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.     

A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

     

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.

     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

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.     

A potential trackable bone filler: Preparation and characterization

This study reported the synthesis of fluorescent hydroxyapatite/alginate/carbon quantum dots (HA/Alg/CQDs) nanocomposites via the co-precipitation technique. The N-doped CQDs as a new class of fluorescent materials were prepared by the citric acid pyrolysis method, with an average size around 4 nm. Physical, chemical, and optical properties of the synthesized nanocomposites were investigated by X-ray diffraction (XRD), Fourier-transformed infrared spectroscopy (FTIR), atomic force microscopy (AFM), field-emission scanning electron microscopy (FESEM), UV–visible spectroscopy, and photoluminescence (PL) spectroscopy, respectively. The PL spectroscopy data verified the favorable in vitro luminescent emission of the HA/Alg/CQDs nanocomposites in comparison with HA/Alg and HA samples. The XRD patterns of the prepared samples confirmed the formation of crystalline HA in all composites, possessing a Ca/P ratio around 1.5 as obtained by EDX elemental analysis. The FESEM analysis exhibited HA nanoplates that homogeneously distributed throughout the alginate matrix. Therefore, the synthesized nanocomposites could be regarded as potential trackable drug carriers for hard tissue engineering applications.