navigation are characterized by the further increase of demands on the precision of hydrographic information, especially of the nautical maps. Thus, precision of both spatial position and depth bathymetric data is important for ensuring safe navigation, and so problem of data filtering and elimination of outliers arises.In the present work, comparison of methods, used for postprocessing of depth data, measured by echosounder, is done.First of all, review of commonly used data filtering and outlier elimination methods is done, and their advantages and disadvantages are analyzed.As improved outlier elimination algorithm and median filtering has their flaws, Kalman filtering is considered as a measure of outlier elimination and real data estimation. It’s shown that Kalman filter can both effectively filter noise and eliminate outliers; however, quality of the filtered data strongly depends on measurement noise covariation and process noise covariation estimates, and respectively. At that, the lower is, the better noise is filtered and the smoother depth profile is; the higher is, the better outliers are eliminated. However, care must be taken, as depth profile is distorted at high values, and noise is almost not filtered at low ones.It’s shown that noise covariation estimate has more influence on data filtering; therefore, one should pay attention to correct estimation. For practical reasons, values of ; =10 are recommended.In the recent works, wavelet filtering is considered as a promising method of data filtering in postprocessing. Therefore, as a next step, comparison of Kalman filtering and wavelet filtering is done using the real-world data. To that end, white noise is added to filtered and smoothed data, and then those data are filtered by methods, mentioned above. Corellation of source and denoised data is chosen as a criterion of filter effectiveness.It’s shown that Kalman filter is somewhat less effective in data postprocessing than wavelet filter. However, as Kalman filter allows one both to filter noises form the measured data and to eliminate outliers, and can be used for “on-the-fly” data filtering, it’s advisable to use Kalman filtering for real-time measurements during surveys, and wavelets for data postprocessing.Future studies may be devoted to improvement of existing and introduction of new data filtering and postrprocessing methods.

Quantifier elimination by dependency sequents

We consider the problem of existential quantifier elimination for Boolean CNF formulas. We present a new method for solving this problem called Derivation of Dependency-Sequents (DDS). A Dependency-sequent (D-sequent) is used to record that a set of quantified variables is redundant under a partial assignment. We introduce the join operation that produces new D-sequents from existing ones. We show that DDS is compositional, i.e., if our input formula is a conjunction of independent formulas, DDS automatically recognizes and exploits this information. We introduce an algorithm based on DDS and present experimental results demonstrating its potential.

ROZBOR AKTUÁLNÍHO VÝVOJE TRHU ZEMĚDĚLSKÉHO POJIŠTĚNÍ V ČESKÉ REPUBLICE

Proactive approach to risk management of agriculture companies is the way to ensure the efficiency of agricultural production even affected by natural disasters, to ensure the continuity of agricultural business and ultimately affect the level of development of rural regions. The instrument that solves the problem of reduction and elimination of risks associated with agricultural production is a systemic approach to the insurance of agricultural production, both crop insurance and livestock insurance, linked to a support program for SME in agriculture.This presented paper aims to identify and discuss the possibility of eliminating risks possibly threate­ning the agricultural production and to analyze forms of covering risks associated with agricultural production on the commercial insurance market in the Czech Republic. The paper analyzes the current situation and current development of the agricultural insurance on the insurance market in the Czech Republic.This paper was written as a part of the research project MSM 6215648904, carried out by the Faculty of Business and Economics, under the title „The Czech economics in the processes of integration and globalization, and the development of the agriculture and service sector in the new conditions of the integrated European market“, following the goals and methodology of the research project.

Solution for selective harmonic optimisation in diode‐clamped inverters using radial basis function neural networks

In this study, a new heuristic based intelligent algorithm is proposed for the elimination of harmonics in diode-clamped inverters. Numerous methods have been introduced for optimising harmonics in diode-clamped inverter such as Newton-Raphson method. These methods are not sometimes applicable because of difficulty of initializing algorithm parameters and singularity problems in Jacobian-based methods. Radial basis function neural network is used in the proposed method which is one of the most useful members of the neural networks family, because of their capability in the approximation problems. They work on the basis of this fact that, it has been proved that any arbitrary non-linear function can be exactly modelled by a combination of some non-linear basis functions. The proposed algorithm can be applied to solve the selective harmonic elimination problem of any multilevel inverters with any number of levels. The method has the benefit of high rate of convergence and accuracy besides simplicity in implementation. Theoretical results are authorised by experiments and simulations for a seven-level diode-clamped inverter. The obtained results prove efficiency and capability of the proposed method.

Quality of Life in Colorectal Cancer Patients according to the Severity of Symptom Clusters Classification

Purpose: The purpose of this study to verify the symptom clusters of colorectal cancer patients and also identify the influence of the symptom clusters upon colorectal patient symptom clusters identify acording exper. Methods: 101 colorectal cancer patients were the subjects of this study. As for a research tool, the global health status/QOL was used including the sub-scales of QLQ-C30. In the functional scales and the symptom scales, the sub-scales of QLQ-C30 and QLQ-CR29 were used. Results: The result of this study shows us there are 7 kinds of symptom clusters. Results: The result of this study shows us there are 7 kinds of symptom clusters, bowel movement problems, abdominal-pelvic problems, feces problems, urination problems, dry mouth-taste problems, side effect problems, and elimination problems. Conclusion: Based on the result, the symptom clusters in colorectal cancer patients were confirmed to be influencing the subjects’ quality of life and functions. The efficient management of symptom clusters that subjects experience leads to promoting colorectal cancer patients' physical and emotional status, thereby contributing to an improvement in their quality of life.

Eliminating Impulse for Descriptor System by Derivative Output Feedback

The problem of impulse elimination for descriptor system by derivative output feedback is investigated in this paper. Based on a novelly restricted system equivalence between matrix pencils, the range of dynamical order of the resultant closed loop descriptor system is given. Then, for the different dynamical order, sufficient conditions for the existence of derivative output feedback to ensure the resultant closed loop system to be impulse free are derived, and the corresponding derivative output feedback controllers are provided. Finally, simulation examples are given to show the consistence with the theoretical results obtained in this paper.

Utilizing the Cuckoo Optimization Algorithm for Selective Harmonic Elimination Strategy in the Cascaded Multilevel Inverter

There are several procedures to solve the selective harmonic elimination (SHE) problem. In this paper, the elimination of undesired harmonics in a multilevel inverter with equal DC sources by using cuckoo evolutionary optimization method is presented. SHE is an efficient method for achieving the desired fundamental component and eliminating selection harmonics. The recently developed evolutionary optimization method named cuckoo (COA) is used to solve the nonlinear transcendental equations of SHE problem. To verify the presented method accuracy, simulation and experimental results are provided for a 7-level cascaded multi-level inverter. The feasibility and effectiveness of the proposed algorithm is evaluated with intensive simulation and experimental studies. The obtained results show that the cuckoo algorithm is more e‑cient than Bee algorithm (BA) and Genetic algorithm (GA) in eliminating the selective harmonics which cause the lower total harmonic distortion (THD) in the output voltage.