Contribution to "Deliverable 4: Activity 6: Pilot Demonstrations: Pilot testing, 2nd milling campaign"

Report for Deliverable 4: Activity 6 of MEDOLICO Project - Mediterranean Cooperation in the Treatment and Valorisation of Olive Mill Wastewater, EU Programme ENPI-CBCMED I-B/2.1/090

Contribution to "Deliverable 5: Activity 7: Feasibility and evaluation of OMW by-products recovery"

Report for Deliverable 5: Activity 7 of MEDOLICO Project - Mediterranean Cooperation in the Treatment and Valorisation of Olive Mill Wastewater, EU Programme ENPI-CBCMED I-B/2.1/090

Contribution to "Deliverable 7: Activity 2. Project Dissemination and Visibility"

Report for Deliverable 7: Activity 2 of MEDOLICO Project - Mediterranean Cooperation in the Treatment and Valorisation of Olive Mill Wastewater, EU Programme ENPI-CBCMED I-B/2.1/090 

Parental contribution to progeny during experimental spawning of jungle perch, Kuhlia rupestris

When releasing captive-bred animals into wild populations, it is essential to maintain the capacity for adaptation and resilience by minimising the effect on population genetic diversity. Populations of the jungle perch (Kuhlia rupestris) have become reduced or locally extinct along the Queensland coast; thus, captive breeding of K. rupestris for restocking is presently underway. Currently, multiple individuals are placed in a tank to produce larvae, yet the number of adults contributing to larval production is unknown. We performed a power analysis on pre-existing microsatellite loci to determine the minimum number of loci and larvae required to achieve accurate assignment of parentage. These loci were then used to determine the number of contributing participants during a series of four spawning events through the summer breeding season in 2012-2013. Not all fish contributed to larval production and no relationship was found between male body size and parentage success. In most cases, there was a high skew of offspring to one mating pair (62% was the average contribution of the most successful pair per tank). This has significant implications for the aquaculture, restocking and conservation of K. rupestris.

English for tourism : contribution to language use through mobile learning

Tourism is growing and is becoming more competitive. Destinations need to find elements which demonstrate their uniqueness, the singularity which allows them to differentiate themselves from others. This struggle for uniqueness makes economies become more competitive and competition is a central element in the dynamics of Tourism. Technology is also an added value for tourism competitiveness, as it allows destinations to become internationalised and known worldwide. In this scenario, research has increased as a means to study Tourism trends in fields such as sociology and marketing. Nevertheless, there are areas in which there is not much research done and which are fundamental: these are the areas concerned with identities, communication and interpersonal relations. In this regard, Linguistics has a major role for different reasons: firstly, it studies language itself and through it, communication, secondly, language conveys culture and, thirdly, it is by enriching language users that innovation in Tourism and in knowledge, as a whole, is made possible. This innovation, on the other hand, has repercussions in areas such as management, internationalisation and marketing as well. It is, therefore, the objective of this thesis to report on how learning experiences take place in Tourism undergraduate English language classes as well as to give an account of enhanced results in classes where mobile learning was adopted. In this way, an alliance between practice and research was established. This is beneficial for the teaching and learning process because by establishing links between research based insight and practice, the outcome is grounded knowledge which helps make solid educational decisions. This research, therefore, allows to better understand if learners accept working with mobile technologies in their learning process. Before introducing any teaching and learning approach, it was necessary to be informed, as well, of how English for tourism programmes are organised. This thesis also illustrates through the premises of Systemic Functional Linguistics that language use can be enhanced by using mobile technology in Tourism undergraduate language classes.

