Nitrogen fluxes from the landscape are controlled by net anthropogenic nitrogen inputs and by climate

The flux of nitrogen (N) to coastal marine ecosystems is strongly correlated with the “net anthropogenic nitrogen inputs” (NANI) to the landscape across 154 watersheds, ranging in size from 16 km2 to 279 000 km2, in the US and Europe. When NANI values are greater than 1070 kg N km−2 yr−1, an average of 25% of the NANI is exported from those watersheds in rivers. Our analysis suggests a possible threshold at lower NANI levels, with a smaller fraction exported when NANI values are below 1070 kg N km−2 yr−1. Synthetic fertilizer is the largest component of NANI in many watersheds, but other inputs also contribute substantially to the N fluxes; in some regions, atmospheric deposition of N is the major component. The flux of N to coastal areas is controlled in part by climate, and a higher percentage of NANI is exported in rivers, from watersheds that have higher freshwater discharge.

A continuum model of a fishing net

A working report for the Department of Agriculture and Fisheries, Scotland, Marine Laboratory Aberdeen

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Mutation in TERMINAL FLOWER1 reverses the photoperiodic requirement for flowering in the wild strawberry, Fragaria vesca

Photoperiodic flowering has been extensively studied in the annual short-day and long-day plants rice and Arabidopsis while less is known about the control of flowering in perennials. In the perennial wild strawberry, Fragaria vesca L. (Rosaceae), short-day and perpetual flowering long-day accessions occur. Genetic analyses showed that differences in their flowering responses are caused by a single gene, the SEASONAL FLOWERING LOCUS which may encode the F. vesca homolog of TERMINAL FLOWER1 (FvTFL1). We show through high-resolution mapping and transgenic approaches that FvTFL1 is the basis of this change in flowering behavior and demonstrate that FvTFL1 acts as a photoperiodically regulated repressor. In short-day F. vesca, long photoperiods activate FvTFL1 mRNA expression and short days suppress it, promoting flower induction. These seasonal cycles in FvTFL1 mRNA level confer seasonal cycling of vegetative and reproductive development. Mutations in FvTFL1 prevent LD suppression of flowering, and the early flowering that then occurs under LD is dependent on the F. vesca homolog of FLOWERING LOCUS T. This photoperiodic response mechanism differs from those described in model annual plants. We suggest that this mechanism controls flowering within the perennial growth cycle in F. vesca, and demonstrate that a change in a single gene reverses the photoperiodic requirements for flowering.

Replacing Norway spruce with European beech: a comparison of biomass and Net Primary Production patterns in young stands

An alteration of species composition in temperate forests – both managed and natural - is one of the expected effects of environmental change. Present forest tree species ranges will be altered by changing environmental conditions. By a combination of continuous and destructive sampling, we compared biomass stocks and annual NPP in naturally regenerated stands of Norway spruce and European beech. We purposely selected a site where future environmental conditions are predicted to favour beech over presently dominant spruce. We found no difference in overall productivity, but biomass allocation differed significantly between the two species. Beech allocated more assimilates to stem and roots than spruce. There was no significant difference between the species in NPP of the fast turnover biomass pool comprising foliage and fine roots. Maximum height growth occurred about a month earlier than in spruce, potentially changing the timing of carbon (C) flow into the soil pools. We show that the replacement of spruce by beech will result in changes in forest biomass allocation and in alterations of belowground C cycle. Such changes will affect forest ecosystem function by modifying the magnitude and timing of certain C fluxes, but also by potentially changing the species composition of forest biota dependent on them.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.

Modeling requirement driven architecture of adaptive healthcare system based on semiotics

In order to improve the quality of healthcare services, the integrated large-scale medical information system is needed to adapt to the changing medical environment. In this paper, we propose a requirement driven architecture of healthcare information system with hierarchical architecture. The system operates through the mapping mechanism between these layers and thus can organize functions dynamically adapting to user’s requirement. Furthermore, we introduce the organizational semiotics methods to capture and analyze user’s requirement through ontology chart and norms. Based on these results, the structure of user’s requirement pattern (URP) is established as the driven factor of our system. Our research makes a contribution to design architecture of healthcare system which can adapt to the changing medical environment.

Optimal frame length for keeping normalized goodput with lowest requirement on BER

This paper presents an adaptive frame length mechanism based on a cross-layer analysis of intrinsic relations between the MAC frame length, bit error rate (BER) of the wireless link and normalized goodput. The proposed mechanism selects the optimal frame length that keeps the service normalized goodput at required levels while satisfying the lowest requirement on the BER, thus increasing the transmission reliability. Numerical results are provided and show that an optimal frame length satisfying the lowest BER requirement does indeed exist. The performance of BER requirement as a function of the MAC frame length is evaluated and compared for transmission scenarios with and without automatic repeat request (ARQ). Furthermore, issues related to the MAC overhead length are also discussed to illuminate the functionality and performance of the proposed mechanism.

