Taenia solium taeniasis and cysticercosis:extinction or outbreak

Taenia solium taeniasis and cysticercosis are neglected zoonotic diseases that affect human health and economies of developing countries.In this work,we formulate and analyze deterministic and continuous time Markov chain(CTMC)stochastic models to determine parameters that drive Taenia solium taeniasis and cysticercosis and the likelihood of their extinction.The basic reproduction number R0 is computed by the next generation matrix approach,sensitivity index of each parameter in R0 is derived by the normalized forward sensitivity index and the likelihood of diseases’extinction is computed by the multitype branching process.The analysis shows that humans with Taenia solium taeniasis,infectious pork and Taenia solium eggs in the environment play an important role in the transmission of Taenia solium taeniasis and cysticercosis,and the model exhibits forward bifurcation at R0=1.This implies that R0<1 is a sufficient condition to eliminate Taenia solium taeniasis and cysticercosis.For CTMC model,analysis shows that the probability of Taenia solium taeniasis and cysticercosis extinction is high if the diseases emerge from humans with Taenia solium cysticercosis and there is an outbreak if the diseases emerge from either humans with Taenia solium taeniasis or infectious pork or Taenia solium eggs in the environment.To control Taenia solium taeniasis and cysticercosis,the intervention strategies should focus on improving hygiene and sanitation for reducing shedding rate of Taenia solium eggs in the environment,inspection of pork for reducing the rate of acquiring Taenia solium taeniasis and spraying of insecticides for killing Taenia solium eggs in the environment.

Probabilistic Safety Analysis of High Speed and Conventional Lines Using Bayesian Networks

A Bayesian network approach is presented for probabilistic safety analysis(PSA)of railway lines.The idea consists of identifying and reproducing all the elements that the train encounters when circulating along a railway line,such as light and speed limit signals,tunnel or viaduct entries or exits,cuttings and embankments,acoustic sounds received in the cabin,curves,etc.In addition,since the human error is very relevant for safety evaluation,the automatic train protection(ATP)systems and the driver behaviour and its time evolution are modelled and taken into account to determine the probabilities of human errors.The nodes of the Bayesian network,their links and the associated probability tables are automatically constructed based on the line data that need to be carefully given.The conditional probability tables are reproduced by closed formulas,which facilitate the modelling and the sensitivity analysis.A sorted list of the most dangerous elements in the line is obtained,which permits making decisions about the line safety and programming maintenance operations in order to optimize them and reduce the maintenance costs substantially.The proposed methodology is illustrated by its application to several cases that include real lines such as the Palencia-Santander and the Dublin-Belfast lines.

A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations

We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks and employ the Fourier positional encodings to enable the approximation of high-frequency modes.We formulate a self-supervised training objective for spectral learning and propose a novel regularization mechanism to ensure that the network finds the exact eigenfunctions instead of a space spanned by the eigenfunctions.Furthermore,we investigate the effect of weight normalization as a mechanism to alleviate the risk of recovering linear dependent modes,allowing us to accurately recover a large number of eigenpairs.The effectiveness of our methods is demonstrated across a collection of representative benchmarks including both local and non-local diffusion operators,as well as high-dimensional time-series data from a video sequence.Our results indicate that the present algorithm can outperform competing approaches in terms of both approximation accuracy and computational cost.

A Novel Segment White Matter Hyperintensities Approach for Detecting Alzheimer

Segmentation has been an effective step that needs to be done before the classification or detection of an anomaly like Alzheimer’s on a brain scan.Segmentation helps detect pixels of the same intensity or volume and group them together as one class or region,where in that particular region of interest(ROI)can be concentrated on,rather than focusing on the entire image.In this paper White Matter Hyperintensities(WMH)is taken as a strong biomarker that supports and determines the presence of Alzheimer’s.As thefirst step a proper segmentation of the lesions has to be carried out.As pointed out in various other research papers,when the WMH area is very small or in places like the Septum Pellucidum the detection of the lesion is hard tofind.To overcome such problem areas a very optimized and accurate Threshold would be required to have a precise segmentation to detect the area of localization.This would help in proper detection and classification of the Anomaly.In this paper an elaborate comparison of various thresholding techniques has been done for segmentation.A novel idea for detection of Alzheimer’s has been presented in this paper,which encompasses the effectiveness of an optimized and adaptive technique.The Unet architecture has been taken as the baseline model with an adaptive kernel model embedded within the architecture.Various state-of-the-art technologies have been used with the dataset and a comparative study has been presented with the current architecture used in the paper.The lesion segmentation in narrow areas has accurately been detected compared to the other models and the number of false positives has been reduced to a great extent.

