Hesperetin-Loaded Solid Lipid Nanoparticles and Nanostructure Lipid Carriers for Food Fortification: Preparation, Characterization, and Modeling

Solid lipid nanoparticles and nanostructure lipid carriers were used to entrap hesperetin and broaden confined knowledge of application of nanocarriers as the functional ingredients in food sectors. The produced nanocarriers using a high mechanical shear method were subjected to size and zeta potential analysis. The developed nanosize carriers had the encapsulation efficiency ranging from 39.90 to 63.08 %. Differential scanning calorimetry, X-ray diffraction, and Fourier transform infrared spectroscopy were also employed to study thermal behavior, crystalline state, and chemical structure. The release behavior of hesperetin in simulated gastrointestinal conditions was investigated and kinetically modeled. The modeling results indicated that the release phenomenon is mostly governed by combination of Fickian and dissolution mechanisms. Stability of the nanocarriers, as analyzed for up to 30 days, at 6 and 25 °C in aqueous suspension, showed no detectable hesperetin leakage. Cryoprotectant effect of different compounds (i.e., glucose, sorbitol, glycerin, lactose, and sucrose) was also examined. Finally, the potential capability of nanocarriers for food fortification was studied using milk as a model food. The fortified milk samples were subjected to sensory analysis and results betokened that the developed nanocarriers did not show any significant difference with blank milk sample and could well mask the bitter taste, after taste, and obviate poor solubility of hesperetin.     

Prediction of Oil Recovery Factor in CO2 Injection Process

In this study, prediction of recovery factor (RF) for CO₂ injection into oil reservoirs based on artificial neural networks (ANNs) and mathematical models were investigated. To design the optimum ANN model, number of neurons, hidden layers, and training function were studied. Finally, efficiency of the models was evaluated using new data. As a result of this work, it can be concluded that it is possible to predict RF in CO₂ injection process by ANN and mathematical model. However, values that obtained from ANN were in the best agreement with the actual values than regression model. The proposed artificial neural network predicted RF during CO₂ injection with error about 0.396%.     

Preparation of superfine ammonium perchlorate particles using some advanced liquid antisolvent crystallization (LASC) methods

The aim of this study is to investigate the crystallization of superfine ammonium perchlorate (AP) particles using some advanced liquid antisolvent crystallization (LASC) methods containing liquid antisolvent equipped with Y-jet mixer (LASY) and evaporation antisolvent crystallization (EASC) methods. Scanning electron microscopy (SEM), optical microscopy and X-ray diffraction techniques were used to investigate the crystal-size distribution (CSD), average particle size (APS), sphericity, uniformity coefficient (UC), and crystallinity of prepared samples. Also, microstructure image processing (MIP) technique in combination with MATLAB and the Rosin–Rammler (RR) equation were used to analyze specifications of the prepared particles. The results of MIP analysis technique were illustrated that the particles obtained with the LASY method represented higher sphericity (0.89–0.92) than EASC method (0.79–0.82). Also according to the Rosin–Rammler analysis results, the highest uniformity coefficient was related to the LASY method (3.83–4.02). The results of this study were shown that the LASY method can be considered as a safe and simple procedure for rapid preparation of superfine ammonium perchlorate particles with regular morphology.     

Designing intelligent agents to support long-term partnership in two echelon e-Supply Networks

Realizing the dynamic nature of information flow and the conflicting objectives of members play vital role in effective design of e-Supply Networks (e-SN). While there are some research in the SN literature proposing different dynamic and intelligent coordination mechanisms, the impact of the proper definition of data structure and long-term relationship in modeling both coordination and negotiation mechanisms have not been addressed deeply. In this paper, we propose three overlapping services including intelligent matching of partners, proposal generations, and long-term contract management. The process begins with the selection of qualified partners based on the similarity of users profiles in a multidimensional space defined by network attributes. Then, a coordination mechanism for long-term agreements is proposed such that the generated proposals in e-SN encourage buyers to reveal their demand in advance. The proposed mechanism introduces the importance of strategic buyers for suppliers in modeling and decision making process. To illustrate the model efficiency, a prototype system has been modeled and is compared to the traditional tendering mechanism. The validation results confirm the model efficiency in providing long-term decisions in a dynamic environment.     

