Formation of carbonate apatite on calcium phosphate coatings containing silver ions

The prerequisite for bioactive materials to bond to living bone is the formation of biologically equivalent carbonate apatite on their surfaces in the body. Calcium phosphate ceramic surfaces can be transformed to a biological apatite through a series of surface reactions including dissolution–precipitation and ion exchange. In the present work, apatite coatings with different crystallinity, compositions and crystal sizes, including a well-crystallized hydroxyapatite coating, were synthesized electrochemically and doped with silver ions in silver nitrate solution at room temperature. The formation of a new carbonate apatite on the surface of these coatings was investigated in an acellular simulated body fluid with ion concentrations comparable with those of human blood plasma, using scanning electron microscopy and Fourier transform-infrared spectroscopy. The results show that small quantities of silver ions incorporated into apatite coatings may have a strong stimulatory effect on the formation of carbonate apatite without adversely affecting the chemical stability of these coatings.     

Multi-criteria evolutionary optimization of a traffic light using genetics algorithm and teaching-learning based optimization

Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi-criteria decision-making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub-optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and Teaching-Learning-based Optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real-world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.     

Risk-Based Design of Flood Control Systems Considering Multiple Dependent Uncertainties

Due to random behavior of flood events and inaccuracies in measurements, design, analysis, and operation of flood control systems are subjected to several uncertainties. An important aspect in developing stochastic models for evaluating and analyzing more than one uncertainty is the dependence or independence of them. In flood control projects, hydrologic, hydraulic, geotechnical, and economic uncertainties are important considerations. In this paper, a stochastic Monte-Carlo simulation – optimization modeling approach is described for risk-based design of flood control levees (as a common structural flood control measure): considering multiple dependent uncertainties. It has been applied to the Leaf River reach in Hattiesburg, Mississippi for testing and evaluation of modeling results. Unlike a deterministic model that yields just one set of values for system dimensions, the stochastic model solution gives a range of values for each of them. One of the major reasons for limited field application of uncertainty analysis is difficulties in performing the modeling results in real world contexts. For closing the gap between theory and reality, design charts are developed in this study. This helps decision makers in identifying design values with desired and accepted risks.     

Effect of egg consumption on inflammatory markers: a systematic review and meta-analysis of randomized controlled clinical trials

There is little evidence about whether eggs affect inflammation. The aim of this meta-analysis was to explore the effects of egg consumption on inflammation. A systematic search of online databases (Institute for Scientific Information (ISI), Scopus, Ovid, PubMed, Cochrane) was used to gather clinical trials that assessed the effect of egg consumption on circulating inflammatory biomarkers. Using a random-effects model, pooled weighted mean differences (WMD) and corresponding standard deviations (SD) were calculated. Of the 21 eligible studies found, nine trials were eligible for analysis. Eight trials assessed high-sensitivity C-reactive protein (hs-CRP), four trials assessed interleukin-6 (IL-6), and five trials assessed tumor necrosis factor-alpha (TNF-α). Egg consumption did not affect hs-CRP (WMD 0.24 mg/L; 95% CI: -0.43, 0.90; I² = 53.8; P = 0.48), IL-6 (WMD 0.20 pg/mL; 95% CI: -0.71, 1.11; I² = 69.3; P = 0.50), and TNF-α (WMD: -0.38 pg/mL; 95% CI: -0.87, 0.10; I² = 0.00; P = 0.12) relative to controls. Overall, this meta-analysis revealed that egg consumption had no significant effect on serum biomarkers of inflammation in adults. © 2019 Society of Chemical Industry     

Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours

Thalamus is an important neuro-anatomic structure in the brain. In this paper, an automated method is presented to segment thalamus from magnetic resonance images (MRI). The method is based on a discrete dynamic contour model that consists of vertices and edges connecting adjacent vertices. The model starts from an initial contour and deforms by external and internal forces. Internal forces are calculated from local geometry of the model and external forces are estimated from desired image features such as edges. However, thalamus has low contrast and discontinues edges on MRI, making external force estimation a challenge. The problem is solved using a new algorithm based on fuzzy C-means (FCM) unsupervised clustering, Prewitt edge-finding filter, and morphological operators. In addition, manual definition of the initial contour for the model makes the final segmentation operator-dependent. To eliminate this dependency, new methods are developed for generating the initial contour automatically. The proposed approaches are evaluated and validated by comparing automatic and radiologist's segmentation results and illustrating their agreement.     

