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The aim of this paper is to assess the causal relationship among innovation in environment-related technologies, per capita income, and three major waste disposal operations (landfill, recycling, and incineration) for Korea. A time-series analysis over the frequency domain (Breitung–Candelon Spectral Granger causality) is applied, followed by Artificial Neural Networks experiments over the 1985–2016 period. Empirical results highlight that economic growth is tightly linked both to the growth of recycled waste and to the increase of environment-related innovations. Findings also highlight that waste recycling operations can spur the level of economic activity.

The aim of this study is to explore the nexus among CO2 emissions, energy use, and GDP in Russia using annual data ranging from 1970 to 2017. We first conduct time-series analyses (stationarity, structural breaks, and cointegration tests). Then, we present a new D2C algorithm, and we run a Machine Learning experiment. Comparing the results of the two approaches, we conclude that economic growth causes energy use and CO2 emissions. However, the critical analysis underlines how the variance decomposition justifies the qualitative approach of using economic growth to immediately implement expenses for the use of alternative energies able to reduce polluting emissions. Finally, robustness checks to validate the results through a new D2C algorithm are performed. In essence, we demonstrate the existence of causal links in sub-permanent states among these variables.

This study aims to investigate the nexus among waste generation, economic growth, and greenhouse gas (GHG) emissions in a circular economy framework for the case of Switzerland. Using two different empirical approaches (Dynamic Auto-Regressive Distributed Lags and Fuzzy Cognitive Maps), time-series results show that municipal waste and economic growth have both a short- and a long-run impact on GHG emissions. Moreover, causality analyses evidence the presence of a unidirectional causal flow running from municipal waste and economic growth to greenhouse gas emissions, while a bidirectional causality between municipal waste and economic growth. The results of the static analysis of the municipal solid waste cognitive map show that the most significant system variables relate to the domains of “policy drivers” (education and awareness campaigns and extended producer responsibility) and “environment and health” (GHG emissions). Findings of the policy scenario simulations reveal that the most effective drivers are those about the mission-oriented policy approach., https://www.sciencedirect.com/science/article/abs/pii/S095965262201174X?dgcid=coauthor

Analysing temporal patterns in plant communities is extremely important to quantify the extent and the consequences of ecological changes, especially considering the current biodiversity crisis. Long-term data collected through the regular sampling of permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms driving vegetation change. We hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a global collection of vegetation time-series derived from the regular monitoring of plant species in permanent plots. With 79 data sets from five continents and 7,789 vegetation time-series monitored for at least 6 years and mostly on an annual basis, LOTVS possibly represents the largest collection of temporally fine-grained vegetation time-series derived from permanent plots and made accessible to the research community. As such, it has an outstanding potential to support innovative research in the fields of vegetation science, plant ecology and temporal ecology., The authors acknowledge institutional support as follows. Nicola J. Day: Te Apārangi Royal Society of New Zealand (Rutherford Postdoctoral Fellowship). Jiří Danihelka: Czech Science Foundation (project no. 19-28491X) and Czech Academy of Sciences (project no. RVO 67985939). Francesco de Bello: Spanish Plan Nacional de I+D+i (project PGC2018-099027-B-I00). Eric Garnier: La Fage INRA experimental station. Tomáš Herben: GAČR grant 20-02901S. Anke Jentsch: German Federal Ministry of Education and Research (grant 031B0516C - SUSALPS) and Oberfrankenstiftung (grant OFS FP00237). Norbert Juergens: German Federal Ministry of Education and Research (grant 01LG1201N - SASSCAL ABC). Frédérique Louault and Katja Klumpp: AnaEE-France (ANR-11-INBS-0001). Robin J. Pakeman: Strategic Research Programme of the Scottish Government's Rural and Environment Science and Analytical Services Division. Meelis Pärtel: Estonian Research Council (PRG609) and European Regional Development Fund (Centre of Excellence EcolChange). Josep Peñuelas: Spanish Government (grant PID2019-110521GB-I00), Fundación Ramon Areces (grant ELEMENTAL-CLIMATE), Catalan Government (grant SGR 2017-1005), and European Research Council (Synergy grant ERC-SyG-2013-610028, IMBALANCE-P). Ute Schmiedel: German Federal Ministry of Education and Research (Promotion numbers 01LC0024, 01LC0024A, 01LC0624A2, 01LG1201A, 01LG1201N). Hana Skálová: GAČR grant 20-02901S. Karsten Wesche: International Institute Zittau, Technische Universität Dresden. Susan K. Wiser: New Zealand Ministry for Business, Innovation and Employment’s Strategic Science Investment Fund. Ben A. Woodcock: NERC and BBSRC (NE/N018125/1 LTS-M ASSIST - Achieving Sustainable Agricultural Systems). Enrique Valencia: Program for attracting and retaining talent of Comunidad de Madrid (no. 2017-T2/AMB-5406) and Community of Madrid and Rey Juan Carlos University (Young Researchers R&D Project. Ref. M2165 – INTRANESTI). Truman P. Young: National Science Foundation (LTREB DEB 19-31224).