Phytoplankton versus macrophyte contribution to primary production and biogeochemical cycles of a coastal mesotidal system. A modelling approach

This study presents an assessment of the contributions of various primary producers to the global annual production and N/P cycles of a coastal system, namely the Arcachon Bay, by means of a numerical model. This 3D model fully couples hydrodynamic with ecological processes and simulates nitrogen, silicon and phosphorus cycles as well as phytoplankton, macroalgae and seagrasses. Total annual production rates for the different components were calculated for different years (2005, 2007 and 2009) during a time period of drastic reduction in seagrass beds since 2005. The total demand of nitrogen and phosphorus was also calculated and discussed with regards to the riverine inputs. Moreover, this study presents the first estimation of particulate organic carbon export to the adjacent open ocean. The calculated annual net production for the Arcachon Bay (except microphytobenthos, not included in the model) ranges between 22,850 and 35,300 tons of carbon. The main producers are seagrasses in all the years considered with a contribution ranging from 56% to 81% of global production. According to our model, the -30% reduction in seagrass bed surface between 2005 and 2007, led to an approximate 55% reduction in seagrass production, while during the same period of time, macroalgae and phytoplankton enhanced their productions by about +83% and +46% respectively. Nonetheless, the phytoplankton production remains about eightfold higher than the macroalgae production. Our results also highlight the importance of remineralisation inside the Bay, since riverine inputs only fulfill at maximum 73% nitrogen and 13% phosphorus demands during the years 2005, 2007 and 2009. Calculated advection allowed a rough estimate of the organic matter export: about 10% of the total production in the bay was exported, originating mainly from the seagrass compartment, since most of the labile organic matter was remineralised inside the bay.

INVERTEBRATE CONTRIBUTION TO LEAF BREAKDOWN IN PYNN’S BROOK, NL.

Leaf bags of fine and coarse mesh were placed at two locations, one with an open tree canopy, the other with a closed tree canopy, in Pynn’s Brook on June 30th 2015. Bags were collected after 2, 30, 37 and 44 days. After collection, invertebrates were counted and leaf material remaining was determined to measure leaf breakdown rate. There was no significant difference in leaf mass remaining (R) between the two sites. Comparisons between mesh types found a difference in leaf breakdown at two collection days. The difference at 2 days was small (2.7%) and may not be biologically meaningful. At 37 days, the difference was larger (8.41%) and may be related to a larger proportion of shredder taxa, seen in coarse mesh bags, or higher absolute numbers of invertebrates. The invertebrate community was dominated by Diptera spp. across all collection days and mesh types, but after 37 days, communities in coarse mesh bags had a higher proportion of shredder orders than did fine mesh bags.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

Influence of the metal precursor on the catalytic behavior of Pt/Ceria catalysts in the preferential oxidation of CO in the presence of H2 (PROX)

The effect of the metal precursor (presence or absence of chlorine) on the preferential oxidation of CO in the presence of H2 over Pt/CeO2 catalysts has been studied. The catalysts are prepared using (Pt(NH3)4)(NO3)2 and H2PtCl6, as precursors, in order to ascertain the effect of the chlorine species on the chemical properties of the support and on the catalytic behavior of these systems in the PROX reaction. The results show that chloride species exert an important effect on the redox properties of the oxide support due to surface chlorination. Consequently, the chlorinated catalyst exhibits a poorer catalytic activity at low temperatures compared with the chlorine-free catalyst, and this is accompanied by a higher selectivity to CO2 even at high reaction temperatures. It is proposed that the CO oxidation mechanism follows different pathways on each catalyst.

The influence of promoters (Zr, La, Tb, Pr) on the catalytic performance of CuO-CeO2 systems for the preferential oxidation of CO in the presence of CO2 and H2O

CuO supported on CeO2 and Ce0.9X0.1O2, where X is Zr, La, Tb or Pr, were synthesized using nitrate precursors, giving rise ceria based materials with a small particle size which interact with CuO species generating a high amount of interfacial sites. The incorporation of cations to the ceria framework modifies the CeO2 lattice parameter, improving the redox behavior of the catalytic system. The catalysts were characterized by X-ray fluorescence spectrometry (XRFS), X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), Raman spectroscopy, thermoprogrammed reduction with H2 (H2-TPR) and X-ray photoelectron spectroscopy (XPS). The catalysts were tested in the preferential oxidation of CO under a H2-rich stream (CO-PROX), reaching conversion values higher than 95% between 115 and 140 °C and being the catalyst with 6 wt.% of Cu supported on Ce0.9Zr0.1O2 (sample 6CUZRCE) the most active catalyst. The influence of the presence of CO2 and H2O was also studied simulating a PROX unit, taking place a decrease of the catalytic activity due to the inhibitor effect both CO2 and H2O.

