Social Preferences and Transport Policy: The case of US speed limits

This article, in reviewing the longrunning US debate on speed limits, illustrates how a different valuation of the trade-off between private mobility needs and safety concerns can shape transport policies. It is argued that the regulatory decentralization debate, together with the speed limit in force in each state, obey the social preferences and valuation given to this tradeoff. Such a view is consistent with evidence that higher speed limits are to be found in states with greater private mobility needs, even though their fatality rates might be among the highest in the country. By contrast, lower speed limits and supporters of a low national speed limit are to be found in states that show a greater concern for safety outcomes and which are less dependent on private mobility. By reviewing these events and examining the role played by the main actors and analyzing their motivations, the article identifies important lessons for similar future discussions on transport policy.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Reliable entertainment: Spanish consumers' preferences regarding a film's country of origin

This article contributes to the study of cinema audiences in Europe by analyzing theactual behavior of Spanish moviegoers and their level of satisfaction. We modeledmoviegoers’ choice of film by country of origin (U.S.A., Spain, and other countries)according to a set of determinants: (1) consumers’ interpretation of several sources of information, (2) motivations and (3) choice rules. We found three clear consumerstereotypes related to each type of film: (1) U.S.A. films were preferred by almost everyone (especially families and younger audiences); (2) Spanish films had audiences composed of middle-age and middle-class moviegoers; and (3) European productions were preferred by a social or intellectual elite. U.S.A. films dominate the Spanish market for the reason that they provide most of what moviegoers prefer, namely, familiar, reliable entertainment in Spanish; three characteristics that are not satisfied by Spanish and European films. Additionally, we discuss the implications for the European cultural policy

Empirical welfare analysis: when preferences matter

Fleurbaey and Maniquet have proposed the criteria of conditional equality and of egalitarian equivalence to assess the equity among individuals in an ordinal setting. Empirical applications are rare and only partially consistent with their framework. We propose a new empirical approach that relies on individual preferences, is consistent with the ordinal criteria and enables to compare them with the cardinal criteria. We estimate a utility function that incorporates individual heterogeneous preferences, obtain ordinal measures of well-being and apply conditional equality and egalitarian equivalence. We then propose two cardinal measures of well-being, that are comparable with the ordinal model, to compute Roemer’s and Van de gaer’s criteria. Finally we compare the characteristics of the worst-off displayed by each criterion. We apply this model to a sample of US micro data and obtain that about 18% of the worst-off are not common to all criteria.

Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.

Removal of nitrogen, phosphorus, copper and zinc from swine breeding waste water by bermudagrass and cattail in constructed wetland systems

This work aimed to study the agronomic performance and capacity of nutrient removal by bermudagrass (Cynodon spp.) and cattail (Typha sp.) when grown in constructed wetlands systems (CWSs) of vertical and horizontal flow, respectively, used in the post-treatment of swine breeding wastewater (ARS). The average yield of dry matter (DM) of bermudagrass in sections of 60-day interval ranged from 14 to 43 t ha-1, while the cultivated cattail produced in a single cut after 200 days of cultivation between 45 and 67 t ha-1 of DM. Bermudagrass extracted up to 17.65 kg ha-1 d-1 of nitrogen, 1.76 kg ha-1 d-1 of phosphorus, 6.67 g ha-1 d-1 of copper and 54.75 g ha-1 d-1 of zinc. Cattail extracted up to 5.10 kg ha-1 d-1 of nitrogen, 1.07 kg ha-1 d-1 of phosphorus, 1.41 g ha-1 d-1 of copper and 16.04 g ha-1 d-1 of zinc. Cattail and bermudagrass were able to remove, respectively, 5.0 and 4.6% of the nitrogen and 11.2 and 5.4% of the phosphorus applied via ARS, being less efficient in extracting N and P when the initial intake of these nutrients is evaluated.

