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Nitrogen is one of the most yield-limiting nutrients for rice production, and proper nitrogen fertilizer management is critical for high yield. In rice research, more time and energy are devoted to managing nitrogen fertilizer than any other nutrient because of the high potential for a return on the fertilizer investment.Considering this, an experiment was conducted at the Soil Science farm, Bangladesh Agricultural University, Mymensingh, Bangladesh during July 2017 to December 2017 in Aman season to examine the effect of different doses of nitrogen fertilizer on growth and yield of T. Aman rice variety (BRRI Hybrid Dhan4). The experiment consisted of four nitrogen levels (viz. 0,40, 80and 100 kg N ha-1) in a randomized complete block design with three replicates. The grain yield increased over control varied from 47% to 53%. The highest grain yield was observed 53% for treatment T3 (N80) and the lowest was recorded 47% in treatment T2 (N40). The highest N uptake was recorded 134.16 kg ha-1for treatment T3 when rice plants were fertilized with 80 kg N ha which was statistically similar to T4 and the lowest 62.45 kg ha-1 in treatment T1 (control). However the overall performance on yield contributing parameters was found on T3 treatment. We concluded that application of the intermediate levelof nitrogen (80 kg N ha-1) was economical and environment-friendly for the cultivation of BRRI Hybrid Dhan4.

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this paper we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time.

Technological advancements in vehicular transportation systems have led to the growth of novel paradigms, in which vehicles and infrastructures collaborate to infer high-level knowledge about phenomena of interest. Vehicular Social Network (VSN) is one such paradigm in which vehicular network entities are considered as part of an Online Social Network (OSN), paving the way for new services derived from social context. Although vehicular crowdsourcing has tremendous benefits, its deployment in real systems requires to solve important challenges including defense against Sybil attacks. This paper proposes a novel fog-assisted system that uses SybilDriver to minimize the presence of Sybil entities in VSN-based crowdsourcing applications. The proposed system exploits the characteristics of Vehicular Ad-hoc NETworks (VANETs) and OSNs to effectively recognize Sybils, and the adoption of fog computing helps reduce the overall network overhead by processing data closer to the vehicles. We perform detailed experiments on real-world publicly available datasets primarily to assess the effectiveness of SybilDriver against different Sybil attack strategies. Our experimental results show that SybilDriver detects Sybils with higher performance than state-of-the-art techniques under different settings. Furthermore, an evaluation of the fog architecture in terms of message complexity demonstrates low impact on the network overhead.