Particle swarm optimisation assisted classification using elastic net prefiltering

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how “Occam's razor” is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.

Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA

Observations of net ecosystem exchange (NEE) of carbon and its biophysical drivers have been collected at the AmeriFlux site in the Morgan-Monroe State Forest (MMSF) in Indiana, USA since 1998. Thus, this is one of the few deciduous forest sites in the world, where a decadal analysis on net ecosystem productivity (NEP) trends is possible. Despite the large interannual variability in NEP, the observations show a significant increase in forest productivity over the past 10 years (by an annual increment of about 10 g C m−2 yr−1). There is evidence that this trend can be explained by longer vegetative seasons, caused by extension of the vegetative activity in the fall. Both phenological and flux observations indicate that the vegetative season extended later in the fall with an increase in length of about 3 days yr−1 for the past 10 years. However, these changes are responsible for only 50% of the total annual gain in forest productivity in the past decade. A negative trend in air and soil temperature during the winter months may explain an equivalent increase in NEP through a decrease in ecosystem respiration.

Elastic net orthogonal forward regression

An efficient two-level model identification method aiming at maximising a model׳s generalisation capability is proposed for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularisation parameters in the elastic net are optimised using a particle swarm optimisation (PSO) algorithm at the upper level by minimising the leave one out (LOO) mean square error (LOOMSE). There are two elements of original contributions. Firstly an elastic net cost function is defined and applied based on orthogonal decomposition, which facilitates the automatic model structure selection process with no need of using a predetermined error tolerance to terminate the forward selection process. Secondly it is shown that the LOOMSE based on the resultant ENOFR models can be analytically computed without actually splitting the data set, and the associate computation cost is small due to the ENOFR procedure. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Changes in global net radiative imbalance 1985-2012

Combining satellite data, atmospheric reanalyses and climate model simulations, variability in the net downward radiative flux imbalance at the top of Earth's atmosphere (N) is reconstructed and linked to recent climate change. Over the 1985-1999 period mean N (0.34 ± 0.67 Wm–2) is lower than for the 2000-2012 period (0.62 ± 0.43 Wm–2, uncertainties at 90% confidence level) despite the slower rate of surface temperature rise since 2000. While the precise magnitude of N remains uncertain, the reconstruction captures interannual variability which is dominated by the eruption of Mt. Pinatubo in 1991 and the El Niño Southern Oscillation. Monthly deseasonalized interannual variability in N generated by an ensemble of 9 climate model simulations using prescribed sea surface temperature and radiative forcings and from the satellite-based reconstruction is significantly correlated (r ∼ 0.6) over the 1985-2012 period.

Rationality of service composition of Workflow Net in a service oriented architecture

Users’ requirements change drives an information system evolution. Consequently, such evolution affects those atomic services which provide functional operations from one state of their composition to another state of composition. A challenging issue associated with such evolution of the state of service composition is to ensure a resultant service composition remaining rational. This paper presents a method of Service Composition Atomic-Operation Set (SCAOS). SCAOS defines 2 classes of atomic operations and 13 kinds of basic service compositions to aid a state change process by using Workflow Net. The workflow net has algorithmic capabilities to compose the required services with rationality and maintain any changes to the services in a different composition also rational. This method can improve the adaptability to the ever changing business requirements of information systems in the dynamic environment.

The Article 51 reporting requirement for self-defense actions

This article examines one legal criterion for the exercise of the right of self-defense that has been significantly overlooked by commentators: the so-called “reporting requirement.” Article 51 of the United Nations (UN) Charter provides, inter alia, that “[m]easures taken by members in the exercise of this right of self-defense shall be immediately reported to the Security Council.” Although the requirement to report all self-defense actions to the Council is clearly set out in Article 51, the Charter offers no further guidance with regard to this obligation. Reference to the practice of states since the UN’s inception in 1945 is therefore essential to understanding the scope and nature of the reporting requirement. As such, this article is underpinned by an extensive original dataset of reporting practice covering the period from January 1, 1998 to December 31, 2013. We know from Article 51 that states “shall” report, but do they, and—if so—in what manner? What are the various implications of reporting, of failing to report, and of the way in which states report? How are reports used, and by whom? Most importantly, this article questions the ultimate value of states reporting their self-defense actions to the Security Council in modern interstate relations.