Order Vector Research Articles

Vector-borne disease outbreak control via instant releases.

This paper is devoted to the study of optimal release strategies to control vector-borne diseases, such as dengue, Zika, chikungunya and malaria. Two techniques are considered: the sterile insect one (SIT), which consists in releasing sterilized males among wild vectors in order to perturb their reproduction, and the Wolbachia one (presently used mainly for mosquitoes), which consists in releasing vectors, that are infected with a bacterium limiting their vectorial capacity, in order to replace the wild population by one with reduced vectorial capacity. In each case, the time dynamics of the vector population is modeled by a system of ordinary differential equations in which the releases are represented by linear combinations of Dirac measures with positive coefficients determining their intensity. We introduce optimal control problems that we solve numerically using ad-hoc algorithms, based on writing first-order optimality conditions characterizing the best combination of Dirac measures. We then discuss the results obtained, focusing in particular on the complexity and efficiency of optimal controls and comparing the strategies obtained. Mathematical modeling can help testing a great number of scenarios that are potentially interesting in future interventions (even those that are orthogonal to the present strategies) but that would be hard, costly or even impossible to test in the field in present conditions.

Improved Sparse Mean Reverting Portfolio Selection Using Simulated Annealing and Extreme Learning Machine

We study the problem of selecting a sparse, mean reverting portfolio from a universe of assets using simulated annealing (SA). Assuming that assets follow a first order vector autoregressive process (VAR(1)), we make a number of improvements in existing methods. First, we extend the underlying asset dynamics to include a time-independent additive term, thereby enriching the model’s applicability. Second, we introduce Extreme Learning Machine (ELM) to decide whether to apply SA or settle for the much faster greedy solution. Finally, we improve the SA method by better calibration of the initial temperature and by determining the exact value of the weights within a selected dimension using the Rayleigh quotient. On real data, these changes result in more than 90% improvement in run time on average and 4.78% improvement in optimized mean reversion in our simulations. We also test the trading performance of our method on both simulated and real data and managed to achieve positive mean trading results in both cases.

Extended spherical harmonic expansion using multipolar bases of equivalent magnetic sources on arbitrary surfaces

Purpose The purpose of this paper is to propose a compact model to represent the magnetic field outside the sources. This model provides the multipolar ordering of a spherical harmonic expansion far from the source while being valid in its close proximity. Design/methodology/approach The authors investigate equivalent surface sources that enable to compute the field very close to any chosen surface that encloses the source. Then the authors present a method to find an appropriate initial basis and its associated inner product that allow to construct multipolar harmonic bases for these equivalent sources, where any vector of order k produces a field that decreases at least as fast as the field produced by a multipole of order k. Finally, those bases are numerically implemented to demonstrate their performances, both far from the source and in its close proximity. Findings The charge distribution and normal dipole distribution are well-suited to construct multipolar harmonic bases of equivalent sources. These bases can be described by as few parameters as the decreasing spherical harmonic expansion. Comparison with other numerical models shows its ability to compute the field both far from the source and close to it. Originality/value A basis for normal dipole distribution has already been described in the literature. This paper presents a general method to construct a multipolar basis for equivalent sources and uses it to construct a basis for single-layer potential.

A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA’s operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis.

First-principles calculation of Hubbard U for Terbium metal under high pressure

Using density functional theory (DFT) and linear response approaches, we compute the on-site Hubbard interaction U of elemental Terbium (Tb) metal in the pressure range ∼ 0–65 GPa. The resulting first-principles U values with experimental crystal structures enable us to examine the magnetic properties of Tb using a DFT+U method. The lowest-energy magnetic states in our calculations for different high-pressure Tb phases—including hcp, α-Sm, and dhcp—are found to be compatible with the corresponding magnetic ordering vectors reported in experiments. The result shows that the inclusion of Hubbard U substantially improves the accuracy and efficiency in modeling correlated rare-earth materials. Our study also provides the necessary U information for other quantum many-body techniques to study Tb under extreme pressure conditions.