Environmental control of the dominant phytoplankton in Aras Reservoir (Iran): A multivariate approach

This study was undertaken to determine the phytoplankton structure and the environmental variables comprising the driving factors leading to cyanobacterial blooms in Aras Reservoir. Sampling was carried out seasonally at five sampling sites along the main body of the reservoir. Samples were collected for phytoplankton identification and enumeration, chemical analyses and chlorophyll‐a (Chl‐a) concentrations at each sampling site. Principal component analysis (PCA) and two‐way unweighted pair group method with arithmetic mean (UPGMA) were performed to determine the environmental variables affecting phytoplankton community dynamics. Seventy‐two species belonging to five divisions were determined during this study. Cyanobacteria contained the highest density (74%) during the study period, with Pseudanabaena limnetica being the most abundant species. The Shannon diversity index was low (0.44–1.87), indicating a high level of cyanobacteria dominance and eutrophic water status. Principal component analysis indicated that total phosphorus and temperature were significantly associated with cyanobacteria growth. Two‐way clustering by UPGMA indicated a close relationship among sampling sites during the same season from the perspective of the phytoplankton density. The findings of this study suggested Aras Reservoir could be highly affected by agricultural runoff and untreated sewage loadings. Thus, the proper use of fertilizers and sewage treatment should be taken into account in considering effective conservation and management plans.     

A numerical method for solving a fractional partial differential equation through converting it into an NLP problem

In this paper, we propose a new approach for solving fractional partial differential equations, which is very easy to use and can also be applied to equations of other types. The main advantage of the method lies in its flexibility for obtaining the approximate solutions of time fractional and space fractional equations. Using this approach, we convert a fractional partial differential equation into a nonlinear programming problem. Several numerical examples are used to demonstrate the effectiveness and accuracy of the method.     

Experimental and modeling study of the kinetics of methane hydrate formation and dissociation

In this work, several experiments were conducted at isobaric and isothermal condition in a CSTR reactor to study the kinetics of methane hydrate formation and dissociation. Experiments were performed at five temperatures and three pressure levels (corresponding to equilibrium pressure). Methane hydrate formation and dissociation rates were modeled using mass transfer limited kinetic models and mass transfer coefficients for both formation and dissociation were calculated. Comparison of results, shows that mass transfer coefficients for methane hydrate dissociation are one order greater than formation conditions. Mass transfer coefficients were correlated by polynomials as relations of pressure and temperature. The results and the method can be applied for prediction of methane production from naturally occurring methane hydrate deposits.     

Physicochemical and Rheological Properties of White-Cheek Shark (Carcharhinus dussumieri) Skin Gelatin

Physicochemical and rheological properties of white-cheek shark (Carcharhinus dussumieri) skin gelatin were determined as a function of either an alkaline-acid or an acid pretreatment. With alkaline-acid pretreatment, the purity of white-cheek shark skin gelatin was increased, with a significantly lower extraction yield, a higher retention of high molecular weight components, and greater preservation of the triple helical structure. Moreover, gelatin from alkaline-acid treated skins showed denser spherical structure, significantly (p < 0.05) different textural properties, better thermostability (T_g = 21°C, T_m = 27.5°C), higher values of both G′ and G″, higher gel strength (330 g), more imino acids (20.3%), and lighter colored gels compared with acid treated white-cheek shark skin gelatin.     

Analysis of Flow Behavior of an Nb-Ti Microalloyed Steel During Hot Deformation

Metallurgical and Materials Transactions A - The hot flow behavior of an Nb-Ti microalloyed steel is investigated through hot compression test at various strain rates and temperatures. By the...