Three-dimensional detonation cellular structures in rectangular ducts using an improved CESE scheme

The three-dimensional premixed H2-O2 detonation propagation in rectangular ducts is simulated using an in-house parallel detonation code based on the second-order space-time conservation element and solution element（CE/SE） scheme.The simulation reproduces three typical cellular structures by setting appropriate cross-sectional size and initial perturbation in square tubes.As the cross-sectional size decreases,critical cellular structures transforming the rectangular or diagonal mode into the spinning mode are obtained and discussed in the perspective of phase variation as well as decreasing of triple point lines.Furthermore,multiple cellular structures are observed through examples with typical aspect ratios.Utilizing the visualization of detailed three-dimensional structures,their formation mechanism is further analyzed.

The Remnant of GW170817: A Trapped Neutron Star with a Massive Incompressible Superfluid Core

Our bimetric spacetime model of glitching pulsars is applied to the remnant of GW170817. Accordingly, pulsars are born with embryonic incompressible superconducting gluon-quark superfluid cores (SuSu-matter) that are embedded in Minkowski spacetime, whereas the ambient compressible and dissipative media (CDM) are imbedded in curved spacetime. As pulsars cool down, the equilibrium between both spacetime is altered, thereby triggering the well-observed glitch phenomena. Based thereon and assuming all neutron stars (NSs) to be born with the same initial mass of , we argue that the remnant of GW170817 should be a relatively faint NS with a massive central core made of SuSu-matter. The effective mass and radius of the remnant are predicted to be and Rrem=10.764 Km, whereas the mass of the enclosed SuSu-core is . Here, about 1/2Mcore is an energy enhancement triggered by the phase transition of the gluon-quark-plasma from the microscopic into macroscopic scale. The current compactness of the remnant is , but predicted to increase as the CDM and cools down, rendering the remnant an invisible dark energy object, and therefore to an excellent black hole candidate.

Character Modelling with Sketches and ODE-Based Shape Creation

Character models have enormous applications in industry.Efficient creation of detailed character models is an important topic.This paper proposes a new and easy-to-use technique to quickly create detailed character models from sketches.The proposed technique consists of two main components:primitive deformer and shape generators.With this technique,2D silhouette contours of a character model are drawn or extracted from an image or sketch.Then,proper geometric primitives are selected and aligned with the corresponding 2D silhouette contours.After that,a primitive deformer is used to create a base mesh and three shape generators are used to add 3D details to the base mesh.The primitive deformer and three shape generators are developed from ODE-driven deformations.The primitive deformer deforms the aligned geometric primitives to exactly match the 2D silhouette contours in one view plane and obtains a base mesh of a character model consisting of deformed primitives.The shape generators are used to add 3D details to the base mesh by creating local 3D models.The experimental results demonstrate that the new technique can quickly create detailed 3D character models from sketches with few manual operations.The new technique is physics-based and easy to learn and use.

Broadband vectorial ultrathin optics with experimental efficiency up to 99%in the visible region via universal approximators

Integrating conventional optics into compact nanostructured surfaces is the goal of flat optics.Despite the enormous progress in this technology,there are still critical challenges for real-world applications due to the limited operational efficiency in the visible region,on average lower than 60%,which originates from absorption losses in wavelengththick(~500 nm)structures.Another issue is the realization of on-demand optical components for controlling vectorial light at visible frequencies simultaneously in both reflection and transmission and with a predetermined wavefront shape.In this work,we developed an inverse design approach that allows the realization of highly efficient(up to 99%)ultrathin(down to 50 nm thick)optics for vectorial light control with broadband input-output responses in the visible and near-IR regions with a desired wavefront shape.The approach leverages suitably engineered semiconductor nanostructures,which behave as a neural network that can approximate a user-defined input-output function.Nearunity performance results from the ultrathin nature of these surfaces,which reduces absorption losses to nearnegligible values.Experimentally,we discuss polarizing beam splitters,comparing their performance with the best results obtained from both direct and inverse design techniques,and new flat-optics components represented by dichroic mirrors and the basic unit of a flat-optics display that creates full colours by using only two subpixels,overcoming the limitations of conventional LCD/OLED technologies that require three subpixels for each composite colour.Our devices can be manufactured with a complementary metal-oxide-semiconductor(CMOS)-compatible process,making them scalable for mass production at low cost.

