Preparation and evaluation of novel nano-bioglass/gelatin conduit for peripheral nerve regeneration

Peripheral nerves are exposed to physical injuries usually caused by trauma that may lead to a significant loss of sensory or motor functions and is considered as a serious health problem for societies today. This study was designed to develop a novel nano bioglass/gelatin conduit (BGGC) for the peripheral nerve regeneration. The bioglass nanoparticles were prepared by sol–gel technique and characterized using transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction analysis. The interfacial bonding interaction between the nano-bioglass and gelatin in the developed conduits was assessed by FTIR. The surface morphology and pore size of the nanocomposite were investigated through scanning electron microscopy with the pore size of the conduits being 10–40 μm. Biocompatibility was assessed by MTT assay which indicated the BGGC to have good cytocompatibility. The guidance channel was examined and used to regenerate a 10 mm gap in the right sciatic nerve of a male Wistar rat. Twenty rats were randomly divided into two experimental groups, one with the BGGC and the other being normal rats. The gastrocnemius muscle contractility was also examined at one, two and three months post-surgery in all groups using electromyography (EMAP). Histological and functional evaluation and the results obtained from electromyography indicated that at three months, nerve regeneration of the BGGC group was statistically equivalent to the normal group (p > 0.05). Our result suggests that the BGGC can be a suitable candidate for peripheral nerve repair.     

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.     

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     

Two Efficient Dual-Band and Wide-Band Low-Power DCO Designs Using Current Starving Gates,DCV and Reconfigurable Schmitt Triggers in 180 nm

Digitally controlled oscillators are the main cores in all-digital phase-locked loops (ADPLL), which are important for determining the range of frequency and power consumption in ADPLLs. In the conventional digitally controlled oscillator (DCO) designs, one single band of operation is assigned to the DCO. The following paper presents a new approach in the design of DCOs, which works in dual-band and wide-band modes with a control unit. In dual-band mode, the DCO works in two different ranges of frequencies simultaneously via digital control bits. The wide-band DCO (WBDCO) works in one wider range of frequencies consecutively. It seems that in the wide-band DCO, the gap width for the dual-band DCO (DBDCO) is zero. The previously mentioned designs allow the designer to have standard frequencies with the help of direct or multiplied frequencies. So, we can have a trade-off between power and performance. This means that we can have low power consumption in low-frequency applications and vice versa. The proposed designs are based on using digitally controlled capacitors, current starving gates and Schmitt triggers in critical points of the DCO loop, while preserving coarse and fine tunings. The non-delay linearity factors are clearly investigated and resolved with the use of a new combined control unit. The simulations of the proposed designs are performed in Hspice with a voltage of \(\mathrm{VDD}=1.8\) v in 180 nm CMOS technology for 64- and 128-bit input coarse codes. Our simulation and evaluation results showed that in the dual-band DCO, a 14.8 ps jitter was calculated at 134 MHz with 1.2131 mW power consumption, while in the wide band with overlap mode, a 68.7 ps jitter was measured at 184.61 MHz with 1.604 mW power consumption. Our designs are proper for reconfigurable and multi-standard ADPLL designs.     

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.     

An Improved UTD Based Model for the Multiple Building Diffraction of Plane Waves in Urban Environments by Using Higher Order Diffraction Coefficients

This paper describes an improved model for multiple building diffraction modeling based on the uniform theory of diffraction. A well-known problem in conventional uniform theory of diffraction (CUTD) is multiple edge transition zone diffraction. Here, higher order diffracted fields are used in order to improve the result. Hence, we use higher order diffraction coefficients to improve a hybrid physical optics (PO)-CUTD model, the results show that the new model can correct errors of the PO-CUTD model. Therefore, the proposed model can find application in the development of theoretical models to predict more realistic path loss in urban environments, when multiple building diffraction is considered.     

Mathematical-Thermodynamic Prediction and Kinetics of Eta Phase Formation in Fe–Ni-Based Superalloys

Time–temperature–precipitation diagram of eta phase formation was established for an Fe–Ni based superalloy by means of mathematical-thermodynamic analysis, kinetic investigation, and microstructural observation. The eta phase started to precipitate at the expense of the γ′ phase after prolonged aging. On the basis of thermodynamic prediction, the interaction between Gibbs free energy of eta phase formation and activation energy for diffusion of elements, especially titanium, was considered. The tip of TTP diagram was obtained at 817 °C, on the basis of mathematical-thermodynamic prediction, and 825–840 and 840 °C, on the basis of microstructural evolution and kinetic analysis, respectively. Kinetic investigations predicted that the eta transformation started with diffusion of Ti into Ni. This indicated that, with the progress of transformation, as n in Avrami’s equation predicts, other eta formation mechanisms such as γ′ to eta transformation, instant nucleation from the matrix, coincidence of eta lamellas, etc. increased n and activation energy.     

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.