ELUM: A spatial modelling tool to predict soil greenhouse gas changes from land conversion to bioenergy in the UK

The ELUM Software Package spatially predicts the net soil greenhouse gas balance of land-use change to grow energy crops in the UK up to 2050. It is able to support a range of analyses of bioenergy, and was developed in consultation with anticipated users. Results can be obtained according to specific interests, viewed in the graphical interface and exported for a variety of purposes. The functionality of the software is demonstrated through different case studies, which show an array of applications.     

A new empirical model to retrieve soil moisture and roughness from C-band radar data

A new empirical model for the retrieval, at a field scale, of the bare soil moisture content and the surface roughness characteristics from radar measurements is proposed. The derivation of the algorithm is based on the results of three experimental radar campaigns conducted under natural conditions over agricultural areas. Radar data were acquired by means of several C-band space borne (SIR-C, RADARSAT) or helicopter borne (ERASME) sensors, operating in different configurations of polarization (HH or VV) and incidence angle. Simultaneously to radar acquisitions, a complete ground truth data base was built up with different surface condition measurements of the mean standard deviation (rms) height s, the correlation length l, and the volumetric surface moisture M_v. This algorithm is more specifically developed using the radar cross-section σ⁰ (HH polarization and 39° incidence angle off nadir), namely, σ_0HH,39, and the differential (HH polarization) radar cross-section Δσ₀=σ_0,23°−σ_0,39° in terms of an original roughness parameter, Z_s, namely Z_s=s²/l, and M_v. A good agreement is observed between model outputs and backscattering measurements over different test fields. Eventually, an inversion technique is proposed to retrieve Z_s and M_v from radar measurements.     

A simple equation solver and its application to financial modelling

We discuss a simple algorithm for solving sets of simultaneous equations. The algorithm can solve systems of linear and some kinds of non-linear equations, although it has nowhere near the power of a general non-linear equation solver. Its principal advantages over more general algorithms are simplicity and speed. Versions of the algorithm have been used in a graphics language and in a system for interactively modifying the equations that constitute financial models. We discuss the second application in more detail here.     

Passive microwave emission from smooth bare soils: Developing a simple model to predict near surface water content

MICRO-SWEAT is a coupled microwave emission and soilvegetation-atmosphere transfer scheme. It can predict, among other things, the L band (21 cm wavelength) microwave brightness temperature ( T B ) for a smooth bare soil from knowledge of the soil hydraulic properties and local meteorological data. T B is the product of the apparent emissivity ( e app ) and the effective physical temperature of the soil profile ( T eff ). The modelled relationship between e app and average 0-2 cm soil water content was used to develop a simple semi-empirical model, which predicts the near surface water content from knowledge of the particle size distribution and e app . T eff was found to be comparable to the soil temperature at 11 cm depth, and was estimated using a simple sinusoidal function based on thermal diffusivity to extrapolate the temperature at 11 cm depth from knowledge of the diurnal variation in air temperature. The dependence of T B on incidence angle was predicted by MICRO-SWEAT and also incorporated. This simple model was then successfully used to predict the 0-2 cm water content from knowledge of T B, the air temperature regime and the particle size distribution, for data collected at various look angles over three different soils using a truck-based radiometer.     

A very simple dynamic soil acidification model for scenario analyses and target load calculations

A very simple dynamic soil acidification model, VSD, is described, which has been developed as the simplest extension of steady-state models for critical load calculations and with an eye on regional applications. The model requires only a minimum set of inputs (compared to more detailed models) and execution time is minimised by reducing the set of model equations to a single non-linear equation. To facilitate the exploration of model behaviour at individual sites, the model is linked to a graphical user interface (GUI). This GUI allows easy (Bayesian) calibration, forward simulation (scenario analyses) and can also be used to compute target loads and delay times between deposition reductions and ecosystem recovery. VSD compares well to other widely used more complex models and is currently used in several European countries in the support of effects-based emission reduction policies.     

