Rhodovulum visakhapatnamense sp. nov.

A Gram-negative, rod-shaped, phototrophic bacterium (JA181T) was isolated from a tidal water sample. On the basis of 16S rRNA gene sequence similarity, strain JA181T was shown to belong to the class Alphaproteobacteria, most closely related to Rhodovulum sulfidophilum (97.8 % similarity to the type strain), Rhodovulum adriaticum (93 %), Rhodovulum robiginosum (93 %), Rhodovulum iodosum (94 %), Rhodovulum imhoffii (94 %), Rhodovulum strictum (95 %), Rhodovulum euryhalinum (94.6 %) and Rhodovulum marinum (94.6 %). DNA–DNA hybridization with Rdv. sulfidophilum DSM 1374T (relatedness of 39 % with strain JA181T) and physiological and biochemical tests allowed genotypic and phenotypic differentiation of strain JA181T from the eight Rhodovulum species with validly published names. Strain JA181T therefore represents a novel species, for which the name Rhodovulum visakhapatnamense sp. nov. is proposed (type strain JA181T =JCM 13531T =ATCC BAA-1274T =DSM 17937T)

Adaptive Mean Value Function Based Quality Assessment of Software Reliable Growth Models

Software growth models aims for reliability of the application over a period of time. Assessment of such models is of great interest since many faults arises with the models during the operation over a span of time. In this paper a adaptive mean value function based testing and estimation of the parameters were discussed. The proposed approach is also compared against the conventional testing approaches and found that the proposed method able to detect the fault under different scenario and proves to give better performance under a constrained environment. Keywords: Software reliability, Growth models, testing analysis, Fault detection and correction

Quality Assessment of Software Reliability Growth Models

The two main important properties of software components are reliability and robustness. Reliability can be defined as the probability of failure free operation and on the other hand the robustness can be defined as how far the software can be able to with stand for intrusion attacks. In both the cases there should be some metric to evaluate the performance of these properties. In this paper metrics are been described which can be used to assess the quality of performance for these properties within a software reliability growth model. Keywords: Software growth model, reliability, robustness, Quality metrics

Rhodobium gokarnense sp. nov., a novel phototrophic alphaproteobacterium from a saltern

A pink-pigmented, phototrophic, purple nonsulfur bacterium, strain JA173T, was isolated in pure culture from a saltern in Gokarna, India, in a medium containing 2 % (w/v) NaCl. Strain JA173T was a non-motile Gram-negative rod that multiplied by budding. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain JA173T clusters with the class Alphaproteobacteria; highest sequence similarity (98 %) was to the type strain of Rhodobium orientis and 94 % similarity was observed to the 16S rRNA gene sequence of the type strain of Rhodobium marinum. However, DNA–DNA hybridization with R. orientis DSM 11290T revealed a relatedness value of only 35.1 % with strain JA173T. Strain JA173T contained lamellar internal membranes, bacteriochlorophyll a and carotenoids of the spirilloxanthin series. Strain JA173T had an obligate requirement for NaCl (optimum growth at 2–6 %, w/v) and grew photoheterotrophically with a number of organic compounds as carbon source or electron donor. Photoautotrophic, chemoautotrophic and fermentative growth could not be demonstrated. Yeast extract was required for growth. Based on 16S rRNA gene sequence analysis, DNA–DNA hybridization data and morphological and physiological characteristics, strain JA173T is sufficiently different from other species of the genus Rhodobium to be recognized as a representative of a novel species, Rhodobium gokarnense sp. nov. The type strain is JA173T (=ATCC BAA-1215T=DSM 17935T=JCM 13532T)

CONCERNING EFFECTIVE ERROR IDENTIFIED WITH SOFTWARE RECORDS DECREASE TECHNIQUES

To reduce time cost in manual work, text classification techniques they can fit on conduct automatic bug triage. In this particular paper, we address the problem of understanding reduction for bug triage, i.e., the simplest way to reduce the scale and improve the grade of bug data. Software companies spend over 45 percent of cost when controlling software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To discover a purchase of applying instance selection and possess selection, we extract attributes from historic bug data sets developing a predictive model for every new bug data set. We combine instance selection with feature selection to concurrently reduce data scale inside the bug dimension coupled with word dimension. The conclusion result shows our data reduction can effectively reduce the data scale and lift a realistic look at bug triage. We empirically investigate performance of understanding reduction on totally 600,000 bug reports of two large free projects, namely Eclipse and Mozilla. Our work supplies a kinds of leveraging techniques on human sources to produce reduced and-quality bug data in software development and maintenance