By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify the economic and environmental channels through which energy and society interfere in this country. In this paper, we investigate whether this structural energy change may adversely impact the growth of the Belgian economy (i) and its ability to meet its long-term greenhouse gas emission targets (ii). A multivariate model comprising production factors (labor, capital, and exports), nuclear and renewable energy uses, total primary energy supply, economic growth, and CO2emissions from the power and heating sector is combined with real time-series data spanning the 1974–2019 period. The analysis consists in sequentially assessing two distinct nexuses (energy-economy and energy-economy-environment) over reduced- and augmented frameworks (excluding and including nuclear energy), and through a two-stage empirical strategy: time-series econometric estimations (Toda-Yamamoto causality test, Impulse Response Functions (IRFs), and the Auto-Regressive Distributed Lags (ARDL) and Machine Learning (ML) experiments with a Partial Differential Equations (PDEs) algorithm. For robustness purposes, we conduct two seminal tests which relate to dynamic predictive processes (T-Mat and Verticality tests). Besides confirming the time-series findings, our ML results highlight the necessity to timely manage the process of nuclear phase-out, along with a progressive deployment of installed renewable energy capacity. This should avoid additional economic costs, energy security threats, and undermining of climate targets. In doing so, this study combines macro-level nexus investigations with the politics and institutional determinants of nuclear energy reliance and seeks to bring inclusive knowledge on this topic.

This paper aims to examine the relationship between FDI and economic growth in Malta over the years 1971–2017. Unit root and stationarity tests reveal that FDI is stationary, while real GDP, trade, and manufacturing are integrated of order 1. The results of causality tests show that the neutrality hypothesis holds, given the fact that any statistically significant causal relation emerges between FDI and economic growth. Moreover, robustness checks used a new Artificial Neural Networks (ANNs) algorithm. This procedure is able to predict the change in FDI concerning Maltese economic growth. Relevant policy suggestions emerged with empirical results.

Network Function Virtualization (NFV) is the key technology that allows modern network operators to provide flexible and efficient services, by leveraging on general-purpose private cloud infrastructures. In this work, we investigate the performance of a number of metric forecasting techniques based on machine learning and artificial intelligence, and provide insights on how they can support the decisions of NFV operation teams. Our analysis focuses on both infrastructure-level and service-level metrics. The former can be fetched directly from the monitoring system of an NFV infrastructure, whereas the latter are typically provided by the monitoring components of the individual virtualized network functions. Our selected forecasting techniques are experimentally evaluated using real-life data, exported from a production environment deployed within some Vodafone NFV data centers. The results show what the compared techniques can achieve in terms of the forecasting accuracy and computational cost required to train them on production data.

