No description available.

[pt] O estudo dos eventos naturais e dos gerados pela atividade humana no mar tem tido uma grande prioridade para o setor de petróleo, isso devido à possibilidade de ter um evento perigoso para o ambiente marinho ou a área de produção. Nesse contexto, o objetivo deste trabalho é a avaliação de abordagens baseadas em aprendizado profundo para a classificação de eventos no mar usando imagens de radar de abertura sintética na área de óleo e gás. Métodos baseados em aprendizado profundo têm mostrado um ótimo desempenho através do uso de camadas convolucionais, onde as características são extraídas automaticamente através da definição de um kernel e stride. As seguintes arquiteturas são avaliadas neste trabalho: Inception V3, Xception, Inception ResNet V2, MobileNet, VGG16 e Deep Attention sampling. A avaliação é feita em uma metodologia de classificação de eventos no mar usando duas bases de dados de imagens de radar: a primeira contém 10 eventos comumente presentes no oceano ártico, e a segunda descreve um derramamento de óleo presente na costa da Louisiana. Nos experimentos realizados se obteve os melhores resultados com as arquiteturas Deep Attention sampling as quais atingiram valores de f1-score e Recall de até 0.82 por cento e 0.87 por cento respectivamente, para a classe de interesse no conjunto de dados de derramamento de óleo. Para o conjuntode dados de eventos naturais no mar, um alto desempenho foi evidenciado para arquiteturas baseadas no uso de módulos de Inception, tendo pontuações mais altas de F1-score e Recall para a arquitetura Xception. Além disso, foi observado uma melhoria de até 10 por cento e 13 por cento nas métricas f1-score e Recall no uso da atenção, em relação à sua arquitetura base (VGG16), e 4 por cento respeito a outras arquiteturas baseadas em módulos Inception, isto para o conjunto de dados de eventos no mar, demonstrando as vantagens de usar amostragem com atenção. / [en] The study of natural events and those generated by human activity at sea has been a high priority for the Oil and Gas industry, due to the possibility of a dangerous event for the marine environment or the production area. In this context, the objective of this work is the evaluation of approaches based on deep learning for the classification of events in the sea using synthetic aperture radar images in the oil and gas area. Methods based on deep learning have shown an excellent performance through the use of convolutional layers, where the characteristics are extracted automatically through the definition of a kernel and stride. The following architectures are evaluated in this work: Inception V3, Xception, Inception ResNet V2, MobileNet, VGG16, and Deep Attention sampling. The assessment is made using a methodology for classifying events at sea using two radar image databases: the first contains 10 events commonly present in the Arctic Ocean, and the second describes an oil spill present near the Louisiana coast. In the experiments performed, the best results were obtained with the Deep Attention sampling architectures, which reached f1- score and Recall values of up to 0.82 a per cent nd 0.87 per cent respectively, for the class of interest in the oil spill dataset. For the dataset of natural events in the sea, high performance was evidenced for architectures based on the non-use of Inception modules, having higher values of F1-score and Recall for an Xception architecture. Also, an improvement of up to 10 per cent and 13 per cent in the metrics f1- score and recall in the use of attention was observed, concerning its base architecture (VGG16), and 4 per cent with other architectures based on Inception modules, this for the dataset of events at sea, demonstrating the advantages of using Attention Sampling carefully.

