Disaster Risk Assessment Based on Variable Fuzzy Sets and Improved Information Diffusion Method

The predictive analysis of natural disasters and their consequences is challenging because of uncertainties and incomplete data. The present article studies the use of variable fuzzy sets (VFS) and the improved information diffusion method (IIDM) to construct a composite method. The proposed method aims to integrate multiple factors and quantification of uncertainties within a consistent system for catastrophic risk assessment. VFS have been selected because of several advantages. These sets can effectively eliminate the border effect on assessment result and monitor estimation standard error. The IIDM theory can obtain as much useful information as possible from the sample and improves system recognition accuracy. Both these methods contribute to a sound risk probability prediction of natural disasters. Furthermore, the application has been verified through flood and drought risk analyses in China, where the risks of different flood and drought grades have been obtained. The results indicate that the proposed method achieves a better detection stability of disaster risk with higher precision compared with other methods. The method is effective, practical, and applicable in disaster rescue and relief.     

Rapid risk assessment of wetland degradation and loss in low-lying coastal zone of Shanghai,China

Coastal wetlands are facing an increasingly high risk of degradation and loss due to a wide variety of human activities and natural processes. Human encroachment, including land reclamation, drainage, and introduction of invasive species, has direct negative effects on wetlands, mainly through changes in hydrology and vegetation. Additionally, accelerated sea level rise (SLR) can result in flooding of wetlands in low-lying coastal zones. In this study, we present a rapid risk assessment method for coastal wetland loss and degradation. The main stress factors, i.e., urban sprawl, agriculture, coastal erosion, and SLR, have been examined and quantified through remote sensing and geographic information system spatial analysis. A weighted factor-based linear model has been used to evaluate the spatial risk levels of wetland loss. The proposed methodology is applied to the low-lying coastal wetlands of Hangzhou Bay in Shanghai, China. The results show that the regions closer to the sea have relatively higher risk levels on the landward side of the coastline, but relatively low risk levels on the seaward side of the coastline. This work emphasizes the need to sustainably use and protect wetlands in order to reduce disaster risks and contribute to the improvement of human well-being.     

Spatial Analysis and Modeling Tool (SAMT): 2. Applications

The Spatial Analysis and Modeling Tool (SAMT) is a GIS-based analytical software for land use study. This paper demonstrates the capabilities of SAMT as applied in studies of land use and landscape change in the catchment area of the River Quillow, North-East Germany. Neural network and fuzzy models are important features that were incorporated in SAMT. The region of study for this application is a catchment area of about 170 km² and is subdivided into 54,000 grid cells for spatial simulations.Three different applications of SAMT are discussed in this article. The first application involves assessment of the potential soil erosion risk on arable land. This user-defined application combines an economic model (LP model), a temperature-driven crop coverage model and a soil erosion risk model (Universal Soil Loss Equation) using data from tables and map information. The second and third applications are winter wheat yield estimates using, respectively, the fuzzy toolbox SAMT_FUZZY in conjunction with expert knowledge, and using the neural network toolbox SAMT_NN based on observations.     

