Theoretical modeling and experimental validation of surface stress in thrombin aptasensor

Adsorption of target molecules on the immobilized microcantilever surface produced beam displacement due to the differential surface stress generated between the immobilized and non-immobilized surface. Surface stress is caused by the intermolecular forces between the molecules. Van der Waals, electrostatic forces, hydrogen bonding, hydrophobic effect and steric hindrance are some of the intermolecular forces involved. A theoretical framework describing the adsorption-induced microcantilever displacement is derived in this paper. Experimental displacement of thrombin aptamer-thrombin interactions was carried out. The relation between the electrostatic interactions involved between adsorbates (thrombin) as well as adsorbates and substrates (thrombin aptamer) and the microcantilever beam displacement utilizing the proposed mathematical model was quantified and compared to the experimental value. This exercise is important to aid the designers in microcantilever sensing performance optimization.

A modified approach to modeling of diffusive transformation kinetics from nonisothermal data and experimental verification

An inverse model is proposed to construct the mathematical relationship between continuous cooling transformation (CCT) kinetics with constant rates and the isothermal one. The kinetic parameters in JMAK equations of isothermal kinetics can be deduced from the experimental CCT kinetics. Furthermore, a generalized model with a new additive rule is developed for predicting the kinetics of nucleation and growth during diffusional phase transformation with arbitrary cooling paths based only on CCT curve. A generalized contribution coefficient is introduced into the new additivity rule to describe the influences of current temperature and cooling rate on the incubation time of nuclei. Finally, then the reliability of the proposed model is validated using dilatometry experiments of a microalloy steel with fully bainitic microstructure based on various cooling routes.

Safeguard information infrastructure against DDoS attacks: experiments and modeling

Nowadays Distributed Denial of Service (DDoS) attacks have made one of the most serious threats to the information infrastructure. In this paper we firstly present a new filtering approach, Mark-Aided Distributed Filtering (MADF), which is to find the network anomalies by using a back-propagation neural network, deploy the defense system at distributed routers, identify and filtering the attack packets before they can reach the victim; and secondly propose an analytical model for the interactions between DDoS attack party and defense party, which allows us to have a deep insight of the interactions between the attack and defense parties. According to the experimental results, we find that MADF can detect and filter DDoS attack packets with high sensitivity and accuracy, thus provide high legitimate traffic throughput and low attack traffic throughput. Through the comparison between experiments and numerical results, we also demonstrate the validity of the analytical model that can precisely estimate the effectiveness of a DDoS defense system before it encounters different attacks.

Modeling of the mechanical behavior of nanostructured HSLA steels

In the present paper the basic strengthening mechanisms operating in microstructures are discussed with respect to their application in submicron/nano materials. This analysis focuses on these strengthening mechanisms in bcc microstructures, where the effect of grain boundaries is very strong. An experimental study of the influence of the thermomechanical history on the microstructure and dislocation substructure was performed using two different grades of HSLA steels. As a result, a modified version of the Khan–Huang–Liang flow stress model (KHL) was developed and is discussed in the light of results from the present study. Comparison with experimental results showed significant diversity in the refinement and mechanical responses of each steel, due to different activity of strengthening mechanisms and microalloying elements in the microstructure evolution process. The effect of mechanical and microstructural inhomogeneity in severe plastic deformation (SPD) on the deformation induced grain refinement and mechanical properties was also considered.

A physical based modeling approach for the dynamic behavior of ultrafine grained structures

This paper discusses some experimental results on the influence of grain refinement on the final mechanical properties of IF and microalloyed steels designed for auto-body components. It shows also some modeling approaches to understanding the dynamic behavior of fine-rained materials. The Zerilli–Armstrong (Z–A) and Khan–Huang–Liang (KHL) models for studied steels were implemented into FEM code in order to simulate the dynamic compression tests with different strain rates.

Microstructure evolution modeling of square-diamond pass hot bar rolling of AISI 4135 steel

In this study, kinetics of the static (SRX) and metadynamic recrystallization (MDRX) of AISI4135 steel was investigated using hot torsion tests. Continuous torsion tests were carried out to determine the critical strain for dynamic recrystallization (DRX). The times for 50% recrystallization of SRX and MDRX were determined, respectively, by means of interrupted torsion tests. Furthermore, austenite grain size (AGS) evolution due to recrystallization (RX) was measured by optical microscopy. With the help of the evolution model established, the AGS for hot bar rolling of AISI4135 steel was predicted numerically. The predicted AGS values were compared with the results using the other model available in the literature and experimental results to verify its validity. Then, numerical predictions depending on various process parameters such as interpass time, temperature, and roll speed were made to investigate the effect of these parameters on AGS distributions for square-diamond pass rolling. Such numerical results were found to be beneficial in understanding the effect of processing conditions on the microstructure evolution better and control the rolling processes more accurately.