Optimality Conditions in Control Problems with Random State Constraints in Probabilistic or Almost Sure Form

In this paper, we discuss optimality conditions for optimization problems involving random state constraints, which are modeled in probabilistic or almost sure form. Although the latter can be understood as the limiting case of the former, the derivation of optimality conditions requires substantially different approaches. We apply them to a linear elliptic partial differential equation with random inputs. In the probabilistic case, we rely on the spherical-radial decomposition of Gaussian random vectors in order to formulate fully explicit optimality conditions involving a spherical integral. In the almost sure case, we derive optimality conditions and compare them with a model based on robust constraints with respect to the (compact) support of the given distribution. Funding: The authors thank the Deutsche Forschungsgemeinschaft [Projects B02 and B04 in the “Sonderforschungsbereich/Transregio 154 Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks”] for support. C. Geiersbach acknowledges support from the Deutsche Forschungsgemeinschaft [Germany’s Excellence Strategy–the Berlin Mathematics Research Center MATH+ Grant EXC-2046/1, Project 390685689]. R. Henrion acknowledges support from the Fondation Mathématique Jacques Hadamard [Program Gaspard Monge in Optimization and Operations Research, including support to this program by Electricité de France].

Fermi Surface Nesting Driving the RKKY Interaction in the Centrosymmetric Skyrmion Magnet Gd_{2}PdSi_{3}.

The magnetic skyrmions generated in a centrosymmetric crystal were recently first discovered in Gd_{2}PdSi_{3}. In light of this, we observe the electronic structure by angle-resolved photoemission spectroscopy and unveil its direct relationship with the magnetism in this compound. The Fermi surface and band dispersions are demonstrated to have a good agreement with the density functional theory calculations carried out with careful consideration of the crystal superstructure. Most importantly, we find that the three-dimensional Fermi surface has extended nesting which matches well the q vector of the magnetic order detected by recent scattering measurements. The consistency we find among angle-resolved photoemission spectroscopy, density functional theory, and the scattering measurements suggests the Ruderman-Kittel-Kasuya-Yosida interaction involving itinerant electrons to be the formation mechanism of skyrmions in Gd_{2}PdSi_{3}.

Quantitative Stylistic Analysis of Middle Chinese Texts Based on the Dissimilarity of Evolutive Core Word Usage

Stylistic analysis enables open-ended and exploratory observation of languages. To fill the gap in the quantitative analysis of the stylistic systems of Middle Chinese, we construct lexical features based on the evolutive core word usage and scheme a Bayesian method for feature parameters estimation. The lexical features are from the Swadesh list, each of which has different word forms along with the language evolution during the Middle Ages. We thus count the varied word of those entries along with the language evolution as the linguistic features. With the Bayesian formulation, the feature parameters are estimated to construct a high-dimensional random feature vector to obtain the pair-wise dissimilarity matrix of all the texts based on different distance measures. Finally, we perform the spectral embedding and clustering to visualize, categorize, and analyze the linguistic styles of Middle Chinese texts. The quantitative result agrees with the existing qualitative conclusions and, furthermore, betters our understanding of the linguistic styles of Middle Chinese from both the inter-category and intra-category aspects. It also helps unveil the special styles induced by the indirect language contact.

Investigation of spin excitations and charge order in bulk crystals of the infinite-layer nickelate LaNiO2

Recent x-ray spectroscopic studies have revealed spin excitations and charge density waves in thin films of infinite-layer (IL) nickelates. However, clarifying whether the origin of these phenomena is intrinsic to the material class or attributable to impurity phases in the films has presented a major challenge. Here we utilize topotactic methods to synthesize bulk crystals of the IL nickelate LaNiO2 with crystallographically oriented surfaces. We examine these crystals using resonant inelastic x-ray scattering (RIXS) at the Ni L3-edge to elucidate the spin and charge correlations in the bulk of the material. While we detect the presence of prominent spin excitations in the crystals, fingerprints of charge order are absent at the ordering vectors identified in previous thin-film studies. These results contribute to the understanding of the bulk properties of LaNiO2 and establish topotactically synthesized crystals as viable complementary specimens for spectroscopic investigations. Published by the American Physical Society 2024

Deep learning‐based seizure prediction using EEG signals: A comparative analysis of classification methods on the CHB‐MIT dataset