I dagsläget överproduceras och överkonsumeras mat, samtidigt slängs 30% av all mat som produceras i världen. Matproduktionen står för en stor del av världens miljöbelastning; främst genom animaliska produkter såsom kött och mejeriprodukter, och därför har ämnet blivit mycket omtalat. I samband med denna debatt har mjölksubstitut introducerats på marknaden. Målet med arbetet var att undersöka hur matbutiker i Stockholm arbetar med svinn och försäljning av mjölk och mjölksubstitut. En jämförelse gjordes mellan Arla Mellanmjölk, Alpro Soya och Alpro Mandeldryck. Datainsamlingsmetoden gjordes dels i kvantitativt format där siffror över försäljning och svinn samlades in, men även i kvalitativt format, i form av intervjuer med sex utvalda matbutiker. Det visade sig att svinnet för Arla Mellanmjölk, Alpro Soya och Alpro Mandeldryck var minimalt med anledning att det alltid finns efterfrågan på dessa produkter och att matbutikerna aktivt arbetar för att hålla svinnet lågt. Detta görs genom att ha ett välkalibrerat automatiskt beställningssystem som tar hand om majoriteten av butikens beställningar in, ha bra kylar i butiken, samt att regelbundet inventera butiken. De produkter som undersöktes ansågs inte behöva exponeras på något speciellt vis då de är basvaror som i princip säljer sig själva. Det är snarare ett problem att mjölksubstituten tar slut från centrallagret emellanåt när efterfrågan blir för stor. Matbutikernas primära mål är att driva ett lönsamt företag och ansträngningarna som görs sker på grund av ekonomiska fördelar. Likväl, även om minskat svinn görs med anledning att inte gå i förlust ekonomiskt blir utfallet detsamma och till fördel för miljön. Konsumenterna verkar tillsynes vara de som har mest makt i att förändra hur matbutikernas verksamhet bedrivs. Genom att välja mjölksubstitut framför mjölk kommer följaktligen miljöbelastningen från mjölkproduktionen att minska. / The food production and consumption today are excessive, while 30% of all produced food in the world goes to waste. Food production has a significant effect on the environment; primarily through animalistic products such as meat and dairy food, it is, therefore, a much-disputed subject. Milk alternatives have, as a reaction to this, been introduced on the market. This project aimed to examine how grocery stores in Stockholm handle food waste and sales of milk and milk alternatives. A comparison was made between Arla semi-skimmed milk, Alpro Soya and Alpro Almond. The data collection methods were both quantitively, as numbers of sales and waste were collected, but also qualitative, with interviews with six selected grocery stores. The food waste for Arla semi-skimmed milk, Alpro Soya and Alpro Almond was minimal as a result of constant demand for these products, and also the grocery stores active work to prevent food waste. This is accomplished through a well calibrated automatic ordering system that controls the majority of the store's orders. Additionally, new refrigerators and continuous inventory are also methods used to minimize the food waste. The grocery stores felt no reason to expose the selected products in any particular way since they are already well-known and gets sold without any effort. It is rather an issue that the demand for the milk substitutes is too high and the central warehouse has no supply. The grocery stores' primary goal is to conduct a profitable business, and when the stores put in an effort, the objective is the economic benefits. Nonetheless, even though the reduced food waste is a product of the grocery stores effort to minimize unnecessary economic losses, the outcome will be the same; a benefit for the environment. The consumers seem to have the most influence on how grocery stores conduct their businesses. By choosing a milk substitute over regular milk, the environmental impact from the production of milk will be reduced.

This study aims to examine the effect of organisational commitment and employee’s pro-environmental behaviour at home on their intention to adopt green practices at the workplace in the context of hotel industry, by taking the theory of planned behaviour as a conceptual framework. Hotel employees play a critical role that affects customers' experiences, which then affects the overall hotel performance. However, the mechanism that affects their behavioural intention has yet to be investigated properly. Thus, a survey was conducted to collect the data from employees working in green and non-green hotels in Malaysia. Overall, there were 407 responses received, which represented a response rate of 55.75 percent. Then, a set of hypotheses was tested using the structural equation modelling. The empirical results indicate that organisational commitments have a positive effect on the attitude for engaging in a green behaviour and subjective norm, which in turn influenced employees’ intention to adopt green practices at work. Meanwhile, employees’ pro environmental behaviour at home has an indirect impact on employee’s intention to adopt green practices in the workplace through their attitude for engaging in a green behaviour, subjective norms, and perceived behavioural control. The findings lead to a theoretical contribution by incorporating another theory into the theory of planned behaviour, which is the social bond theory through organisational commitment and spill-over effect through pro environmental behaviour at home. Subsequently, a practical recommendation from this research is attainable to policy makers and hotel providers in order for them to understand and increase employees’ willingness to adopt green practices at the workplace. / The full text will be available at the end of the embargo: 15th Dec 2026

No description available.

