s. a. mostafavi

Recently, water injection has been widely studied as one of the most popular enhanced/improved oil recovery methods. Most of the studies focus on low salinity and seawater injection, emphasizing the quality of the injection fluid. In addition, receiving the required quantity of injection water and high-quality injection fluid is crucial. Produced water with oil and urban wastewater can be regarded as appropriate resources for water injection projects to mitigate the environmental concerns and the cost of water treatment. This study examines several water resources suggested for injection into an oil reservoir near the Persian Gulf to find the optimal choice. The studied resources are low-salinity water, seawater (from the Persian Gulf), produced water, and urban wastewater. Injection fluid quality, compatibility with formation brine, scale formation, and usability were the primary factors considered when selecting the optimal water resource. Accordingly, geo-chemical software OLI Scale Chem was used for compatibility check and estimation of scale type and amount. As a result, the least amount of scale formed after urban wastewater injection, whereas seawater was the most incompatible water source. It should be emphasized that various non-technical factors, such as the climate of the region, the water transmission route and associated costs, economic conditions of the country, environmental concerns, and the priority of wastewater usage may significantly impact resource selection.

Fog computing is a new paradigm which enables offloading IOT data (tasks) to the network devices. The aim of this approach is reducing the response time for delay-sensitive applications and improving the quality of service for users. This paper presents a task offloading scheme with taking advantages of the Software-Defind Networks. In this research a mixed integer linear programming (MILP) optimization model is presented with the aim of minimizing delay and mobility cost of things, which considers local computing, fog nodes participation, applications distribution and resource limitations. Whereas the presented mathematical model is Np-hard, a meta-heuristic algorithm based on the ant colony optimization is proposed by considering constraints of the mathematical model. The results obtained from evaluation of the proposed method is compared with the optimal value obtained from the mathematical model, random method and a heuristic algorithm presented in related works. The results of evaluation show that the delay and the total offloading cost in the proposed method are 22% and 28.75% higher than the optimal values, respectively. Also, the proposed method is capable to reduce the delay by 20% and reduce the migration cost of computing results by 40% compared to the heuristic method in state-of-the-art.

The network overhead and multiple networks disconnection faults are the main challenges of anonymous servers  implemented in VANETs. The block chain technology has been entered into the wide range of preserving privacy. The robust anonymity mechanism existence and the traceability of all transactions are the main advantages of this  technology. The primary model of the block chain was able to complete the process with the anonymity stored data. In distributed models, the authentication, storage and retrieval of transactions are applied by all user’s consensus. The asymmetric cryptography, preserves the identity anonymity and aggregating transactions of different users into a block which is ready to send, preserves the path anonymity. The proposed method is aimed to ensure anonymity by mounting the block chain on VANETs. Before delivering any transaction to the block chain, the risk of user’s privacy is high. To achieve low risk, we combine the graph processing methods with Silent Period, Cloaking-Region and  Dummy Node methods. The block chain simulation on VANET is driven by python and the anonymity risks are simulated with ARX. The results suggest that the block chain is stabled and the optimal risk reduction is achieved on the VANET.

With increasing demand of bandwidth-intensive application in cellular networks, coexistence of Device-to-Device (D2D) communications with cellular subscribers is a promising solution for high spectrum efficiency and network throughput. In cellular D2D communications, intelligent resource sharing among the network subscribers and paired devices is of significant importance. The most state-of-the-art works are relied on the exact values of Channel State Information (CSI) and subscribers’ transmission rate feedback which are not available in the real cases. In this paper, we propose a novel reinforcement-learning-based approach for mode selection and spectrum allocation called RL-D2D which shares efficiently resources amongst the D2D users and cellular subscribers with the need for CSI, achieving high network throughput. The results of evaluations show that RL-D2D achieves near-optimal performance and low outage rate in despite of lack of CSI and users’ transmission rate feedback.