Statistical analysis of finite element modeling in sheet metal forming and springback analysis

This work focuses on development of a method to statistically study forming and springback problems of TRansformation Induced Plasticity (TRIP) through an industrial case study. A Design of Experiments (DOE) approach was used to study the sensitivity of predictions to four user input parameters in implicit and explicit sheet metal forming codes. Numerical results were compared to experimental measurements of parts stamped in an industrial production line. The accuracy of forming strain predictions for TRIP steel were comparable with conventional steel, but the springback predictions of TRIP steel were far less accurate. The statistical importance of selected parameters for forming and springback prediction is also discussed. Changes of up to ±10% in Young's modulus and coefficient of friction were found to be insignificant in improving or deteriorating the statistical correlation of springback accuracies.

Microstructural modeling of dynamic recrystallization using irregular cellular automata

Cellular automaton (CA) was used to simulate dynamic recrystallization (DRX) during thermomechanical deformation. Initial grain size, initial grain orientation and dislocation density were used as input data to the CA model. Flow curve, dislocation density, final grain size and orientation, and DRX volume fraction were the output data which were compared with experimental data to validate the model. The model proposed in this work considered the thermomechanical parameters (e.g., temperature and strain rate) and their role on the nucleation and growth kinetics during DRX. It was shown that the CA model can predict the final microstructure and flow curve to a high degree of accuracy and was able to successfully simulate the volume fraction of DRX as a function of strain for a wide range of deformation conditions.

Modeling of enzyme–substrate complexes for the metalloproteases MMP-3, ADAM-9 and ADAM-10

The matrix metalloproteases (MMPs) and the ADAMs (A Disintegrin And Metalloprotease domain) are proteolytic enzyme families containing a catalytic zinc ion, that are implicated in a variety of normal and pathological processes involving tissue remodeling and cancer. Synthetic MMP inhibitors have been designed for applications in pathological situations. However, a greater understanding of substrate binding and the catalytic mechanism is required so that more effective and selective inhibitors may be developed for both experimental and clinical purposes. By modeling a natural substrate spanning P4-P4‘ in complex with the catalytic domains, we aim to compare substrate-specificities between Stromelysin-1 (MMP-3), ADAM-9 and ADAM–10, with the aid of molecular dynamics simulations. Our results show that the substrate retains a favourable antiparallel beta-sheet conformation on the P-side in addition to the well-known orientation of the P'-region of the scissile bond, and that the primary substrate selectivity is dominated by the sidechains in the S1' pocket and the S2/S3 region. ADAM-9 has a hydrophobic residue as the central determinant in the S1' pocket, while ADAM-10 has an amphiphilic residue, which suggests a different primary specificity. The S2/S3 pocket is largely hydrophobic in all three enzymes. Inspired by our molecular dynamics calculations and supported by a large body of literature, we propose a novel, hypothetical, catalytic mechanism where the Zn-ion polarizes the oxygens from the catalytic glutamate to form a nucleophile, leading to a tetrahedral oxyanion anhydride transition state.

Machine learning approaches for modeling spammer behavior

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Naïve Bayesian classifier (Naive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.

Modeling bacterial growth in drinking water : effect of nutrients

This article presents a model of growth of naturally occurring heterotrophic bacteria in the bulk water phase in the absence of disinfectant. The model considers growth with carbon, phosphorus, and nitrogen balance, death and lysis of bacteria, and conversion of less biodegradable organic carbon to assimilable organic carbon. Experimental data from two raw and two treated waters were used to test the model. The model describes the increase of live and dead bacterial cells in the water phase, and its output closely matches the experimental data. Such a model has the ability to characterize water nutrient status as well as to predict behavior of indigenous heterotrophic bacteria. The ability to predict bacterial population dynamics with respect to nutrients is beneficial for water treatment optimization. The model, based on microbiological measurements, helps to characterize treated water quality and project performance in terms of water quality into a distribution system.

Modeling a dynamic data replication strategy to increase system availability in cloud computing environments

Failures are normal rather than exceptional in the cloud computing environments. To improve system avai-lability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes: 1) analyzing and modeling the relationship between system availability and the number of replicas; 2) evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3) calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4) designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.

Modeling propagation dynamics of social network worms

Social network worms, such as email worms and facebook worms, pose a critical security threat to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and develop countermeasures. Although several analytical models have been proposed for modeling propagation dynamics of social network worms, there are two critical problems unsolved: temporal dynamics and spatial dependence. First, previous models have not taken into account the different time periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the problem of spatial dependence results from the improper assumption that the states of neighboring nodes are independent. These two problems seriously affect the accuracy of the previous analytical models. To address these two problems, we propose a novel analytical model. This model implements a spatial-temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our model overcomes the computational challenge of spatial dependence and provides a stronger approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII) model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic models and the spatial-temporal model, the experimental results show our SII model achieves a greater accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-infectious- recovered models. The results show that our model is more suitable for modeling the propagation of social network worms.

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.