Made available in DSpace on 2015-03-13T17:08:40Z (GMT). No. of bitstreams: 1 ClenubioFS.pdf: 1330843 bytes, checksum: f44365144a87dbadb85a95129360dfdc (MD5) Previous issue date: 2006-10-09 / The northern coast of Rio Grande do Norte State (RN) shows areas of Potiguar basin with high activity in petroleum industry. With the goal of avoiding and reducing the accident risks with oil it is necessary to understand the natural vulnerability, mapping natural resources and monitoring the oil spill. The use of computational tools for environmental monitoring makes possible better analyses and decisions in political management of environmental preservation. This work shows a methodology for monitoring of environment impacts, with purpose of avoiding and preserving the sensible areas in oil contact. That methodology consists in developing and embedding an integrated computational system. Such system is composed by a Spatial Decision Support System (SDSS). The SDSS shows a computational infrastructure composed by Web System of Geo-Environmental and Geographic Information - SWIGG , the System of Environmental Sensibility Maps for Oil Spill AutoMSA , and the Basic System of Environmental Hydrodynamic ( SisBAHIA a System of Modeling and Numerical Simulating SMNS). In a scenario of oil spill occurred coastwise of Rio Grande do Norte State s northern coast, the integration of such systems will give support to decision agents for managing of environmental impacts. Such support is supplied through a system of supporting to spatial decisions / O litoral norte do Estado do Rio Grande do Norte (RN) apresenta ?reas da bacia Potiguar com intensa atividade da ind?stria petrol?fera. Com a finalidade de prevenir e minimizar os riscos de acidentes com ?leo, faz-se necess?rio compreender a vulnerabilidade natural, mapear os recursos naturais e monitorar os derrames de ?leo. O uso de ferramentas computacionais para o monitoramento ambiental, possibilita uma melhor an?lise e tomada de decis?o no planejamento de pol?ticas de conserva??o ambiental. A presente disserta??o apresenta uma metodologia de trabalho para o monitoramento de impactos ambientais, com o prop?sito de avaliar e proteger as regi?es sens?veis ao contato do ?leo. A metodologia consiste em desenvolver e implantar um sistema integrado, constituindo um Sistema de Apoio ? Decis?o Espacial (SADE). O SADE apresenta uma infra-estrutura computacional composta pelo SWIGG (Sistema Web de Informa??es Geogr?ficas e Geoambientais), o AutoMSA (Automatizador de Mapas de Sensibilidade Ambiental para derramamentos de ?leo) e o Sistema BAse de HIdrodin?mica Ambiental (SisBAHIA um Sistema de Modelagem e Simula??o Num?rica - SMSN). Num cen?rio de derramamento de ?leo ocorrido pr?ximo ?s ?reas costeiras do litoral norte do Estado do RN a integra??o destes sistemas disponibilizar? aos agentes respons?veis pelo gerenciamento dos danos ambientais, um sistema de suporte a tomada de decis?o espacial

The 2010 Deepwater Horizon oil spill resulted in the accidental release of millions of barrels of crude oil into the Gulf of Mexico (GoM). Photo-induced toxicity following co-exposure to ultraviolet (UV) radiation is one mechanism by which polycyclic aromatic hydrocarbons (PAHs) from oil spills may exert toxicity. Blue crab (Callinectes sapidus) are an important commercial and ecological resource in the Gulf of Mexico and their largely transparent larvae may make them sensitive to PAH photo-induced toxicity. Mahi-mahi (Coryphaena hippurus), an important fishery resource, have positively buoyant, transparent eggs. These characteristics may result in mahi-mahi embryos being at particular risk from photo-induced toxicity. Red drum (Sciaenops ocellatus) and speckled seatrout (Cynoscion nebulosus) are both important fishery resources in the GoM. They spawn near-shore and produce positively buoyant embryos that hatch into larvae in about 24 h. The goal of this body of work was to determine whether exposure to UV as natural sunlight enhances the toxicity of crude oil to early lifestage GoM species. Larval and embryonic organisms were exposed to several dilutions of water accommodated fractions (WAF) from several different oils collected in the field under chain of custody during the 2010 spill and two to three gradations of natural sunlight in a factorial design. Here, we report that co-exposure to natural sunlight and oil significantly reduced larval survival and embryo hatch compared to exposure to oil alone.