AbstractEpilepsy is a brain disorder that causes patients to have multiple seizures. About 30% of patients with epilepsy are not treated with medication or surgery. The abnormal activity of brain before occurring of a seizure (about a few minutes before a seizure occurs) are known as the preictal area. Therefore, if we can predict this state, we can control possible seizures by using appropriate medications. In this study, we present a method for predicting epileptic seizures using electroencephalogram (EEG) signals. The method can identify the preictal region that occurs before the onset of seizures. In our proposed method, first the noise removal of EEG signals is performed, and then the necessary features are extracted using a convolution neural network. Finally, we use the feature vectors in order to train multiple classifiers, fully connected layer, random forest, and support vector machines with linear kernel. Additionally, we apply maximum voting, which is an ensemble method, to classify preictal segments from interictal ones. In this study, using EEG signals of patients from CHB‐MIT dataset, we were able to achieve sensitivity of 90.76%.

Research of cognitive data processing in radio communication systems with permutation decoding

Background. The need to use permutation decoding tools in radio communication systems is explained by the increased error correction capabilities of this method. In this case, complex matrix calculations during the search for equivalent codes according to the classical scheme of permutation decoding are replaced by a list of ready-made solutions. These solutions are calculated a priori and entered into the cognitive cards of the decoder processor, which makes the method a convenient tool in the procedure for ensuring information reliability when controlling, for example, unmanned vehicles via radio channels. In fact, matrix calculations on board are replaced by searching the list of cognitive maps for the right solution corresponding in real time to the current permutation of reliable character numerators. However, data processing in the decoder’s cognitive map requires a special description.
 Aim. The study of methods for identifying permutations of character numerators of code vectors in order to effectively transform them in a system of cognitive maps of a permutation decoder.
 Methods. The paper reveals the subtle structure of cognitive maps of productive and unproductive permutations of numerators, which allows on a regular basis to obtain an alternative solution for switching to a set of productive permutations when the receiver receives an unproductive permutation, thereby excluding the use of trial and error.
 Results. The efficiency of the permutation decoder increases due to the implementation of permutations that were originally included in a set of solutions introduced into the cognitive map of unproductive permutations.
 Conclusion. A family of microcontrollers is proposed to implement the principle of interaction of cognitive maps with a system of alternative solutions.

Estimation for vector autoregressive model under multivariate skew-t-normal innovations

Current procedures for estimating the parameters of [Formula: see text]th order vector autoregressive (VAR [Formula: see text]) model are usually based on assuming that the ensuing error distribution is multivariate normal. But there exists large body of evidence that several data encountered in real life are skewed; thereby making estimators derived based on normality assumption not suitable in such scenarios. This prompts for the search of appropriate methods for skewed distributions. Therefore, this article proposes estimators for the mean and covariance matrices of the [Formula: see text] model under multivariate skew- [Formula: see text]-normal (MSTN) distribution. Also, estimators for the shape and skewness parameters are provided. The expectation conditional maximization (ECM) and its extension the expectation conditional maximization either (ECME) algorithms are the tools used to derive the estimators. The performance of the estimators were examined through extensive simulations, and results show that they compete favourably with other numerical methods especially when the underlying distribution is skewed. The usefulness of our estimators was illustrated using a real data set on some US economic indicators. The VAR [Formula: see text] model under MSTN distribution provides a good fit, better than [Formula: see text] model under the assumption of normality.

Linear Programming-Based Fuzzy Alternative Ranking Order Method Accounting for Two-Step Normalization for Comprehensive Evaluation of Digital Economy Development in Provincial Regions

As digital technologies continue to reshape economic landscapes, the comprehensive evaluation of digital economy (DE) development in provincial regions becomes a critical endeavor. This article proposes a novel approach, integrating the linear programming method, fuzzy logic, and the alternative ranking order method accounting for two-step normalization (AROMAN), to assess the multifaceted facets of DE growth. The primary contribution of the AROMAN is the coupling of vector and linear normalization techniques in order to produce accurate data structures that are subsequently utilized in calculations. The proposed methodology accommodates the inherent uncertainties and complexities associated with the evaluation process, offering a robust framework for decision-makers. The linear programming aspect optimizes the weightings assigned to different evaluation criteria, ensuring a dynamic and context-specific assessment. By incorporating fuzzy logic, the model captures the vagueness and imprecision inherent in qualitative assessments, providing a more realistic representation of the DE’s multifaceted nature. The AROMAN further refines the ranking process, considering the interdependencies among the criteria and enhancing the accuracy of the evaluation. In order to ascertain the efficacy of the suggested methodology, a case study is undertaken pertaining to provincial areas, showcasing its implementation in the evaluation and a comparison of DE progress in various geographical settings. The outcomes illustrate the capacity of the model to produce perceptive and implementable insights for policymakers, thereby enabling them to make well-informed decisions and implement focused interventions that promote the expansion of the DE. Moreover, managerial implications, theoretical limitations, and a comparative analysis are also given of the proposed method.

