Biochemical and rheological characterization of a protease from fruits of Withania coagulans with a milk-clotting activity

A protease from Withania coagulans fruits was evaluated for milk-clotting properties. The optimum temperature and pH for enzyme activity were 70°C and 4, respectively. The protease also exhibited an excellent thermal stability at 60°C for 30 min. Fractional precipitation using ammonium sulfate showed that the 40–50% fraction (F5) possessed the highest milk-clotting activity. The F5 fraction showed a Mw band of 66 kDa by SDS-PAGE. Gel formation was monitored using low amplitude oscillatory rheology at different temperatures. An Arrhenius plot was used to describe the temperature dependence of the gelation rate parameter. Time sweep testing showed that an increase in temperature was accompanied by a decrease in the gelation onset time, a higher gel formation rate, and higher values for final gel strength. W.coagulans fruits have potential for use as a rennet substitute.     

Ionic Liquid-based Ultrasound-Assisted In Situ Solvent Formation Microextraction Combined with Electrothermal Atomic Absorption Spectrometry as a Practical Method for Preconcentration and Trace Determination of Vanadium in Water and Food Samples

A new and practical sample enrichment method termed ionic liquid-based ultrasound-assisted in situ solvent formation microextraction (IL-UA-ISFME) was combined with electrothermal atomic absorption spectrometry (ETAAS) for preconcentration and trace determination of vanadium in real samples. In this sample enrichment methodology, a hydrophilic ionic liquid (IL) ([Hmim][BF₄]) was added to the aqueous media containing an ion-exchange reagent (NaPF₆), in order to obtain a hydrophobic IL ([Hmim][PF₆]) as the microextraction solvent. The hydrophobic extraction solvent formed under these conditions was completely dispersed into the sample solution using ultrasonic radiation. Vanadium was complexed with N-benzoyl-N-phenylhydroxylamine (BPHA), and extracted into the IL phase during the dispersion of the hydrophobic IL. Main variables affecting the recommended method was studied in details and optimized. Under the optimum conditions, the combined methodology provided a linear dynamic range of 15–2,500 ng l^?1, a limit of detection (LOD) of 4.7 ng l^?1 and a relative standard deviation (RSD) of 4.0 %. The accuracy and validity of the method was checked by analyzing a certified standard reference material of water (SRM-1643e). Finally, the developed method was utilized for quantitation of vanadium in real water and milk samples.     

Effect of reprocessing on shrinkage and mechanical properties of ABS and investigating the proper blend of virgin and recycled ABS in injection molding

In this research, the effect of reprocessing of acrylonitrile–butadiene–styrene (ABS) on the mechanical properties of the polymer was studied through a five-stage reprocessing. To this end, the injection mold of the standard samples was manufactured. Then, using an injection molding machine, all of the virgin material was processed. After sampling, the rest of the parts were ground and then reprocessed under the same conditions. In order to get a proper combinational ratio of virgin and recycled ABS with respect to shrinkage and mechanical properties, another experiment was designed. In order to do this, virgin material was blended with 20%, 35% and 50% of the recycled material. The blends were reprocessed and samples were obtained. Impact, tensile, flexural and shrinkage tests, selected from ASTM standard, were used to investigate the mechanical properties of the polymer as well as its shrinkage. Furthermore, viscosity test was used to investigate the changes in the structure of the polymer. As the reprocessing cycles increased, shrinkage decreased and tensile and flexural ultimate strengths increased, more in flexural strength than in tensile. While Young's modulus slightly increased, viscosity decreased and consequently molecular weight decreased too. The proper blend for the least shrinkage was 50% whereas the best mechanical properties were achievable by the 20% blend. The obtained results suggest that reprocessing causes polymer degradation which is a result of the break in the bonds of poly butadiene. Moreover, it can be concluded that reprocessing in ABS can lead to the loss of effectiveness of some additives.     

Data-Fusion Approaches and Applications for Construction Engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.     

