Compatible Functions Research Articles

Hybrid Metasurfaces for Infrared-Multiband Radar Stealth-Compatible Materials Applications

The compatible stealth functionality in the infrared (IR) and radar wave bands is the most important research topic in the field of stealth material technology. Here, a new hybrid metasurface (HMS) for infrared-multiband radar stealth-compatible materials was proposed and studied. Two specifically designed metasurface layers that can control the infrared emission and microwave absorption were combined to realize radar and IR bi-stealth. The simulated and experimental results show that the HMS has five strong absorption peaks at f 1 = 6.35, f 2 = 8.38, f 3 = 12.10, f 4 = 15.37 and f 5 = 18.05 GHz. In addition, the emissivity of the proposed HMS is less than 0.32 from 3 to 14 μm and shows low emissivity characteristics in the infrared band. These results demonstrate that the proposal has practical application to multispectral stealth technology.

Meshless fracture analysis of 3D planar cracks with generalized thermo-mechanical stress intensity factors

This paper implements three dimensional meshless local Petrov–Galerkin method in the linear elastic fracture mechanics analyses of isotropic functionally graded and homogeneous materials under different thermo-mechanical loads. Energy equation based on Lord–Shulman non-Fourier heat conduction law coupled with equation of motion is applied to evaluate the effect of theoretical and realistic relaxation times on transient thermal stress intensity factors along 3D thorough or semielliptical cracks under thermo-mechanical shock. A new coordinate transform method with linear weight function is introduced to evaluate stress intensity factors by generalized interaction integral method based on incompatibility formulation. Simple linear test function is introduced, which is approximated via compatible radial basis functions and leads in eliminating internal boundary integrals of meshless method and reducing computational time. Shape functions are constructed in every Gauss point by determining the closest domain point method in preprocessing. Parametric studies are carried out in order to determine suitable radial basis functions shape parameters and penalty method parameter. Reasonable accuracy and the small number of used points are the main advantages of this method over finite element method.

TOWARDS A SCALE DEPENDENT FRAMEWORK FOR CREATING VARIO-SCALE MAPS

Abstract. Traditionally, the content for vario-scale maps has been created using a ‘one fits all’ approach equal for all scales. Initially only the delete/merge operation was used to create the vario-scale data using the importance and the compatibility functions defined at class level (and evaluated at instance level) to create the tGAP structure with planar partition as basis. In order to improve the generalization quality other operators and techniques have been added during the past years; e.g. simplify, collapse (change area to line representation), split, attractiveness regions and the introduction of the concept of linear network topology. However, the decision which operation to apply has been hard coded in our software, making it not very flexible. Further, we want to include awareness of the current scale when deciding what generalization operation to apply. For this purpose we propose the scale dependent framework (SDF), which at its core contains the encoding of the generalization knowledge in the SDF conceptual model. This SDF model covers the representation of scale dependent class importance, scale dependent class compatibility values, scale dependent attractiveness regions and last but not least specification of generalization operations that are scale and class dependent. By changing the settings in the SDF configuration and re-running the vario-scale generalization process, we can easily experiment in order to find best settings (for specific map user needs). In this paper we design the SDF conceptual model and explicitly motivate and define the scope of its expressiveness. We further present the improved scale dependent tGAP creation software and present initial results in the form of better created vario-scale map content.

Triple Verification Network for Generalised Zero-shot Learning.

Conventional Zero-shot Learning approaches often suffer from severe performance degradation in the Generalised Zero-shot Learning (GZSL) scenario, i.e. to recognise test images that are from both seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a Triple Verification problem and propose a unified optimisation of regression and compatibility functions, i.e. two main streams of existing ZSL approaches. The complementary losses mutually regularise the same model to mitigate the CO problem. Furthermore, we implement a deep extension paradigm to linear models and significantly outperforms state-of-the-art methods in both GZSL and ZSL scenarios on the four standard benchmarks.

Microwave‐Assisted and Base‐Promoted Domino Reaction of Cyclic N‐Sulfonyl Ketimines with α,β‐Disubstituted Nitroalkenes: A Green Access to 2‐Hydroxyarylpyridines

AbstractMicrowave‐assisted and organobase‐promoted eco‐friendly one‐pot protocol has been developed to construct an interesting class of pyridyl‐substituted phenols/α‐naphthols containing a carboxylate or aroyl group at C2 position on the pyridine ring. A series of N‐sulfonyl ketimines and different kinds of bielectrophiles, such as MBH acetates of nitroalkenes/α‐arylacetylenyl‐β‐arylnitroolefins, were involved in this cyclization process, which delivers good to high yields of aforementioned heterocycles and excels with several compatible functionalities.