Boreal forest fire impacts on lower troposphere carbon monoxide and ozone levels at the regional to hemispheric scales

Tropospheric ozone (O3) and carbon monoxide (CO) pollution in the Northern Hemisphere is commonly thought to be of anthropogenic origin. While this is true in most cases, copious quantities of pollutants are emitted by fires in boreal regions, and the impact of these fires on CO has been shown to significantly exceed the impact of urban and industrial sources during large fire years. The impact of boreal fires on ozone is still poorly quantified, and large uncertainties exist in the estimates of the fire-released nitrogen oxides (NO x ), a critical factor in ozone production. As boreal fire activity is predicted to increase in the future due to its strong dependence on weather conditions, it is necessary to understand how these fires affect atmospheric composition. To determine the scale of boreal fire impacts on ozone and its precursors, this work combined statistical analysis of ground-based measurements downwind of fires, satellite data analysis, transport modeling and the results of chemical model simulations. The first part of this work focused on determining boreal fire impact on ozone levels downwind of fires, using analysis of observations in several-days-old fire plumes intercepted at the Pico Mountain station (Azores). The results of this study revealed that fires significantly increase midlatitude summertime ozone background during high fire years, implying that predicted future increases in boreal wildfires may affect ozone levels over large regions in the Northern Hemisphere. To improve current estimates of NOx emissions from boreal fires, we further analyzed ΔNOy /ΔCO enhancement ratios in the observed fire plumes together with transport modeling of fire emission estimates. The results of this analysis revealed the presence of a considerable seasonal trend in the fire NOx /CO emission ratio due to the late-summer changes in burning properties. This finding implies that the constant NOx /CO emission ratio currently used in atmospheric modeling is unrealistic, and is likely to introduce a significant bias in the estimated ozone production. Finally, satellite observations were used to determine the impact of fires on atmospheric burdens of nitrogen dioxide (NO2 ) and formaldehyde (HCHO) in the North American boreal region. This analysis demonstrated that fires dominated the HCHO burden over the fires and in plumes up to two days old. This finding provides insights into the magnitude of secondary HCHO production and further enhances scientific understanding of the atmospheric impacts of boreal fires.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) - Part 1: Model description and evaluation

We introduce the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS). CATT-BRAMS is an on-line transport model fully consistent with the simulated atmospheric dynamics. Emission sources from biomass burning and urban-industrial-vehicular activities for trace gases and from biomass burning aerosol particles are obtained from several published datasets and remote sensing information. The tracer and aerosol mass concentration prognostics include the effects of sub-grid scale turbulence in the planetary boundary layer, convective transport by shallow and deep moist convection, wet and dry deposition, and plume rise associated with vegetation fires in addition to the grid scale transport. The radiation parameterization takes into account the interaction between the simulated biomass burning aerosol particles and short and long wave radiation. The atmospheric model BRAMS is based on the Regional Atmospheric Modeling System (RAMS), with several improvements associated with cumulus convection representation, soil moisture initialization and surface scheme tuned for the tropics, among others. In this paper the CATT-BRAMS model is used to simulate carbon monoxide and particulate material (PM(2.5)) surface fluxes and atmospheric transport during the 2002 LBA field campaigns, conducted during the transition from the dry to wet season in the southwest Amazon Basin. Model evaluation is addressed with comparisons between model results and near surface, radiosondes and airborne measurements performed during the field campaign, as well as remote sensing derived products. We show the matching of emissions strengths to observed carbon monoxide in the LBA campaign. A relatively good comparison to the MOPITT data, in spite of the fact that MOPITT a priori assumptions imply several difficulties, is also obtained.