Impact of super absorbent polymers on early age behavior of ultra-high performance concrete walls

Early age cracking, a common problem for Ultra-High Performance Concrete (UHPC), is caused by Autogenous Shrinkage (AS) and self-desiccation arising from the chemical shrinkage during the cement hydration reactions when the deformation is restrained. However, to avoid the crack development initiated by AS, several solutions can be adopted; one example is the addition of a promising material, considered as an internal curing agent, the Super Absorbent Polymers (SAP) which limits the capillary depressions that can enhance the formation of the crack. In this study the main goal is to mitigate the shrinkage using SAPs in infrastructure under severe conditions. Therefore, a demonstrator wall was built simulating a typical case with high risk of cracking. With the help of fiber optic SOFO sensors embedded in the wall, real-time deformations are recorded and compared the demountable mechanical strain gauges (DEMEC) measurements to further investigate the behavior of SAPs in real scale infrastructure. The amount of extra water (in SAP) needed to mitigate shrinkage was determined by performing chemical shrinkage tests on different cement paste combinations. Tests of autogenous shrinkage were performed on mortars using corrugated tubes and showed that SAPs reduce to some extent the AS. Under restrained conditions via ring tests, SAP specimens did not crack. Therefore, SAPs were found promising towards mitigating the shrinkage and enhancing the early age behavior of concrete for a better durability

Learning about Actions and Events in Shared NeMuS

The categorization process of information from pure data or learned in unsuper- vised artificial neural networks is still manual, especially in the labeling phase. Such a process is fundamental to knowledge representation [6], especially for symbol-based systems like logic, natural language processing and textual infor- mation retrieval. Unfortunately, applying categorization theory in large volumes of data does not lead to good results mainly because there is no generic and systematic way of categorizing such data processed by artificial neural networks and joining investigated conceptual structures. Connectionist approaches are capable of extracting information from arti- ficial neural networks, but categorizing them as symbolic knowledge have been little explored. The obstacle lies on the difficulty to find logical justification from response patterns of these networks [2]. This gets worse when considering induc- tive learning from dynamic data which is very important to Cognitive Sciences that considers categorization as a mental operation of classifying objects, actions and events [1]. We shall address the discoveries of our on-going investigation on the problem of inductively learning (IL) from dynamic data by applying a novel framework for neural-symbolic representation and reasoning called share Neural Multi-Space (NeMuS) used in the Amao system[4]. Instead of woking like traditional ap- proaches for ILP, e.g. [5], Amao uses a shared NeMuS of a give background knowledge (BK) and uses inverse unification as the generalization mechanism of a set of logically connected expressions from the Herbrand Base (HB) of BK that defines positive examples

Quantum phase transitions in alternating spin-(1/2, 5/2) Heisenberg chains

The ground state spin-wave excitations and thermodynamic properties of two types of ferrimagnetic chains are investigated: the alternating spin-1/2 spin-5/2 chain and a similar chain with a spin-1/2 pendant attached to the spin-5/2 site. Results for magnetic susceptibility, magnetization and specific heat are obtained through the finite-temperature Lanczos method with the aim in describing available experimental data, as well as comparison with theoretical results from the semiclassical approximation and the low-temperature susceptibility expansion derived from Takahashi's modified spin-wave theory. In particular, we study in detail the temperature vs. magnetic field phase diagram of the spin-1/2 spin-5/2 chain, in which several low-temperature quantum phases are identified: the Luttinger Liquid phase, the ferrimagnetic plateau and the fully polarized one, and the respective quantum critical points and crossover lines

Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil, a limited number of pupils leaving high schools continue their education (up to 20%). Initial pioneering efforts of universities and companies to support pupils from underprivileged backgrounds, to be able to succeed in being accepted by universities include personalised learning solutions. However, initial findings show that typical distance learning problems occur with the pupil population: isolation, demotivation, and lack of engagement. Thus, researchers and companies proposed gamification. However, gamification design is traditionally exclusively based on theory-driven approaches and usually ignore the data itself. This paper takes a different approach, presenting a large-scale study that analysed, statistically and via machine learning (deep and shallow), the first batch of students trained with a Brazilian gamified intelligent learning software (called CamaleOn), to establish, via a grassroots method based on learning analytics, how gamification elements impact on student engagement. The exercise results in a novel proposal for real-time measurement on Massive Open Online Courses (MOOCs), potentially leading to iterative improvements of student support. It also specifically analyses the engagement patterns of an underserved community

Zeta function method and repulsive Casimir forces for an unusual pair of plates at finite temperature

We apply the generalized zeta function method to compute the Casimir energy and pressure between an unusual pair of parallel plates at finite temperature, namely: a perfectly conducting plate and an infinitely permeable one. The high and low temperature limits of these quantities are discussed; relationships between high and low temperature limits are estabkished by means of a modified version of the temperature inversion symmetry.Comment: latex file 9 pages, 3 figure

Decreased dentin tubules density and reduced thickness of peritubular dentin in hyperbilirubinemia-related green teeth

It is stated anecdotally that patients with liver diseases in childhood who develop green teeth have increased risk for rampant caries, which may be secondary to changes in dental structure. The aim of this study was to test the hypothesis that hyperbilirubinemia affects the dentin morphology of green teeth. Sixteen primary teeth were prepared and divided into two groups (green teeth, n = 8 and control, n = 8), which were transversely fractured across the cervical third of the dental crowns; dentin was prepared and sputter-coated with gold, and examined under a scanning electron microscope. The mean density and mean diameter of dentin tubules, as well as the thickness of peritubular dentin, were compared. Hyperbilirubinemia was associated with a decrease in the density of the dentin tubules (p< .01) and the thickness of peritubular dentin of green teeth (p< .01). There was a correlation between childhood hyperbilirubinemia and changes in the dentin morphology, including a decrease in the density of the dentin tubules and a reduction in the thickness of peritubular dentin in green teeth