GeoMaTree : Geometric and Mathematical Model Based Digital Tree Authoring System

This study proposes a method to develop an authoring system(GeoMaTree) for diverse trees that constitute a virtual landscape. The GeoMaTree system enables the simple, intuitive production of an efficient structure, and supports real-time processing. The core of the proposed system is a procedural modeling based on a mathematical model and an application that supports digital content creation on diverse platforms. The procedural modeling allows users to control the complex pattern of branch propagation through an intuitive process. The application is a multi-resolution 3D model that supports appropriate optimization for a tree structure. The application and a compatible function, with commercial tools for supporting the creation of realistic synthetic images and virtual landscapes, are implemented, and the proposed system is applied to a variety of 3D image content.

Antibacterial biocompatible arginine functionalized mono-layer graphene: No more risk of silver toxicity.

Antibacterial ability is vital in biological approaches as well as functional biomaterials. Besides, cytocompatibility aspect of biologic media, tissue and organs is always concern for appropriate synthesis. From the past, metallic/oxide phases of silver (Ag) material in various macro, micro or nano configurations have been widely used for antibacterial targets. While, background of Ag toxicity within particle, film and composites is posing gradual ion release affected by molecular bounding. Recent researches conducted to control, optimize and neutralize Ag limitations finding the benefits of ideal (∼ 100%) mediation against both Gram-negative and Gram-positive bacteria. Whereas, non-degradable releases history is still a challenge and its longer accumulation may cause to disrupt biostructures and disease risk. Thus, facile development of large-area organic materials with switchable bacteria toxicity and normal cell compatibility function is interesting for concerned approaches. Here, smart positively-charged stable arginine amino acid incorporated mono layer graphene (Arg-EMGr) nanobiocomposite introduced as useful antibacterial and safe bactericidal agent competitive with Ag direct. The immunity characteristic versus Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) comparably assessed with graphene oxide (GO) and different concentrations GO-AgNPs morphology. As cell viability matter, 1,3,5,7-days vitro culture assay shown attachment proliferation and cytotoxicity due to short interaction.

Dual Attention Network for Product Compatibility and Function Satisfiability Analysis

Product compatibility and functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products. Due to the huge number of products available online, it is infeasible to enumerate and test the compatibility and functionality of every product. In this paper, we address two closely related problems: product compatibility analysis and function satisfiability analysis, where the second problem is a generalization of the first problem (e.g., whether a product works with another product can be considered as a special function). We first identify a novel question and answering corpus that is up-to-date regarding product compatibility and functionality information. To allow automatic discovery product compatibility and functionality, we then propose a deep learning model called Dual Attention Network (DAN). Given a QA pair for a to-be-purchased product, DAN learns to 1) discover complementary products (or functions), and 2) accurately predict the actual compatibility (or satisfiability) of the discovered products (or functions). The challenges addressed by the model include the briefness of QAs, linguistic patterns indicating compatibility, and the appropriate fusion of questions and answers. We conduct experiments to quantitatively and qualitatively show that the identified products and functions have both high coverage and accuracy, compared with a wide spectrum of baselines.

On Principal Congruences in Distributive Lattices with a Commutative Monoidal Operation and an Implication

In this paper we introduce and study a variety of algebras that properly includes integral distributive commutative residuated lattices and weak Heyting algebras. Our main goal is to give a characterization of the principal congruences in this variety. We apply this description in order to study compatible functions.

Almost compatible functions and infinite length games

${\mathcal{A}}'(\kappa)$ asserts the existence of pairwise almost compatible finite-to-one functions $A\to \omega$ for each countable subset $A$ of $\kappa$. The existence of winning $2$-Markov strategies in several infinite-length games, including the Menger game on the one-point Lindelofication $\kappa^\dagger$ of $\kappa$, are guaranteed by ${\mathcal{A}}'(\kappa)$. ${\mathcal{A}}'(\kappa)$ is implied by the existence of cofinal Kurepa families of size $\kappa$, and thus, holds for all cardinals less than $\aleph _\omega$. It is consistent that ${\mathcal{A}}'({\aleph _\omega })$ fails; however, there must always be a winning $2$-Markov strategy for the second player in the Menger game on $\omega_\omega^\dagger$.

