Comparative study on damage identification from lso-Eigen-Value-Change contours and smeared damage model

The paper proposes two methodologies for damage identification from measured natural frequencies of a contiguously damaged reinforced concrete beam, idealised with distributed damage model. The first method identifies damage from Iso-Eigen-Value-Change contours, plotted between pairs of different frequencies. The performance of the method is checked for a wide variation of damage positions and extents. The method is also extended to a discrete structure in the form of a five-storied shear building and the simplicity of the method is demonstrated. The second method is through smeared damage model, where the damage is assumed constant for different segments of the beam and the lengths and centres of these segments are the known inputs. First-order perturbation method is used to derive the relevant expressions. Both these methods are based on distributed damage models and have been checked with experimental program on simply supported reinforced concrete beams, subjected to different stages of symmetric and un-symmetric damages. The results of the experiments are encouraging and show that both the methods can be adopted together in a damage identification scenario.

Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in downscaling

Regional impacts of climate change remain subject to large uncertainties accumulating from various sources, including those due to choice of general circulation models (GCMs), scenarios, and downscaling methods. Objective constraints to reduce the uncertainty in regional predictions have proven elusive. In most studies to date the nature of the downscaling relationship (DSR) used for such regional predictions has been assumed to remain unchanged in a future climate. However,studies have shown that climate change may manifest in terms of changes in frequencies of occurrence of the leading modes of variability, and hence, stationarity of DSRs is not really a valid assumption in regional climate impact assessment. This work presents an uncertainty modeling framework where, in addition to GCM and scenario uncertainty, uncertainty in the nature of the DSR is explored by linking downscaling with changes in frequencies of such modes of natural variability. Future projections of the regional hydrologic variable obtained by training a conditional random field (CRF) model on each natural cluster are combined using the weighted Dempster-Shafer (D-S) theory of evidence combination. Each projection is weighted with the future projected frequency of occurrence of that cluster (''cluster linking'') and scaled by the GCM performance with respect to the associated cluster for the present period (''frequency scaling''). The D-S theory was chosen for its ability to express beliefs in some hypotheses, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The methodology is tested for predicting monsoon streamflow of the Mahanadi River at Hirakud Reservoir in Orissa, India. The results show an increasing probability of extreme, severe, and moderate droughts due to limate change. Significantly improved agreement between GCM predictions owing to cluster linking and frequency scaling is seen, suggesting that by linking regional impacts to natural regime frequencies, uncertainty in regional predictions can be realistically quantified. Additionally, by using a measure of GCM performance in simulating natural regimes, this uncertainty can be effectively constrained.

Change in spectrum of Brownian fluctuations of optically trapped red blood cells due to malarial infection

We study the properties of single red blood cells (RBCs) held in an optical-tweezers trap. We observe a change in the spectrum of Brownian fluctuations between RBCs from normal and malaria-infected samples. The change, caused by infection-induced structural changes in the cell, appears as a statistical increase in the mean (by 25%) and standard deviation (by 200%) of the corner frequency measured over similar to 100 cells. The increase is observed even though the ensemble of cells being measured consists mostly of cells that do not actually host the parasite, but are from an infected pool. This bystander effect appears to vindicate other observations that infected cells can affect the biomechanical properties of uninfected cells. The change is also observed to be independent of the stage of infection and its duration, highlighting its potential for disease detection. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3427142].

Climate change, sustainable development and India: Global and national concerns

Climate change is one of the most important global environmental challenges, with implications for food production, water supply, health, energy, etc. Addressing climate change requires a good scientific understanding as well as coordinated action at national and global level. This paper addresses these challenges. Historically, the responsibility for greenhouse gas emissions' increase lies largely with the industrialized world, though the developing countries are likely to be the source of an increasing proportion of future emissions. The projected climate change under various scenarios is likely to have implications on food production, water supply, coastal settlements, forest ecosystems, health, energy security, etc. The adaptive capacity of communities likely to be impacted by climate change is low in developing countries. The efforts made by the UNFCCC and the Kyoto Protocol provisions are clearly inadequate to address the climate change challenge. The most effective way to address climate change is to adopt a sustainable development pathway by shifting to environmentally sustainable technologies and promotion of energy efficiency, renewable energy, forest conservation, reforestation, water conservation, etc. The issue of highest importance to developing countries is reducing the vulnerability of their natural and socio-economic systems to the projected climate change. India and other developing countries will face the challenge of promoting mitigation and adaptation strategies, bearing the cost of such an effort, and its implications for economic development.

The ability of prolactin to change the sensitivity of the pituitary of the lactating rat to luteinising hormone releasing hormonein vitro

The ability of prolactin to influence the responsiveness of the lactating rat pituitary to luteinising hormone releasing hormone has been examinedin vitro. The pituitary responsivenessin vivo to luteinising hormone releasing hormone decreased as a function of increase in the lactational stimulus. Prolactin inhibited the spontaneousin vitro release of luteinising hormone and follicle stimulating hormone to a small extent, from the pituitary of lactating rats with the suckling stimulus. However, it significantly inhibited the release of these two hormones from luteinising hormone releasing hormone-stimulated pituitaries. The responsiveness of pituitaries of rats deprived of their litter 24 h earlier, to luteinising hormone releasing hormone was also inhibited by prolactin, although minimal. It was concluded that prolactin could be influencing the functioning of the pituitary of the lactating rat by (a) partially suppressing the spontaneous release of gonadotropin and (b) inhibiting the responsiveness of the pituitary to luteinising hormone releasing hormone.

