Stochastic Computational Heuristic for the Fractional Biological Model Based on Leptospirosis

The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient,called ANNs-SCG.The fractional derivatives have been applied to get more reliable performances of the system.The mathematical form of the biological Leptospirosis system is divided into five categories,and the numerical performances of each model class will be provided by using the ANNs-SCG.The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results.The reference solutions have been obtained by using theAdams numerical scheme.For these investigations,the data selection is performed at 82%for training,while the statics for both testing and authentication is selected as 9%.The procedures based on the recurrence,mean square error,error histograms,regression,state transitions,and correlation will be accomplished to validate the fitness,accuracy,and reliability of the ANNs-SCG scheme.

Plasma-activated hydrogel:fabrication,functionalization,and effective biological model

Hydrogels are biomaterials with 3D networks of hydrophilic polymers.The generation of hydrogels is turning to the development of hydrogels with the help of enabling technologies.Plasma can tailor the hydrogels’properties through simultaneous physical and chemical actions,resulting in an emerging technology of plasma-activated hydrogels(PAH).PAH can be divided into functional PAH and biological tissue model PAH.This review systematically introduces the plasma sources,plasma etching polymer surface,and plasma cross-linking involved in the fabrication of PAH.The‘diffusion-drift-reaction model’is used to study the microscopic physicochemical interaction between plasma and biological tissue PAH models.Finally,the main achievements of PAH,including wound treatment,sterilization,3D tumor model,etc,and their development trends are discussed.

Positive Almost Periodic Solution on a Nonlinear Logistic Biological Model with Grazing Rates

In this paper, we study the following nonlinear biological modeldx(t)/dt = x(t)[a(t)-b(t)x α (t)] + f(t, xt),by using fixed pointed theorem, the sufficient conditions of the existence of unique positive almost periodic solution for the above system are obtained, by using the theories of stability, the sufficient conditions which guarantee the stability of the positive almost periodic solution are derived.

Study on optimization of parameters in a biological model

According to the data observed in a China- Japan Joint Investigation, the parameters of an ecosystem dynamics model (Qiao et al., 2000) were optimized. The values of eighteen parameters for the model were obtained, with nutrient half saturation constant, Kn= 1.4μmol/dm3, Kp = 0.129 μmol/dm3 and K3= 1. 16μmol/dm3 for the diatom and Kn= 0.345μmol/dm3, Kp=0. 113μmol/dm3 for the flagellate. The proposals to set up a function for this multiple objective problem were discussed in detail.

Existence of Positive Periodic Solutions for a Time-Delay Biological Model

Based on the classic Lotlk-Volterra cooperation model, we establish a time-delay model of which a species cannot survive independently. By continuation theorem, we discuss existence of positive periodic solutions of the model.

Numerical Investigations of a Class of Biological Models on Unbounded Domain

This paper is concerned with numerical computations of a class of biologi-cal models on unbounded spatial domains.To overcome the unboundedness of spatial domain,we first construct efficient local absorbing boundary conditions(LABCs)to re-formulate the Cauchy problem into an initial-boundary value(IBV)problem.After that,we construct a linearized finite difference scheme for the reduced IVB problem,and provide the corresponding error estimates and stability analysis.The delay-dependent dynamical properties on the Nicholson’s blowflies equation and the Mackey-Glass equa-tion are numerically investigated.Finally,numerical examples are given to demonstrate the efficiency of our LABCs and theoretical results of the numerical scheme.

