The Effect of Multiple Surface Treatments on Biological Properties of Ti-6Al-4V Alloy

In this research, the effect of various surface treatments including laser processing, grit blasting and anodizing on chemical structure, surface topography, and bioactivity of Ti-6Al-4V was investigated. Six groups of samples were prepared by a combination of two alternative laser processes, grit blasting and anodizing. Selected samples were first evaluated using microanalysis techniques and contact roughness testing and were then exposed to in vitro environment. Scanning electron microscopy was used to characterize the corresponding final surface morphologies. Weight measurement and atomic absorption tests were employed for determination of bioactivity limits of different surface conditions. Based on the data obtained in this study, low-energy laser processing generally yields a better biological response. The maximum bioactivity was attained in those samples exposed to a three step treatment including low-energy laser treatment followed by grit blasting and anodizing.     

Preparation of alginate and chitosan nanoparticles using a new reverse micellar system

Alginate and chitosan nanoparticles were prepared using a new reverse micelle system, composed of cetyltrimethylammonium bromide (CTAB) as a surfactant, isooctane as a solvent, and 1-hexananol as a co-solvent. The obtained nanoparticles were characterized by FTIR, DLS and TEM techniques. The main objective of this study was to investigate the effects of polymer concentration, water content, and volumetric ratio of co-solvent to solvent on the physical and morphological properties of the prepared nanoparticles. To evaluate the results, the design of experimental was initially carried out and then the obtained data were statistically analyzed using the Qualitek-4^? software. Results revealed that the size of the prepared alginate and chitosan nanoparticles varied in the range 220?C490 and 210?C1,050?nm, respectively. Furthermore, increasing either alginate or chitosan concentration increased the size of their nanoparticles. The results also showed that the size of nanoparticles was decreased with increasing the volumetric ratio of co-solvent/solvent. Finally, the size of alginate nanoparticles was increased by increasing the water content while it decreased the size of chitosan nanoparticles. Considering the statistical analysis of experiments, the polymer concentration is the major parameter affecting nanoparticles?? size. In contrast, water content has the smallest effect on the size of nanoparticles. However, the difference between the particle sizes of chitosan and alginate nanoparticles cab be attributed to the electrostatic repulsion between chitosan and CTAB.     

Theoretical Analysis of the Underwater Incremental Adaptive Network Performance Based on the VLC Technology

In this paper, the underwater implementation of the incremental adaptive networks is proposed based on the visible light communication technology. The underwater distance between transmitter and receiver nodes and the salinity and temperature levels of the considered water determines the stochastical properties of the underwater link that is modeled with the Log-normal distribution. The incremental network performance can be expressed with the excess mean square error and mean square deviation values and we used them in this paper for our theoretical analysis. Our findings showed that the distances between the nodes must not be more than 10 m or the incremental network will diverge from its estimation goal. The network performance is analyzed through multiple link distances and the results are presented with several simulations. The simulation results are devised in order to elaborate the effects of the underwater turbulent links on the performances of estimating adaptive network. Also, the impacts of different salinity and temperature levels are analyzed theoretically and the results are compared with the simulation results.

     

An Adaptive and Cost-Based Intrusion Response System

An Adaptive and Cost-Based Intrusion Response System (ACBIRS) is presented in this paper. The designed system analyzes alerts from the Intrusion Detection System (IDS) and evaluates the attack cost, based on the probable damage of attacks on the protected system. Later on, a response is deployed to thwart the attack and prevent the attacker from reaching his/her goals. The proposed response selection approach is a cost-based method that considers attack features, including type of the attack, severity of the attack, value of targeted host/hosts services, and their data to prioritize alerts. Alerts will be responded with respect to their priorities. The selected responses are based on a measure called Response Merit (RM). The balance between attack damage cost, response cost together with the effectiveness of the response to countermeasure previous attacks determine the RM. In contrast to other Intrusion Response Systems (IRS), ACBIRS not only consists of the attack and response measures but also includes response feedback supervision that is proposed in this paper for the first time. ACBIRS allows responses to be adaptive in changing environments through success and failure assessment of previously deployed responses. Experiments show that ACBIRS can successfully prevent 92% of intrusions with only 3% disruption on benign traffic.