Green synthesis of zinc oxide nanoparticles using Amygdalus scoparia Spach stem bark extract and their applications as an alternative antimicrobial,anticancer, and anti-diabetic agent

For the first time in this study, Zinc oxide nanoparticles were biosynthesized by the eco-friendly and cost-effective procedure using Amygdalus scoparia stem bark extract then used as antibacterial, antifungal, anticancer, and anti-diabetic agents. The characterization techniques confirmed the biosynthesis, crystalline nature, structure, size, elemental composition of ZnO NPs and bioactive compounds that exist in A. scoparia extract accounting for Zn²⁺ ion reduction, capping and stabilization of ZnO NPs. The ZnO NPs displayed remarkable inhibitory activity against E. coli, E. aerigenes, S. aureus, P. oryzae, F. thapsinum, and F. semitectum compared to antibiotic standards. The ZnO NPs showed significant inhibitory effects on cancer cell lines, while it had no toxic effect on Vero normal cell line. The ZnO NPs (30 mg/kg)-treated diabetic rats showed significantly higher levels of insulin and lower AST, ALT and blood glucose compared with the STZ induced diabetic group and other treated groups (P < 0.05). The ZnO NPs- and extract-treated rats showed significantly higher levels of IR, GluT2, and GCK expression and lower TNFα expression compared with the STZ induced diabetic rats. Our findings showed that ZnO NPs represented an outstanding performance for biological applications.     

Statistical Optimization for Production of Light Olefins in a Fluidized‐Bed Reactor

The methanol‐to‐olefins reaction (MTO) was studied in a small‐scale fluidized‐bed reactor over synthesized silicoaluminophosphate (SAPO‐34) catalysts. Mesoporous nanocrystalline SAPO‐34 molecular sieves were synthesized hydrothermally by ultrasonic and microwave‐assisted aging processes in the presence of hexadecyltrimethylammonium bromide (CTAB) and tetradecyldimethyl(3‐trimethoxysilylpropyl)ammonium chloride (TPOAC) as surfactant agents. The Box‐Behnken experimental design method was applied to determine the optimum operating parameters of this process conducted in the fluidized‐bed reactor. The optimum conditions in terms of reaction temperature, ratio of inlet gas velocity to minimum fluidizing velocity, and MeOH weight fraction were evaluated.     

Access point selection in the network of Internet of things (IoT) considering the strategic behavior of the things and users

The Journal of Supercomputing - With the expansion in the use of IoT, increasing the efficiency of these networks has become even more significant. Objects need reliable communications at suitable...     

Endothelin receptors mediating vasoconstriction in rat pressurized small arteries

The effects of endothelin-1 (ET-1), a nonselective ETA and ETB receptor agonist, and sarafotoxin S6c, a selective ETB agonist, were investigated in the presence and absence of BQ123 and BQ788, ETA- and ETB-selective antagonists, respectively, in rat mesenteric small arteries, using a perfusion pressurized arteriograph in which segments of vessels were cannulated and exposed to constant pressure and flow. ET-1 (10(-13)-10(-7) M) induced vasoconstriction in both intact and endothelium-denuded arteries in a concentration-dependent manner. BQ123 (10(-7) and 10(-6) M) inhibited the effect of ET-1, displacing the concentration-response curve to the right in a concentration-dependent manner. The effect of ET-1 was not significantly affected by BQ788 (10(-7) and 10(-6) M), a selective antagonist of ETB receptors. Sarafotoxin S6c (10(-11)-10(-7) M) also induced a slight concentration-dependent vasoconstriction. The effect of sarafotoxin S6c (10(-8) M) was inhibited by the ETB-selective antagonist BQ788 (10(-7) M), but was not significantly changed by BQ123 (10(-7) M). Vasoconstriction induced by sarafotoxin S6c (10(-8) M) in a single bolus concentration was significantly greater than the contraction induced by the same concentration as part of a cumulative concentration-response curve, indicating desensitization or downregulation of ETB receptors during the latter. Repeated application of single concentrations of sarafotoxin S6c (10(-8) M) caused progressively smaller contraction of arteries. These results show the existence of both ETA and ETB vasoconstrictor receptors located on smooth muscle of small arteries. They also show that ETB receptors induce a smaller constrictor effect, and rapidly undergo desensitization after sustained or repeated activation.     

