Taxonomic characteristics, environmental protection and the problem of the\nprotection of vegetation at biogeocenosis, as well as flora is a topical issue from the\nscientific point of view in modern times. Taking into account this modern problem,\necological-phytocenological characteristics of the Shirvan National Park (hereinafter\nreferred to as SNP), established in the territory of the Caspian Sea in the area of\n54373.5 hectares, were studied on the basis of progressive geobotanical methods.\nScientifically non-protection of natural phytocenoses which is spread over the\nSNP, soil and climate. environmental factors, as well as adverse anthropogenic and\ntechnogenic impacts of vegetation covering (wind erosion and salting), wane of\npsammophytes desert vegetation, flooding or transfer are observed.\nMain purpose of the establishment of the SNP is to refine the favorable\nenvironmental for the semi-desert and desert landscape of the area, as well as the\ngazelles (Gazella subgutturoza) and birds fallen into the \"Red Book of Azerbaijan\" and\nfor conservation of other fauna species.
The principal object of this study is to find an analytical and numerical investigation to examine the modified projective synchronization (MPS) of the chaotic nonlinear systems with complex variables and uncertain parameters. Based on the adaptive control routine and the Lyapunov function a plan is created to gain MPS of chaotic attractors of these systems. The MPS of two identical complex Chen systems are exercised as an example to prove the usefulness of the presented plan. Numerical simulations are computed to illustrate the effectiveness of the recommended synchronization plan and confirm the analytical outcomes.
This paper proposes “a Crop Risk Reasoning System based on Fuzzy using weather information and Farm Data” which collects the weather information based on the location of farms and processes four modules to reason the risk of crops. First, a Cloudlet based Data Management Module(CDMM) collects and manages the private farm data about crops and the shared data about local weather in a Cloud Server. Second, an Internal and External Information collection Module(IEICM) collects the weather information according to the location of a Private Farm Server, Third, a Risk Computation Module(RCM) computes the External Risk (ER) dependent on an external environment such as sunshine amount, temperature, and humidity by using the weather information and the real time private farm data about crops collected from the Private Farm Server, the Soil Status Risk (SSR) dependent on essential nutrient supply status of crops by collecting the element information like C, O, H, N, etc. and moisture content included in the soil in real time and the Total Risk (TR) on the final risk. Fourth, a Risk Alarm Module(RAM) informs a farm manager of the risk information according to the TR value computed by the RCM. Therefore, this paper reasons the crop risk information and analyses performance. It prevents the disease of crops happening from various Internal and external environment in advance and provides farmers with the optimized environment in which crops can grows well.
This paper proposes a Cloud Situation Awareness (CSA) Framework which is used to be aware of the situation around autonomous vehicles using Internet of Vehicles (IoV) and Cloud. The framework consists of two layers: an infrastructure layer and a cloud layer. The infrastructure layer consists of three modules: an Interface Module (IM), a Data Collection and Pre-processing Module (DCPM), and a Communication Module (CM). The cloud layer is a Situation Assessment Module (SAM). This paper focused more on the SAM in the cloud layer. The SAM consists of two processes: a situation and impact assessment process and a decision making process. The situation and impact assessment is processed by an Extended Situation-Specific Fuzzy Bayesian Network (ESSFBN). The decision making is processed by a Fuzzy Super Vector Machine (F-SVM). The simulation shows that the ESSFBN is more sensitive than SSFBN. The simulation also shows that the ESSFBN obtains better precision because of the proposed F-SVM.