Instructional aid for tactical wargame model

A tactical gaming model for wargame play between two teams A and B through a control unit C has been developed, which can be handled using IBM personal computers (XT and AT models) having a local area network facility. This simulation model involves communication between the teams involved, logging and validation of the actions of the teams by the control unit. The validation procedure uses statistical and also monte carlo techniques. This model has been developed to evaluate the planning strategies of the teams involved. This application software using about 120 files has been developed in BASIC, DBASE and the associated network software. Experience gained in the instruction courses using this model will also be discussed.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. (C) 2010 Elsevier Ltd. All rights reserved.

Heat conduction with a phase change in a cylindrical mould

Analytical short time solution of moving boundary in heat conduction in a cylindrical mould under prescribed flux boundary condition has been studied in this paper. Partial differential equations are converted to integro-differential equations. These integro-differential equations which are coupled have been solved analytically for short time by choosing suitable series expansions for the unknown quantitities.

X-ray studies on crystalline complexes involving amino acids and peptides XIX. Effects of change in chirality in the complexes of succinic acid with dl- and l-arginine

Crystals of dl-arginine hemisuccinate dihydrate (I)(monoclinic; P21/c; a = 5.292, b = 16.296, c = 15.203 Å; α= 92.89°; Z = 4) and l-arginine hemisuccinate hemisuccinic acid monohydrate (II) (triclinic; P1; a = 5.099; b = 10.222, c = 14.626 Å; α= 77.31, β= 89.46, γ= 78.42°; Z = 2) were grown under identical conditions from aqueous solutions of the components in molar proportions. The structures were solved by direct methods and refined to R = 0.068 for 2585 observed reflections in the case of (I) and R = 0.036 for 2154 observed reflections in the case of (11). Two of the three crystallographically independent arginine molecules in the complexes have conformations different from those observed so far in the crystal structures containing arginine. The succinic acid molecules and the succinate ions in the structures are centrosymmetric and planar. The crystal structure of (II) is highly pseudosymmetric. Arginine-succinate interactions in both the complexes involve specific guanidyl-carboxylate interactions. The basic elements of aggregation in both the structures are ribbons made up of alternating arginine dimers and succinate ions. However, the ribbons pack in different ways in the two structures. (II) presents an interesting case in which two ionisation states of the same molecule coexist in a crystal. The two complexes provide a good example of the effect of change in chirality on stoichiometry, conformation, aggregation, and ionisation state in the solid state.

An infrared spectroscopic study of the low-spin-high-spin transition in FexMn1-x(Phen)2(NCS)2: A composition-induced change in the order of the spin-state transition

Infrared spectroscopy provides a valuable tool to investigate the spin-state transition in Fe(II) complexes of the type Fe(Phen)2(NCS)2. With progressive substitution of Fe by Mn, the first-order transition changes over to a second-order transition, with a high residual population of the high-spin state even at very low temperatures

A delta13C record of late Quaternary climate change from tropical peats in southern India

STABLE-ISOTOPE ratios of carbon in soils or lake sediments1-3 and of oxygen and hydrogen in peats4,5 have been found to reflect past moisture variations and hence to provide valuable palaeoclimate records. Previous applications of the technique to peat have been restricted to temperate regions, largely because tropical climate variations are less pronounced, making them harder to resolve. Here we present a deltaC-13 record spanning the past 20 kyr from peats in the Nilgiri hills, southern India. Because the site is at high altitude (>2,000 m above sea level), it is possible to resolve a clear climate signal. We observe the key climate shifts that are already known to have occurred during the last glacial maximum (18 kyr ago) and the subsequent deglaciation. In addition, we observe an arid phase from 6 to 3.5 kyr ago, and a short, wet phase about 600 years ago. The latter appears to correspond to the Mediaeval Warm Period, which previously was believed to be confined to Europe and North America6,7. Our results therefore suggest that this event may have extended over the entire Northern Hemisphere.

Impact of climate change on Indian forests: a dynamic vegetation modeling approach

We make an assessment of the impact of projected climate change on forest ecosystems in India. This assessment is based on climate projections of the Regional Climate Model of the Hadley Centre (HadRM3) and the dynamic global vegetation model IBIS for A2 and B2 scenarios. According to the model projections, 39% of forest grids are likely to undergo vegetation type change under the A2 scenario and 34% under the B2 scenario by the end of this century. However, in many forest dominant states such as Chattisgarh, Karnataka and Andhra Pradesh up to 73%, 67% and 62% of forested grids are projected to undergo change. Net Primary Productivity (NPP) is projected to increase by 68.8% and 51.2% under the A2 and B2 scenarios, respectively, and soil organic carbon (SOC) by 37.5% for A2 and 30.2% for B2 scenario. Based on the dynamic global vegetation modeling, we present a forest vulnerability index for India which is based on the observed datasets of forest density, forest biodiversity as well as model predicted vegetation type shift estimates for forested grids. The vulnerability index suggests that upper Himalayas, northern and central parts of Western Ghats and parts of central India are most vulnerable to projected impacts of climate change, while Northeastern forests are more resilient. Thus our study points to the need for developing and implementing adaptation strategies to reduce vulnerability of forests to projected climate change.