Prevedere la probabilità di eventi futuri è un problema comune. L'approccio più utilizzato assume una struttura fissa per questa probabilità, detta modello, dipendente da variabili latenti dette parametri. Dopo aver osservato dei dati è possibile inferire una distribuzione per queste variabili non osservabili. Il procedimento di inferenza non è sempre immediato, siccome selezionare un singolo valore per i parametri potrebbe portare a scarsi risultati, mentre approssimare una distribuzione usando MCMC potrebbe essere complicato. L'inferenza variazionale (VI) sta ricevendo una crescente attenzione come alternativa per approssimare la distribuzione a posteriori tramite un problema di ottimo. Tuttavia, VI spesso impone una struttura parametrica alla distribuzione proposta. Il primo contributo della tesi, detto Hierarchical Variational Inference (HVI), è una metodologia che utilizza reti neurali per creare un'approssimazione semi-parametrica della distribuzione a posteriori. HVI richiede gli stessi requisiti minimi di un Metropolis-Hastings o di un Hamiltonian MCMC, per essere applicata. Il secondo contributo è un pacchetto Python per l'inferenza variazionale su serie storiche usando modelli media-covarianza. Questo utilizza HVI e tecniche di VI standard combinate con reti neurali. I risultati sperimentali, su dati econometrici e finanziari, mostrano un consistente miglioramento della previsione usando VI, rispetto a stime puntuali dei parametri, in particolare producendo stimatori con minor variabilità. Probabilistic forecasting is a common task. The usual approach assumes a fixed structure for the outcome distribution, often called model, that depends on unseen quantities called parameters. It uses data to infer a reasonable distribution over these latent values. The inference step is not always straightforward, because single-value can lead to poor performances and overfitting while handling a proper distribution with MCMC can be challenging. Variational Inference (VI) is emerging as a viable optimisation based alternative that models the target posterior with instrumental variables called variational parameters. However, VI usually imposes a parametric structure on the proposed posterior. The thesis's first contribution is Hierarchical Variational Inference (HVI) a methodology that uses Neural Networks to create semi-parametric posterior approximations with the same minimum requirements as Metropolis-Hastings or Hamiltonian MCMC. The second contribution is a Python package to conduct VI on time-series models for mean-covariance estimate, using HVI and standard VI techniques combined with Neural Networks. Results on econometric and financial data show a consistent improvement using VI, compared to point estimate, obtaining lower variance forecasting.

Existing studies reveal opposing results regarding the economic growth and infrastructure nexus, which emanates from the differences in scale, timing and stage of development. In this paper, we explore the relationship between railway networks and real GDP controlling for energy consumption, over the period 1861–1970 in Italy. The empirical strategy uses both AutoRegressive Distributed Lags (ARDL) model and the Wavelet Analysis (WA), which is able to adopt to scale and time support, thereby enabling one to escape the Heisenberg's curse. Our applied findings show that the two series are generally positive correlated (being in phase), but also that railway networks cause real value added in the long-run. Thus, through an innovative approach, we can confirm previous results in literature: in fact, railway networks represent a determinant of economic growth in the Italian case., https://www.sciencedirect.com/science/article/pii/S0739885921000986?dgcid=author JEL classification: C22; L92; N13

Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into three major areas related to the interpretation of human motion: tracking of human motion using virtual pan-tilt-zoom (vPTZ) camera, recognition of human motions and human behaviors segmentation. In the field of human motion tracking, a virtual environment for PTZ cameras (vPTZ) is presented to overcame the mechanical limitations of PTZ cameras. The vPTZ is built on equirectangular images acquired by 360° cameras and it allows not only the development of pedestrian tracking algorithms but also the comparison of their performances. On the basis of this virtual environment, three novel pedestrian tracking algorithms for 360° cameras were developed, two of which adopt a tracking-by-detection approach while the last adopts a Bayesian approach. The action recognition problem is addressed by an algorithm that represents actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. The proposed method learns a codebook of frequent sequential patterns by means of an apriori-like algorithm. An action is then represented with a Bag-of-Frequent-Sequential-Patterns approach. In the last part of this thesis a methodology to semi-automatically annotate behavioral data given a small set of manually annotated data is presented. The resulting methodology is not only effective in the semi-automated annotation task but can also be used in presence of abnormal behaviors, as demonstrated empirically by testing the system on data collected from children affected by neuro-developmental disorders.