Les radars à synthèse d’ouverture (RSO) polarimétriques sont devenus incontournables dans le domaine de la télédétection, grâce à leur zone de couverture étendue, ainsi que leur capacité à acquérir des données dans n’importe quelles conditions atmosphériques de jour comme de nuit. Au cours des trois dernières décennies, plusieurs RSO polarimétriques ont été utilisés portant une variété de modes d’imagerie, tels que la polarisation unique, la polarisation double et également des modes dits pleinement polarimétriques. Grâce aux recherches récentes, d’autres modes alternatifs, tels que la polarisation hybride et compacte, ont été proposés pour les futures missions RSOs. Toutefois, un débat anime la communauté de la télédétection quant à l’utilité des modes alternatifs et quant au compromis entre la polarimétrie double et la polarimétrie totale. Cette thèse contribue à ce débat en analysant et comparant ces différents modes d’imagerie RSO dans une variété d’applications, avec un accent particulier sur la surveillance maritime (la détection des navires et de marées noires). Pour nos comparaisons, nous considérons un paramètre fondamental, appelé le degré de polarisation (DoP). Ce paramètre scalaire a été reconnu comme l’un des paramètres les plus pertinents pour caractériser les ondes électromagnétiques partiellement polarisées. A l’aide d’une analyse statistique détaillée sur les images polarimétriques RSO, nous proposons des estimateurs efficaces du DoP pour les systèmes d’imagerie cohérente et incohérente. Ainsi, nous étendons la notion de DoP aux différents modes d’imagerie polarimétrique hybride et compacte. Cette étude comparative réalisée dans différents contextes d’application dégage des propriétés permettant de guider le choix parmi les différents modes polarimétriques. Les expériences sont effectuées sur les données polarimétriques provenant du satellite Canadian RADARSAT-2 et le RSO aéroporté Américain AirSAR, couvrant divers types de terrains tels que l’urbain, la végétation et l’océan. Par ailleurs nous réalisons une étude détaillée sur les potentiels du DoP pour la détection et la reconnaissance des marées noires basée sur les acquisitions récentes d’UAVSAR, couvrant la catastrophe de Deepwater Horizon dans le golfe du Mexique. / Polarimetric Synthetic Aperture Radar (SAR) systems have become highly fruitful thanks to their wide area coverage and day and night all-weather capabilities. Several polarimetric SARs have been flown over the last few decades with a variety of polarimetric SAR imaging modes; traditional ones are linear singleand dual-pol modes. More sophisticated ones are full-pol modes. Other alternative modes, such as hybrid and compact dual-pol, have also been recently proposed for future SAR missions. The discussion is vivid across the remote sensing society about both the utility of such alternative modes, and also the trade-off between dual and full polarimetry. This thesis contributes to that discussion by analyzing and comparing different polarimetric SAR modes in a variety of geoscience applications, with a particular focus on maritime monitoring and surveillance. For our comparisons, we make use of a fundamental, physically related discriminator called the Degree of Polarization (DoP). This scalar parameter has been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. Based on a detailed statistical analysis of polarimetric SAR images, we propose efficient estimators of the DoP for both coherent and in-coherent SAR systems. We extend the DoP concept to different hybrid and compact SAR modes and compare the achieved performance with different full-pol methods. We perform a detailed study of vessel detection and oil-spill recognition, based on linear and hybrid/compact dual-pol DoP, using recent data from the Deepwater Horizon oil-spill, acquired by the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). Extensive experiments are also performed over various terrain types, such as urban, vegetation, and ocean, using the data acquired by the Canadian RADARSAT-2 and the NASA/JPL Airborne SAR (AirSAR) system.

The 2010 BP Deepwater Horizon oil spill in the Gulf of Mexico awakened communities to the increased risk of large-scale damage along their coastlines presented by new technology in deep water drilling. Normal accident theory and high reliability theory offer a framework through which to view the 2010 spill that features predictive criteria linked to a qualitative assessment of risk presented by technology and organizations. The 2010 spill took place in a sociotechnical system that can be described as complex and tightly coupled, and therefore prone to normal accidents. However, the entities in charge of managing this technology lacked the organizational capacity to safely operate within this sociotechnical system.