Fundamentals of the logarithmic measure for revealing multimodal diffusion

We develop a theoretical foundation for a time-series analysis method suitable for revealing the spectrum of diffusion coefficients in mixed Brownian systems, for which no prior knowledge of particle distinction is required. This method is directly relevant for particle tracking in biological systems, in which diffusion processes are often nonuniform. We transform Brownian data onto the logarithmic domain, in which the coefficients for individual modes of diffusion appear as distinct spectral peaks in the probability density. We refer to the method as the logarithmic measure of diffusion, or simply as the logarithmic measure. We provide a general protocol for deriving analytical expressions for the probability densities on the logarithmic domain. The protocol is applicable for any number of spatial dimensions with any number of diffusive states. The analytical form can be fitted to data to reveal multiple diffusive modes. We validate the theoretical distributions and benchmark the accuracy and sensitivity of the method by extracting multimodal diffusion coefficients from two-dimensional Brownian simulations of polydisperse filament bundles. Bundling the filaments allows us to control the system nonuniformity and hence quantify the sensitivity of the method. By exploiting the anisotropy of the simulated filaments, we generalize the logarithmic measure to rotational diffusion. By fitting the analytical forms to simulation data, we confirm the method’s theoretical foundation. An error analysis in the single-mode regime shows that the proposed method is comparable in accuracy to the standard mean-squared displacement approach for evaluating diffusion coefficients. For the case of multimodal diffusion, we compare the logarithmic measure against other, more sophisticated methods, showing that both model selectivity and extraction accuracy are comparable for small data sets. Therefore, we suggest that the logarithmic measure, as a method for multimodal diffusion coefficient extraction, is ideally suited for small data sets, a condition often confronted in the experimental context. Finally, we critically discuss the proposed benefits of the method and its information content.     

Determining informativity of marker typing for genetic counseling in a pedigree

Summary For the situation of a Mendelian disease linked to a genetic marker, a new method is described that allows evaluating for genetic counseling the information potentially available from the linked marker before the marker data are actually obtained, that is, prior to drawing blood for marker typing. For a consultand in a family pedigree, the method determines the risk distribution (small families) or an approximation to it (larger families) and calculates the probability that the risk will deviate beyond certain limits from its a priori value, which exists without marker data, for example, that the risk will be smaller than 0.10 or larger than 0.90. The method was applied here to a pedigree of 15 individuals for which analytical calculations would be difficult to carry out.     

Assessing ecological risk through automated drainage extraction and watershed delineation

Decision makers are frequently involved in projects requiring ecological risk definition, which are inherent to biological conservation process. It is important to recognize these risks in order to invest wisely in the management and protection of biological resources. In this matter, Geographic Information System tools and remote sensing data have been used frequently as important components in planning and management of conservation units, Rabus et al. (2003), Valeriano et al. (2009) and Valeriano et al. (2010) stressed the advantages of using data that were gathered during the Shuttle Radar Topographic Mission (SRTM) for biological and geomorphologic purposes. For Brazil's national territory, the SRTM data were refined (Valeriano, 2008) and offered as free access on the TOPODATA Project website (http://www.dsr.inpe.br/topodata) where geomorphometric information (including elevation data) at a resolution of 30 m are provided. The aim of this paper is to demonstrate an example of how TOPODATA products have been applied in order to determine the ecological risk of the border of a Conservation Unit, located in the State of São Paulo—Brazil, in the Brazilian Atlantic Forest, using automated drainage network and watershed extraction. A comparison between SRTM, TOPODATA, and ASTER DEM was carried out, showing an advantage of TOPODATA drainage network product. The vectors generated using this data are more similar to the official drainage network vectors than the drainage network extracted using ASTER-DEM or SRTM. The network product generated using ASTER-DEM produced many commission errors and the one generated using SRTM produced a poor network, with generalized vectors, less detailed than the others. The results showed that using the TOPODATA Project‘s Digital Elevation Model (DEM) can provide important data for ecological analysis and significant additional information for decision making, regarding drainage networks and watershed features. The produced map for border ecological risk showed to fit perfectly to the field work analyses, produced in other works. Furthermore, the extracted watershed polygons might furnish important information unrevealing best conservation unit boundaries, which means more efficient management and best biological conservation results.     