Coccolithophore export fluxes were investigated via the analysis of sinking matter, obtained from Eastern Mediterranean time-series sediment traps moored in three open sites of the north-eastern Mediterranean Sea located in the Athos Basin of North Aegean (M2 site), Cretan Sea of South Aegean (M3 site) and at Ionian Sea (Nestor site). The aim of our study was to determine the spatial, temporal and seasonal variability in coccolithophore fluxes, as well as to estimate coccolith biogenic carbonate contribution to the sedimentation process. Data from an additional time-series sediment trap located in the southwestern Black Sea were also considered for the comparison of the oligotrophic Eastern Mediterranean setting with the eutrophic Black Sea. Coccolithophore fluxes revealed a highly seasonal pattern during February–March in the North Aegean (peak in late February 2015: 85.6 x 105 coccospheres m−2 day−1; 27.9 x 108 coccoliths m−2 day−1), during March–May in the Cretan Sea (peak in late March 2015: 33.7 x 105 coccospheres m−2 day−1; 19.5 x 108 coccoliths m−2 day−1) and during February–March and May–June in the Ionian Sea (peak in late May 2012: 14.3 x 105 coccospheres m−2 day−1; 1.53 x 108 coccoliths m−2 day−1). The recorded maxima coincide with low sea surface temperatures, increased precipitation and high PIC fluxes. Coccosphere fluxes were dominated by Emiliania huxleyi comprising ~70% of the total abundance, in the North Aegean and ~50% in the Cretan and Ionian Seas. Syracosphaera pulchra was also prominent in the study sites, where its abundance reached 14% in the North Aegean and ~10% in the Cretan and Ionian Seas respectively. Florisphaera profunda represented one of the major taxa in the coccolith fluxes of all three Eastern Mediterranean sites (~25% in North Aegean, ~20% in Cretan and Ionian Seas), while Algirosphaera robusta and Umbilicosphaera sibogae were the most abundant among the minor taxa. The North Aegean Sea exhibited a considerably higher coccolith flux when compared to other sediment traps due to the prominent seasonal peak of E. huxleyi during winter (February–March) (>95% of the total abundance). In contrast to the Eastern Mediterranean sediment traps, the time-series data from the Black Sea showed presence of monospecific E. huxleyi assemblage increasing its abundance during late September–November (max 320 x 105 coccospheres m−2 day−1; at least 7.79 x 108 coccoliths m−2 day−1, coccolith flux derived only from coccospheres converted to coccoliths). In the Eastern Mediterranean, biogenic carbonate fluxes followed the general pattern of the total mass flux in all investigated areas, with the Black Sea coccolithophore CaCO3 flux being the lowest due to low the E. huxleyi coccolith mass. Overall, in the North Aegean Sea, coccolithophore fluxes are strongly dependent on surface waters nutrients enrichment due to winter vertical water column mixing, riverine inputs and Black Sea water inflows, while the fertilization and/or formation of fast-sinking aggregates due to episodic dust input event are affecting the coccolithophore fluxes in the Cretan and Ionian Seas. The intercomparison of the coccolith export fluxes in the studied NE-SW mooring transects implies a north-south and east-west decreasing pattern, depending on the variable oceanographic regimes and the associated environmental factors controlling the investigated areas.

This paper presents the first empirical test of coherence (i.e., consistency of policies within a framework), causality (i.e., logical priority of objectives over policies), and effectiveness (i.e., ability of policies to tackle challenges as defined by objectives) of the overall EU environmental policy system. To do so, I applied complementary statistical (cross-sectional and time series) and econometric (dynamic tri-probit) analyses to an original panel dataset, based on addressed issues (i.e., pollution vs. resource, trans-boundary vs. domestic; flow vs. stock) rather than on implemented policies (i.e., EU legislation aiming at optimal pollution production, optimal resource use, and market competition). It would be impossible to perform similar analyses by referring to realized policies rather than tackled issues due to the unbalanced sample characterizing the former with respect to the latter, as well as due to overlapping objectives, interrelated policies, and non-univocal relationships between objectives and policies. In contrast with previous studies of single EU environmental policies, characteristics of the EU environmental policy, or EU environmental objectives, I found that the overall EU environmental policy system is internally coherent, dynamically causal, and ex ante effective. Moreover, the evidence suggests that many issues are correlated: trans-boundary issues became more relevant in 2012, pollution production was more significant than resource use, and flow issues were more important than stock issues from 1995 to 2010. Finally, I show that few objectives overlapped: a “safe environment” objective (1987–1997) was preferred to a “greenhouse gas (GHG) reduction” objective (2003–2012, but pursued with a 2-year lag), although the latter has recently become preferred to the former. In addition, a “GHG reduction” objective was preferred to “a sustainable development” objective (1998–2002).

