Design,Facile Synthesis and Characterization of Porphyrin-Zirconium-Ferrite@SiO2 Core-Shell and Catalytic Application in Cyclohexane Oxidation

Silicon - In this study, a new magnetic ZrFe2O4@SiO2-TCPP nanocatalyst with high efficiency was used for the oxidation of cyclohexane to cyclohexanone (Ke) and cyclohexanol (Al). The mesoporous...     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

     

A Model for Identifying the Behavior of Alzheimer’s Disease Patients in Smart Homes

Wireless Personal Communications - In recent years, Smart Cities and Smart Homes have been studied as an important field of research. The design and construction of smart homes have flourished so...     

A comparative study between the augmented Lagrangian method and the complementarity approach for modeling the contact problem

The increasing demand for real-time high-fidelity multibody dynamics simulations in several modern fields such as robotics and computer game industries has motivated many researches to propose novel approaches to model multibody systems with several contacts. The possibility of different contact conditions in a system with several contacts yields a combinatorial problem of potentially large size. Rigid contact model which is the most common model used for real-time simulations yields a non-smooth dynamic formulation. The solution of such a system can be governed using different methods. In this paper a comparison between the complementarity approaches and the augmented Lagrangian based formulations to deal with non-smooth contact models is presented via numerical examples, and the advantages and shortcomings of each method are discussed.     

Antibacterial activity of zinc oxide nanoparticle suspensions on food‐borne pathogens

Nanoparticles (NPs) are increasingly recognised for their utility in biological applications including nanomedicine and food safety. The present study investigated the antibacterial activity of zinc oxide (ZnO) when tested against the Gram‐negative bacteria Escherichia coli as well as the Gram‐positive bacterium Staphylococcus aureus, and the effect was more pronounced with the Gram‐positive than with the Gram‐negative bacteria. ZnO NPs also exhibited a preferential ability to suppress growth of E. coli and S. aureus in milk. This study suggested that the application of ZnO NPs as antibacterial agents in food systems and medicine may be effective at inhibiting certain pathogens.     

Preparation, Characterization and Photocatalytic Properties of Ba-Cd-Sr-Ti Doped Fe3O4 Nanohollow Spheres on Removal of Congo Red Under Visible-Light Irradiation

In this study, Ba-Cd-Sr-Ti doped Fe₃O₄ nanohollow spheres were successfully prepared via a simple solvothermal method. The crystal size, structure, morphology and elemental analysis of the as-prepared sample were investigated in detail by X-ray diffraction (XRD), FT-IR, scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy, respectively. Magnetic hysteresis measurement was carried out on a vibrant sample magnetometer (VSM) showing the soft ferromagnetic property at room temperature. The synthesized nanohollow spheres were employed as a photocatalyst to study the photocatalytic degradation of dye contaminations. The UV-Vis results showed that the specimen could well catalyze the decolorizing of congo red (CR) solution and a removal efficiency of 99.5 % was obtained at pH 6. The optical characteristic of the products was studied by estimating the band-gap energy based on diffuse-reflectance spectroscopy (DRS) which represented the doped magnetite with semiconductor metals to be more prone in the visible region as compared to UV region. Some factors such as initial dye concentration, pH, and contact time influencing the decomposition of CR were evaluated.     

Thermosensitive folic acid-targeted poly (ethylene-<Emphasis Type="Italic">co</Emphasis>-vinyl alcohol) hemisuccinate polymeric nanoparticles for delivery of epirubicin to breast cancer cells

A novel thermosensitive folic acid (FA)-targeted succinylated poly (ethylene-co-vinyl alcohol) (EVOH) (EVOHS-FA) nanocarrier was synthesized for the specific delivery of epirubicin (EPI) to MCF-7 breast cancer cell line. Three different ratios of synthesized EVOH-Suc were reacted with FA. The structure of the desired products (EVOHS₄₀-FA, EVOHS₆₀-FA and EVOHS₈₀-FA) was confirmed by ¹H NMR and FTIR techniques. Nanoparticles were obtained by nano-precipitation procedure using DMSO/H₂O as solvent/anti-solvent. The particle size, zeta potential, entrapment efficacy and in vitro release profile of the final formulations in different temperatures were measured. The optimized nanoparticles had the particle size of 214 ± 8.5 nm, zeta potential of ?29.6 mV, PDI of 0.198 ± 0.04, and a high encapsulation efficiency that released the drug efficiently within 450 h at the temperature of 40 °C compared to 37 °C. The morphology of nanoparticles was studied by scanning electron microscopy. The in vitro cytotoxicity was evaluated using the MTT assay on MCF-7 cell lines in response to temperatures of 37 and 40 °C. The MTT assay indicated that the targeted nanoparticles carrying EPI were significantly more cytotoxic than the non-targeted nanoparticles and the free drug at 40 °C.     

Investigation on the effects of microwave and conventional heating methods on the phytochemicals of pomegranate (Punica granatum L.) and black mulberry juices

Black mulberry and pomegranate juices were concentrated by conventional and microwave heating at different operational pressures (7.3, 12, 38.5, and 100 kPa). The effects of each heating method on the phytochemical changes (total anthocyanin content and antioxidant activity) of juices were investigated. The effect of various heating methods on the profile of the pomegranate anthocyanins were conducted using high-performance liquid chromatography–mass spectrometry (HPLC–MS). The main anthocyanins of pomegranate quantified by HPLC–MS were cyanidin, delphidin, pelargonidin, 3-glucoside, and 3, 5-diglucoside. In black mulberry juice, the final juice concentration of 42° Brix was achieved in 140, 120, and 95 min at 100, 38.5, and 7.3 kPa respectively and the final pomegranate juice concentration of 40° Brix was achieved in 140, 127, and 109 min at 100, 38.5, and 12 kPa respectively by using a rotary evaporator. Applying microwave energy decreased the required times to 115, 95, and 60 min for black mulberry juice and to 118, 95, and 75 min for pomegranate juice. Results showed that anthocyanin degradation and consequently decrease in antioxidant activity were more pronounced in rotary evaporation compared to microwave heating method.     

An efficient improvement remote user mutual authentication and session key agreement scheme for E-health care systems

The E-health care systems allow patients to gain the health monitoring facility and access medical services remotely. A secure mechanism for mutual authentication and session key agreement is the most important requirements for E-Health Care Systems. Recently, Amin et al.’s proposed a mutual authentication and session key agreement protocol and claimed that their scheme is secure against all possible attacks. In this paper, we show that not only their scheme is vulnerable to privileged-insider attack, replay attack, session key disclosure attack, but also does not provide patient untraceability and backward secrecy. In order to withstand the mentioned security weaknesses, we propose an efficient remote mutual authentication scheme for the systems which are using ECC and Fuzzy Extractor. The proposed scheme not only resists against different security attacks, but it also provides an efficient registration, login, mutual authentication, session key agreement, and password and biometric update phases. During the experimentation, it has been observed that the proposed scheme is secure against various known attacks. Beside, our scheme is robust against privileged-insider attack that it rarely checked in security analysis. The informal analysis will ensure that our scheme provides well security protection against the different security attacks. Furthermore, we analyzed the security of the scheme using AVISPA software and Random Oracle Model. The formal analysis results and performance evaluation vouch that our scheme is also secure and efficient in computation and communication cost.     

A self-tuning ANN model for simulation and forecasting of surface flows

Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.