Flexural behavior of hybrid PVA fiber and AR-Glass textile reinforced geopolymer composites

Textile reinforced mortar or concrete, a thin cementitious composite reinforced by non-corrosive polymer textile fabric, was developed and has been researched for its role on repair and strengthening of reinforced concrete (RC) structures. Due to embedment of polymeric textile fabric inside the cementitious matrix, many researchers argued the superiority of this technology than the externally bonded fiber reinforced polymer (FRP) sheet in RC in terms of prevention of debonding of FRP and durability in fire. However, due to use of cement rich matrix the existing development of textile reinforced concrete (TRC) need to be more environmental friendly by replacing cement based binder with geopolymeric binder. This paper presents a first study on the flexural behavior of alkali resistant glass fiber textile reinforced geopolymer (TRG). In this study, two types of geopolymer binder is considered. One is fly ash based heat cured geopolymer and the other is fly ash/slag blended ambient air cured geopolymer binder. Both geopolymer types are considered in the TRG and the results are benchmarked with the current cement based TRC. The effect of short polyvinyl alcohol (PVA) fiber as hybrid reinforced with alkali-resistant (AR) glass fiber textile on the flexural behavior of above TRC and TRGs is also studied. Results show deflection hardening behavior of both TRGs with higher flexural strength in heat cured TRG and higher deflection capacity at peak load in ambient air cured TRG. The increase in PVA fiber volume fraction from 1% to 1.5% did not show any improvement in flexural strength of both TRGs although TRC showed good improvement. In the case of deflection at peak load, an opposite phenomenon is observed where the deflection at peak load in both TRGs is increased due to increase in PVA fiber volume fractions

Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate.Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition

INTERNATIONAL PROFILE OF U.S. FOOD PROCESSORS

Agribusiness, International Relations/Trade,

Narrow band microwave radiation from a biased single-Cooper-pair transistor

We show that a single-Cooper-pair transistor (SCPT) electrometer emits narrow-band microwave radiation when biased in its sub-gap region. Photo activation of quasiparticle tunneling in a nearby SCPT is used to spectroscopically detect this radiation, in a configuration that closely mimics a qubit-electrometer integrated circuit. We identify emission lines due to Josephson radiation and radiative transport processes in the electrometer, and argue that a dissipative superconducting electrometer can severely disrupt the system it attempts to measure.Comment: 4 pages, 3 figure

A Hybrid Multicast-Unicast Infrastructure for Efficient Publish-Subscribe in Enterprise Networks

One of the main challenges in building a large scale publish-subscribe infrastructure in an enterprise network, is to provide the subscribers with the required information, while minimizing the consumed host and network resources. Typically, previous approaches utilize either IP multicast or point-to-point unicast for efficient dissemination of the information. In this work, we propose a novel hybrid framework, which is a combination of both multicast and unicast data dissemination. Our hybrid framework allows us to take the advantages of both multicast and unicast, while avoiding their drawbacks. We investigate several algorithms for computing the best mapping of publishers' transmissions into multicast and unicast transport. Using extensive simulations, we show that our hybrid framework reduces consumed host and network resources, outperforming traditional solutions. To insure the subscribers interests closely resemble those of real-world settings, our simulations are based on stock market data and on recorded IBM WebShpere subscriptions