Regional variations in the nonlinearity and anisotropy of bovine aortic elastin

Arterial walls have a regular and lamellar organization of elastin present as concentric fenestrated networks in the media. In contrast, elastin networks are longitudinally oriented in layers adjacent to the media. In a previous model exploring the biomechanics of arterial elastin, we had proposed a microstructurally motivated strain energy function modeled using orthotropic material symmetry. Using mechanical experiments, we showed that the neo-Hookean term had a dominant contribution to the overall form of the strain energy function. In contrast, invariants corresponding to the two fiber families had smaller contributions. To extend these investigations, we use biaxial force-controlled experiments to quantify regional variations in the anisotropy and nonlinearity of elastin isolated from bovine aortic tissues proximal and distal to the heart. Results from this study show that tissue nonlinearity significantly increases distal to the heart as compared to proximally located regions (). Distally located samples also have a trend for increased anisotropy (), with the circumferential direction stiffer than the longitudinal, as compared to an isotropic and relatively linear response for proximally located elastin samples. These results are consistent with the underlying tissue histology from proximally located samples that had higher optical density (), fiber thickness (), and trend for lower tortuosity () in elastin fibers as compared to the thinner and highly undulating elastin fibers isolated from distally located samples. Our studies suggest that it is important to consider elastin fiber orientations in investigations that use microstructure-based models to describe the contributions of elastin and collagen to arterial mechanics.

Integrating biodiversity and conservation with modern agricultural landscapes

To achieve food security and meet the demands of the ever-growing human populations, farming systems have assumed unsustainable practices to produce more from a finite land area. This has been cause for concern mainly due to the often-irreversible damage done to the otherwise productive agricultural landscapes. Agro-ecology is proclaimed to be deteriorating due to eroding integrity of connected ecological mosaics and vulnerability to climate change. This has contributed to declining species diversity, loss of buffer vegetation, fragmentation of habitats, and loss of natural pollinators or predators, which eventually leads to decline in ecosystem services. Currently, a hierarchy of conservation initiatives is being considered to restore ecological integrity of agricultural landscapes. However, the challenge of identifying a suitable conservation strategy is a daunting task in view of socio-ecological factors that may constrain the choice of available strategies. One way to mitigate this situation and integrate biodiversity with agricultural landscapes is to implement offset mechanisms, which are compensatory and balancing approaches to restore the ecological health and function of an ecosystem. This needs to be tailored to the history of location specific agricultural practices, and the social, ecological and environmental conditions. The offset mechanisms can complement other initiatives through which farmers are insured against landscape-level risks such as droughts, fire and floods. For countries in the developing world with significant biodiversity and extensive agriculture, we should promote a comprehensive model of sustainable agricultural landscapes and ecosystem services, replicable at landscape to regional scales. Arguably, the model can be a potential option to sustain the integrity of biodiversity mosaic in agricultural landscapes.

Formulation of a mathematical approach to regional frequency analysis

Estimation of design quantiles of hydrometeorological variables at critical locations in river basins is necessary for hydrological applications. To arrive at reliable estimates for locations (sites) where no or limited records are available, various regional frequency analysis (RFA) procedures have been developed over the past five decades. The most widely used procedure is based on index-flood approach and L-moments. It assumes that values of scale and shape parameters of frequency distribution are identical across all the sites in a homogeneous region. In real-world scenario, this assumption may not be valid even if a region is statistically homogeneous. To address this issue, a novel mathematical approach is proposed. It involves (i) identification of an appropriate frequency distribution to fit the random variable being analyzed for homogeneous region, (ii) use of a proposed transformation mechanism to map observations of the variable from original space to a dimensionless space where the form of distribution does not change, and variation in values of its parameters is minimal across sites, (iii) construction of a growth curve in the dimensionless space, and (iv) mapping the curve to the original space for the target site by applying inverse transformation to arrive at required quantile(s) for the site. Effectiveness of the proposed approach (PA) in predicting quantiles for ungauged sites is demonstrated through Monte Carlo simulation experiments considering five frequency distributions that are widely used in RFA, and by case study on watersheds in conterminous United States. Results indicate that the PA outperforms methods based on index-flood approach.

How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals?