High Data Utility by Employing the Large Scale Data Sampling and Length Constraint Strategy

We intend an original differential private frequent item sets mining algorithm for big data by integration the ideas which has improved presentation due to the new example and improved truncation performance. We construct our algorithm on FP-Tree for recurrent item sets mining. In arrange to resolve the trouble of structure FP-Tree with large-scale data; we initial utilize the sampling idea to get hold of delegate data to colliery probable congested recurrent item sets, which are presently used to come across the last everyday items in the large-scale data. In count, we occupy the length constriction policy to get to the bottom of the predicament of elevated global compassion. In particular, we use sequence corresponding thoughts to realize the most an alogous string in the source dataset, and put into operation transaction truncation for attain the lowest information defeat. We lastly add the Laplace noise for frequent item sets to make certain privacy guarantees

Study on the qualitative assessment of in-vessel food waste compost by indexing method

The consumption of different food-based goods produces a considerable amount of waste that needs to be conserved in an eco-friendly manner. A study was carried out on food waste compost made from the in-vessel compost process for use in agriculture and its marketability for its fertility and contamination potential. Food waste samples were collected from the canteen and hostels of GITAM University,  Visakhapatnam (Andhra Pradesh), India and were transferred to a 125Kg in-vessel food waste composter (Molten Mind F125) and allowed to digest for 24 hrs followed by curing for seven days. After curing, the samples were characterized for nutrient content for fertility index (FI) and heavy metal contamination for clean index (CI). The compost quality index was derived from FI and CI to assess its suitability for agriculture. The pH of the food waste compost sample was reported as 8.4 and the C/N ratio was 28, which was higher than the standard ratio (15-20). The other physicochemical characteristics were analyzed using the standard methods and the concentration of metals was analyzed using Inductively Coupled Plasma Mass Spectrometry ( ICPMS). From the analysis, it was evident that heavy metal concentrations were well within the permissible limits. Further, the compost was characterized to know the fertility index (FI) and contamination index (CI) and its suitability to the soil. FI value was reported as more than 3.1 and CI value more than 4, which indicated that compost was best in quality, having high-value potential and low heavy-metal content, which will be suitable for high-value crops such as organic farming.

Thiophaeococcus mangrovi gen. nov., sp. nov., a photosynthetic marine gammaproteobacterium isolated from Bhitarkanika mangrove forest, India

A coccoid, phototrophic purple sulfur bacterium was isolated in pure culture from a mud sample collected from brackish water in the Bhitarkanika mangrove forest of Orissa, India, in a medium containing 2 % NaCl (w/v). This bacterium, strain JA304T, was Gram-negative and had a requirement for NaCl. Intracellular photosynthetic membranes were of the vesicular type. The colour of the phototrophically grown culture was saddle-brown. Bacteriochlorophyll a and the carotenoid lycopene were present as photosynthetic pigments. Strain JA304T was able to grow photolithoautotrophically and could photoassimilate a number of organic substrates. Yeast extract was required for growth of strain JA304T. The DNA G+C content was 68.1–68.9 mol%. 16S rRNA gene sequence comparisons indicate that the isolate represents a member of the Chromatiaceae within the class Gammaproteobacteria. According to sequence comparison data, strain JA304T is positioned distinctly outside the group formed by the four genera Thiocystis, Chromatium, Allochromatium and Thermochromatium, with only 86.7–91.0 % sequence similarity. Distinct morphological, physiological and genotypic differences from these previously described taxa support the classification of this isolate as a representative of a novel species in a new genus, for which the name Thiophaeococcus mangrovi gen. nov., sp. nov. is proposed. The type strain of Thiophaeococcus mangrovi is JA304T (=JCM 14889T =DSM 19863T). PABA, para-aminobenzoic acid The GenBank/EMBL/DDBJ accession number for the 16S rRNA gene sequence of strain JA304T is AM748925. A phase-contrast micrograph and whole-cell absorption spectrum and an acetone spectrum of extracted pigments of strain JA304T are available as supplementary material with the online version of this paper