Bayesian analysis of uncertainty in the GlobCover 2009 land cover product at climate model grid scale

Land cover data derived from satellites are commonly used to prescribe inputs to models of the land surface. Since such data inevitably contains errors, quantifying how uncertainties in the data affect a model’s output is important. To do so, a spatial distribution of possible land cover values is required to propagate through the model’s simulation. However, at large scales, such as those required for climate models, such spatial modelling can be difficult. Also, computer models often require land cover proportions at sites larger than the original map scale as inputs, and it is the uncertainty in these proportions that this article discusses. This paper describes a Monte Carlo sampling scheme that generates realisations of land cover proportions from the posterior distribution as implied by a Bayesian analysis that combines spatial information in the land cover map and its associated confusion matrix. The technique is computationally simple and has been applied previously to the Land Cover Map 2000 for the region of England and Wales. This article demonstrates the ability of the technique to scale up to large (global) satellite derived land cover maps and reports its application to the GlobCover 2009 data product. The results show that, in general, the GlobCover data possesses only small biases, with the largest belonging to non–vegetated surfaces. In vegetated surfaces, the most prominent area of uncertainty is Southern Africa, which represents a complex heterogeneous landscape. It is also clear from this study that greater resources need to be devoted to the construction of comprehensive confusion matrices.

Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

The knowledge economy and innovation: certain uncertainty and the risk economy

The knowledge economy is a dominant force in today's world, and innovation policy and national systems of innovation are central to it. In this article, we draw on different sociological and economic theories of risk to engage critically with innovation policy and national systems of innovation. Beck's understanding of a risk society, Schumpeter's innovation thesis, and Perez's techno-economic paradigm are used to consider the risk economy, and the broader risk implications of knowledge economy policies and their associated innovation systems. Derrida's theory of haunting provides the methodological framework for our discussion. We use his notion of “hauntology” to conceptualize the risk economy as a ghost that haunts knowledge economy policies and systems. The spectral risk economy draws attention to the inherent instability of the knowledge economy, and challenges the certainty of its economic dogma by offering an alternative perspective. The risk economy problematizes knowledge economy policies and systems by revealing the uncertain and “undecidable” future of social, political and cultural hazards ignored in the interest of commercial gain.

Development of a food composition database for the estimation of dietary intakes of glucosinolates, the biologically active constituents of cruciferous vegetables

Evidence indicates that cruciferous vegetables are protective against a range of cancers with glucosinolates and their breakdown products considered the biologically active constituents. To date, epidemiological studies have not investigated the intakes of these constituents due to a lack of food composition databases. The aim of the present study was to develop a database for the glucosinolate content of cruciferous vegetables that can be used to quantify dietary exposure for use in epidemiological studies of diet–disease relationships. Published food composition data sources for the glucosinolate content of cruciferous vegetables were identified and assessed for data quality using established criteria. Adequate data for the total glucosinolate content were available from eighteen published studies providing 140 estimates for forty-two items. The highest glucosinolate values were for cress (389 mg/100 g) while the lowest values were for Pe-tsai chinese cabbage (20 mg/100 g). There is considerable variation in the values reported for the same vegetable by different studies, with a median difference between the minimum and maximum values of 5·8-fold. Limited analysis of cooked cruciferous vegetables has been conducted; however, the available data show that average losses during cooking are approximately 36 %. This is the first attempt to collate the available literature on the glucosinolate content of cruciferous vegetables. These data will allow quantification of intakes of the glucosinolates, which can be used in epidemiological studies to investigate the role of cruciferous vegetables in cancer aetiology and prevention.

Education for social justice in an uncertain world

There is a broad body of literature that examines the notion of ‘uncertainty’ in education and, indeed, the themes of this 13th international conference on learning acknowledge that the world is in flux. Barnett (2004), in particular, promotes a renewed approach to education — one that he believes transcends the traditional scope of higher education. Barnett notes that higher education has focused traditionally on knowledge, but, in an uncertain world, this is no longer enough. He encourages teachers in higher education to consider reconstructing curriculum and pedagogy so that a focus on knowing and acting is retained but is complemented by a pedagogy that is designed to enhance students' being in the world. This paper focuses on the potential synergies or difficulties that arise from an analysis of the ‘education for uncertainty’ literature and the goals of education for social justice. Does education for being provide greater possibilities for the enhancement of social justice?

The influence of cultural values on brand loyalty

It is well documented that culture can influence consumer attitudes and behavior. While there have been numerous studies on how culture influences the four Ps of the marketing mix, few researchers have examined its effect on customer loyalty. More specifically, how consumers who identify more with certain cultural traits are likely to be more brand loyal. Using Hofstede’s cultural dimensions, this study empirically examines cultural effects on consumer-reported “proneness” to brand loyalty and finds that those who scored highly in individualism and uncertainty avoidance have greater affinity for exhibiting loyalty to a brand.

Minimizing bounded uncertainty impact on scheduling with earliest start and due-date constraints via interval computation

Bounded uncertainty is a major challenge to real life scheduling as it increases the risk and cost depending on the objective function. Bounded uncertainty provides limited information about its nature. It provides only the upper and the lower bounds without information in between, in contrast to probability distributions and fuzzymembership functions. Bratley algorithm is usually used for scheduling with the constraints of earliest start and due-date. It is formulated as . The proposed research uses interval computation to minimize the impact of bounded uncertainty of processing times on Bratley’s algorithm. It minimizes the uncertainty of the estimate of the objective function. The proposed concept is to do the calculations on the interval values and approximate the end result instead of approximating each interval then doing numerical calculations. This methodology gives a more certain estimate of the objective function.

Design of a norm-bounded LQG controller for power distribution networks with distributed generation

This paper presents a novel excitation control design to improve the voltage profile of power distribution networks with distributed generation and induction motor loads. The system is linearised by perturbation technique. Controller is designed using the linear-quadratic-Gaussian (LQG) controller synthesis method. The LQG controller is addressed with norm-bounded uncertainty. The approach considered in this paper is to find the smallest upper bound on the H∞ norm of the uncertain system and to design an optimal controller based on this bound. The design method requires the solution of a linear matrix inequality. The performance of the controller is tested on a benchmark power distribution system. Simulation results show that the proposed controller provides impressive oscillation damping compared to the conventional excitation controller.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Extending scheduling algorithms to minimise the impact of bounded uncertainty using interval programming

 This research proposed a new methodology to extend algorithms to accept interval-based uncertain parameters. The methodology is applied on scheduling algorithms, including heuristic and meta-heuristic algorithms and produced optimal results with higher accuracy. The research outcomes are effective for decision making process using uncertain or predicted data.

Global, regional, and national levels and causes of maternal mortality during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013

BACKGROUND: The fifth Millennium Development Goal (MDG 5) established the goal of a 75% reduction in the maternal mortality ratio (MMR; number of maternal deaths per 100,000 livebirths) between 1990 and 2015. We aimed to measure levels and track trends in maternal mortality, the key causes contributing to maternal death, and timing of maternal death with respect to delivery. METHODS: We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to analyse a database of data for 7065 site-years and estimate the number of maternal deaths from all causes in 188 countries between 1990 and 2013. We estimated the number of pregnancy-related deaths caused by HIV on the basis of a systematic review of the relative risk of dying during pregnancy for HIV-positive women compared with HIV-negative women. We also estimated the fraction of these deaths aggravated by pregnancy on the basis of a systematic review. To estimate the numbers of maternal deaths due to nine different causes, we identified 61 sources from a systematic review and 943 site-years of vital registration data. We also did a systematic review of reports about the timing of maternal death, identifying 142 sources to use in our analysis. We developed estimates for each country for 1990-2013 using Bayesian meta-regression. We estimated 95% uncertainty intervals (UIs) for all values. FINDINGS: 292,982 (95% UI 261,017-327,792) maternal deaths occurred in 2013, compared with 376,034 (343,483-407,574) in 1990. The global annual rate of change in the MMR was -0·3% (-1·1 to 0·6) from 1990 to 2003, and -2·7% (-3·9 to -1·5) from 2003 to 2013, with evidence of continued acceleration. MMRs reduced consistently in south, east, and southeast Asia between 1990 and 2013, but maternal deaths increased in much of sub-Saharan Africa during the 1990s. 2070 (1290-2866) maternal deaths were related to HIV in 2013, 0·4% (0·2-0·6) of the global total. MMR was highest in the oldest age groups in both 1990 and 2013. In 2013, most deaths occurred intrapartum or postpartum. Causes varied by region and between 1990 and 2013. We recorded substantial variation in the MMR by country in 2013, from 956·8 (685·1-1262·8) in South Sudan to 2·4 (1·6-3·6) in Iceland. INTERPRETATION: Global rates of change suggest that only 16 countries will achieve the MDG 5 target by 2015. Accelerated reductions since the Millennium Declaration in 2000 coincide with increased development assistance for maternal, newborn, and child health. Setting of targets and associated interventions for after 2015 will need careful consideration of regions that are making slow progress, such as west and central Africa. FUNDING: Bill & Melinda Gates Foundation.

Transformational leadership and job involvement in the Middle East: the moderating role of individually held cultural values

This study examines whether individually held cultural values moderate the relationship between transformational leadership behavior of supervisors and the job involvement of subordinates in the Middle Eastern organizational context. Hierarchical regression analysis was used to analyze survey data from 229 employees of 10 organizations in the United Arab Emirates. In line with the findings of studies in Western countries, transformational leadership was found to influence job involvement positively. In addition, the cultural value orientations of individuals were found to moderate this relationship. Collectivism positively influenced the relationship between transformational leadership and job involvement, whereas uncertainty avoidance had a negative effect. These findings provide an insight into how transformational leadership may be used to motivate culturally diverse groups of employees within the Middle East. To enhance job involvement, organizations need to realize that the attitudinal response of subordinates to transformational leadership can depend on their cultural values. This has significant implications regarding the training and effective deployment of transformational leaders within Middle Eastern organizations.

Effect Of Different Hydrological Model Structures On The Assimilation Of Distributed Uncertain Observations

The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of different structure of a semi-distributed hydrological model in the assimilation of distributed uncertain discharge observations. The study was applied to the Bacchiglione catchment, located in Italy. The first methodological step was to divide the basin in different sub-basins according to topographic characteristics. Secondly, two different structures of the semi-distributed hydrological model were implemented in order to estimate the outflow hydrograph. Then, synthetic observations of uncertain value of discharge were generated, as a function of the observed and simulated value of flow at the basin outlet, and assimilated in the semi-distributed models using a Kalman Filter. Finally, different spatial patterns of sensors location were assumed to update the model state as response of the uncertain discharge observations. The results of this work pointed out that, overall, the assimilation of uncertain observations can improve the hydrologic model performance. In particular, it was found that the model structure is an important factor, of difficult characterization, since can induce different forecasts in terms of outflow discharge. This study is partly supported by the FP7 EU Project WeSenseIt.

Uncertainty Characterization Of A Conceptual Rainfall-Runoff Model By Using Grey Numbers

A procedure for characterizing global uncertainty of a rainfall-runoff simulation model based on using grey numbers is presented. By using the grey numbers technique the uncertainty is characterized by an interval; once the parameters of the rainfall-runoff model have been properly defined as grey numbers, by using the grey mathematics and functions it is possible to obtain simulated discharges in the form of grey numbers whose envelope defines a band which represents the vagueness/uncertainty associated with the simulated variable. The grey numbers representing the model parameters are estimated in such a way that the band obtained from the envelope of simulated grey discharges includes an assigned percentage of observed discharge values and is at the same time as narrow as possible. The approach is applied to a real case study highlighting that a rigorous application of the procedure for direct simulation through the rainfall-runoff model with grey parameters involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the grey numbers representing the simulated discharges. Relying on this simplified procedure, the conceptual rainfall-runoff grey model is thus calibrated and the uncertainty bands obtained both downstream of the calibration process and downstream of the validation process are compared with those obtained by using a well-established approach, like the GLUE approach, for characterizing uncertainty. The results of the comparison show that the proposed approach may represent a valid tool for characterizing the global uncertainty associable with the output of a rainfall-runoff simulation model.