Comparison of functional properties and SDS-PAGE patterns between fish protein isolate and surimi produced from silver carp

The purpose of this study was to investigate and compare the physicochemical properties of fish protein isolate (FPI) produced from silver carp (Hypophthalmichthys molitrix), which is a relatively cheap fish with low consumption in Fars (a state of Iran). In this research, proteins were isolated using pH shifts method. Results showed that in terms of production efficiency, protein recovery, lipid reduction and functional properties such as water-holding capacity, emulsifying capacity, foaming capacity and water absorption, FPI was better than conventional surimi. The treated samples at pH?=?2.5 represented better functional properties relative to other samples. The electrophoretic patterns demonstrated that acidic pH led to vanish high molecular weight protein bands because these proteins were converted to low molecular weight proteins, whereas basic pH caused the formation of high molecular weight proteins relative to myosin heavy chain. It seems that in basic pH, the proteins were linked together after denaturation and then aggregated. Overall, the results showed that physicochemical qualitative properties of FPI were better than those of conventional surimi.     

Fluid flow characterization of liquid�Cliquid mixing in mixer-settler

Agitation in a mixer-settler is one of the most common operations, yet presents one of the greatest challenges in the area of computer simulation. Mixer-settlers typically contain an impeller mounted on a shaft, and optionally can contain baffles. The hydrodynamic characteristics of mixer-settlers have been studied in the present study. The effect of different geometrical parameters on the efficiency characteristics of the system has been investigated. The effects of different width of impellers, impeller speed, inlet velocity, impeller diameter, etc. have been studied. Computational fluid dynamics (CFD) model has been developed to predict the efficiency characteristics. The model has been validated with the help of experimental data for different velocity outlets used in the work. This work has enabled developing efficiency that can produce higher condition than those reported in previous literature. From the CFD simulations results, optimum mixer-settler geometry has been proposed.     

The GA Optimization Performance in the Microstructure and Mechanical Properties of MMNCs

This paper is aimed at development of an effective approach based on the combination of GA, FEM and artificial intelligent methods to determine the optimum conditions of Al Matrix nano composites in terms of microstructure and mechanical properties. Using the stir casting method, alumina nano-particles were incorporated into the Al–Si aluminum alloy. Characterization of the mechanical properties showed that the presence of nano particles significantly improved the hardness and strength of the composites. Then artificial neural network and finite element technique were implemented in order to predict the mechanical properties and genetic algorithms were used for the process conditions optimization. It was revealed that the proposed model is a useful and efficient method to find the optimal process conditions in stir casting.     

Trade-Off Analysis of Energy Consumption and Image Quality for Multihop Wireless Sensor Networks

It has become necessary in recent years to observe and monitor some physical phenomena. This was made possible by the emergence of wireless sensor networks. The main characteristic of such networks is nodes with scarce resources. Given the stringent resource constraints, nodes are limited in energy, memory and computational power. These resource constraints pose serious difficulties for image processing and transmission to the destination. Therefore, image transfer in wireless sensor networks presents major challenge which raises issues related to its representation, its storage and its transmission. Based on wavelet transform an Adaptive Energy Efficient Wavelet Image Compression Algorithm is proposed in order to be suitable for wireless sensor network. In addition, an identification of the wavelet image compression parameters is investigated to analyze the trade-offs between the energy saving, and the image quality. Performance studies indicate that the proposed scheme enabling significant reductions in computation as well as communication energy needed, with minimal degradation in image quality.     

The use of Artificial Neural Network models for CO2 capture plants

Artificial Neural Networks (ANN) are multifaceted tools that can be used to model and predict various complex and highly non-linear processes. This paper presents the development and validation of an ANN model of a CO₂ capture plant. An evaluation of the concept is made of the usefulness of the ANN model as well as a discussion of its feasibility for further integration into a conventional heat and mass balance programme. It is shown that the trained ANN model can reproduce the results of a rigorous process simulator in fraction of the simulation time. A multilayer feed-forward form of Artificial Neural Network was used to capture and model the non-linear relationship between inputs and outputs of the CO₂ capture process. The data used for training and validation of the ANN were obtained using the process simulator CO2SIM. The ANN model was trained by performing fully automatic batch simulations using CO2SIM over the entire range of actual operation for an amine based absorption plant. The trained model was then used for finding the optimum operation for the example plant with respect to lowest possible specific steam duty and maximum CO₂ capture rate. Two different algorithms have been used and compared for the training of the ANN and a sensitivity analysis was carried out to find the minimum number of input parameters needed while maintaining sufficient accuracy of the model. The reproducibility shows error less than 0.2% for the closed loop absorber/desorber plant. The results of this study show that trained ANN models are very useful for fast simulation of complex steady state process with high reproducibility of the rigorous model.