From composites to factors: Bridging the gap between PLS and covariance‐based structural equation modelling

Partial least squares (PLS) methods possess desirable characteristics that have led to their extensive use in the field of information systems, as well as many other fields, for path analyses with latent variables. Such variables are typically conceptualized as factors in structural equation modelling (SEM). In spite of their desirable characteristics, PLS methods suffer from a fundamental problem: Unlike covariance‐based SEM, they do not deal with factors, but with composites, and as such do not fully account for measurement error. This leads to biased parameters, even as sample sizes grow to infinity. Anchored on a new conceptual foundation, we discuss a method that builds on the consistent PLS technique and that estimates factors, fully accounting for measurement error. We provide evidence that this new method shares the property of statistical consistency with covariance‐based SEM but, like classic PLS methods, has greater statistical power. Moreover, our method provides correlation‐preserving estimates of the factors, which can be used in a variety of other tests. For readers interested in trying it, the new method is implemented in the software WarpPLS. Our detailed discussion should facilitate the implementation of the method in any numeric computing environment, including open source environments such as R and GNU Octave.     

A simple expected volatility (SEV) index: Application to SET50 index options

In 2003, the Chicago Board Options Exchange (CBOE) made two key enhancements to the volatility index (VIX) methodology based on S&P options. The new VIX methodology seems to be based on a complicated formula to calculate expected volatility. In this paper, with the use of Thailand's SET50 Index Options data, we modify the VIX formula to a very simple relationship, which has a higher negative correlation between the VIX for Thailand (TVIX) and SET50 index options. We show that TVIX provides more accurate forecasts of option prices than the simple expected volatility (SEV) index, but the SEV index outperforms TVIX in forecasting expected volatility. Therefore, the SEV index would seem to be a superior tool as a hedging diversification tool because of the high negative correlation with the volatility index.     

A measurement-based model to predict the performance impact ofsystem modifications: a case study

The paper presents a performance case study of parallel jobs executing in real multi user workloads. The study is based on a measurement based model capable of predicting the completion time distribution of the jobs executing under real workloads. The model constructed is also capable of predicting the effects of system design changes on application performance. The model is a finite state, discrete time Markov model with rewards and costs associated with each state. The Markov states are defined from real measurements and represent system/workload states in which the machine has operated. The paper places special emphasis on choosing the correct number of states to represent the workload measured. Specifically, the performance of computationally bound, parallel applications executing in real workloads on an Alliant FX/80 is evaluated. The constructed model is used to evaluate scheduling policies, the performance effects of multiprogramming overhead, and the scalability of the Alliant FX/8O in real workloads. The model identifies a number of available scheduling policies which would improve the response time of parallel jobs. In addition, the model predicts that doubling the number of processors in the current configuration would only improve response time for a typical parallel application by 25%. The model recommends a different processor configuration to more fully utilize extra processors. The paper also presents empirical results which validate the model created     

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVI's response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a region's FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.     

Nicolog: A simple yet powerful cc(FD) language

In this paper, we describe Nicolog, a language with capabilities similar to recently developed constraint logic programming (CLP) languages such as CLP(BNR), clp(FD), and cc(FD). Central to Nicolog are projection constraints (PCs), a sublanguage for compiling and optimizing constraint propagation in numeric and Boolean domains. PCs are an interesting generalization of the indexical constraints introduced in cc(FD) and also found in clp(FD). Nicolog compiles a very general class of built-in constraints into equivalent sets of PCs, allowing an arbitrary mixture of integer (easily extensible to real) and Boolean operations. Nicolog also lets the user program PCs directly, making it possible to implement new sophisticated propagation procedures. We show that PCs are a simple, efficient, and flexible way to implement most of the propagation procedures possible in other FD CLP systems. These include procedures for cardinality, constructive disjunction, implication, and mixed Boolean/numeric constraints. Empirical results with a simple prototype Nicolog implementation based on the WAM architecture show it can solve hard problems with speed comparable to the fastest existing CLP systems.     

