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Recommender Systems are useful information filtering tools that have reduced information overload over the web. Collaborative filtering (CF) is one of the extensively used recommendation techniques. Traditional CF captures user-item ratings in a two-dimensional rating matrix which does not sufficiently convey user preferences. Ratings based on several criteria are incorporated into CF to develop multi-criteria recommender systems (MCRS). MCRS are more efficient and cater to the users’ needs with more satisfaction. However, there are certain issues like multidimensionality, sparsity, and cold start associated with MCRS. This paper aims to study the MCRS and investigate efficient solutions for existing issues. In this direction, we proposed a modified similarity measure that improves the accuracy of neighborhood generation and rating prediction. In the proposed approach, the users are clustered based on multi-criteria ratings, which reduces the data sparsity and multidimensionality issues in MCRS. The supremacy of the proposed approach is verified by conducting experiments on a benchmark data set and evaluating the performance using some standard evaluation measures.

Drought is the most important factor limiting the activity of rhizobia during N-fixation and plant growth. In the present study, we isolated Bradyrhizobiumspp. from root nodules of higher trehalose-accumulating soybean genotypes and examined for moisture stress tolerance on a gradient of polyethylene glycol (PEG 6000) amended in yeast extract mannitol (YEM) broth. In addition, the bradyrhizobial strains were also evaluated for symbiotic effectiveness on soybean. Based on 16S rDNA gene sequences, four bradyrhizobial species were recovered from high trehalose-accumulating genotypes, i.e., two Bradyrhizobium liaoningensestrains (accession number KX230053, KX230054) from EC 538828 and PK-472, respectively, one Bradyrhizobium daqingense(accession number KX230052) from PK-472, and one Bradyrhizobium kavangense(accession number MN197775) from Valder genotype having low trehalose. These strains, along with two native strains, viz., Bradyrhizobium japonicum(JF792425), Bradyrhizobium liaoningense(JF792426), and one commercial rhizobium, were studied for nodulation, leghaemoglobin, and N-fixation abilities on soybean under sterilized sand microcosm conditions in a completely randomized design. Among all the strains, D-4A (B. daqingense) followed by D-4B (B. liaoningense) was found to have significantly higher nodulation traits and acetylene reduction assay (ARA) activity when compared to other strains and commercial rhizobia. The bradyrhizobia isolates showed plant growth promotion traits such as indole acetic acid (IAA), exopolysaccharide (EPS), and siderophore production, phosphate-solubilizing potential, and proline accumulation. The novel species B. daqingensewas reported for the first time from Indian soil and observed to be a potential candidate strain and should be evaluated for conferring drought tolerance in soybean under simulated stress conditions.

The exponential growth of recommender systems research has drawn the attention of the scientific community recently. These systems are very useful in reducing information overload and providing users with the items of their need. The major areas where recommender systems have contributed significantly include e-commerce, online auction, and books and conference recommendation for academia and industrialists. Book recommender systems suggest books of interest to users according to their preferences and requirements. In this article, we have surveyed machine learning techniques which have been used in book recommender systems. Moreover, evaluation metrics applied to evaluate recommendation techniques is also studied. Six categories for book recommendation techniques have been identified and discussed which would enable the scientific community to lay a foundation of research in the concerned field. We have also proposed future perspectives to improve recommender system. We hope that researchers exploring recommendation technology in general and book recommendation in particular will be finding this work highly beneficial.

Integrating renewable energy technologies in a single system is becoming more reliable to meet electrical demand of remote locations. Here, integration and the optimal use of various available energy resources in a stand-alone microgrid are investigated. An integrated renewable energy system (IRES) approach has been proposed and analyzed using homer software. Seven scenarios with different combinations of energy sources and storage systems have been investigated based on their levelized cost of energy (LCOE) supply and net present cost (NPC). The proposed IRES, which includes photovoltaic (PV), wind, and biogas, gives the least LCOE as $0.207/kW h without any policy intervention. This LCOE reduces to $0.12/kW h with policy intervention and consideration of carbon abetment cost. Moreover, sensitivity analysis has been carried out with variation in load, solar radiation, and wind speed. The NPC is found to be most sensitive to the variation of load and least sensitive to the variation of wind speed.

Inherited cataract is a clinically and genetically heterogeneous disease that most often presents as a congenital autosomal dominant trait. Here we report linkage of a three-generation family of Pakistani origin with autosomal dominant cataract "zonular nuclear" pulverulent type (CZNP) on chromosome 1q21.1. Genome wide-linkage analysis excluded all the known cataract loci except on chromosome 1q. Significantly positive 2-point lod score values (Z=3.01 at θ=0) were obtained for markers D1S305 and D1S2721, which are known to flank the gene for connexin 50 (Cx50) or gap junction protein alpha-8 (Gja8). Previously a mutation in this gene has been reported in a British family with zonular pulverulent cataract (CZP).Here we describe a second mutation (E48K) in connexin 50 that confirms the involvement of this gene in cataractogenesis.