Informatics Publishing Limited

Mandeep Kaur ¹, Rajesh Tiwari ¹, B. R. Kumawat ¹

Affiliations
1 Department of Fruit Science, College of Horticulture (R.V.S.K.V.V.) Mandsaur, M.P., IN

Abstract

An experiment was conducted during 2011-2012 at the Department of Fruit Science, K.N.K. College of Horticulture, Mandsaur (M.P.) on seven year old guava tree cv. L-49 with two levels of nitrogen i.e., 600 g N (100% N) and 450 g N (75% N), two levels of phosphorus i.e., 400 g P₂O₅ (100% P₂O₅) and 300 g P₂O₅ (75% P₂O₅) and a common dose of potassium i.e., 300 g K₂O and 30 g each of Azospirillium and PSB inoculation and 10 kg Vermicompost. The results revealed that the age fruit weight (220.37 g), number of fruits per tree (384), TSS (11.67 0Brix), total sugar (8.06%), reducing sugar (4.17%), non-reducing sugar (3.89%), pectin (0.81%), ascorbic acid (207.90 mg/100 g pulp) and lowest acidity (0.20%) were obtained with treatment T₉. The combined application of recommended dose of NPK with vermicompost (10 kg) and biofertilizers (30 g /plant) gave significantly higher fruit yield per tree (84.66 kg) with higher B: C ratio (1:5.27).

Keywords

Guava, L-49, Integrated Nutrient Management, Yield, Quality

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Shikha Agrawal ¹, Rajesh Tiwari ¹

Affiliations
1 Shri Sankarachary College of Engineering & Technology, Bhilai, IN

Abstract

High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommendations may incorporate this information. This paper focuses on networks that represent trust and recommendation systems that incorporate these trust relationships. The goal of a trust-based recommendation system is to generate personalized recommendations by aggregating the opinions of other users in the trust network .In analogy to prior work on voting and ranking systems, we use the axiomatic approach from the theory of social choice. We develop a set of five natural axioms that a trust- based recommendation system might be expected to satisfy. Then, we show that no system can simultaneously satisfy all the axioms. However, for any subset of four of the five axioms we exhibit a recommendation system that satisfies those axioms. Next we consider various ways of weakening the axioms, one of which leads to a unique recommendation system based on random walks. We consider other recommendation systems, including systems based on personalized Page Rank, majority of majorities, and minimum cuts, and search for alternative axiomatizations that uniquely characterize these systems. Finally, we determine which of these systems is incentive compatible, meaning that groups of agents interested in manipulating recommendations cannot induce others to share their opinion by lying about their votes or modifying their trust links. This is an important property for systems deployed in a monetized environment.

Keywords

Recommendation Systems, Reputation Systems, Axiomatic Approach, Trust Networks.

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