A T-Cell Model for the Biological Role of Selenium-Dependent Glutathione Peroxidase

The cytosolic form of selenium-dependent glutathione peroxidase detoxifies both hydrogen and lipid peroxides and therefore represents a major component of the cellular anti-oxidant defenses. In order to study the biological role of this enzyme, we generated an expression construct in a retroviral vector, which when introduced into immortalized human T-cells, resulted in significant increases in the activity of this important enzyme. This effect is stable over extended maintenance in culture. The anti-oxidant defenses in these same cells are also shown to be attenuated hy chemically reducing cellular glutathione levels. Collectively, the abllity to both increase and decrease the anti-oxidant defenses in human T cells results in a useful model system for the study of oxidative stress and signaling in this cell type

Dynamics of Fractional Differential Model for Schistosomiasis Disease

In the present study,a design of a fractional order mathematical model is presented based on the schistosomiasis disease.To observe more accurate performances of the results,the use of fractional order derivatives in the mathematical model is introduce based on the schistosomiasis disease is executed.The preliminary design of the fractional order mathematical model focused on schistosomiasis disease is classified as follows:uninfected with schistosomiasis,infected with schistosomiasis,recovered from infection,susceptible snail unafflicted with schistosomiasis disease and susceptible snail afflicted with this disease.The solutions to the proposed system of the fractional order mathematical model will be presented using stochastic artificial neural network(ANN)techniques in conjunction with the LevenbergMarquardt backpropagation(LMBP),referred to as ANN-LMBP.To illustrate the preciseness of the ANN-LMBP method,mathematical presentations of three different values focused on fractional order will be performed.These statics performances are taken in these investigations are 78%and 11%for both learning and certification.The accuracy of the ANN-LMBP method is determined by comparing the values obtained by the database Adams-Bash forth-Moulton scheme.The simulation-based error histograms(EHs),MSE,recurrence,and state transitions(STs)will be offered to achieve the capability,accuracy,steadiness,abilities,and finesse of the ANN-LMBP method.

OPTIMAL CONTROL OF A POPULATION DYNAMICS MODEL WITH HYSTERESIS

This paper addresses a nonlinear partial differential control system arising in population dynamics.The system consist of three diffusion equations describing the evolutions of three biological species:prey,predator,and food for the prey or vegetation.The equation for the food density incorporates a hysteresis operator of generalized stop type accounting for underlying hysteresis effects occurring in the dynamical process.We study the problem of minimization of a given integral cost functional over solutions of the above system.The set-valued mapping defining the control constraint is state-dependent and its values are nonconvex as is the cost integrand as a function of the control variable.Some relaxationtype results for the minimization problem are obtained and the existence of a nearly optimal solution is established.

Nonlinear dynamical wave structures of Zoomeron equation for population models

The nonlinear dynamical exact wave solutions to the non-fractional order and the time-fractional order of the biological population models are achieved for the first time in the framwork of the Paul-Painlevéapproach method(PPAM).When the variables appearing in the exact solutions take specific values,the solitary wave solutions will be easily obtained.The realized results prove the efficiency of this technique.

Properties of Solutions of Kolmogorov-Fisher Type Biological Population Task with Variable Density

In this paper, we discussed population model of two competing populations with non-linear double diffusion and variable density which described by nonlinear system of competing individuals. We identify new properties, such as finite speed of propagation, and localization of the outbreaks in a specific area.

Schistosoma mansoni Infection in Holochilus sciureus Shows Sex-Related Differences in Parasitological Patterns

Due to the different parasitological patterns found between sexes in human populations and in different biological models during Schistosoma mansoni infection, we proposed to investigate such differences using Holochilus sciureus rodent, besides confirming whether this rodent is suitable for experimental infections. In this sense, males and females of H. sciureus were infected with 200 cercariae from a human strain of S. mansoni. The number of eggs per gram of feces (epg) and the worms were quantified, besides histopathological analysis. Thus, it was shown that females have fewer epg, as well as a longer prepatent period. On the other hand, males had a lower recovery rate of adult worms. The histopathological analysis did not show differences between the sexes. Thus, we suggest that infection in H. sciureus females provides a favorable environment for the development of adult worms, despite impairing the parasite fecundity. In addition, H. sciureus may be an excellent biological model for S. mansoni experimental infections.