Synthesis of poly(methyl methacrylate)/silica nanocomposite through emulsion polymerization using dimethylaminoethyl methacrylate

Polymer/Silica nanocomposite latex particles were prepared by emulsion polymerization of methyl methacrylate (MMA) with dimethylaminoethyl methacrylate (DM). The reaction was performed using a nonionic surfactant and in the presence of silica nanoparticles as the seed. The polymer‐coated silica nanoparticles with polymer content and number average particle sizes ranged from 32 to 93 wt % and 114–310 nm, respectively, were obtained depending on reaction conditions. Influences of some synthetic conditions such as MMA, DM, surfactant concentration, and the nature of initiator on the coating of the silica nanoparticles were studied. Electrostatic attraction between anionic surface of silica beads and cationic amino groups of DM is the main driving force for the formation of the nanocomposites. It was demonstrated that the ratio of DM/MMA is important factor in stability of the system. The particle size, polymer content, efficiency of the coating reaction, and morphology of resulted nanocomposite particles showed a dependence on the amount of the surfactant. Zeta potential measurements confirmed that the DM was located at the surface of the nanocomposites particles. Thermogravimeteric analysis indicated a relationship between the composition of polymer shell and polymer content of the nanocomposites. The nanocomposites were also characterized by FTIR and differential scanning calorimetry techniques. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Environmental damage costs in Iran by the energy sector

On the basis of the energy supply and demand, this paper assesses the environmental damage from air pollution in Iran using the Extern-E study that has extended over 10 years and is still in progress in the European Union (EU) commission. Damage costs were transferred from Western European practice to the conditions of Iran by scaling according to GDP per capital measured in PPP terms.     

ECFGM: enriched control flow graph miner for unknown vicious infected code detection

Vicious codes, especially viruses, as a kind of impressive malware have caused many disasters and continue to exploit more vulnerabilities. These codes are injected inside benign programs in order to abuse their hosts and ease their propagation. The offsets of injected virus codes are unknown and their targets usually are latent until they are executed and activated, what in turn makes viruses very hard to detect. In this paper enriched control flow graph miner, ECFGM in short, is presented to detect infected files corrupted by unknown viruses. ECFGM uses enriched control flow graph model to represent the benign and vicious codes. This model has more information than traditional control flow graph (CFG) by utilizing statistical information of dependent assembly instructions and API calls. To the best of our knowledge, the presented approach in this paper, for the first time, can recognize the offset of infected code of unknown viruses in the victim files. The main contributions of this paper are two folds: first, the presented model is able to detect unknown vicious code using ECFG model with reasonable complexity and desirable accuracy. Second, our approach is resistant against metamorphic viruses which utilize dead code insertion, variable renaming and instruction reordering methods.     

Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

Experience classification for transfer learning in traffic signal control

In recent years, due to the drastic rise in the number of vehicles and the lack of sufficient infrastructure, traffic jams, air pollution, and fuel consumption have increased in cities. The optimization of timing for traffic lights is one of the solutions for the mentioned problems. Many methods have been introduced to deal with these problems, including reinforcement learning. Although a great number of learning-based methods have been used in traffic signal control, they suffer from poor performance and slow learning convergence. In this paper, a transfer learning-based method for traffic signal control has been proposed. Multi-agent system has also been used for modelling the traffic network and transfer learning has been used to make reinforcement learning agents transfer their experience to each other. Furthermore, a classifier has been utilized to classify the transferred experiences. The results show that using the proposed method leads to a significant improvement on average delay time and convergence time of the learning process.

     

A graph mining approach for detecting unknown malwares

Nowadays malware is one of the serious problems in the modern societies. Although the signature based malicious code detection is the standard technique in all commercial antivirus softwares, it can only achieve detection once the virus has already caused damage and it is registered. Therefore, it fails to detect new malwares (unknown malwares). Since most of malwares have similar behavior, a behavior based method can detect unknown malwares. The behavior of a program can be represented by a set of called API's (application programming interface). Therefore, a classifier can be employed to construct a learning model with a set of programs' API calls. Finally, an intelligent malware detection system is developed to detect unknown malwares automatically. On the other hand, we have an appealing representation model to visualize the executable files structure which is control flow graph (CFG). This model represents another semantic aspect of programs. This paper presents a robust semantic based method to detect unknown malwares based on combination of a visualize model (CFG) and called API's. The main contribution of this paper is extracting CFG from programs and combining it with extracted API calls to have more information about executable files. This new representation model is called API-CFG. In addition, to have fast learning and classification process, the control flow graphs are converted to a set of feature vectors by a nice trick. Our approach is capable of classifying unseen benign and malicious code with high accuracy. The results show a statistically significant improvement over n-grams based detection method.