Modeling of tension stiffening in reinforced cement composites: Part I. Theoretical modeling

This is the second part of a two-part paper involving a numerical model for simulations of tensile behaviour of reinforced cement-based composites. The model simulates the tensile stress strain response of a brittle matrix composite, tension stiffening effect of cracked matrix, and crack spacing evolution in tension members. The paper presents the simulations of four independent experimental results obtained from literature: steel reinforced concrete, concrete reinforced with steel and glass fiber reinforced plastic (GFRP), alkali resistant (AR) glass textile reinforced concrete and AR glass fabric reinforced cement pastes. The first and third experiments had complete input information for the simulations, and the predicted responses compare quite well to the experimental results. The second and last experiments did not have complete input data but, the properties can be estimated from other sources or by means of back calculations. The predicted responses reasonably agreed with the experimental results.     

Defending recommender systems: detection of profile injection attacks

Collaborative recommender systems are known to be highly vulnerable to profile injection attacks, attacks that involve the insertion of biased profiles into the ratings database for the purpose of altering the system’s recommendation behavior. Prior work has shown when profiles are reverse engineered to maximize influence; even a small number of malicious profiles can significantly bias the system. This paper describes a classification approach to the problem of detecting and responding to profile injection attacks. A number of attributes are identified that distinguish characteristics present in attack profiles in general, as well as an attribute generation approach for detecting profiles based on reverse engineered attack models. Three well-known classification algorithms are then used to demonstrate the combined benefit of these attributes and the impact the selection of classifier has with respect to improving the robustness of the recommender system. Our study demonstrates this technique significantly reduces the impact of the most powerful attack models previously studied, particularly when combined with a support vector machine classifier. This research was supported in part by the National Science Foundation Cyber Trust program under Grant IIS-0430303 and the National Science Foundation IGERT program under Grant DGE-0549489.     

Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization

Web usage mining, possibly used in conjunction with standard approaches to personalization such as collaborative filtering, can help address some of the shortcomings of these techniques, including reliance on subjective user ratings, lack of scalability, and poor performance in the face of high-dimensional and sparse data. However, the discovery of patterns from usage data by itself is not sufficient for performing the personalization tasks. The critical step is the effective derivation of good quality and useful (i.e., actionable) aggregate usage profiles from these patterns. In this paper we present and experimentally evaluate two techniques, based on clustering of user transactions and clustering of pageviews, in order to discover overlapping aggregate profiles that can be effectively used by recommender systems for real-time Web personalization. We evaluate these techniques both in terms of the quality of the individual profiles generated, as well as in the context of providing recommendations as an integrated part of a personalization engine. In particular, our results indicate that using the generated aggregate profiles, we can achieve effective personalization at early stages of users' visits to a site, based only on anonymous clickstream data and without the benefit of explicit input by these users or deeper knowledge about them.     

Micromechanical Modeling of Filament Wound Cement-Based Composites

A theoretical model to predict the response of laminated cement-based composites is developed. The micromechanical model simulates the mechanical response of a multilayer cement-based composite laminate under uniaxial, biaxial, and flexural loading modes. Tsai-Wu Criterion is used for each lamina and the stacking sequence is utilized to obtain the overall stiffness matrix. The effect of distributed cracking on the stiffness degradation of the cross ply layers under tensile loading is measured using a scalar damage parameter that is empirically related to the apparent strain. The model is calibrated by predicting the load versus deformation response of unidirectional, cross ply, and angle ply laminates under tensile and flexural loading. Results are then compared to the experimental results cross ply and angle composites with various stacking sequences.     

Closed-Form Solutions for Flexural Response of Fiber-Reinforced Concrete Beams

A constitutive law for fiber-reinforced concrete materials consisting of an elastic perfectly plastic model for compression and an elastic-constant postpeak response for tension is presented. The material parameters are described by using Young’s modulus and first cracking strain in addition to four nondimensional parameters to define postpeak tensile strength, compressive strength, and ultimate strain levels in tension and compression. The closed-form solutions for moment-curvature response are derived and normalized with respect to their values at the cracking moment. Further simplification of the moment-curvature response to a bilinear model, and the use of the moment-area method results in another set of closed-form solutions to calculate midspan deflection of a beam under three- and four-point bending tests. Model simulations are correlated with a variety of test results available in literature. The simulation of a three- and four-point bending test reveals that the direct use of uniaxial tensile response underpredicts the flexural response.     

A Near-Maximum-Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming

In multiple-input multiple-output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this problem is known to be NP-hard. In this paper, a quasi-ML algorithm based on semi-definite programming (SDP) is proposed. We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models. The proposed relaxation models are also used for soft output decoding in MIMO systems.     

Producing PLGA fine particles containing high magnetite nanoparticles by using the electrospray technique

Loading magnetite (Fe₃O₄) nanoparticles (NPs), extensively using magnetic agents in magnetic resonance imaging (MRI) and drug delivery in a matrix of polymeric fine particles (FPs), can optimize not only the delivery of these diagnostic and therapeutic agents but also the design of multifunctional drugs. In an effort to use a new method for producing high magnetite loaded polymeric particles, oleic acid (OA) capped magnetite NPs were synthesized and loaded into biocompatible and biodegradable FPs of poly lactic-co-glycolic acid (PLGA) by using the electrospray (ES) technique; and the effect of voltage, flow rate and magnetite content on the morphology, size, size distribution, uniformity and magnetic properties of fabricated magnetic FPs (MFPs) were studied. Results of SEM images and calculations showed that solution flow rate is a major factor in ES and the particle size of magnetite loaded PLGA FPs increases considerably as the flow rate increases. Particle size did not change considerably due to an increase in voltage; however, particle uniformity first increased and then decreased due to an increase in flow rate or voltage. High magnetite content of 72% was achieved for magnetite loaded PLGA FPs and an increase in the magnetite content resulted in an increase in the saturation magnetization of magnetite loaded PLGA FPs; though, their sphericity decreased.     

Strain rate and gage length effects on tensile behavior of Kevlar 49 single yarn

In the present paper, Kevlar® 49 single yarns with different gage lengths were tested under both quasi-static loading at a strain rate of 4.2 × 10^?4 s^?1 using a MTS load frame and dynamic tensile loading over a strain rate range of 20–100 s^?1 using a servo-hydraulic high-rate testing system. The experimental results showed that the material mechanical properties are dependent on gage length and strain rate. Young’s modulus, tensile strength, maximum strain and toughness increase with increasing strain rate under dynamic loading; however the tensile strength decreases with increasing gage length under quasi-static loading. Weibull statistics were used to quantify the degree of variability in yarn strength at different gage lengths and strain rates. This data was then used to build an analytical model simulating the stress–strain response of single yarn under dynamic loading. The model predictions agree reasonably well with the experimental data.     

Chlorella sp.: A new strain with highly saturated fatty acids for biodiesel production in bubble-column photobioreactor

The biodiesel production from a naturally isolated strain of Chlorella in 2 L bubble-column photobioreactor was studied. The microalgal strain was isolated from the rice paddy-field soil samples during a screening program. After 17 days, at the end of exponential phase of growth, the total content of the lipids was extracted. The extracted fatty acids were first esterified and then identified using GC/MS analysis. Several types of fatty acid methyl esters (FAMEs) were identified in the isolated microalga and the presence of saturated fatty acids in Chlorella sp. MCCS 040 was approved. The composition of fatty acids in the studied species of microalga was mainly palmitic acid methyl ester, myristic acid methyl ester, stearic acid methyl ester and undecanoic acid methyl ester. This strain because of its highly saturated fatty acids content can be an ideal candidate for biodiesel production.