Effect of fortification with asparagus powder on the qualitative properties of processed cheese

The qualitative properties of processed cheese (PC) fortified with different levels of asparagus powder (AP) (0.5%, 1% and 1.5% wt/wt) were evaluated during storage. AP decreased the pH and lipolysis indexes and increased the phenolic content, antioxidant activity and proteolysis of the processed cheeses. AP made the structure of the cheese more elastic, increased the rigidity and decreased the spreadability compared with the control sample, which corresponded to the results obtained using dynamic oscillatory rheometry. The results showed that AP as a rich source of bioactive components could be used for the fortification of processed cheeses.     

Investigation into the effect of carboxylic acid monomer on particle nucleation and growth in emulsifier-free emulsion copolymerization of styrene-butadiene-acrylic acid

Latexes of carboxylated styrene-butadiene rubber were prepared via batch emulsion copolymerization with different amounts of acrylic acid in the absence of emulsifier. The effect of acid monomer was investigated in the particle formation and growth. It was observed that the amount of acrylic acid strongly affected the particle formation. The number of particles and thus polymerization rate increased with increasing of the acid content. There was no significant difference in the polymerization rate per particle in all experiments. The results show that in this case particle growth process is less dependent on the acrylic acid amount in comparison with its influence on nucleation stage and then particle number. Several parameters such as polymerization rate and number of latex particle per unit volume of the aqueous phase were calculated. Attempt was made to evaluate the average number of growing chain per particle. Also average particle diameter of the above carboxylated SBR latexes was obtained through some calculations from the direct measurement of average particle diameter in the swollen state by light scattering technique for the first time.     

A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L₁ and L₂/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.     

Relating Sediment Yield Estimations to the Wet Front Term Using Rainfall Simulator Field Experiments

Water Resources Management - Depth to wet front is generally considered as the amount of water that penetrates into soil and wets the internal soil layer. This is an important variable especially...     

Knowledge Diffusion of the Internet of Things (IoT): A Main Path Analysis

Wireless Personal Communications - The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT...     

An Approach to Synthesize Thick α- and γ-Al2O3 Coatings by High-Speed Physical Vapor Deposition

Crystalline α- and γ-Al₂O₃ exhibit in many applications high wear resistance, chemical resistance, and hot hardness, making them interesting materials for production engineering. To synthesize α-Al₂O₃ with high coating thickness of s ≥ 10 μm, chemical vapor deposition at temperatures T > 1000 °C is well established. However, there are almost no studies dealing with the synthesis of thick α-Al₂O₃ by physical vapor deposition (PVD) at high temperatures T > 700 °C. High-temperature deposition of thick coatings can be realized by means of the dense hollow cathode plasma, combined with the transport function of the plasma gas in high-speed (HS) PVD. Herein, crystalline α- and γ-Al₂O₃ films are deposited on cemented carbides at substrate temperatures T _s ≈ 570 °C and T _s ≈ 780 °C by HS-PVD. These coatings exhibit a thickness up to s = 20 μm. Moreover, phase analysis presents α-phases in coatings synthesized at substrate temperature of T _s ≈ 780 °C with significant higher hardness than films by T _s ≈ 570 °C. These release the potential of HS-PVD to synthesize α-Al₂O₃ coatings with high thickness. Thereby, a higher thickness of these coatings is beneficial for the wear protection of turning and die casting tools.     

Modeling industrial thickener using computational fluid dynamics (CFD), a case study: Tailing thickener in the Sarcheshmeh copper mine

Separation of particles from liquid in the large gravitational tanks is widely used in mining and industrial wastewater treatment process. Thickener is key unit in the operational processes of hydrometallurgy and is used to separate solid from liquid. In this study, population balance models were combined with computational fluid dynamics (CFD) for modeling the tailing thickener. Parameters such as feed flow rate, flocculant dosage, inlet solid percent and feedwell were investigated. CFD was used to simulate the industrial tailing thickener with settled bed of 120 m diameter which is located in the Sarcheshmeh copper mine. Important factor of drag force that defines the rake torque of rotating paddles on the bed was also determined. Two phases turbulence model of Eulerian/Eulerian in accordance with turbulence model of k-ε was used in the steady-state. Also population balance model consists of 15 groups of particle sizes with Luo and Lehr kernel was used for aggregation/breakage kernel. The simulation results showed good agreement with the operational data.     

Effect of galvanic contact on the flotability of galena in the presence and absence of a collector

In this paper, the effect of various electrochemical environments in the galena flotation is investigated. The electrochemical environments consisted of a ball mill for grinding of galena ore and a Denver flotation cell for flotation of galena in the laboratory scale. In order to achieve the maximum recovery with sodium hyposulfite, the concentrations of 0.06 and 0.12 M of air and nitrogen gases have been used to control the pulp potential in the Denver flotation cell. The galena sample was from the “Era mine” which is located in the Kiyasar area, north of Iran. This mine contains: Galena (PbS) at least 22%, Fluorite (CaF₂) 73.37%, Quartz (SiO₂) 2.54% and other minerals such as Cerussite (PbCO₃) and Kaolinite (Al₂Si₂O₅(OH)₄). Flotation of Galena was conducted in a 0.12 M of sodium hyposulfite solution. It was found that the amount of recovery by this process was 85% and 63% in the presence and absence of potassium amyl xanthate (KAX) and at an estimated potential of 280 to 350 mV respectively while the recovery was 70% and 52% at an approximate potential of 175 to 210 mV in water in the presence and absence of collector (KAX).     

A high-performance and scalable multi-core aware software solution for network monitoring

In recent years, the need for high-performance network monitoring tools, which can cope with rapidly increasing network bandwidth, has become vital. A possible solution is to utilize the processing power of multi-core processors that nowadays are available as commercial-off-the-shelf (COTS) hardware. In this paper, we introduce a software solution for wire-speed packet capturing and transmission for TCP/IP networks under Linux operating system, called DashCap. The results of our experimental evaluations show that the proposed solution causes more than two times performance boost for packet capturing in comparison to the existing software solutions under Linux. We have proposed a scalable software architecture for network monitoring tools called DashNMon, which is based on DashCap. Multi-core awareness is a distinguished property of this architecture. Comparing to the existing cluster-based solutions, DashNMon can be used with COTS multi-core processors. In order to evaluate the proposed solutions, we have developed several prototype tools. The results of the experiments carried out using these tools show the scalability and high performance of the network monitoring tools that are based on the proposed architecture. Using the proposed architecture, it is possible to design and implement high-performance multi-threaded network intrusion detection systems (NIDSs) or application-layer firewalls, completely in the user space and with better utilization of the computational resources of multi-processor/multi-core systems.     

Stable identification of nonlinear systems using neural networks: theory and experiments

This paper presents an approach for stable identification of multivariable nonlinear system dynamics using a multilayer feedforward neural network. Unlike most of the previous neural network identifiers, the proposed identifier is based on a nonlinear-in-parameters neural network (NLPNN). Therefore, it is applicable to systems with higher degrees of nonlinearities. Both parallel and series-parallel models are used with no a priori knowledge about the system dynamics. The method can be considered both as an online identifier that can be used as a basis for designing a neural network controller as well as an offline learning scheme for monitoring the system states. A novel approach is proposed for the weight updating mechanism based on the modification of the backpropagation (BP) algorithm. The stability of the overall system is shown using Lyapunov's direct method. To demonstrate the performance of the proposed algorithm, an experimental setup consisting of a three-link macro-micro manipulator (M/sup 3/) is considered. The proposed approach is applied to identify the dynamics of the experimental robot. Experimental and simulation results are given to show the effectiveness of the proposed learning scheme.