This thesis present a detailed study of the composition of high temperature fluid from the Lucky Strike hydrothermal field (37°N, Mid Ocean Ridge) collected during three sampling campaigns within the framework of the deep sea observatory EMSO-Azores. The hydrothermal field has developped around a fossil lava lake framed by three ancient volcanic cones. In 2013, the discovery of a new active site to the East of the system, and presenting an unprecedented fluid composition at Lucky Strike (low Cl concentration and high Fe and Mn concentration), lead to a new model of hydrothermal circulation based on chemical geothermobarometer (Si; Si-Cl) and geothermometer (Fe-Mn) applied to 13 venting sites. We defined 5 groups of sites based on their chlorinity and location around the lava lake. It appears that vapor-dominated Capelinhos fluids were extracted relatively fast from the phase separation zone (estimated at ~2600mbsf). Nevertheless, fluids in the vicinity of the lava lake, both vapor and brine dominated, display P and T conditions of equilibration lower than for Capelinhos fluids. This highlights on-going equilibration process through conductive cooling and/or brine entrainment in the upflow zone up to the layer 2A of the oceanic crust. Chlorinity variations highlight the varying residence time in the upflow of the fluids between vents which depends on physical characteristics of the crust. We studied the temporal variability of fluid composition collected between 2009 and 2015. Two time scales have been evidenced. The first is the sampling scale, i.e. ~1h, and corresponds to subsurface processes indicating that a hydrothermal fluid, conductively cooled (T

Free Space Optics (FSO) links are affected by several impairments: optical turbulence, scattering, absorption, and pointing. In particular, atmospheric optical turbulence generates optical power fluctuations at the receiver that can degrade communications with fading events, especially in high data rate links. Innovative solutions require an improvement of FSO link performances, together with testing models and appropriate channel codes. In this paper, we describe a high-resolution time-correlated channel model able to predict random temporal fluctuations of optical signal irradiance caused by optical turbulence. Concerning the same channel, we also report simulation results on the error mitigation performance of Luby Transform, Raptor, and RaptorQ codes.

In this paper,we calculated the cost of economic fluctuations in Brazil. Using quarterly data from real per capita consumption between the beginning of Plano Real and the third quarter of 2013,we computed the cost of economic fluctuations enabling different parametric forms for the stochastic process of the consumption series. We estimated a cost of economic fluctuations in Brazil in approximately 6,48% of the per capita consumption using the most suitable specification for consumption stochastic process Neste artigo, calculamos o custo das flutuações econômicas no Brasil. Utilizamos dados trimestrais do consumo real entre o início do Plano Real e o terceiro trimestre de 2013 e computamos o custo das flutuações permitindo diferentes formas paramétricas para o processo estocástico da série. EstimamosqueocustodasflutuaçõeseconômicasnoBrasiléaproximadamente6,48%do consumo per capita utilizando a melhor das especificações para o processo estocástico do consumo

