Impact of the solidification path of FeOx–SiO2 slags on the resultant inorganic polymers

Aiming to reduce the carbon dioxide emissions associated with cement production, alternative binders such as inorganic polymers currently receive substantial attention and slags from the non-ferrous metallurgy are promising precursors. However, studies  that correlate their chemistry and crystallinity with the newly formed binder remain limited. In this work, the effect of three different solidification methods on glass formation and reactivity of FeO_x–SiO₂ slags, as well as on the molecular structure of the resultant Fe-rich inorganic polymers, was investigated. The inorganic polymers were synthesized by mixing the slags (approximate molar ratio FeO/SiO₂ = 1.6) with an alkali silicate solution (molar ratios SiO₂/Na₂O = 1.6 and H₂O/Na₂O = 25). Results demonstrated that higher cooling rates promoted higher glass formation and faster reaction kinetics when the slags were activated. ⁵⁷Fe Mössbauer spectroscopy indicated that all the slags consisted predominantly of Fe²⁺ ions with a minor amount of Fe³⁺ ions, regardless of the variability in glass content. The binder phase of all inorganic polymers consisted of iron in both Fe²⁺ and Fe³⁺ states, after 28 days of curing. After pulverizing the inorganic polymer pastes and exposing the powder to air for 28 additional days, the Fe²⁺ state in the binder transformed to Fe³⁺. The compressive strength evolution of the three slags showed that the 2-day strength was higher for the samples with a higher amorphous fraction, while after 28 days, this influence was less pronounced.     

Detection and Identification of Probiotic Lactobacillus plantarum Strains by Multiplex PCR Using RAPD-Derived Primers

Lactobacillus plantarum 2035 and Lactobacillus plantarum ACA-DC 2640 are two lactic acid bacteria (LAB) strains that have been isolated from Feta cheese. Both display significant potential for the production of novel probiotic food products. The aim of the present study was the development of an accurate and efficient method for the molecular detection and identification of the above strains in a single reaction. A multiplex PCR assay was designed for each strain, based on specific primers derived from Random Amplified Polymorphic DNA (RAPD) Sequenced Characterized Amplified Region (SCAR) analysis. The specificity of the assay was tested with a total of 23 different LAB strains, for L. plantarum 2035 and L. plantarum ACA-DC 2640. The multiplex PCR assay was also successfully applied for the detection of the above cultures in yogurt samples prepared in our lab. The proposed methodology may be applied for monitoring the presence of these strains in food products, thus evaluating their probiotic character. Moreover, our strategy may be adapted for other novel LAB strains with probiotic potential, thus providing a powerful tool for molecular discrimination that could be invaluable to the food industry.     

A sparse nonparametric hierarchical Bayesian approach towards inductive transfer for preference modeling

In this paper, we present a novel methodology for preference learning based on the concept of inductive transfer. Specifically, we introduce a nonparametric hierarchical Bayesian multitask learning approach, based on the notion that human subjects may cluster together forming groups of individuals with similar preference rationale (but not identical preferences). Our approach is facilitated by the utilization of a Dirichlet process prior, which allows for the automatic inference of the most appropriate number of subject groups (clusters), as well as the employment of the automatic relevance determination (ARD) mechanism, giving rise to a sparse nature for our model, which significantly enhances its computational efficiency. We explore the efficacy of our novel approach by applying it to both a synthetic experiment and a real-world music recommendation application. As we show, our approach offers a significant enhancement in the effectiveness of knowledge transfer in statistical preference learning applications, being capable of correctly inferring the actual number of human subject groups in a modeled dataset, and limiting knowledge transfer only to subjects belonging to the same group (wherein knowledge transferability is more likely).     

Model-free robot joint position regulation and tracking with prescribed performance guarantees

The problem of robot joint position control with prescribed performance guarantees is considered; the control objective is the error evolution within prescribed performance bounds in both problems of regulation and tracking. The proposed controllers do not utilize either the robot dynamic model or any approximation structures and are composed by simple PID or PD controllers enhanced by a proportional term of a transformed error through a transformation related gain. Under a sufficient condition for the damping gain, the proposed controllers are able to guarantee (i) predefined minimum speed of convergence, maximum steady state error and overshoot concerning the position error and (ii) uniformly ultimate boundedness (UUB) of the velocity error. The use of the integral term reduces residual errors allowing the proof of asymptotic convergence of both velocity and position errors to zero for the regulation problem under constant disturbances. Performance is a priori guaranteed irrespective of the selection of the control gain values. Simulation results of a three dof spatial robotic manipulator and experimental results of one dof manipulator are given to confirm the theoretical findings.     

Oligosaccharide model of the vascular endothelial glycocalyx in physiological flow

Experiments have consistently revealed the pivotal role of the endothelial glycocalyx layer in vasoregulation and the layer’s contribution to mechanotransduction pathways. However, the exact mechanism by which the glycocalyx mediates fluid shear stress remains elusive. This study employs atomic-scale molecular simulations with the aim of investigating the conformational and orientation properties of highly flexible oligosaccharide components of the glycocalyx and their suitability as transduction molecules under hydrodynamic loading. Fluid flow was shown to have nearly no effect on the conformation populations explored by the oligosaccharide, in comparison with static (diffusion) conditions. However, the glycan exhibited a significant orientation change, when compared to simple diffusion, aligning itself with the flow direction. It is the tethered end of the glycan, an asparagine amino acid, which experienced conformational changes as a result of this flow-induced bias. Our results suggest that shear flow through the layer can have an impact on the conformational properties of saccharide-decorated transmembrane proteins, thus acting as a mechanosensor.     

Community detection in Social Media

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.     

Classical and alternative fuel mix optimization in cement production using mathematical programming

In this paper a systematic methodology is presented for the simultaneous optimal selection of raw materials, fossil fuels and alternative fuels in cement production. The aim is to offer a generic mathematical formulation that can be used as the basis for developing case specific mathematical formulations that can assist the strategic decision-making process. The mathematical model presented takes into consideration the essential elements of a cement plant operation. The final formulation is a mixed integer linear programming problem that aims at minimizing the overall operating cost. A realistic case study is presented which demonstrates the usefulness of the proposed mathematical programming methodology.     

Pressure- and Temperature-Driven Flow Through Triangular and Trapezoidal Microchannels

A detailed study of pressure- and temperature-driven flows through long channels of triangular and trapezoidal cross sections is carried out. Due to the imposed pressure and temperature gradients there is a combined gas flow consisting of a thermal creep flow from the cold toward the hot reservoir and a Poiseuille flow from the high- toward the low-pressure reservoir. The formulation is based on the linearized Shakhov model subject to Maxwell boundary conditions, and it is solved numerically using a finite-difference scheme in the physical space and the discrete velocity method in the molecular velocity space. The results are valid in the whole range of the Knudsen number. In addition to the dimensionless flow rates, a methodology is presented to estimate for a certain set of input data the mass flow rates and the pressure distribution along the channel. Finally, special attention is given to the case of zero net mass flow and to the computation of the coefficient of the thermomolecular pressure difference.