Exact solutions of nonlinear fractional differential equations by (G'/G)-expansion method

In this paper, we use the fractional complex transform and the （G＇/G）-expansion method to study the nonlinear fractional differential equations and find the exact solutions. The fractional complex transform is proposed to convert a partial fractional differential equation with Jumarie＇s modified Riemann-Liouville derivative into its ordinary differential equation. It is shown that the considered transform and method are very efficient and powerful in solving wide classes of nonlinear fractional order equations.

An efficient technique for two-dimensional fractional order biological population model

In this paper,we find the solutions for two-dimensional biological population model having fractional order using fractional natural decomposition method(FNDM).The proposed method is a graceful blend of decomposition scheme with natural transform,and three examples are considered to validate and illustrate its efficiency.The nature of FNDM solution has been captured for distinct arbitrary order.In order to illustrate the proficiency and reliability of the considered scheme,the numerical simulation has been presented.The obtained results illuminate that the considered method is easy to apply and more effective to examine the nature of multi-dimensional differential equations of fractional order arisen in connected areas of science and technology.

Nonlinearity of muscle stiffness

In this letter, a comparison between three types （two linear and one nonlinear） of models of skeletal muscle stiffness is shown. Results are compared with experimental data for biceps brachii in the case of muscle stretching and with the Hill equation for a biological muscle. It is shown that results for nonlinear stiffness model in case of length-force relationship fits to the experimental data.

A survey of the pursuit-evasion problem in swarm intelligence

For complex functions to emerge in artificial systems,it is important to understand the intrinsic mechanisms of biological swarm behaviors in nature.In this paper,we present a comprehensive survey of pursuit–evasion,which is a critical problem in biological groups.First,we review the problem of pursuit–evasion from three different perspectives:game theory,control theory and artificial intelligence,and bio-inspired perspectives.Then we provide an overview of the research on pursuit–evasion problems in biological systems and artificial systems.We summarize predator pursuit behavior and prey evasion behavior as predator–prey behavior.Next,we analyze the application of pursuit–evasion in artificial systems from three perspectives,i.e.,strong pursuer group vs.weak evader group,weak pursuer group vs.strong evader group,and equal-ability group.Finally,relevant prospects for future pursuit–evasion challenges are discussed.This survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios.

Advances in adaptive nonlinear manifolds and dimensionality reduction

Recent decades have witnessed a much increased demand for advanced,effective and efficient methods and tools for analyzing,understanding and dealing with data of increasingly complex,high dimensionality and large volume.Whether it is in biology,neuroscience,modern medicine and social sciences or in engineering and computer vision,data are being sampled,collected and cumulated in an unprecedented speed.It is no longer a trivial task to analyze huge amounts of high dimensional data.A systematic,automated way of interpreting data and representing them has become a great challenge facing almost all fields and research in this emerging area has flourished.Several lines of research have embarked on this timely challenge and tremendous progresses and advances have been made recently.Traditional and linear methods are being extended or enhanced in order to meet the new challenges.This paper elaborates on these recent advances and discusses various state-of-the-art algorithms proposed from statistics,geometry and adaptive neural networks.The developments mainly follow three lines:multidimensional scaling,eigen-decomposition as well as principal manifolds.Neural approaches and adaptive or incremental methods are also reviewed.In the first line,traditional multidimensional scaling(MDS)has been extended not only to be more adaptive such as neural scale,curvilinear component analysis(CCA)and visualization induced self-organizing map(ViSOM)for online learning,but also to be more local scaling such as Isomap for enhanced flexibility for nonlinear data sets.The second line extends linear principal component analysis(PCA)and has attracted a huge amount of interest and enjoyed flourishing advances with methods like kernel PCA(KPCA),locally linear embedding(LLE)and Laplacian eigenmap.The advantage is obvious:a nonlinear problem is transformed into a linear one and a unique solution can then be sought.The third line starts with the nonlinear principal curve and surface and links up with adaptive neural network approaches such as self-organizing map(SOM)and ViSOM.Many of these frameworks have been further improved and enhanced for incremental learning and mapping function generalization.This paper discusses these recent advances and their connections.Their application issues and implementation matters will also be briefly enlightened and commented on.