FSO is an optical wireless line-of-sight communication system able to offer good broadband performance, electromagnetic interference immunity, high security, license-free operation, low power consumption, ease of relocation, and straightforward installation. It represents a modern technology, significantly functional when it is impossible, expensive or complex to use physical connections or radio links. Unfortunately, since the transmission medium in a terrestrial FSO link is the air, these communications are strongly dependent on various atmospheric phenomena (e.g., rain, snow, optical turbulence and, especially, fog) that can cause losses and fading. Therefore, in worst-case conditions, it could be necessary to increase the optical transmission power, although, at the same time, it is needed to comply to safety regulations. The effects of the already mentioned impairments are: scattering (i.e., Rayleigh and Mie) losses, absorption and scintillation. The first two can be described by proper attenuation coefficients and increase if the atmospheric conditions get worst. As regards scintillation, it is a random phenomenon, appreciable even under clear sky. Because of scintillation, in FSO links, the irradiance fluctuates and could drop below a threshold under which the receiver is not able to detect the useful signal. In this case, communications suffer from erasure errors, which cause link outages. This phenomenon becomes relevant at high distance, but it can also be observed in 500m-long FSO links. Moreover, the optical turbulence intensity can change of an order of magnitude during the day: it reaches its maximum around midday (when the temperature is the highest) and, conversely, it is lower during the night. In order to reduce or eliminate these impairments, different methods (both hardware and software) were studied and reported in literature. Hardware solutions focus on aperture averaging effects to reduce irradiance fluctuations, in particular by using a bigger detector or multi-detector systems. On the other hand, software techniques mostly focus on transmission codes. Rateless codes are an innovative solution, suitable for channels affected by erasure or burst errors. They add a redundant coding (also settable on the fly) to the source data, allowing the receiver to successfully recover the whole payload that, otherwise, would be corrupted or partially lost. To test rateless codes, recovery capabilities in FSO channels, detailed information about the occurring signal fading are needed: in particular, its depth, temporal duration and statistics. For this reason, I have implemented a time-correlated channel model able to generate an irradiance time-series at the receiver side, at wide range of turbulence conditions (from weak to strong). The time-series represents a prediction of temporal irradiance fluctuations caused by scintillation. In this way, I was able to test the recovery capabilities of several types of rateless codes. I have performed measurement campaigns in order to characterize Free Space Optics links affected by the optical turbulence. In particular, I have used three different setups placed in the Laboratory of Optics of the University of Palermo and in the Optical Communication Laboratory of the Northumbria University. Thanks to an in-depth post-processing of the collected data, I was able to extract useful information about the FSO link quality and the turbulence strength, thus proving the effectiveness of the Gamma-Gamma model under several turbulence conditions. In Chapter 1, I will introduce the theory of optical wireless communications and, in particular, of Free Space Optics communications. In detail, I will describe the advantages and the impairments that characterize this kind of communication and discuss about its applications. In Chapter 2, the adopted channel models are presented. In particular, these models are able to predict irradiance fluctuations at the detector in Free Space Optics links and were designed for terrestrial and space-to-ground communications at different link specifications, turbulence conditions and temporal covariance. Firstly, a brief description of the employed irradiance distribution and of the irradiance covariance functions is presented. The details of the above mentioned channel model implementation and the performance are then described. Finally, in order to detail the channel model features, several examples of irradiance fluctuation predictions are depicted. In Chapter 3, the details of a measurement campaign, focused on the analysis of optical turbulence effects in a FSO link, will be treated. Three different measurement setups composed of different typologies of laser sources, detectors and turbulent channels will be described. Data post-processing will be discussed. Moreover, a performance evaluation of the terrestrial channel model described in Chapter 2 will be discussed. In Chapter 4, rateless codes will be presented. These codes introduce a redundancy by means of repair symbols, associated to the source data, and, in case of losses, they are able to recover the source data without any need for retransmission. They can also manage large amounts of data and offer very interesting features for erasure channels and multicast/broadcast applications. Three different classes of rateless codes will be described and, in particular: Luby Transform, Raptor and RaptorQ codes. Moreover, the performance of the rateless codes in Free Space Optics links will be investigated. The implemented simulators are based on the channel models presented in Chapter 2 and focus on the study of rateless codes recovery capabilities when erasure errors due to fadings occur. The results on the performance of three rateless codes typologies, in two different FSO links, will be illustrated. All the research work was supported by the European Space Agency (grant no. 5401001020). Experimental activities were performed in collaboration with the Optical Communications Research Group of the Northumbria University and within the COST IC1101 European Action.

In this work, we propose an indoor experimental set-up able to generate several optical turbulence conditions in a Free Space Optics link. Using this set-up, we prove the effectiveness of an irradiance time-series generator based on the Gamma-Gamma model and able to predict the irradiance fluctuations at the receiver, under both weak and moderate turbulence conditions.

In this manuscript, a Geostationary satellite-toground Free Space Optics (FSO) downlink channel model has been implemented, which is able to predict temporal irradiance fluctuations caused by scintillation at a wide range of turbulence conditions and for different values of the zenith angle. In order to mitigate fading events that occur in FSO communications, we have also tested the performance of three different families of Rateless Codes (Luby Transform, Raptor and RaptorQ) into our model and found that RaptorQ is the best candidate to mitigate errors in FSO links.

