Banaras Hindu University

About: Banaras Hindu University is a education organization based out in Varanasi, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Catalysis. The organization has 11858 authors who have published 23917 publications receiving 464677 citations. The organization is also known as: Kashi Hindu Vishvavidyalay & Benares Hindu University.

Outcome of teenage pregnancy

Abstract: Objective The objective of the study was to evaluate the obstetric, fetal and neonatal outcomes of teenage pregnancy in a tertiary care teaching hospital.

Selective Naked-Eye Detection of Hg2+ through an Efficient Turn-On Photoinduced Electron Transfer Fluorescent Probe and Its Real Applications

Abstract: A simple molecular fluorescent probe 5 has been designed and synthesized by appending anthracene and benzhydryl moieties through a piperazine bridge. The probe upon interaction with different metal ions showed high selectivity and sensitivity (2 ppb) for Hg2+ through fluorescence “turn-on” response in HEPES buffer. The significant fluorescence enhancement (∼10-fold) is attributable to PET arrest due to complexation with nitrogen atoms of the piperazine unit and Hg2+ in 1:2 stoichiometry, in which a naked-eye sensitive fluorescent blue color of solution changed to a blue-green (switched-on). As a proof of concept, promising prospects for application in environmental and biological sciences 5 have been utilized to detect Hg2+ sensitively in real samples, on cellulose paper strips, in protein medium (like BSA), and intracellularly in HeLa cells. Moreover, the optical behavior of 5 upon providing different chemical inputs has been utilized to construct individual logic gates and a reusable combinational logic...

Neuro-genetic prediction of software development effort

Abstract: Prediction of resource requirements of a software project is crucial for the timely delivery of quality-assured software within a reasonabletimeframe. Many conventional (model-based) and AI-oriented (model-free) resource estimators have been proposed in the recent past. Thispaper presents a novel genetically trained neural network (NN) predictor trained on historical data. We demonstrate substantial improvementin prediction accuracy by the neuro-genetic approach as compared to both a regression-tree-based conventional approach, as well asbackpropagation-trained NN approach reported recently. The superiority of this new predictor is established usingn-fold cross validationand Student’s t-test on various partitions of merged Cocomo and Kemerer data sets incorporating data from 78 real-life software projects.q 2000 Elsevier Science B.V. All rights reserved. Keywords: Neuro-genetic prediction; Neural network predictor; Genetically trained neural network 1. IntroductionReasonably accurate prediction of software developmenteffort has a profound effect on all stages of the softwaredevelopment cycle. Underestimates of resource require-ments for a software project lead to: (a) underestimationof the cost; (b) unrealistic time schedule; (c) considerablework pressure on the engineers; and (d) compromises indevelopment methodology, documentation and testing. Onthe other hand, overestimates are likely to cause: (a) a lostcontract due to prohibitive costs; (b) over allocation of engi-neers to the project leading to constraints on other projects;(c) low productivity levels of engineers; and (d) easy-goingwork habit in the organization. Resource requirementprediction for software projects is, therefore, an activeresearch area.Various conventional model-based methods have metwith limited success, whereas, intelligent prediction usingneurocomputing has proven its worth in many diverse appli-cation areas [1]. McCullagh et al. [2] have used neuralnetwork (NN) to estimate rainfall in Australia and havereported results superior to conventional model-basedapproach. NN predictors are playing major roles in diverseapplications and are being successfully applied to load fore-casting, medical diagnosis, communications, robot naviga-tion, software production etc., for example see Ref. [3].Recently, software engineers have started using NNs invarious stages of software production with significantsuccess. Karunanithi [4] has applied NN for software relia-bility prediction in the presence of code churn. This work isa major step forward in software reliability estimation sincethe conventional reliability growth models made the unrea-listic assumption that the complete code for the system isavailable before testing starts and the code remains frozenduring testing. Due to their power of generalization, NNsare able to accurately predict reliability in the presence ofcode churn. In a unique application of NN-based classifier,Khoshgoftaar et al. [5] have developed a system for identi-fying high-risk, error-prone modules early in the develop-ment cycle to allow optimal resource allocation for themodules. Specification-level software size estimates havebeen obtained by Hakkarainen et al. [6] by training an NNwith structured analysis (SA) descriptions as inputs, and sizemetric values as outputs. The authors used training and testdata set consisting of randomly generated SA descriptionsas input data and corresponding algorithm-based size metricvalues as output data. The size metrics used in their experi-ments were—DeMarco’s Function Bang metric, Albrecht’sFunction Points and Symons’ Mark II Function Points.Function Bang is based on the complexity of data flowsand the types of operation on these data flows. It measuresthe number of data-tokens around the boundary of variousfunctional primitives in a data flow diagram; whereas,

A reference time scale for Site U1385 (Shackleton Site) on the SW Iberian Margin

Abstract: We produced a composite depth scale and chronology for Site U1385 on the SW Iberian Margin. Using log(Ca/Ti) measured by core scanning XRF at 1-cm resolution in all holes, a composite section was constructed to 166.5 meter composite depth (mcd) that corrects for stretching and squeezing in each core. Oxygen isotopes of benthic foraminifera were correlated to a stacked δ18O reference signal (LR04) to produce an oxygen isotope stratigraphy and age model. Variations in sediment color contain very strong precession signals at Site U1385, and the amplitude modulation of these cycles provides a powerful tool for developing an orbitally-tuned age model. We tuned the U1385 record by correlating peaks in L* to the local summer insolation maxima at 37°N. The benthic δ18O record of Site U1385, when placed on the tuned age model, generally agrees with other time scales within their respective chronologic uncertainties. The age model is transferred to down-core data to produce a continuous time series of log(Ca/Ti) that reflect relative changes of biogenic carbonate and detrital sediment. Biogenic carbonate increases during interglacial and interstadial climate states and decreases during glacial and stadial periods. Much of the variance in the log(Ca/Ti) is explained by a linear combination of orbital frequencies (precession, tilt and eccentricity), whereas the residual signal reflects suborbital climate variability. The strong correlation between suborbital log(Ca/Ti) variability and Greenland temperature over the last glacial cycle at Site U1385 suggests that this signal can be used as a proxy for millennial-scale climate variability over the past 1.5 Ma. Millennial climate variability, as expressed by log(Ca/Ti) at Site U1385, was a persistent feature of glacial climates over the past 1.5 Ma, including glacial periods of the early Pleistocene (‘41-kyr world’) when boundary conditions differed significantly from those of the late Pleistocene (‘100-kyr world’). Suborbital variability was suppressed during interglacial stages and enhanced during glacial periods, especially when benthic δ18O surpassed ~ 3.3–3.5‰. Each glacial inception was marked by appearance of strong millennial variability and each deglaciation was preceded by a terminal stadial event. Suborbital variability may be a symptomatic feature of glacial climate or, alternatively, may play a more active role in the inception and/or termination of glacial cycles.