Quality of service and trustworthiness of data is of high importance for decision making in Internet-of-Things (IoT) applications. Malicious nodes and devices may compromise the quality of service and experience for other nodes through providing invalid data and evaluations. Hence, a trust management system to assess the trust level of users and gathered data is deemed to be essential to every IoT system. The current approach in the literature for computing the trust level is entity-centric trust in which the trust level of end users are estimated. However, the trustworthiness of data is equally important in many applications. In this paper, we propose, Trusty, a hybrid trust computation approach, aiming at trust assessment for both entities as well as data. In our proposed approach, a Bayesian learning method is used for computing the entity trust, while Dempster-Shafer theory is exploited to data fusion and data trustworthiness assessment. We implement Trusty in a smart parking system scenario to investigate the performance of our model in the different settings for misbehavior nodes and faulty sensors. As shown by the extensive simulation experiments, Trusty outperforms the competing approaches in terms of convergence for both data trustworthiness and entity trust.

Prunus domestica (plum) has been considered as a useful remedy for several disorders in Iranian Traditional Medicine (ITM). It has cold and wet temperament and is used as syrup for hot temperament diseases such as hot headache and stomach disorders. In the present study, plum syrup has been formulated according to ITM manuscripts and quality control evaluations have been accomplished to present a suitable formulation.

The fruits of Prunus domestica L. were macerated in water, then decocted. The mixture was filtered. The filtrate was concentrated to have a suitable viscosity. The extract was sweetened by adding sugar (1:2) and heated till sugar was completely dissolved. The final product was evaluated physicochemically and microbiologically according to standard protocols and total phenolics content of the syrup stability was determined. The syrup was assessed in accelerated condition (40 ºC) during 6 months.

The prepared formulation was a viscose and brown syrup with plum flavor and fragrance. No precipitation and cap locking were observed in the syrup. Dry residue, pH, density, viscosity and total phenolics of the syrup were found 43.1%, 3.49, 1.27 g/ml, 6.5 cP and 152.3 mg/100ml, respectively. No microbial growth was observed in the formulation. In the accelerated stability tests, no remarkable changes were seen in the product. Total phenolics content was decreased 2.2% during 6 months in 40 ºC.

The formulated Prunus domestica syrup could be introduced for further mass production after completing the final required evaluations.

Niosomes are non-ionic surfactant vesicles that have potential applications in the delivery of hydrophilic and hydrophobic drugs. The topical form of N-acetyl glucosamine (NAG) recently has been considered in the treatment of hyperpigmentation disorders due to its inhibitory effect on thyrosinase enzymes in melanocytes. To improve NAG penetration into the skin we formulated the drug in niosomes and investigated its flux across excised rat skin using Franz diffusion cells. The drug assay was performed by a novel and specific high performance liquid chromatography method. Niosomal vesicles were further characterized by optical and scanning electron microscopy and particle size analysis. Niosomes prepared with Span 40 produced a drug encapsulation of about 50%. The vesicle size was markedly dependent on the composition of the niosome formulations and was in range of 500-4500 nm (Span 80 < Span 60 < Span 40 niosomes). Span 40-niosomes provided a higher NAG flux across the skin than Span 60- and Span 80-nisomes. All formulations significantly improved the extent of drug assessed to be localized in the skin (P< 0.05), as compared to NAG hydroalcoholic (HA) solution. Our study demonstrated the potential of niosomes for improved NAG localization in the skin, as needed in hyperpigmentation disorders.

Acebutolol (AC) is a chiral b-adrenergic blocking drug, which is useful clinically as the racemate in treating hypertension and is metabolized to an equipotent chiral metabolite, diacetolol (DC). In this paper we report a case of a 32 year old woman who was receiving AC during her pregnancy and lactating time for management of hypertension. The maternal plasma level and breast milk as well as cord blood collected to see whether the drug is transferred to the fetus through the mother stereoselectively. A stereospecific high-performance liquid chromatographic (HPLC) assay was used to measure the enantiomeric concentrations of AC and DC. AC and DC enantiomers are stereoselectively excreted into the milk, although the concentrations of DC enantiomers were higher than those of AC. AC concentrations were very low in both cord artery and vein samples four hours after taking the drug however, the stereoselective concentrations of metabolite, DC, was found in these samples. In conclusion, AC and DC enantiomers are excreted in human breast milk in concentrations much higher than that in maternal plasma. Furthermore, DC was found in the infant’s plasma indicating that accumulation of metabolite did occur in this infant.