Abstract Nanocellulose is a renewable, biodegradable, and easily available material that is considered as an attractive resource for many different value-added applications in the emerging bio-based economy. Its outstanding properties, such as strength, lightness, transparency, and good thermal insulation, have inspired research and product development around nanocellulose. The potential of nanocellulose to replace synthetic chemicals made from non-renewable sources, for example, is considered to be very promising. Chemical functionalization, that is, the modification of the cellulosic surface properties, is seen to be beneficial in applications such as those in which higher hydrophobicity is needed. In this thesis, the ability of cellulose nanoparticles to stabilize oil droplets in oil-in-water emulsions was studied. The aim of the study was to explore the possibility of developing a new type of "green" oil spill chemical from cellulose. Therefore, the cellulose was chemically modified in an aquatic environment with a sequential periodate oxidation and chlorite oxidation followed by reductive amination reaction, which increased the hydrophobicity of the produced nanocellulose. In addition, the use of deep-eutectic solvents in the preparation of modified (succinylated and carboxylated) and non-modified cellulose nanoparticles was studied. Chemical (kraft) pulp, dissolving pulp, and semi-chemical fine fibers were used as raw materials in this research. The results demonstrated that chemically modified cellulose nanoparticles work well as stabilizers for oil-water emulsions resulting in small, stable oil droplets and impeding creaming, which is a typical phenomenon for particle stabilized emulsions. The modification of cellulose nanoparticles improved their ability to partition at the oil-water interface, which enabled efficient and irreversible adsorption. It was found that because of their small size, the cellulose nanocrystals can be compressed more tightly onto the surface of the oil droplet, while longer and more flexible cellulose nanofibrils formed a web structure between the oil droplets. All cellulose nanoparticle-stabilized emulsions were stable against droplet coalescence, and even at low temperatures, they retained their droplet size and stability. Salinity, on the other hand, improved stability when CNCs from chemical pulp were used, but it negatively affected stability when nanocrystals from semichemical pulp were used. / Tiivistelmä Uusiutuva, biohajoava ja helposti saatavilla oleva nanoselluloosa on merkittävä tulevaisuuden raaka-aine useissa erilaisissa käyttökohteissa. Sen ylivertaiset ominaisuudet, kuten lujuus, keveys, läpinäkyvyys ja lämmöneristävyys ovat olleet innoittamassa nanoselluloosan tutkimusta ja tuotekehitystä. Nanoselluloosan mahdollisuuksia ja käyttöä eri sovelluksissa korvaamaan esimerkiksi uusiutumattomista luonnonvaroista valmistettuja kemikaaleja, pidetään erittäin lupaavina. Kemiallisesta funktionalisoinnista eli selluloosan pintaominaisuuksien muokkauksesta nähdään olevan hyötyä, kun tavoitellaan nanoselluloosan toiminnallisuutta esimerkiksi hydrofobista luonnetta vaativissa sovelluksissa pinta-aktiivisen aineen tavoin. Tässä työssä tutkittiin erityisesti nanoselluloosapartikkeleiden kykyä stabiloida öljypisaroita dieselöljy-vesiemulsioissa. Tutkimuksen päämääränä oli selvittää mahdollisuutta kehittää uudentyyppistä, ”vihreää” öljyntorjuntakemikaalia selluloosasta. Tämän vuoksi selluloosaa muokattiin kemiallisesti vesiympäristössä yhdistetyllä hapetus- ja aminointikäsittelyllä, mikä lisäsi valmistetun nanoselluloosan hydrofobisuutta. Toisena käsittelyvaihtoehtona tutkittiin syväeutektisten liuottimien käyttöä sekä muokattujen (sukkinyloidut ja karboksyloidut) että muokkaamattomien nanoselluloosapartikkeleiden  valmistuksessa. Raaka-aineina työssä käytettiin kemiallista sellumassaa, liukosellua sekä puolikemiallista hienokuitua. Työn tuloksena voidaan todeta, että nanoselluloosasta valmistetut kemiallisesti muokatut (funktionalisoidut) nanopartikkelit toimivat hyvin öljy-vesiemulsiossa estäen emulsion öljypisaroiden yhteensulautumista. Nanopartikkelit stabiloivat emulsiossa olevan öljyn hyvin pieniksi pisaroiksi hidastaen kermottumista eli emulsion yleistä faasierottumista. Nanoselluloosan funktionalisointi paransi sen kykyä hakeutua öljy-vesi rajapintaan, mahdollistaen tehokkaan ja palautumattoman adsorption. Havaittiin, että pienen kokonsa vuoksi selluloosananokiteet pystyivät pakkautumaan tiiviimmin öljyn pinnalle, kun taas selluloosananokuidut, jotka ovat pidempiä, muodostivat verkkomaisen rakenteen myös öljypisaroiden väliin. Suolan lisäys vaikutti emulsion stabiilisuuteen vaihtelevasti eri näytteiden välillä, kun taas kylmät olosuhteet poikkeuksetta paransivat stabiilisuutta.