A visualization approach for robustness analysis in multicriteria disaggregation–aggregation approaches

The assessment of criteria weights in multicriteria decision aid methods of value system approaches, often leads to the assessment of preference models with a low degree of robustness due to the assessment of several compatible value functions. This research work presents a methodological frame, which aims to three issues: (a) to explain the nature of low robustness of the estimated preference models, utilising tomographical techniques, visual tools, and specific indices for the robustness measurement, (b) to support the Decision Makers (DMs) towards a deeper understanding of his/her value system, and (c) to acquire additional preference information by the DM estimating a value functions of higher robustness through focused interactive feedbacks. The main scope of this research work is to exploit and enrich the interactive nature of the multicriteria disaggregation–aggregation approach. The proposed methodology is applied on a real world case study along with the utilisation of the RAVI (Robustness Analysis with Visual and Interactive approaches) software developed to support the above mentioned methodological approach.

Automatic contouring of geologic fabric and finite strain data on the unit hyperboloid

Fabric and finite strain analysis, an integral part of studies of geologic structures and orogenic belts, is commonly done by the analysis of particles whose shapes can be approximated as ellipses. Given a sample of such particles, the mean and confidence intervals of particular parameters can be calculated, however, taking the extra step of plotting and contouring the density distribution can identify asymmetries or modes related to sedimentary fabrics or other factors. A common graphical strain analysis technique is to plot final ellipse ratios, Rf, versus orientations, ϕf on polar Elliott or Rf/ϕ plots to examine the density distribution. The plot may be contoured, however, it is desirable to have a contouring method that is rapid, reproducible, and based on the underlying geometry of the data. The unit hyperboloid, H2, gives a natural parameter space for two-dimensional strain, and various projections, including equal-area and stereographic, have useful properties for examining density distributions for anisotropy. An index, Ia, is given to quantify the magnitude and direction of anisotropy. Elliott and Rf/ϕ plots can be understood by applying hyperbolic geometry and recognizing them as projections of H2. These both distort area, however, so the equal-area projection is preferred for examining density distributions. The algorithm presented here gives fast, accurate, and reproducible contours of density distributions calculated directly on H2. The algorithm back-projects the data onto H2, where the density calculation is done at regular nodes using a weighting value based on the hyperboloid distribution, which is then contoured. It is implemented as an Octave compatible MATLAB function that plots ellipse data using a variety of projections, and calculates and displays contours of their density distribution on H2.

New single-image super-resolution reconstruction using MRF model

Abstract Learning-based single-image super-resolution reconstruction (LSISRR) problem using Markov Random Field (LSISRR-MRF) is a robust and integrated framework for both natural and face image super-resolution. The spatial relationships between patches are learned from the training images using the Markov network, and the missing details in the unknown high-resolution (HR) images are predicted using the learned relationship. However, selection of efficient training images with better structural similarity to the input low-resolution (LR) image and appropriate selection of compatibility constraints to measure the correlation between patches affect a lot to the reconstruction results. Besides these, selection of optimal candidate patches for each input LR patch from a pool of training patches is a time-consuming process. To avoid these pitfalls, an improved and fast LSISRR-MRF model is proposed here. A statistical framework, i.e., spatiogram based matching is used to choose efficient training images. Searching of candidate patches is performed in a compact and well-structured search space known as epitomic representation. Smoothness preserving compatibility functions are utilized to well constraint the correlation between patches. Efficacy of the suggested method for generating a high-quality HR image is validated via several experimental analyses on both synthetic and real-time images over some of the state of art methods.

YARP-ROS Inter-Operation in a 2D Navigation Task.

This paper presents some recent developments in YARP middleware, aimed to improve its integration with ROS. They include a new mechanism to read/write ROS transform frames and a new set of standard interfaces to intercommunicate with the ROS navigation stack. A novel set of YARP companion modules, which provide basic navigation functionalities for robots unable to run ROS, is also presented. These modules are optional, independent from each other, and they provide compatible functionalities to well-known packages available inside ROS framework. This paper also discusses how developers can customize their own hybrid YARP-ROS environment in the way it best suits their needs (e.g., the system can be configured to have a YARP application sending navigation commands to a ROS path planner, or vice versa). A number of available possibilities is presented through a set of chosen test cases applied to both real and simulated robots. Finally, example applications discussed in this paper are also made available to the community by providing snippets of code and links to source files hosted on github repository https://github.com/robotology.1

Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery

Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.

