Diazotierte aromatische Amine oder Sulfonamide reagieren mit einem β‐Ketoester zu substituierten ZZ‐Phenylhydrazono‐Z,3‐dioxo‐buttersäureethylestern (I), die mit Isonicotinsäurehydrazid (II) zu den substituierten Pyrazolinonen (III) umgesetzt werden.
Estimation of suspended sediment transport in a catchment area is very important to manage water resources, construction of dam and barrage, as well as to protect the surrounding environment. The daily monsoon sediment and flow were observed physically and quantity of total sediment input by the two major rivers of the south Mahanadi deltaic rivers to Lagoon Chilika were calculated during pre Naraj barrage (FY 2000 to 2003) and post Naraj Barrage period (FY’s 2004, 2012, 2013) establishing an observatory in the rivers the Daya and the Bhargovi.[b] The non-linear complex relationship between quantity of suspended sediment transport and volume of river-discharge inflicts challenge to the estimation process. In this paper, two southern-most distributaries, the Daya and the Bhargovi of the Mahanadi River System which flow into Chilika lagoon are studied. Random Forest, an ensemble machine learning algorithm is used to estimate the transport of sediment by these two distributaries using predictive modeling. Predicted figures based on the gathered data from these distributaries during pre-barrage period 2000-2003 have been compared with the observed data gathered in post-barrage years 2004, 2012 and 2013. Comparative data suggests that the construction of Naraj barrage has significantly reduced the concentration of sediment influx into Chilika lagoon while controlling the discharge through effective barrage management.
Accurate predictions of vehicle mobility and density are necessary for a wide range of mobile applications, including VANETs, crowdsourcing, participatory sensing, network provisioning, and shared transportation. The difficulty of forecasting is exacerbated by the scarcity and scale of vehicular mobility data. Crowd management and navigation analysis of vehicular networks that make use of deep learning techniques are the focus of this study. Multihop path based edge computing is used to analyze vehicular network navigation, and a markov spatio reinforcement neural network is used to manage vehicular crowds. The number of vehicles in the network and its navigation analysis are the basis for the experimental analysis. Throughput, data transmission rate, latency, network traffic analysis, and scalability are the parameters analyzed.proposed technique attained data transmission rate of 94%, latency of 62%, throughput of 61%, network traffic analysis of 59%, scalability of 63%.
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