Classification, error detection, and reconciliation of process information in complex biochemical systems

Bioprocess identification starts with collection of process information. Usually there is a variety of information available, consisting of actual measurement data, historical data, empirical kinetic and yield correlations, and general knowledge available from literature. A central problem is to find out how the various pieces of information should be integrated. In addition, one should know how to deal with missing, inconsistent, or too inaccurate data. Recently, a general systematic method for dealing with these problems, based on conservation constraints, was published, and application shown to simple black box systems. In this article, the scope is generalized by including metabolic network data and dispersed process information of diverse type and nature, such as multiple sources of the value of one particular quantity, use of kinetic expressions, analytical problems, cometabolism or mixed substrate utilization, and chemical reactions. The alkalophilic bacterium Acinetobacter calcoaceticus is used as a model organism, growing on acetate and converting xylose into xylonolactone. It is shown that all relevant pieces of information can be straightforwardly and systematically treated, by considering them as constaints. In general, it is illustrated how the search for directed process improvements starts with an optimal selection of information sources, followed by an accurate analysis of possible metabolic bottlenecks. In this particular case it is shown that the yield of A. calcoaceticus on acetate at varying xylose/acetate feed ratios can be accurately predicted using dispersed process information. (c) 1996 John Wiley & Sons, Inc.     

Measuring rice farmers’ risk perceptions and attitude: Evidence from Pakistan

Abstract

The agriculture sector is confronting numerous types of risk. Like other crops’ farmers, rice growers are also facing multifarious challenges, including production and environmental risk. On the one hand, they are suffering from water shortage. On the other hand, if a flood occurs, there is no proper water reservation management, especially in developing countries. Global warming is another erotic topic for agriculture sustainability. To address the growers’ perceptions and their attitudes toward such types of risks, a farm base survey was carried out, and rice farmers were investigated in Pakistan. Methods of equally likely certainty equivalent and risk matrix were applied to investigate the farmers’ risk perceptions and attitude. A probit model was also used to estimate the factors that influence farmers’ perceptions and attitudes toward risks. The outcomes of the study show that flood, input high prices, increasing temperature, and crop diseases are the perceiving risks among rice farmers. Different types of socio-economic factors are influencing farmers’ risk perceptions and their attitudes, such as age, education level, a distance of farms from rivers, and non-farm income. This study provides valuable information to the policymakers and concerning authorities to apprehend agricultural risks among rice farmers and farming sustainability, especially in developing countries.     

A HIVE model for regional integrated environmental risk assessment: A case study in China

Regional environmental risk assessment has been a significant means of environmental management and decision-making. To assess the regional integrated environmental risk at a nationwide scale, a new index system named the Hazard, Intensity, Vulnerability, and Effectiveness (HIVE) model was designed and evolved from previous researches. The HIVE model consisted of a relatively complete framework with accessible indexes related to environment and social economy, and it considered different risk sources, pathways, and receptors as well as the influence of the risk control. As an important segment of the assessment process, a simple gridding information diffusion method was also proposed to assess the diffusion effect of risk factors in nationwide rivers. Taking China as a case study, this study calculated the environmental risk value of every sub-unit. Through the clustering function of Statistical Package for Social Sciences and the visual representation of a geographic information system, the study area was divided into nine zones that were visualized on maps according to their different risk levels and risk characteristics. This study also identified the dominant factors of each zone, which could provide the foundation for regional environmental management. Moreover, the results implied some significant correlations between risk system components and the regional social economy in China.     

Multivariate survival models induced by genetic frailties,with application to linkage analysis

We derive a multivariate survival model for age of onset data of a sibship from an additive genetic gamma frailty model constructed basing on the inheritance vectors, and investigate the properties of this model. Based on this model, we propose a retrospective likelihood approach for genetic linkage analysis using sibship data. This test is an allele-sharing-based test, and does not require specification of genetic models or the penetrance functions. This new approach can incorporate both affected and unaffected sibs, environmental covariates and age of onset or age at censoring information and, therefore, provides a practical solution for mapping genes for complex diseases with variable age of onset. Small simulation study indicates that the proposed method performs better than the commonly used allele-sharing-based methods for linkage analysis, especially when the population disease rate is high. We applied this method to a type 1 diabetes sib pair data set and a small breast cancer data set. Both simulated and real data sets also indicate that the method is relatively robust to the misspecification to the baseline hazard function.     