In the field of traffic simulation, the calibration of uncertain inputs against real data is usually taken to cover both the epistemic uncertainty regarding the un-modeled details of the phenomena and the aleatory not predicted by the models. For this reason, model parameters are usually indirectly estimated within an optimization framework which tries to maximize the fit between real and simulated measures of the traffic system. This is the case, for example, of the calibration of car-following models’ parameters against vehicle trajectory data. Only recently, it has been proven that the capability of the optimization framework to provide the parameters’ values that allow the car-following model reproducing real trajectories at its best is strictly connected to the setting of the optimization framework itself. This, in particular, entails the necessity to carefully choose an appropriate combination of optimization algorithm and measure of goodness of fit (GOF). In this study, the authors focus attention on this latter issue. Specifically, it is claimed here that the commonly used GOFs are not able to capture the dynamics of the time-series which calibration is performed against. Therefore, a spectral analysis based approach to evaluate the overall performance of the simulation model in the objective function is proposed. The new measure of goodness of fit is tested in the calibration of the Intelligent Driver Model against synthetic and real trajectory data. Results with synthetic data, in particular, confirm that such a new optimization setting is always able to find the global optimum of the problem., JRC.F.8-Sustainable Transport

We review current knowledge and understanding of the biology and ecology of the calanoid copepod Calanus helgolandicus in European waters, as well as provide a collaborative synthesis of data from 18 laboratories and 26 sampling stations in areas distributed from the northern North Sea to the Aegean and Levantine Seas. This network of zooplankton time-series stations has enabled us to collect and synthesise seasonal and multi-annual data on abundance, body size, fecundity, hatching success and vertical distribution of C. helgolandicus. An aim was to enable comparison with its congener Calanus finmarchicus, which has been studied intensively as a key component of European and north east Atlantic marine ecosystems. C. finmarchicus is known to over-winter at depth, whereas the life-cycle of C. helgolandicus is less well understood. Overwintering populations of C. helgolandicus have been observed off the Atlantic coast between 400 and 800 m, while in the Mediterranean there is evidence of significant deep-water populations at depths as great as 4200 m. The biogeographical distribution of C. helgolandicus in European coastal waters covers a wide range of habitats, from open ocean to coastal environments, and its contribution to mesozooplankton biomass ranges from 6% to 93%. Highest abundances were recorded in the Adriatic and off the west coast of Spain. C. helgolandicus is generally found in 9–20 C water, with maximum abundances from 13–17 C. In contrast, C. finmarchicus is found in cooler water between 0 and 15 C, with peak abundances from 0 to 9 C. As water has warmed in the North Atlantic over recent decades, the range of C. helgolandicus and its abundance on the fringes of its expanding range have increased. This review will facilitate development of population models of C. helgolandicus. This will not only help answer remaining questions but will improve our ability to forecast future changes, in response to a warming climate, in the abundance and distribution of this important species. Versión del editor

The aim of this study is to investigate and quantify changes in the rainfall regime of the metropolitan area of Palermo characterised by increasingly strong urbanisation. The rainfall data, considered in this study, were collected on a yearly basis from eight rain gauges within and outside the metropolitan area of Palermo, Sicily, Italy. A preliminary analysis made on the annual total rainfall depths showed a global reduction of total annual rainfall, with two different trends: more regular for the series observed in the rain gauges within the urbanised area and more variable for the series observed in the rain gauges outside the area. A further analysis has been performed using the series of maximum intensity for fixed duration (1, 3, 6, 12, 24 hrs) and annual daily maxima. The analysis of the trend in the extreme rainfall series has been performed by estimating the maximum rainfall depth corresponding to a fixed return period using the EV1 distribution with parameters estimated using L-moments. The analysis of all series indicates a global reduction of rainfall intensities, both for internal and external series, in disagreement with the results obtained by other authors.

This paper presents a general methodology for processing bioclimatic data in the temporal domain. Two different methods are used to assess the presence of temporal trends in the time-series of bioclimatic indices at each mea- surement station. A preliminary stage checks for the statistical homogeneity in the data set and for the presence of serial autocorrelation in the data, applying the proper methods to remove these effects. The methodology has been applied to a case study in Apulia, Italy, using the popular De Martonne index as a bioclimatic indicator.