Feeding 9-10billion people by 2050 and preventing dangerous climate change are two of the greatest challenges facing humanity. Both challenges must be met while reducing the impact of land management on ecosystem services that deliver vital goods and services, and support human health and well-being. Few studies to date have considered the interactions between these challenges. In this study we briefly outline the challenges, review the supply- and demand-side climate mitigation potential available in the Agriculture, Forestry and Other Land Use AFOLU sector and options for delivering food security. We briefly outline some of the synergies and trade-offs afforded by mitigation practices, before presenting an assessment of the mitigation potential possible in the AFOLU sector under possible future scenarios in which demand-side measures codeliver to aid food security. We conclude that while supply-side mitigation measures, such as changes in land management, might either enhance or negatively impact food security, demand-side mitigation measures, such as reduced waste or demand for livestock products, should benefit both food security and greenhouse gas (GHG) mitigation. Demand-side measures offer a greater potential (1.5-15.6Gt CO2-eq. yr(-1)) in meeting both challenges than do supply-side measures (1.5-4.3Gt CO2-eq. yr(-1) at carbon prices between 20 and 100US$ tCO(2)-eq. yr(-1)), but given the enormity of challenges, all options need to be considered. Supply-side measures should be implemented immediately, focussing on those that allow the production of more agricultural product per unit of input. For demand-side measures, given the difficulties in their implementation and lag in their effectiveness, policy should be introduced quickly, and should aim to codeliver to other policy agenda, such as improving environmental quality or improving dietary health. These problems facing humanity in the 21st Century are extremely challenging, and policy that addresses multiple objectives is required now more than ever.

Development of a regional model for the North Indian Ocean

We have developed a one-way nested Indian Ocean regional model. The model combines the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory's (GFDL) Modular Ocean Model (MOM4p1) at global climate model resolution (nominally one degree), and a regional Indian Ocean MOM4p1 configuration with 25 km horizontal resolution and 1 m vertical resolution near the surface. Inter-annual global simulations with Coordinated Ocean-Ice Reference Experiments (CORE-II) surface forcing over years 1992-2005 provide surface boundary conditions. We show that relative to the global simulation, (i) biases in upper ocean temperature, salinity and mixed layer depth are reduced, (ii) sea surface height and upper ocean circulation are closer to observations, and (iii) improvements in model simulation can be attributed to refined resolution, more realistic topography and inclusion of seasonal river runoff. Notably, the surface salinity bias is reduced to less than 0.1 psu over the Bay of Bengal using relatively weak restoring to observations, and the model simulates the strong, shallow halocline often observed in the North Bay of Bengal. There is marked improvement in subsurface salinity and temperature, as well as mixed layer depth in the Bay of Bengal. Major seasonal signatures in observed sea surface height anomaly in the tropical Indian Ocean, including the coastal waveguide around the Indian peninsula, are simulated with great fidelity. The use of realistic topography and seasonal river runoff brings the three dimensional structure of the East India Coastal Current and West India Coastal Current much closer to observations. As a result, the incursion of low salinity Bay of Bengal water into the southeastern Arabian Sea is more realistic. (C) 2013 Elsevier Ltd. All rights reserved.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Self-organized public key management in MANETs with enhanced security and without certificate-chains

In the self-organized public key management approaches, public key verification is achieved through verification routes constituted by the transitive trust relationships among the network principals. Most of the existing approaches do not distinguish among different available verification routes. Moreover, to ensure stronger security, it is important to choose an appropriate metric to evaluate the strength of a route. Besides, all of the existing self-organized approaches use certificate-chains for achieving authentication, which are highly resource consuming. In this paper, we present a self-organized certificate-less on-demand public key management (CLPKM) protocol, which aims at providing the strongest verification routes for authentication purposes. It restricts the compromise probability for a verification route by restricting its length. Besides, we evaluate the strength of a verification route using its end-to-end trust value. The other important aspect of the protocol is that it uses a MAC function instead of RSA certificates to perform public key verifications. By doing this, the protocol saves considerable computation power, bandwidth and storage space. We have used an extended strand space model to analyze the correctness of the protocol. The analytical, simulation, and the testbed implementation results confirm the effectiveness of the proposed protocol. (c) 2014 Elsevier B.V. All rights reserved.

Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study

Precise information on streamflows is of major importance for planning and monitoring of water resources schemes related to hydro power, water supply, irrigation, flood control, and for maintaining ecosystem. Engineers encounter challenges when streamflow data are either unavailable or inadequate at target locations. To address these challenges, there have been efforts to develop methodologies that facilitate prediction of streamflow at ungauged sites. Conventionally, time intensive and data exhaustive rainfall-runoff models are used to arrive at streamflow at ungauged sites. Most recent studies show improved methods based on regionalization using Flow Duration Curves (FDCs). A FDC is a graphical representation of streamflow variability, which is a plot between streamflow values and their corresponding exceedance probabilities that are determined using a plotting position formula. It provides information on the percentage of time any specified magnitude of streamflow is equaled or exceeded. The present study assesses the effectiveness of two methods to predict streamflow at ungauged sites by application to catchments in Mahanadi river basin, India. The methods considered are (i) Regional flow duration curve method, and (ii) Area Ratio method. The first method involves (a) the development of regression relationships between percentile flows and attributes of catchments in the study area, (b) use of the relationships to construct regional FDC for the ungauged site, and (c) use of a spatial interpolation technique to decode information in FDC to construct streamflow time series for the ungauged site. Area ratio method is conventionally used to transfer streamflow related information from gauged sites to ungauged sites. Attributes that have been considered for the analysis include variables representing hydrology, climatology, topography, land-use/land- cover and soil properties corresponding to catchments in the study area. Effectiveness of the presented methods is assessed using jack knife cross-validation. Conclusions based on the study are presented and discussed. (C) 2015 The Authors. Published by Elsevier B.V.

Analytical approach to quantile estimation in regional frequency analysis based on fuzzy framework

Regional frequency analysis is widely used for estimating quantiles of hydrological extreme events at sparsely gauged/ungauged target sites in river basins. It involves identification of a region (group of watersheds) resembling watershed of the target site, and use of information pooled from the region to estimate quantile for the target site. In the analysis, watershed of the target site is assumed to completely resemble watersheds in the identified region in terms of mechanism underlying generation of extreme event. In reality, it is rare to find watersheds that completely resemble each other. Fuzzy clustering approach can account for partial resemblance of watersheds and yield region(s) for the target site. Formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix obtained based on the approach. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies. In this study, it is analytically shown that the strategy results in under-prediction of quantiles. To address this, a mathematical approach is proposed in this study and its effectiveness in estimating flood quantiles relative to DFA and TS is demonstrated through Monte-Carlo simulation experiments and case study on Mid-Atlantic water resources region, USA. (C) 2015 Elsevier B.V. All rights reserved.

Maximum magnitude estimation considering the regional rupture character

The main objective of the paper is to develop a new method to estimate the maximum magnitude (M (max)) considering the regional rupture character. The proposed method has been explained in detail and examined for both intraplate and active regions. Seismotectonic data has been collected for both the regions, and seismic study area (SSA) map was generated for radii of 150, 300, and 500 km. The regional rupture character was established by considering percentage fault rupture (PFR), which is the ratio of subsurface rupture length (RLD) to total fault length (TFL). PFR is used to arrive RLD and is further used for the estimation of maximum magnitude for each seismic source. Maximum magnitude for both the regions was estimated and compared with the existing methods for determining M (max) values. The proposed method gives similar M (max) value irrespective of SSA radius and seismicity. Further seismicity parameters such as magnitude of completeness (M (c) ), ``a'' and ``aEuro parts per thousand b `` parameters and maximum observed magnitude (M (max) (obs) ) were determined for each SSA and used to estimate M (max) by considering all the existing methods. It is observed from the study that existing deterministic and probabilistic M (max) estimation methods are sensitive to SSA radius, M (c) , a and b parameters and M (max) (obs) values. However, M (max) determined from the proposed method is a function of rupture character instead of the seismicity parameters. It was also observed that intraplate region has less PFR when compared to active seismic region.

Modelling the influence of land-use changes on biophysical and biochemical interactions at regional and global scales

Land-use changes since the start of the industrial era account for nearly one-third of the cumulative anthropogenic CO2 emissions. In addition to the greenhouse effect of CO2 emissions, changes in land use also affect climate via changes in surface physical properties such as albedo, evapotranspiration and roughness length. Recent modelling studies suggest that these biophysical components may be comparable with biochemical effects. In regard to climate change, the effects of these two distinct processes may counterbalance one another both regionally and, possibly, globally. In this article, through hypothetical large-scale deforestation simulations using a global climate model, we contrast the implications of afforestation on ameliorating or enhancing anthropogenic contributions from previously converted (agricultural) land surfaces. Based on our review of past studies on this subject, we conclude that the sum of both biophysical and biochemical effects should be assessed when large-scale afforestation is used for countering global warming, and the net effect on global mean temperature change depends on the location of deforestation/afforestation. Further, although biochemical effects trigger global climate change, biophysical effects often cause strong local and regional climate change. The implication of the biophysical effects for adaptation and mitigation of climate change in agriculture and agroforestry sectors is discussed. center dot Land-use changes affect global and regional climates through both biochemical and biophysical process. center dot Climate effect from biophysical process depends on the location of land-use change. center dot Climate mitigation strategies such as afforestation/reforestation should consider the net effect of biochemical and biophysical processes for effective mitigation. center dot Climate-smart agriculture could use bio-geoengineering techniques that consider plant biophysical characteristics such as reflectivity and water use efficiency.

Universal Hashing for Information-Theoretic Security

The information-theoretic approach to security entails harnessing the correlated randomness available in nature to establish security. It uses tools from information theory and coding and yields provable security, even against an adversary with unbounded computational power. However, the feasibility of this approach in practice depends on the development of efficiently implementable schemes. In this paper, we review a special class of practical schemes for information-theoretic security that are based on 2-universal hash families. Specific cases of secret key agreement and wiretap coding are considered, and general themes are identified. The scheme presented for wiretap coding is modular and can be implemented easily by including an extra preprocessing layer over the existing transmission codes.

A recursive multi-scaling approach to regional flood frequency analysis

Scaling approaches are widely used by hydrologists for Regional Frequency Analysis (RFA) of floods at ungauged/sparsely gauged site(s) in river basins. This paper proposes a Recursive Multi-scaling (RMS) approach to RFA that overcomes limitations of conventional simple- and multi-scaling approaches. The approach involves identification of a separate set of attributes corresponding to each of the sites (being considered in the study area/region) in a recursive manner according to their importance, and utilizing those attributes to construct effective regional regression relationships to estimate statistical raw moments (SMs) of peak flows. The SMs are then utilized to arrive at parameters of flood frequency distribution and quantile estimate(s) corresponding to target return period(s). Effectiveness of the RMS approach in arriving at flood quantile estimates for ungauged sites is demonstrated through leave-one-out cross-validation experiment on watersheds in Indiana State, USA. Results indicate that the approach outperforms index-flood based Region-of-Influence approach, simple- and multi-scaling approaches and a multiple linear regression method. (C) 2015 Elsevier B.V. All rights reserved.

Evaluation of the Index-Flood Approach Related Regional Frequency Analysis Procedures

Index-flood related regional frequency analysis (RFA) procedures are in use by hydrologists to estimate design quantiles of hydrological extreme events at data sparse/ungauged locations in river basins. There is a dearth of attempts to establish which among those procedures is better for RFA in the L-moment framework. This paper evaluates the performance of the conventional index flood (CIF), the logarithmic index flood (LIF), and two variants of the population index flood (PIF) procedures in estimating flood quantiles for ungauged locations by Monte Carlo simulation experiments and a case study on watersheds in Indiana in the U.S. To evaluate the PIF procedure, L-moment formulations are developed for implementing the procedure in situations where the regional frequency distribution (RFD) is the generalized logistic (GLO), generalized Pareto (GPA), generalized normal (GNO) or Pearson type III (PE3), as those formulations are unavailable. Results indicate that one of the variants of the PIF procedure, which utilizes the regional information on the first two L-moments is more effective than the CIF and LIF procedures. The improvement in quantile estimation using the variant of PIF procedure as compared with the CIF procedure is significant when the RFD is a generalized extreme value, GLO, GNO, or PE3, and marginal when it is GPA. (C) 2015 American Society of Civil Engineers.

Regional research priorities in brain and nervous system disorders

The characteristics of neurological, psychiatric, developmental and substance-use disorders in low-and middle-income countries are unique and the burden that they have will be different from country to country. Many of the differences are explained by the wide variation in population demographics and size, poverty, conflict, culture, land area and quality, and genetics. Neurological, psychiatric, developmental and substance-use disorders that result from, or are worsened by, a lack of adequate nutrition and infectious disease still afflict much of sub-Saharan Africa, although disorders related to increasing longevity, such as stroke, are on the rise. In the Middle East and North Africa, major depressive disorders and post-traumatic stress disorder are a primary concern because of the conflict-ridden environment. Consanguinity is a serious concern that leads to the high prevalence of recessive disorders in the Middle East and North Africa and possibly other regions. The burden of these disorders in Latin American and Asian countries largely surrounds stroke and vascular disease, dementia and lifestyle factors that are influenced by genetics. Although much knowledge has been gained over the past 10 years, the epidemiology of the conditions in low-and middle-income countries still needs more research. Prevention and treatments could be better informed with more longitudinal studies of risk factors. Challenges and opportunities for ameliorating nervous-system disorders can benefit from both local and regional research collaborations. The lack of resources and infrastructure for health-care and related research, both in terms of personnel and equipment, along with the stigma associated with the physical or behavioural manifestations of some disorders have hampered progress in understanding the disease burden and improving brain health. Individual countries, and regions within countries, have specific needs in terms of research priorities.