The impact of home freezing on the sensory characteristics of ready-to-use leafy vegetables

BACKGROUND: Owing to the increasing trend of consumption of ready‐to‐use leafy vegetables, the necessity of determining the best conditions for their frozen storage and the considerable impact of freezing on their sensory attributes, research was carried out to determine the best freezing temperature and storage time for a mixture of Allium ampeloprasum, Lepidium sativum and Stureia hortensis. RESULTS: The results for freezing temperature at three different storage times showed that colour and overall acceptability at ? 18 °C were always ranked first (P < 0.05), while taste at ? 18 °C was ranked first on days 120 and 150. The results for frozen storage time at three different temperatures indicated that colour, taste and acceptability were not significantly different. CONCLUSION: Overall, the results of this research indicated that the sensory attributes of leafy vegetables during 180 days of frozen storage were affected mainly by freezing temperature rather than frozen storage time. Copyright © 2010 Society of Chemical Industry     

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies.     

Influence of Static Low Electromagnetic Field on Copper Corrosion in the Presence of Multispecies Aerobic Bacteria

The effects of low electromagnetic field(EMF)( B = 2 mT) on the corrosion of pure copper in the absence and presence of multispecies marine aerobic bacteria were investigated in this work. The results showed that EMF has an inhibitory effect on copper metals and decreases the corrosion rate of copper metals in sterile artificial seawater. However, microbiologically influenced corrosion of Cu was increased in the presence of electromagnetic field due to its effect on the biofilm morphology and structure. EMF reduced the growth rate of bacteria and decreased bacterial attachment, thereby forming a heterogeneous and non-stable biofilm on the Cu surface in the presence of EMF. Moreover, the biofilm was dispersed throughout the surface after 7 days, whereas the scattered bacteria were observed on the surface after 10 days. Confocal laser scanning microscopy images showed large and deep pits on the surface in the presence of EMF and confirmed the acceleration of Cu corrosion in the presence of EMF and multispecies bacteria. Furthermore, XPS and FTIR results demonstrated that the corrosion products and metabolic by-products were significantly changed in the presence of EMF.     

Inhibitory Effect of Vibrio neocaledinocus sp. and Pseudoalteromonas piscicida Dual-Species Biofilms on the Corrosion of Carbon Steel

Multi-species biofilms are found in various bacterial habitats and have industrial relevance. These complex bacterial communities have synergetic effects, unlike a single species. Therefore, it is critical to evaluate these complex communities as a whole. Here, the inhibitory effect of single- and dual-species biofilms of Vibrio neocaledinocus sp. and Pseudoalteromonas piscicida for A36 carbon steel corrosion was investigated. The results demonstrated that the synergistic interactions of the monoculture increased the overall biomass production of the dual-species biofilm, but the growth rate was reduced in the presence of the dual-species culture due to a lack of nutrients. Field emission scanning electron microscopy images also confirmed the development of biofilms—they became more homogenized via exposure time in both the mono- and dual-species cultures. The corrosion resistance of A36 carbon steel positively increased because of the dual-species interactions. This reached the highest value after four weeks of exposure. The highest corrosion inhibition efficiency of 99.8% was achieved in the dual-species cultures. Microbial community analysis revealed the high relative abundance of Pseudoalteromonas piscicida during the initial days of exposure, demonstrating the dominant role of this bacterium in the biofilm structure.