C^0DISCONTINUOUS GALERKIN METHODS FOR A PLATE FRICTIONAL CONTACT PROBLEM

Numerous C^0 discontinuous Galerkin (DG) schemes for the Kirchhoff plate bending problem are extended to solve a plate frictional contact problem, which is a fourth-order elliptic variational inequality of the second kind. This variational inequality contains a nondifferentiable term due to the frictional contact. We prove that these C^0 DG methods are consis tent and st able, and derive optimal order error estima tes for the quadratic element. A numerical example is presented to show the performance of the C^0 DG methods;and the numerical convergence orders confirm the theoretical prediction.

NUMERICAL ANALYSIS OF ELLIPTIC HEMIVARIATIONAL INEQUALITIES FOR SEMIPERMEABLE MEDIA

In this paper, we consider elliptic hemivariational inequalities arising in applications in semipermeable media. In its general form, the model includes both interior and boundary semipermeability terms. Detailed study is given on the hemivariational inequality in the case of isotropic and homogeneous semipermeable media. Solution existence and uniqueness of the problem are explored. Convergence of the Galerkin method is shown under the basic solution regularity available from the existence result. An optimal order error estimate is derived for the linear finite element solution under suitable solution regularity assumptions. The results can be readily extended to the study of more general hemivariational inequalities for non-isotropic and heterogeneous semipermeable media with interior semipermeability and/or boundary semiperrneability. Numerical examples are presented to show the performance of the finite element approximations;in particular, the theoretically predicted optimal first order convergence in H' norm of the linear element solutions is clearly observed.

Anomalous Sorption Kinetics of Self-Interacting Particles by a Spherical Trap

In this paper we propose a computational framework for the investigation of the correlated motion between positive and negative ions exposed to the attraction of a bubble surface that mimics the(oscillating)cell membrane.Specifically we aim to investigate the role of surface traps with substances freely diffusing around the cell.The physical system we want to model is an anchored gas drop submitted to a diffusive flowof charged surfactants(ions).When the diffusing surfactantsmeet the surface of the bubble,they are reversibly adsorbed and their local concentration is accurately measured.The correlated diffusion of surfactants is described by a Poisson-Nernst-Planck(PNP)system,in which the drift term is given by the gradient of a potential which includes both the effect of the bubble and the Coulomb interaction between the carriers.The latter term is obtained from the solution of a self-consistent Poisson equation.For very short Debye lengths one can adopt the so called Quasi-Neutral limit which drastically simplifies the system,thus allowing for much faster numerical simulations.The paper has four main objectives.The first one is to present a PNP model that describes ion charges in presence of a trap.The second one is to provide benchmark tests for the validation of simplified multiscale models under current development[1].The third one is to explore the relevance of the term describing the interaction among the apolar tails of the anions.The last one is to quantitatively explore the validity of the Quasi-Neutral limit by comparison with detailed numerical simulation for smaller and smaller Debye lengths.In order to reach these goals,we propose a simple and efficient Alternate Direction Implicit method for the numerical solution of the non-linear PNP system,which guarantees second order accuracy both in space and time,without requiring solution of nonlinear equation at each time step.New semi-implicit scheme for a simplified PNP system near quasi neutrality is also proposed.

Optimal control of alcoholism spreading through awareness over multiplex network

This paper proposes the SISRS epidemic model to represent alcohol addiction among people.The spreading of alcohol addiction is controlled by creating awareness among the people and also by treating them to overcome it.Multiplex network is used to study the dynamics of addiction.Alcoholism spreads over the physical contact layer and follows the SISRS process whereas human awareness spreads over the virtual contact layer and follows the UAU process.Based on the Microscopic Markov Chain Approach competing dynamics of spreading of alcohol addiction and human awareness diffusion are studied.Necessary conditions for the existence of an alcohol-free population are found.An optimal control problem using a suitable cost index is formulated to reduce the alcohol addicts and the optimal control strategy using Pontryagin’s Minimum Principle is determined.Numerical results are developed to find the effect of various parameters and to analyze the effects of different control strategies.The results obtained from this model are closer to the data collected in the National Survey of Drug Use and Health(NSDUH)from 2002 to 2018.