Environmental accounting and reporting in Italian local governments: a longitudinal analysis of changes in context, content and process

This research aims at contributing to a better understanding of changes in local governments’ accounting and reporting practices. Particularly, ‘why’, ‘what’ and ‘how’ environmental aspects are included and the significance of changes across time. It adopts an interpretative approach to conduct a longitudinal analysis of case studies. Pettigrew and Whipp’s framework on context, content and process is used as a lens to distinguish changes under each dimension and analyse their interconnections. Data is collected from official documents and triangulated with semi-structured interviews. The legal framework defines as boundaries of the accounting information the territory under local governments’ jurisdiction and their immediate surrounding area. Organisational environmental performance and externalities are excluded from the requirements. An interplay between the local outer context, political commitment and organisational culture justifies the implementation of changes beyond what is regulated and the implementation of transformational changes. Local governments engage in international networks to gain access to funding and implement changes, leading to adopting the dominant environmental agenda. Key stakeholders, like citizens, are not engaged in the accounting and reporting process. Thus, there is no evidence that the environmental aspects addressed and related changes align with stakeholders’ needs and expectations, which jeopardises its significance. Findings from the current research have implications in other EU member states due to the harmonisation of accounting and reporting practices and the common practice across the EU in using external funding to conceptualise and implement changes. This implies that other local governments could also be representing a limited account related to environmental aspects.

Contrasting Effects Of Land Use Intensity And Exotic Host Plants On The Specialization Of Interactions In Plant-herbivore Networks.

Human land use tends to decrease the diversity of native plant species and facilitate the invasion and establishment of exotic ones. Such changes in land use and plant community composition usually have negative impacts on the assemblages of native herbivorous insects. Highly specialized herbivores are expected to be especially sensitive to land use intensification and the presence of exotic plant species because they are neither capable of consuming alternative plant species of the native flora nor exotic plant species. Therefore, higher levels of land use intensity might reduce the proportion of highly specialized herbivores, which ultimately would lead to changes in the specialization of interactions in plant-herbivore networks. This study investigates the community-wide effects of land use intensity on the degree of specialization of 72 plant-herbivore networks, including effects mediated by the increase in the proportion of exotic plant species. Contrary to our expectation, the net effect of land use intensity on network specialization was positive. However, this positive effect of land use intensity was partially canceled by an opposite effect of the proportion of exotic plant species on network specialization. When we analyzed networks composed exclusively of endophagous herbivores separately from those composed exclusively of exophagous herbivores, we found that only endophages showed a consistent change in network specialization at higher land use levels. Altogether, these results indicate that land use intensity is an important ecological driver of network specialization, by way of reducing the local host range of herbivore guilds with highly specialized feeding habits. However, because the effect of land use intensity is offset by an opposite effect owing to the proportion of exotic host species, the net effect of land use in a given herbivore assemblage will likely depend on the extent of the replacement of native host species with exotic ones.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Characterization of subgraph relationships and distribution in complex networks

A network can be analyzed at different topological scales, ranging from single nodes to motifs, communities, up to the complete structure. We propose a novel approach which extends from single nodes to the whole network level by considering non-overlapping subgraphs (i.e. connected components) and their interrelationships and distribution through the network. Though such subgraphs can be completely general, our methodology focuses on the cases in which the nodes of these subgraphs share some special feature, such as being critical for the proper operation of the network. The methodology of subgraph characterization involves two main aspects: (i) the generation of histograms of subgraph sizes and distances between subgraphs and (ii) a merging algorithm, developed to assess the relevance of nodes outside subgraphs by progressively merging subgraphs until the whole network is covered. The latter procedure complements the histograms by taking into account the nodes lying between subgraphs, as well as the relevance of these nodes to the overall subgraph interconnectivity. Experiments were carried out using four types of network models and five instances of real-world networks, in order to illustrate how subgraph characterization can help complementing complex network-based studies.

Finding Quasi-Optimal Network Topologies for Information Transmission in Active Networks

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Border detection in complex networks

One important issue implied by the finite nature of real-world networks regards the identification of their more external (border) and internal nodes. The present work proposes a formal and objective definition of these properties, founded on the recently introduced concept of node diversity. It is shown that this feature does not exhibit any relevant correlation with several well-established complex networks measurements. A methodology for the identification of the borders of complex networks is described and illustrated with respect to theoretical (geographical and knitted networks) as well as real-world networks (urban and word association networks), yielding interesting results and insights in both cases.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

The role of exon shuffling in shaping protein-protein interaction networks

Background: Physical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results: We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions: Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Hybrid Algorithms for Routing and Assignment Wavelengths in Optical Networks

This paper presents a strategy for the solution of the WDM optical networks planning. Specifically, the problem of Routing and Wavelength Allocation (RWA) in order to minimize the amount of wavelengths used. In this case, the problem is known as the Min-RWA. Two meta-heuristics (Tabu Search and Simulated Annealing) are applied to take solutions of good quality and high performance. The key point is the degradation of the maximum load on the virtual links in favor of minimization of number of wavelengths used; the objective is to find a good compromise between the metrics of virtual topology (load in Gb/s) and of the physical topology (quantity of wavelengths). The simulations suggest good results when compared to some existing in the literature.

FWM Constraints Management for Lightpath Establishment in GMPLS Networks

In all-optical networks, management of physical layer restrictions should collaborate in lightpath establishment. Label-Switched Path validation in Generalized MultiProtocol Label Switching on Dense Wavelength Division Multiplexing network requires the treatment of the physical impairment-related parameters along the provisioned route. In this paper we propose, for the first time in our view, the generation of an optical layer database by simulation that specifically characterizes the dynamic FWM impairments for the lightpaths provisioned in a GMPLS/DWDM network.

Implementation of a Control Loop Experiment in a Network-Based Control System With LonWorks Technology and IP Networks

Considering the increasing popularity of network-based control systems and the huge adoption of IP networks (such as the Internet), this paper studies the influence of network quality of service (QoS) parameters over quality of control parameters. An example of a control loop is implemented using two LonWorks networks (CEA-709.1) interconnected by an emulated IP network, in which important QoS parameters such as delay and delay jitter can be completely controlled. Mathematical definitions are provided according to the literature, and the results of the network-based control loop experiment are presented and discussed.

Hybrid Method to Assess Sensitive Process Interruption Costs Due to Faults in Electric Power Distribution Networks

This paper shows a new hybrid method for risk assessment regarding interruptions in sensitive processes due to faults in electric power distribution systems. This method determines indices related to long duration interruptions and short duration voltage variations (SDVV), such as voltage sags and swells in each customer supplied by the distribution network. Frequency of such occurrences and their impact on customer processes are determined for each bus and classified according to their corresponding magnitude and duration. The method is based on information regarding network configuration, system parameters and protective devices. It randomly generates a number of fault scenarios in order to assess risk areas regarding long duration interruptions and voltage sags and swells in an especially inventive way, including frequency of events according to their magnitude and duration. Based on sensitivity curves, the method determines frequency indices regarding disruption in customer processes that represent equipment malfunction and possible process interruptions due to voltage sags and swells. Such approach allows for the assessment of the annual costs associated with each one of the evaluated power quality indices.