Thermal, mechanical, dielectric, and morphological study of dielectric filler–based thermoplastic nanocomposites for electromechanical applications

Dielectric nanocomposite elastomers based on poly(styrene-ethylene/butylene-styrene) (SEBS) and SEBS-grafted-maleic anhydride (SEBS-g-MA) with barium titanate (BT) suitable for electroactive applications were successfully manufactured by using two corotating twin extrusion systems. The main purpose of the work was to investigate the thermal, mechanical, dielectric, and morphological effects of additives on SEBS and SEBS-g-MA to widen their applications for electroactive applications using fast and more cost-effective simple production process. The morphological characterization showed a good and bad dispersion of BT into SEBS-g-MA and SEBS with 34.9% and −3% dielectric permittivity change in SEBS-g-MA and SEBS upon addition of 10 wt% BT. In addition, dielectric permittivity change, thermal change (enthalpy relaxation and thermal transitions), and mechanical (Young’s modulus, hysteresis loss under multiple stress cycles, storage modulus, loss modulus, and tan δ) properties of elastomers were found to be a function of additive concentration, compatibility and interaction between elastomers and additive type, orientation of additives, and reinforcing factors of additives in elastomers. A simple and effective modeling technique was used to demonstrate the effects of dielectric properties on nanocomposites due to poor dispersion of additives.

Heterotelechelic and In-Chain Polar Functionalized Stereoregular Poly(dienes)

Stereoregular trans-poly(dienes) with three different types of functional groups were synthesized by insertion polymerization. The functional groups are located simultaneously in the polymer backbone and at both chain ends, yielding heterotelechelic and in-chain functionalized polymers. A combination of three compatible functionalization methods allows for the syntheses of these particular polymers. Nd(BH4)3·(THF)3 catalyzes, after activation with MgR2, the direct copolymerization of 1,3-butadiene or isoprene with polar functionalized dienes, thus enabling the functionalization of the polymer’s backbone. The use of functionalized organo-Mg compounds, e.g., (PhS-C5H10)2Mg, for the activation of Nd(BH4)3·(THF)3 enables the functionalization of the initiating chain-end and quenching of the polymerization with suitable reagents; e.g., Si(OEt)4 allows for the functionalization of the terminating chain-end.

User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment’s dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.

Hypothesis: Local variations in the speed of individual DNA replication forks determine the phenotype of daughter cells

How a cell coordinates its thousands of different constituents to achieve coherent – but different – phenotypes is far from fully understood. It is clear though that daughter cells with different phenotypes can be generated by the cell cycle, which comprises the events of chromosome replication, chromosome segregation and cell division. In line with this, recent experiments are consistent with an intimate relationship in bacteria between the speed of chromosome replication at a fork(s) and metabolism. The process of chromosome replication progressively changes the copy number of genes and sites in a linear order. This raises the possibility that speeding up or slowing down or even pausing replication for different times at different sites in the chromosome might be combined with various mechanisms leading to local cooperation (for example, the transcription of a gene leading to more transcription of that gene) and to global competition (for example, a gene having to compete with all the other genes for the transcriptional apparatus). If so, such replication-phenotype coupling could produce different patterns of gene expression and metabolic activity. Indeed, replication-phenotype coupling may constitute a powerful and fundamental way of generating coherent phenotypes, that is, phenotypes in which the cell's constituents perform compatible functions (rather than, for example, trying to maintain growth and to shut down growth simultaneously). In this hypothesis, such coupling would involve the dynamics of the spatially extended assemblies of molecules and macromolecules termed 'hyperstructures'. As a prelude to testing this hypothesis, we discuss some of the parameters that will need to be explored by bench experimentation and computer simulation.

The Effects of Thermoplastic Polyurethane on the Structure and Mechanical Properties of Modified Polypropylene Blends

In this study, a melt-compounding process was used to produce ordinary polypropylene (PP)/thermoplastic polyurethane (TPU) blends and modified impact-resistant polypropylene (MPP)/TPU blends. The influences of TPU on the blending morphology, melting behavior, crystallization behavior, and mechanical properties of these blends were investigated. Differential scanning calorimetry results show that using TPU influences the crystalline and melting behavior of PP/TPU blends, but not of MPP/TPU blends. X-ray diffraction analysis indicated that the presence of TPU neither influences the crystalline structure of PP nor creates new crystalline peaks. The mechanical property test results revealed that PP/TPU blends demonstrate better tensile strength, Young’s modulus, and flexural strength, whereas MPP/TPU blends exhibit higher energy absorption and impact strength. This study is expected to produce optimal products with the preferred continuous and dispersive phases for end users, designing multiple and compatible functionalities of blends.