INVESTIGATION OF PHYSICO-CHEMICAL PROPERTIES OF RHIZOSPHERE SEDIMENTS FROM EAST COAST REGION, TAMIL NADU, INDIA

Objective: The objective of the present study was aimed the physicochemical properties of rhizosphere sediments from the East coast region of Tamil Nadu, India, have been investigated for soil pH, ion contents, organic contents, N and P, as well as obtaining the defined data from samples collected at different depths.Methods: A total of 25 sediment samples from five different locations was collected at a depth of 5–20 cm from the earth's surface and analyzed for the physicochemical parameters by standard methods.Results: The physical parameters of sediment show pH 8.02–8.36, salinity shows high in the aqueous solution of clayey sediment, ranging from a minimum of 3.2 and maximum of 5.4 dsm−1. Lime content and texture shows silt to clay loam, respectively. The chemical parameters include macronutrients such as nitrogen (N), phosphorus (P), potassium (K) and micronutrients such as zinc (Zn), copper (Cu), iron (I), and manganese (Mn) were analyzed. The N, P, and K ranged from 87.5–110.5 (kg/ac), 2.9–4.5 (kg/ac), 132–169 (ppm) and the micronutrients ranged from 1.2–1.36, 0.70–1.06, 5.63–9.64, and 3.06–3.63 mg/kg, respectively.Conclusion: The nutrient contents of the coastal sediment may vary depending on the fluctuation of the nutrient cycle from high to low. The physical properties of the soil were strongly correlated with soil fertility. Favorable physical properties occurs in highly weathered and nutrient depleted soils and limiting physical properties occurs in the least weathered and more fertile soils. Hence, they require frequent analysis of physicochemical parameters to enhance the growth of plants in a successful manner.Keywords: East coastal sediments, Physico-chemical parameters, Macro and micronutrients

Adversarial Sample Generation using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) and Classification for IEEE 802.11 using the Deep Deterministic Policy Gradient (DDPG)

One of today's most promising developments is wireless networking, as it enables people across the globe to stay connected. As the wireless networks' transmission medium is open, there are potential issues in safeguarding the privacy of the information. Though several security protocols exist in the literature for the preservation of information, most cases fail with a simple spoof attack. So, intrusion detection systems are vital in wireless networks as they help in the identification of harmful traffic. One of the challenges that exist in wireless intrusion detection systems (WIDS) is finding a balance between accuracy and false alarm rate. The purpose of this study is to provide a practical classification scheme for newer forms of attack. The AWID dataset is used in the experiment, which proposes a feature selection strategy using a combination of Elastic Net and recursive feature elimination. The best feature subset is obtained with 22 features, and a deep deterministic policy gradient learning algorithm is then used to classify attacks based on those features. Samples are generated using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) to evaluate classification outcomes using adversarial samples. The meta-analysis reveals improved results in terms of feature production (22 features), classification accuracy (98.75% for testing samples and 85.24% for adversarial samples), and false alarm rates (0.35%).&nbsp

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks

A survey on power management strategies of hybrid energy systems in microgrid

The power generation through renewable energy resources is increasing vastly, Solar energy and Wind Energy are the most abundantly available renewable energy resources. The growth of small scale distributed grid networks increasing rapidly in the modern power systems and Distributed Generation (DG) plays a predominant role. Microgrid is one among the emerging techniques in power systems. Power Management is mainly required to have control over the real and reactive power of individual DG and for smooth operation, maintaining stability and reliability. This paper presents a survey of the research works already reported focusing on power management of hybrid energy systems such as mainly solar and wind systems in microgrid. Six different approaches have been studied in detail for AC,DC and hybrid AC/DC microgrid

(E)-6-Methyl-3-(2-methyl­benzyl­idene)­chroman-2-one

In the title compound, C18H16O2, the heterocyclic ring of the chroman-2-one system adopts a slightly distorted screw-boat conformation. The dihedral angle between the least-squares planes of the coumarin ring system and the benzene ring is 67.5 (1)°. The crystal packing features C—H⋯O hydrogen bonds, which link the mol­ecules into centrosymmetric R22(8) dimers, and C—H⋯π inter­actions