A reference data model to support biomass supply chain modelling and optimisation

This paper presents a generic and flexible reference data model meant as the blueprint of the database component of information and decision support systems related to different types of biomass-based supply chains (e.g. first to fourth generation biomass for production of bioenergy and biomaterials). The data model covers the biomass types and handling operations as characterised by their attributes and mutual relationships resulting from a life cycle inventory analysis. The data model enables the identification of the possible operation sequences in the specified chain. This functionality is demonstrated in a case study in which biomass from tall herb communities and mesotrophic grasslands is supplied for biogas or compost production. A comparative analysis has pointed out that the data model includes the required object types to add specific attributes of biomass supply chain simulation and optimisation models (such as spatial and temporal dimensions).     

A hybrid data assimilation scheme for model parameter estimation: Application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.     

A contextualized and personalized model to predict user interest using location-based social networks

The accurate determination of user interest in terms of geographic information is essential to numerous mobile applications, such as recommender systems and mobile advertising. User interest is greatly influenced by the usage context and varies across individuals; therefore, a user interest model should incorporate these individual needs and propensities. In this paper, we present an approach to model user interest in a contextualized and personalized manner based on location-based social networks. Multinomial logistic regression is employed to quantify the relationship between user interest and usage context at both the aggregate and individual levels. The proposed approach is tested in a real-world application using Foursquare check-ins issued between February and June 2014 in the three major cities of Chicago, Los Angeles and New York. Results demonstrate the capability of the contextualization process for capturing contextual influences on user interest, and that such influences can be observed at a fine-grained scale at the individual level through the personalization process. The proposed approach therefore enables contextualized and personalized estimation of user interest, thereby contributing useful information to follow-up mobile applications.     

A graph-theoretic approach to efficiently reason about partially ordered events in (Modal) Event Calculus

In this paper, we show how well-known graph-theoretic techniques can be successfully exploited to efficiently reason about partially ordered events in Kowalski and Sergot's Event Calculus and in its skeptical and credulous modal variants. To overcome the computational weakness of the traditional generate-and-test algorithm of (Modal) Event Calculus, we propose two alternative graph-traversal algorithms that operate on the underlying directed acyclic graph of events representing ordering information. The first algorithm pairs breadth-first and depth-first visits of such an event graph in a suitable way, while the second one operates on its transitive closure and reduction. We prove the soundness and completeness of both algorithms, and thoroughly analyze and compare their computational complexity. This revised version was published online in June 2006 with corrections to the Cover Date.     

A model (in)validation approach to gait classification

This paper addresses the problem of human gait classification from a robust model (in)validation perspective. The main idea is to associate to each class of gaits a nominal model, subject to bounded uncertainty and measurement noise. In this context, the problem of recognizing an activity from a sequence of frames can be formulated as the problem of determining whether this sequence could have been generated by a given (model, uncertainty, and noise) triple. By exploiting interpolation theory, this problem can be recast into a nonconvex optimization. In order to efficiently solve it, we propose two convex relaxations, one deterministic and one stochastic. As we illustrate experimentally, these relaxations achieve over 83 percent and 86 percent success rates, respectively, even in the face of noisy data.     

A mathematical model to predict surface roughness and pattern thickness in roll-to-roll gravure printed electronics

The roll-to-roll printing processes have recently been applied for the manufacturing of printed electronics due to their advantages, such as their high-throughput capabilities and low associated costs. In a roll-to-roll printing machine, a web or substrate is moved from an unwinding roll to a winding roll. During printing, the operating tension is important for correct substrate handling to prevent substrate defects, such as wrinkles, scratches and breaks. Accordingly, the operating conditions of the moving web can affect the quality of the printed pattern. In this study, a mathematical model has been developed to predict the thickness and surface roughness of printed patterns. Because the dynamics of roll-to-roll printing systems are complicated and non-linear, a statistical model is preferred. A full factorial method has been used with four independent variables: operating tension, print speed, ink viscosity and theoretical transfer volume. This model accurately predicted the surface roughness and thickness of the printed pattern.     