Knockdown of NEAT1 prevents post-stroke lipid droplet agglomeration in microglia by regulating autophagy

BackgroundLipid droplets (LD), lipid-storing organelles containing neutral lipids like glycerolipids and cholesterol, are increasingly accepted as hallmarks of inflammation. The nuclear paraspeckle assembly transcript 1 (NEAT1), a long non-coding RNA with over 200 nucleotides, exerts an indispensable impact on regulating both LD agglomeration and autophagy in multiple neurological disorders. However, knowledge as to how NEAT1 modulates the formation of LD and associated signaling pathways is limited.MethodsIn this study, primary microglia were isolated from newborn mice and exposed to oxygen-glucose-deprivation/reoxygenation (OGD/R). To further explore NEAT1-dependent mechanisms, an antisense oligonucleotide (ASO) was adopted to silence NEAT1 under in vitro conditions. Studying NEAT1-dependent interactions with regard to autophagy and LD agglomeration under hypoxic conditions, the inhibitor and activator of autophagy 3-methyladenine (3-MA) and rapamycin (RAPA) were used, respectively. In a preclinical stroke model, mice received intraventricular injections of ASO NEAT1 or control vectors in order to yield NEAT1 knockdown. Analysis of readout parameters included qRT-PCR, immunofluorescence, western blot assays, and behavioral tests.ResultsMicroglia exposed to OGD/R displayed a temporal pattern of NEAT1 expression, peaking at four hours of hypoxia followed by six hours of reoxygenation. After effectively silencing NEAT1, LD formation and autophagy-related proteins were significantly repressed in hypoxic microglia. Stimulating autophagy in ASO NEAT1 microglia under OGD/R conditions by means of RAPA reversed the downregulation of LD agglomeration and perilipin 2 (PLIN2) expression. On the contrary, application of 3-MA promoted repression of both LD agglomeration and expression of the LD-associated protein PLIN2. Under in vivo conditions, NEAT1 was significantly increased in mice at 24 h post-stroke. Knockdown of NEAT1 significantly alleviated LD agglomeration and inhibited autophagy, resulting in improved cerebral perfusion, reduced brain injury and increased neurological recovery.ConclusionNEAT1 is a key player of LD agglomeration and autophagy stimulation, and NEAT1 knockdown provides a promising therapeutic value against stroke.Graphical abstract

Improved Modulated Model Predictive Control Technique for PMSM Drives Using the Vector Projection

Compared to the model predictive control (MPC) technique, the modulated MPC (M2PC) method applies multiple voltage vectors in order within one switching cycle, enabling a fixed switching frequency and better motor control performance. However, the M2PC technique suffers from a significant computational burden and imprecise allocation method of the vector action time, affecting the control performance. To address these two issues, this paper proposes an improved M2PC method that utilizes the information of projection of the reference vector on the selected voltage vectors for efficiently selecting the optimal active vectors and also accurately calculating their action time. Compared to the conventional M2PC, the proposed technique demonstrates both excellent dynamic performance with reduced execution time by 15.3% and superior steady‐state performance in terms of lower torque/speed ripple as well as fewer current harmonics. Those benefits have been proven by the results of a 2.5k rpm permanent magnet synchronous motor drive. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Energy saving of cluster computing by CPU frequency Tuning using genetic algorithm

Dynamic voltage and frequency scaling (DVFS) is a tool used primarily to decrease computer processor energy consumption by lowering its operational frequency. Their only downside is that they distract from the efficiency of parallel applications while operating on parallel platforms. In a heterogeneous cluster architecture, however, a genetic algorithm is being implemented and applied to model the best trade-off between energy-saving and parallel application performance degradation. The proposed algorithm selects the best frequency vector in order to accomplish these objectives by providing the same compromise. So, the objective function of the genetic algorithm at the same time gives limited energy consumption and minimum decreases in performance. The SimGrid simulator will be used for all experiments. The suggested algorithm saves the average energy by (20 %) and the application performance degrades to the limit (0.15 %).

Optimal Design of Fractional Order Vector Controller Using Hardware-in-Loop (HIL) and Opal RT for Wind Energy System

Optimal Design of Fractional Order Vector Controller Using Hardware-in-Loop (HIL) and Opal RT for Wind Energy System

Stability of nonlinear impulsive higher order differential – fractional integral delay equations with nonlocal initial conditions

The aim of this paper is to investigate some types of stability such as generalized Hyers-Ulam- Rassias stability(G-H-U-R-stabile) and the relation with Hyers-Ulam(H-U-stabile) stable and Hyers-Ulam-Rassias stable (H-U-R-stabile) and generalized Hyers-Ulam stable (G-H-U- stable) to obtain which one guarantee to satisfy stability of equations included a nonlinear function some of them contains a delay time of solution and the other contain a vector of different order of derivatives for the solution to n-time and vector of fractional order of integrals with different fractional orders and that was the for using a claculse of fractional calculus to satisfies the issue of this techniques. Moreover, the nonlocal initial values for the proposal equation of nonlinear impulsive higher order differential – fractional integral delay time equations which are adding more interesting for nonlinear analytic object of nonlinear higher order integro – fractional order impulsive classes, and the impulsive difference of the equation has some necessary conditions to prove the results of solution to be stable with certain type has related with other types. The necessary and sufficient conditions which assumed on this nonlinear higher order integro-differential impulsive equation have been achieved the stability with interesting certain estimates obtain through the proving technique. Also the uniqueness of solution has been studied with same conditions was presented for stability and used for that issue a contraction fixed point theorem.

MIRROR (LEFT-RECURSIVE) GRAY CODE

Here we consider a version of the Binary Reflected Gray Code (BRGC, or Gray code for short), called the mirror Gray code – in parallel and in comparison to the Gray code. We discuss some sources where the two codes are not distinguished and the reasons why. We present our arguments for treating the two codes as distinct, starting with a definition of the mirror Gray code and showing its main properties. Among the most important of these is the relationship between the two codes – the codewords of the mirror Gray code are “left-right mirror images” of those in the Gray code and vice versa. Other arguments we show are the differences in the algorithms for generating the codewords. We also present another approach that involves representing the codewords by integers (their serial numbers), generating and examining the two codes by using their sequences of serial numbers. For this purpose, we use the bijections that relate the pairs of combinatorial objects: binary vectors in a certain order and the corresponding integer sequences and subsets. In this way, we get different algorithms, sequences, and functions for successor and predecessor, for ranking and unranking at the mirror Gray code. We believe that this article provides at least one more perspective, as well as expands and enriches the knowledge of the Gray code.

Design of multi-objective guided filter infrared thermal image fusion for hypervelocity impact damages detection

Spacecraft hypervelocity impact damage assessment is becoming increasingly important as the space debris environment deteriorates. Common single-target guided filtering methods are hardly capable of coping with the complex damage infrared thermal image fusion requirements of hypervelocity impacts. In this paper, a two-layer infrared reconstructed image fusion algorithm based on Multi-objective guided filtering (MOGF) is proposed. The aim is to keep the filtered input and output images similar while reducing thermal image noise and retaining as much of the tiny defects as possible in the fusion process. MOGF combines a multi-objective evolutionary optimization algorithm to simultaneously optimize multiple guided filter cost functions in the form of vectors in order to obtain the optimal set of guided filter weight parameters. Given the problem of irregular frontier surfaces in practical multi-objective optimization problems, a weight vector adaptive adjustment strategy is introduced to obtain a more uniform population. Finally, the optimal weight set is passed to the full pixel fusion layer to complete the complex damage infrared reconstructed image fusion. The experiments demonstrate that the proposed fusion method considering multiple targets has higher performance in hypervelocity impact complex damage fusion compared to the general single target fusion method.