Improving parameter estimation for cell surface FRAP data

Fluorescence Recovery After Photobleaching (FRAP) using the confocal laser scanning microscope has become a standard method used to determine the diffusion coefficient and mobile fraction of cell surface proteins. A common experimental approach is to bleach a stripe on the cell surface and fit the ensuing FRAP curve to a 1D diffusion model. This model is derived from the time course of recovery to an infinitely long stripe bleached on an infinite flat plane. This choice of model dictates the use of a long bleach stripe. We demonstrate that, in the case of a long bleach stripe, the finite extent of the cell leads to significant errors in parameter estimation. We further show that these errors are reduced when a relatively small stripe is bleached. Unfortunately, diffusion to such a region is fundamentally two dimensional and therefore applying the 1D model of diffusion leads to significant errors. We derive an equation suitable for fitting to FRAP data acquired from small bleach regions and analyze its accuracy using simulated data. We propose that the use of a small bleach region along with a two dimensional diffusion model is the ideal protocol for cell surface FRAP.     

On estimating HLA/disease association with application to a study of aplastic anemia

A multiplicative model is described relating HLA typing information to disease incidence. A likelihood-based method for estimating parameters in this model is proposed for use with data sets in which HLA haplotype information is available on a series of cases and their parents. This approach is extended to incorporate information from a matched control series for the purpose of estimating HLA and environmental risk factor effects simultaneously. The method is applied to data from aplastic anemia patients treated by bone marrow transplantation and the results are compared to unmatched case-control analyses using the same case series and several different control series.     

Does drainage pay? Quantifying agricultural profitability associated with wetland drainage practices and canola production in Alberta

Conversion of wetlands in cultivated agricultural landscapes is one of the primary drivers of wetland loss in Alberta, Canada, despite a provincial wetland policy that prioritizes wetland avoidance. While other sectors of the agricultural industry have established initiatives to maintain wetlands, a common narrative within the conventional cropping sector is that wetland retention leads to lost acreage and overlap of crop inputs, and that there are financial benefits associated with wetland drainage. The objective of this research was to explicitly quantify crop productivity within drained wetland basins, in an effort to better understand the extent to which producers financially benefit from drainage practices. Working collaboratively with canola producers in central Alberta over the 2019 growing season, wetland basins within four quarter sections were mapped using an Unmanned Aerial Vehicle, and wetland basins with clear evidence of surface drainage were identified. Agricultural input and yield data provided by producers was then used to quantify profitability within each drained basin. Average profit for drained basins for each producer ranged between ? $145/acre and $76/acre, with an average of $55/acre across all operations. This is compared to an average profit of $203/acre for non-wetland areas across all operations. The results suggest that the financial benefits of drainage are highly variable, and for many drained basins, producers may experience financial losses that may be overlooked when profits are examined only at the field- or operation-level. While this study included a small number of operations, and was limited to one type of crop over a single growing season, the results still provide important insight into the extent to which producers benefit financially from the practice of wetland drainage.

     

Analysis of exponential growth data for yoghurt cultures

Obtaining accurate estimates of maximum specific growth rate, growth yield, and product yield is important for many fermentation processes. A systematic procedure is presented to select the exponential growth region and estimate the maximum specific growth rate using the covariate adjustment method with all the available measured variables (i.e. biomass, substrate, and product). The procedure is applied to data collected during growth of pure and mixed cultures of Lactobacillus bulgaricus and Streptococcus thermophilus on 3% dry milk under anaerobic conditions. The estimation procedure gives good estimates with relatively narrow confidence intervals even though biomass concentration is measured by an indirect method. The estimated values of maximum specific growth rate range from 0.2805 h(-1) for S. thermophilus (ATCC-19258) to 0.4672 h(-1) for S. thermophilus (Microlife). Growth and product yields are estimated using regression analysis and the data for the exponential growth region. The growth yields are compared to their theoretical maximum values.     