Effects of corn on C- and L-band radar backscatter: A correction method for soil moisture retrieval

This paper discusses the effects of vegetation on C- (4.75 GHz) and L- (1.6 GHz) band backscattering (σ^o) measured throughout a growth cycle at incidence angles of 15, 35 and 55°. The utilized σ^o data set was collected by a truck mounted scatterometer over a corn field and is supported by a comprehensive set of ground measurements, including soil moisture and vegetation biomass. Comparison of σ^o measurement against simulations by the Integral Equation Method (IEM) surface scattering model (Fung et al., 1992) shows that the σ^o measurements are dominated either by an attenuated soil return or by scattering from vegetation depending on the antenna configuration and growth stage. Further, the measured σ^o is found to be sensitive to soil moisture even at peak biomass and large incidence angles, which is attributed to scattering along the soil-vegetation pathway.For the simulation of C-band σ^o and the retrieval of soil moisture two methods have been applied, which are the semi-empirical water cloud model (Attema & Ulaby, 1978) and a novel method. This alternative method uses the empirical relationships between the vegetation water content (W) and the ratio of the bare soil and the measured σ^o to correct for vegetation. It is found that this alternative method is superior in reproducing the measured σ^o as well as retrieving soil moisture. The highest retrieval accuracies are obtained at a 35° incidence angle leading to RMSD's of 0.044 and 0.037 m³ m^− 3 for the HH and VV-polarization, respectively. In addition, the sensitivity of these soil moisture retrievals to W and surface roughness parameter uncertainties is investigated.     

A downscaling method for distributing surface soil moisture within a microwave pixel: Application to the Monsoon '90 data

A downscaling method for the near-surface soil moisture retrieved from passive microwave sensors is applied to the PBMR data collected during the Monsoon '90 experiment. The downscaling method requires (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature and (3) the fine-scale distribution of surface conditions composed of atmospheric forcing and the parameters involved in the modeling of land surface-atmosphere interactions. During the Monsoon '90 experiment, eight ground-based meteorological and flux stations were operating over the 150 km² study area simultaneously with the acquisition of the aircraft-based L-band PBMR data. The heterogeneous scene is hence composed of eight subpixels and the microwave pixel is generated by aggregating the microwave emission of all sites. The results indicate a good agreement between the downscaled and ground-based soil moisture as long as the intensity of solar radiation is sufficiently high to use the soil temperature as a tracer of the spatial variability of near-surface soil moisture.     

The relationship between ERS-2 SAR backscatter and soil moisture: Generalization from a humid to semi-arid transect

The sources of variation (environment, genotype and date of measurement) of spectral reflectance indices describing biomass and its physiological status, and their potential use for providing accurate and non‐destructive estimates of crop phenology and yield, were studied on canopies of several collections of durum wheat genotypes showing adaptation to different Mediterranean environments. Spectral reflectance was measured during grain filling. All spectral indices and grain yields showed significant differences between contrasting environments in terms of water availability. Photosynthetic area indices and senescence indices were good indicators, for all genotype collections, of biomass and phenology, respectively, when comparing a wetter site with a drier site. When crop development was accelerated by growing plants under high temperature, provided by a spring‐sown trial under Mediterranean conditions, all spectral indices showed significant variation within a period of one week through grain filling, reflecting the changes in crop phenology and the onset of senescence. The reported changes in the values, and even the signs, of the correlation coefficients across genotypes between grain yield and some reflectance indices might reflect genotypic differences in response (by avoidance) to high temperature and drought during late grain filling. Spectral reflectance data may help to understand phenological characteristics of durum wheat canopies, such as crop duration, provided the date of measurement is well chosen.