Probabilistic contact preferences in protein-ligand and protein-protein complexes

Modeller för intermolekulär växelvärkan utnyttjas brett inom biologin. Analys av kontakter mellan proteiner och läkemedelsforskning representerar typiska tillämpningsområden för dylika modeller. En modell som beskriver sådana molekylära växelverkningar kan utformas med hjälp av biofysisk teori, vilket tenderar att resultera i ytterst tung beräkningsbörda även för enkla tillämpningar. Ett alternativt sätt att formulera modeller är att utnyttja stora databaser som innehåller strukturmätningar gjorda med hjälp av till exempel röntgendiffraktion. Då man använder sig av empiriska mätdata direkt, möjliggör en statistisk modell att osäkerheten och inexaktheten i datat tas till hänsyn på ett adekvat sätt, samtidigt som beräkningsbördan håller sig på en rimligare nivå jämfört med kvantmekaniska metoder som i princip borde ge de optimala resultaten. I avhandlingen utvecklades en 3D modell för numerisk undersökning av intermolekulär växelverkan baserad på Bayesiansk statistik. Modellens syfte är att åstadkomma prognoser för det hurdana eller vilka molekylstrukturer prefereras i en given kontext, d.v.s. är mer sannolika inom ramen för interaktion. Modellen testades i essentiella molekyläromgivningar - en liten molekyl vid sin bindningsplats hos ett protein och en gränsyta mellan proteinerna i ett komplex. De erhållna numeriska resultaten motsvarar väl experimentella resultat som tidigare rapporterats i litteraturen, exempelvis kvalitativa bindningsaffiniteter och kemisk kännedom av vissa aminosyrors rumsliga förmågor att utgöra bindningar. I avhandlingen gjordes ytterligare preliminära tester av den statistiska ansatsen för modellering av den centrala molekylära strukturella anpassningsbarheten. I praktiken är den utvecklade modellen ämnad som ett led i en mer omfattande analysmetod, så som en s.k. farmakofor modell. Molekyylivuorovaikutusten mallintamista hyödynnetään laajasti biologisten kysymysten tarkastelussa. Tyypillisiä esimerkkejä sovelluskohteista ovat proteiinien väliset kontaktit ja lääkesuunnittelu. Vuorovaikutuksia kuvaavan mallin lähtökohta voi olla molekyyleihin liittyvä teoria, jolloin soveltamiseen liittyvä laskenta saattaa olla erityisen raskasta, tai suuri havaintojoukko joka on saatu aikaan esimerkiksi mittaamalla rakenteita röntgendiffraktio menetelmällä. Tilastollinen malli mahdollistaa havaintoaineistossa olevan epätarkkuuden ja epävarmuuden huomioimisen, samalla pitäen laskennallisen kuorman pienempänä verrattuna periaatteessa parhaan tuloksen antavaan kvanttimekaaniseen mallinnukseen. Väitöstyössä kehitettiin bayesiläiseen tilastotieteeseen perustuva 3D malli molekyylien välisten vuorovaikutusten laskennalliseen tarkasteluun. Mallin tehtävä on tuottaa ennusteita sen suhteen, minkä tai millaisten molekyylirakenteiden väliset kompleksit ovat etusijalla, toisin sanoen todennäköisempiä, vuorovaikutustilanteessa. Työssä kehitetyn menetelmän toimivuutta testattiin käyttötarkoituksen suhteen olennaisissa molekyyliympäristöissä - pieni molekyyli sitoutumiskohdassaan proteiinissa sekä rajapinta kahden proteiinin välilllä proteiinikompleksissa. Saadut laskennalliset tulokset vastasivat hyvin vertailuun käytettyjä kirjallisuudesta saatuja kokeellisia tuloksia, kuten laadullisia sitoutumisaffiniteetteja, sekä kemiallista tietoa esimerkiksi tiettyjen aminohappojen avaruudellisesta sidoksenmuodostuksesta. Väitöstyössä myös alustavasti testattiin tilastollista lähestymistapaa tärkeän molekyylien rakenteellisen mukautuvuuden mallintamiseen. Käytännössä malli on tarkoitettu osaksi jotakin laajempaa analyysimenetelmää, kuten farmakoforimallia.

STUDY OF AUTUMN-WINTER WEEDS IN PORTUGAL AND ITS ECOLOGICAL PREFERENCES

ABSTRACTThe conclusion of the dam project located in Alqueva, in Southern Portugal, has resulted in a significant increase of new irrigated areas, since 2006. This has meant that, in recent years, there have been progressive flora changes in farming systems traditionally implemented in the Alentejo region. The present work has analyzed the weed flora in an early stage of these changes, and the impact of environmental factors on the distribution of natural vegetation under Mediterranean climate conditions in the influence area of Alqueva. In 2007, 105 floristic surveys were carried out in autumn-winter crop plots or other soil use, and 264 species were identified. Families with higher expression were: Asteraceae, Poaceae, and Fabaceae. Only three species have been identified in more than half of farms, Avena sterilis, Phalaris minor and Lolium rigidum and they were part of the 15 species that revealed high and very high infestation degrees. Soil texture and extractable phosphorus have been determined as active ecological factors, according to the method of ecological profiles and Mutual Information. Therefore, these factores were those with the greatest influence on the species distribution. L.rigidum distribution showed to be associated with medium soil texture and A.sterilis distribution also showed to be associated with medium and fine soil texture soils, without showing ecological preference by extractable phosphorus. The distribution of P.minorwas not related to the soil texture but showed preference for soils with medium phosphorus content.

Geological sketch map of New Zealand constructed from official surveys and the explorations of F. von Hochstetter, Julius Haast and others

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan

Geological map of Europe exhibiting the different systems of rocks according to the most recent researches and inedited materials by Roderick I. Murchison and James Nicol ; constructed by A. Keith Johnston

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan

Consumer preferences of genetically modified foods of vegetal and animal origin in Chile

Given the debate generated by Genetically Modified (GM) foods in developed and developing countries, the aim was to evaluate the importance of determining factors in the preference of consumers in Temuco and Talca in central-southern Chile for GM foods using conjoint analysis and to determine the existence of different market segments using a survey of 800 people. Using conjoint analysis, it was established that, in general, genetic modification was a more important factor than either brand or price in the consumer's decision to purchase either food. Cluster analysis identified three segments: the largest (51.4%) assigned greatest importance to brand and preferred genetically modified milk and tomato sauce; the second group (41.0%) gave greatest importance to the existence of genetic manipulation and preferred non-genetically modified foods; the smallest segment (7.6%) mainly valued price and preferred milk and tomato sauce with no genetic manipulation. The three segments rejected the store brand and preferred to pay less for both foods. The results are discussed based on studies conducted in developed and developing countries.

Sensory profile and drivers of liking for grape nectar among smoker and nonsmoker consumers

Decreased gustatory and olfactory capacity is one of the problems caused by tobacco use. The objectives of this study were to determine the sensory profile of six grape nectar samples sweetened with different sweeteners and to verify the drivers of liking in two distinct consumer groups: smokers and nonsmokers. The sensory profile was constructed by twelve trained panelists using quantitative descriptive analysis (QDA). Consumer tests were performed with 112 smokers and 112 nonsmokers. Partial least squares regression analyses was used to identify the drivers of acceptance and rejection of the grape nectars among the two consumer groups. According to the QDA, the samples differed regarding six of the nineteen attributes generated. The absolute averages of the affective test were lower in the group of smokers; possibly because smoking influences acceptance and eating preferences, especially with regard to sweet foods. The results showed that the grape flavor was the major driver of preference for acceptance of the nectar, while astringency, wine aroma, bitterness and sweetness, and bitter aftertaste were drivers of rejection in the two groups of consumers, with some differences between the groups.

Fish consumption preferences and factors influencing it

Fish consumption preferences are affected by individuals’ socioeconomic characteristics. The aims of the present paper were (i) to obtain information on fish consumption level and frequency; (ii) to investigate the associations between the socioeconomic characteristics of consumers and their preferences; and (iii) to examine the influence of determinants on fish consumption. Data were gathered through a questionnaire completed by a total of 127 randomly selected individuals from different socioeconomic backgrounds from the Antakya, Turkey. The average consumption was found to be 2.98 kg/person/year for fish. Anchovies, gilt-head sea bream, and sea bass were reported as the most consumed three species, respectively. Significant differences in fish consumption were found among age groups, gender groups, and education groups, as well as between marital statuses. A majority of the consumers eat fish once a month throughout the year or only during the winter months. Fish consumption level and frequency were significantly positively correlated with education (p<0.01), income (p<0.05) and total meat consumption (p<0.01). The stepwise multiple regression model explained 41.7% (p<0.01) of the total variance for fish consumption. The amount and frequency of the consumption in the region, which is very far below the world and Turkey average especially for lower socioeconomic groups and for less-consumed fish species, can be increased by certain policies, such as training, advertising and different marketing strategies. Moreover, consumption should be distributed equally throughout the year instead of consuming only in certain seasons.