Hybrid colloid analysis combining analytical ultracentrifugation and flow-field flow fractionation

Ferritin, the iron storage protein, is an organic-inorganic hybrid colloid consisting of a hollow protein capsule, which is filled with ferrihydride with up to 4500 iron atoms. Owing to the varying iron content and the resulting density differences, as well as the protein oligomerization, a particle size distribution is superimposed with a density distribution, making a precise analysis of ferritin by analytical ultracentrifugation difficult. This study describes how the information of the sedimentation coefficient distribution can be combined with the diffusion coefficient distribution obtained from flow-field flow fractionation to yield the buoyant molar mass of the oligomers in the mixture, extending the information content of each individual analytical method. In addition, the sedimentation and diffusion coefficients are compatible with a simple hard-sphere aggregation model, suggesting that the ferritin oligomers up to the pentamer have a globular solution structure.Presented at the conference for Advances in Analytical Ultracentrifugation and Hydrodynamics, 8–11 June 2002, Grenoble, France     

Extending the Life Cycle Methodology to Cover Impacts of Land Use Systems on the Water Balance (7 pp)

Goal, Scope and Background Whilst initially designed for industrial production systems, environmental life cycle assessment (LCA) has recently been increasingly applied to agriculture and forestry projects. Several authors suggested that the standard LCA methodology needs to be refined to cover the particularities of agri- and silvicultural production systems. Until now, water quantity received little attention in these methodological revisions, notwithstanding the well-known impact of agriculture and forestry on issues like water availability, drought and flood risk. This paper proposes an add-on to existing LCA methods in the form of an indicator set that integrates water quantity impacts of agri- and silvicultural production. Method First, system boundaries are discussed in order to identify the water flows between the production system and the environment. These flows are attributed to impact categories, linked to environmental burdens and to the areas of protection. Appropriate indicators are selected for each potential burden. Results and Discussion At the present, two input related impact categories deal with water quantity: Abiotic resource depletion and land use. The list of output related impact categories presented by Udo de Haes et al. (1999) does not include water quantity impacts like flood and drought risk. A new impact category “regional water balance” is introduced to cover these risks. Exceedance probabilities are used as indicators for these temporal variations in streamflow. Conclusion and Outlook The method presented in this paper can bring a life cycle assessment closer to real world concerns. The main drawback, however, is the increasing data requirement that might hinder the feasibility of the method. Future research should focus on this problem, for instance by applying a relatively simple numerical model that can calculate the indicator scores from more easily accessible data.     

The Concept of Data Utility in Health Risk Assessment: A Multi-Disciplinary Perspective

Human and ecological health risk assessments and the decisions that stem from them require the acquisition and analysis of data. In agencies that are responsible for health risk decision-making, data (and/or opinions/judgments) are obtained from sources such as scientific literature, analytical and process measurements, expert elicitation, inspection findings, and public and private research institutions. Although the particulars of conducting health risk assessments of given disciplines may be dramatically different, a common concern is the subjective nature of judging data utility. Often risk assessors are limited to available data that may not be completely appropriate to address the question being asked. Data utility refers to the ability of available data to support a risk-based decision for a particular risk assessment. This article familiarizes the audience with the concept of data utility and is intended to raise the awareness of data collectors (e.g., researchers), risk assessors, and risk managers to data utility issues in health risk assessments so data collection and use will be improved. In order to emphasize the cross-cutting nature of data utility, the discussion has not been organized into a classical partitioning of risk assessment concerns as being either human health- or ecological health-oriented, as per the U.S. Environmental Protection Agency's Superfund Program.     

Risk prediction with imperfect survival outcome information from electronic health records

Readily available proxies for the time of disease onset such as the time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow-up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on the current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error model for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initially estimator solely based on the labeled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semisupervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in a finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Mass General Brigham Healthcare Biobank.     

A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data

MOTIVATION: Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem. RESULTS: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure.