Establishment of a Cross-European Field Site Network in the ALARM Project for Assessing Large-Scale Changes in Biodiversity

The field site network (FSN) plays a central role in conducting joint research within all Assessing Large-scale Risks for biodiversity with tested Methods (ALARM) modules and provides a mechanism for integrating research on different topics in ALARM on the same site for measuring multiple impacts on biodiversity. The network covers most European climates and biogeographic regions, from Mediterranean through central European and boreal to subarctic. The project links databases with the European-wide field site network FSN, including geographic information system (GIS)-based information to characterise the test location for ALARM researchers for joint on-site research. Maps are provided in a standardised way and merged with other site-specific information. The application of GIS for these field sites and the information management promotes the use of the FSN for research and to disseminate the results. We conclude that ALARM FSN sites together with other research sites in Europe jointly could be used as a future backbone for research proposals

ALARM: Assessing large-scale environment risks for biodiversity with tested methods

The EU-funded research project ALARM will develop and test methods and protocols for the assessment of large-scale environmental risks in order to minimise negative human impacts. Research focuses on the assessment and forecast of changes in biodiversity and in the structure, function, and dynamics of ecosystems. This includes the relationships between society, the economy and biodiversity.

Analysis of the calving pattern of herds in small-scale dairy systems in central Mexico

This study sets out to find the best calving pattern for small-scale dairy systems in Michoacan State, central Mexico. Two models were built. First, a linear programming model was constructed to optimize calving pattern and herd structure according to metabolizable energy availability. Second, a Markov chain model was built to investigate three reproductive scenarios (good, average and poor) in order to suggest factors that maintain the calving pattern given by the linear programming model. Though it was not possible to maintain the optimal linear programming pattern, the Markov chain model suggested adopting different reproduction strategies according to period of the year that the cow is expected to calve. Comparing different scenarios, the Markov model indicated the effect of calving interval on calving pattern and herd structure.

Analysis of feeding strategies for small-scale dairy systems in central Mexico using linear programming

A limitation of small-scale dairy systems in central Mexico is that traditional feeding strategies are less effective when nutrient availability varies through the year. In the present work, a linear programming (LP) model that maximizes income over feed cost was developed, and used to evaluate two strategies: the traditional one used by the small-scale dairy producers in Michoacan State, based on fresh lucerne, maize grain and maize straw; and an alternative strategy proposed by the LIP model, based on ryegrass hay, maize silage and maize grain. Biological and economic efficiency for both strategies were evaluated. Results obtained with the traditional strategy agree with previously published work. The alternative strategy did not improve upon the performance of the traditional strategy because of low metabolizable protein content of the maize silage considered by the model. However, the Study recommends improvement of forage quality to increase the efficiency of small-scale dairy systems, rather than looking for concentrate supplementation.

Forage production and nutrient availability in small-scale dairy systems in central Mexico using linear programming and partial budgeting

Small-scale dairy systems play an important role in the Mexican dairy sector and farm planning activities related to resource allocation have a significant impact on the profitability of such enterprises. Linear programming is a technique widely used for planning and ration formulation, and partial budgeting is a technique for assessing the impact of changes on the profitability of an enterprise. This study used both methods to optimise land use for forage production and nutrient availability, and to evaluate the economic impact of such changes in small-scale Mexican dairy systems. The model showed satisfactory performance when optimal solutions were compared with the traditional strategy. The strategy using fresh ryegrass, maize silage and oat hay, and the strategy using a combination of alfalfa hay, maize silage, fresh ryegrass and oat hay appeared attractive options for providing a better nutrient supply and maintaining a higher stocking rate throughout the year than the traditional strategy.

A collaborative working environment for a large scale environmental model

Large scientific applications are usually developed, tested and used by a group of geographically dispersed scientists. The problems associated with the remote development and data sharing could be tackled by using collaborative working environments. There are various tools and software to create collaborative working environments. Some software frameworks, currently available, use these tools and software to enable remote job submission and file transfer on top of existing grid infrastructures. However, for many large scientific applications, further efforts need to be put to prepare a framework which offers application-centric facilities. Unified Air Pollution Model (UNI-DEM), developed by Danish Environmental Research Institute, is an example of a large scientific application which is in a continuous development and experimenting process by different institutes in Europe. This paper intends to design a collaborative distributed computing environment for UNI-DEM in particular but the framework proposed may also fit to many large scientific applications as well.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

MIMO Wiener model identification for large scale fading of wireless mobile communications links

The large scale fading of wireless mobile communications links is modelled assuming the mobile receiver motion is described by a dynamic linear system in state-space. The geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A Wiener system subspace identification algorithm in conjunction with polynomial regression is used to identify a model from time-domain estimates of the field intensity assuming a multitude of emitters and an antenna array at the receiver end.

A comparative study of Java and C performance in two large-scale parallel applications

In the 1990s the Message Passing Interface Forum defined MPI bindings for Fortran, C, and C++. With the success of MPI these relatively conservative languages have continued to dominate in the parallel computing community. There are compelling arguments in favour of more modern languages like Java. These include portability, better runtime error checking, modularity, and multi-threading. But these arguments have not converted many HPC programmers, perhaps due to the scarcity of full-scale scientific Java codes, and the lack of evidence for performance competitive with C or Fortran. This paper tries to redress this situation by porting two scientific applications to Java. Both of these applications are parallelized using our thread-safe Java messaging system—MPJ Express. The first application is the Gadget-2 code, which is a massively parallel structure formation code for cosmological simulations. The second application uses the finite-domain time-difference method for simulations in the area of computational electromagnetics. We evaluate and compare the performance of the Java and C versions of these two scientific applications, and demonstrate that the Java codes can achieve performance comparable with legacy applications written in conventional HPC languages. Copyright © 2009 John Wiley & Sons, Ltd.