An elasto-plastic damage model for concrete

Constitutive modeling of concrete using continuum damage mechanics and plasticity theory is presented in this work. In order to derive the constitutive equations the strain equivalence hypothesis is adopted. Menetrey-William type yield function (in the effective stress space) with multiple hardening functions is used to define plastic loading of the material. Non-associated plastic flow rule is used to control inelastic dilatancy. DruckerPrager type function is chosen as a plastic potential. Damage is assumed to be isotropic and two damage variables are used to represent tensile and compressive damage independently. Damage parameter is driven based on the plastic strain. Fully implicit integration scheme is employed and the consistent elastic-plastic-damage tangent operator is also derived. The overall performance of the proposed model is verified by comparing the model predictions to various numerical simulations, cyclic uniaxial tensile and compressive tests, monotonic biaxial compression test and reinforced concrete beam test

Parasite motility is critical for virulence of African trypanosomes.

African trypanosomes, Trypanosoma brucei spp., are lethal pathogens that cause substantial human suffering and limit economic development in some of the world's most impoverished regions. The name Trypanosoma ("auger cell") derives from the parasite's distinctive motility, which is driven by a single flagellum. However, despite decades of study, a requirement for trypanosome motility in mammalian host infection has not been established. LC1 is a conserved dynein subunit required for flagellar motility. Prior studies with a conditional RNAi-based LC1 mutant, RNAi-K/R, revealed that parasites with defective motility could infect mice. However, RNAi-K/R retained residual expression of wild-type LC1 and residual motility, thus precluding definitive interpretation. To overcome these limitations, here we generate constitutive mutants in which both LC1 alleles are replaced with mutant versions. These double knock-in mutants show reduced motility compared to RNAi-K/R and are viable in culture, but are unable to maintain bloodstream infection in mice. The virulence defect is independent of infection route but dependent on an intact host immune system. By comparing different mutants, we also reveal a critical dependence on the LC1 N-terminus for motility and virulence. Our findings demonstrate that trypanosome motility is critical for establishment and maintenance of bloodstream infection, implicating dynein-dependent flagellar motility as a potential drug target

The Collatz tree as a Hilbert hotel:a proof of the 3x + 1 conjecture

The Collatz conjecture maintains that each natural number $n$ is a node in the Collatz tree with a root path to $1$ generated by iterations of the Collatz function, which connects even $n$ to $n/2$ and odd $n$ to $3n+1$. The conjecture holds if a binary subtree $T_{\ge 0}$ exists with as nodes $\textit{all}$ branching numbers constituting the congruence classes ${\left[4,16\right]}_{18}$ with density $2/18$. For each branching number, the $\textit{upward}$ and $\textit{rightward}$ function generate arrows to two branching numbers in the directions $2n$ and $(n-1)/3$. In the automorphism graph of tree $T_{\ge 0}$. The iterates and conjugates of these two functions build outer paths from tree $T_{\ge 0}$ at its root node to two infinite sequences of nodes containing nested subtrees and disjoint cotrees. The nodes and conjugate inner arrows within each subtree and within each cotree correspond one-to-one to the nodes and arrows within tree $T_{\ge 0}$. The cumulative density of the congruence classes of numbers in disjoint cotrees of $T_{\ge 0}$ amounts to the density $2/18$ of all branching numbers, thus proving the conjecture.Comment: Collatz paths between branching classes $[4,16]_18$ and partitioning of the binary tree $T_{ge 0}$ into disjoint cotrees now validate in v5 the density proofs in previous versions (v1 31 Aug 2000

Misclassification Risk and Uncertainty Quantification in Deep Classifiers

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors

Heuristic-based approaches for (CP)-nets in negotiation

CP-Nets have proven to be an effective representation for capturing preferences. However, their use in multiagent negotiation is not straightforward. The main reason for this is that CP-Nets capture partial ordering of preferences, whereas negotiating agents are required to compare any two outcomes based on the request and offers. This makes it necessary for agents to generate total orders from their CP-Nets. We have previously proposed a heuristic to generate total orders from a given CP-Net. This paper proposes another heuristic based on Borda count, applies it in negotiation, and compares its performance with the previous heuristic

Introduction to the special issue of optical biosensors

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A baseline for non-linear bilateral negotiations: the full results of the agents competing in ANAC 2014

In the past few years, there is a growing interest in automated negotiation in which software agents facilitate negotiation on behalf of their users and try to reach joint agreements. The potential value of developing such mechanisms becomes enormous when negotiation domain is too complex for humans to find agreements (e.g. e-commerce) and when software components need to reach agreements to work together (e.g. web-service composition). Here, one of the major challenges is to design agents that are able to deal with incomplete information about their opponents in negotiation as well as to effectively negotiate on their users’ behalves. To facilitate the research in this field, an automated negotiating agent competition has been organized yearly. This paper introduces the research challenges in Automated Negotiating Agent Competition (ANAC) 2014 and explains the competition set up and results. Furthermore, a detailed analysis of the best performing five agents has been examined