To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network’s realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts.
Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.
This paper discusses the wear and friction with the 2 W% Al2O3 nanocomposite content of pure Mg and AZ91D Mg alloys. Sliding speeds of 0.5 and 1.5 m/s in cast materials with normal stress conditions have been used in sliding distances up to 2000 m/s (0.5, 1.0, and 1 MPa). In order to evaluate the work hardness of the materials measured on temperature similar to the contact surface, we used hardness patterns and hot-compression flow curves. Mg and AZ91D magnesium alloy pure monolithic Mg are low wear resistant due to an increase in contact temperature due to the adjustment of working conditions, but the wear rate was significantly lower in composite materials, mainly because of nanoparticle strength improvements. Although wear generally contributes to grain refining, increased wear capacity, and greater durability, wear resilience due to dislocation resistance and nanoparticles is seen as the primary wear mechanism in the existing nanocomposites.
Renewable energy-based distributed generators (DGs) are gaining more penetration in modern grids to meet the growing demand for electrical energy. The anticipated techno-economic benefits of these eco-friendly resources require their judicious and properly sized allocation in distribution networks (DNs). The preeminent objective of this research is to determine the sizing and optimal placing of DGs in the condensed DN of a smart city. The placing and sizing problem is modeled as an optimization problem to reduce the distribution loss without violating the technical constraints. The formulated model is solved for a radial distribution system with a non-uniformly distributed load utilizing the selective particle swarm optimization (SPSO) algorithm. The intended technique decreases the power loss and perfects the voltage profile at the system’s nodes. MATLAB is used for the simulation, and the obtained results are also validated by the Electrical Transient Analysis Program (ETAP). Results show that placing optimally sized DGs at optimal system nodes offers a considerable decline in power loss with an improved voltage profile at the network’s nodes. Distribution system operators can utilize the proposed technique to realize the reliable operation of overloaded urban networks.
The current study evaluated the effect of pomegranate peel-based edible coating on chicken nuggets in order to develop a functional and safe product, high in nutritional value. For this purpose, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and total phenolic content (TPC) assays were performed to check the potential antioxidant activity of chicken nuggets; microbial control, including total aerobic count and coliforms population, was performed for quality and safety purposes; and thiobarbituric acid reactive substances (TBARS) and peroxide value (POV) were performed to determine the oxidative stability of chicken nuggets. Different treatments were applied at different storage periods (0th, 7th, 14th and 21st day). The higher value of total aerobic count (5.09 ± 0.05 log CFU/g) and coliforms (3.91 ± 0.06 log CFU/g) were obtained for the uncoated samples, while the lower population was enumerated in the combination of sodium alginate (SA) and pomegranate peel powder (PPP). However, DPPH (64.65 ± 2.15%) and TPC (135.66 ± 3.07 GAE/100 g) values were higher in the coated chicken nuggets (SA (1.5%) and PPP (1.5%)) and lowest in the control samples. The higher value of TBARS (1.62 ± 0.03 MDA/kg) and POV (0.92 ± 0.03 meq peroxide/kg) were observed in the uncoated chicken nuggets. In the Hunter color system, L*, a*, and b* peak values were determined in the coated chicken nuggets with SA (1.5%) + PPP (1.5%) at the 21st day of storage. The uncoated chicken nuggets had different sensory characteristics (appearance, color, taste, texture, and overall acceptability) compared to the coated samples. Conclusively, coating based on the combination of SA (1.5%) and PPP (1.5%) increased the quality, safety, and nutritional properties of chicken nuggets.
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