Fine tuning of on-growing diets for farmed marine fish species as a key tool to sustain the aquaculture development

The aim of this thesis was to investigate some important key factors able to promote the prospected growth of the aquaculture sector. The limited availability of fishmeal and fish oil led the attention of the aquafeed industry to reduce the dependency on marine raw materials in favor of vegetable ingredients. In Chapter 2, we reported the effects of fishmeal replacement by a mixture of plant proteins in turbot (Psetta maxima L.) juveniles. At the end of the trial, it was found that over the 15% plant protein inclusion can cause stress and exert negative effects on growth performance and welfare. Climate change aroused the attention of the aquafeed industry toward the production of specific diets capable to counteract high temperatures. In Chapter 3, we investigated the most suitable dietary lipid level for gilthead seabream (Sparus aurata L.) reared at Mediterranean summer temperature. In this trial, it was highlighted that 18% dietary lipid allows a protein sparing effect, thus making the farming of this species economically and environmentally more sustainable. The introduction of new farmed fish species makes necessary the development of new species-specific diets. In Chapter 4, we assessed growth response and feed utilization of common sole (Solea solea L.) juveniles fed graded dietary lipid levels. At the end of the trial, it was found that increasing dietary lipids over 8% led to a substantial decline in growth performance and feed utilization indices. In Chapter 5, we investigated the suitability of mussel meal as alternative ingredient in diets for common sole juveniles. Mussel meal proved to be a very effective alternative ingredient for enhancing growth performance, feed palatability and feed utilization in sole irrespectively to the tested inclusion levels. This thesis highlighted the importance of formulating more specific diets in order to support the aquaculture growth in a sustainable way.

Gravitational Lensing as a Tool on Galactic and Cosmological Scales

This work considers the reconstruction of strong gravitational lenses from their observed effects on the light distribution of background sources. After reviewing the formalism of gravitational lensing and the most common and relevant lens models, new analytical results on the elliptical power law lens are presented, including new expressions for the deflection, potential, shear and magnification, which naturally lead to a fast numerical scheme for practical calculation. The main part of the thesis investigates lens reconstruction with extended sources by means of the forward reconstruction method, in which the lenses and sources are given by parametric models. The numerical realities of the problem make it necessary to find targeted optimisations for the forward method, in order to make it feasible for general applications to modern, high resolution images. The result of these optimisations is presented in the \textsc{Lensed} algorithm. Subsequently, a number of tests for general forward reconstruction methods are created to decouple the influence of sourced from lens reconstructions, in order to objectively demonstrate the constraining power of the reconstruction. The final chapters on lens reconstruction contain two sample applications of the forward method. One is the analysis of images from a strong lensing survey. Such surveys today contain $\sim 100$ strong lenses, and much larger sample sizes are expected in the future, making it necessary to quickly and reliably analyse catalogues of lenses with a fixed model. The second application deals with the opposite situation of a single observation that is to be confronted with different lens models, where the forward method allows for natural model-building. This is demonstrated using an example reconstruction of the ``Cosmic Horseshoe''. An appendix presents an independent work on the use of weak gravitational lensing to investigate theories of modified gravity which exhibit screening in the non-linear regime of structure formation.

The Politics of Purchasing Power: Feminist Tourism and Women's Development in Nepal

My purpose in this essay is to explore how ideas about women and development are created and circulated at the moment of consumption of wares produced at a women's development project in Nepal. I analyze the project as an example of the ways that women's development is an object of material and discursive consumption. Artifacts produced and sold by Nepali women, and purchased by tourists from the "first world," become part of an international exchange of power, money, and meaning. Based on a survey of consumers and ethnographic observations, I conclude that feminist tourists forge relations with disempowered "Others" through the pleasurable activity of an alienated market transaction. Consumers of crafts produced at a women's development project assume a position of empowerment and enlightenment, ready to help out their "women" counterparts through their support of an enterprise with circular logic: within the industry of development (although not necessarily for feminist tourists themselves), at least one of the central projects of development is the development project itself. At the same time, feminist tourists locate themselves outside the oppressive structures and ideologies affecting their "third-world sisters." This is a relation of sympathy and imagined empathy, with no sense of differential location within systems of oppression. They fail to examine or articulate the global link between their own purchasing power and local living conditions of Maithil women; the connection is effectively built out of the discourse.

EmsB, a tandem repeated multi-loci microsatellite, new tool to investigate the genetic diversity of Echinococcus multilocularis

In order to explore the genetic diversity within Echinococcus multilocularis (E. multilocularis), the cestode responsible for the alveolar echinococcosis (AE) in humans, a microsatellite, composed of (CA) and (GA) repeats and designated EmsB, was isolated and characterized in view of its nature and potential field application. PCR-amplification with specific primers exhibited a high degree of size polymorphism between E. multilocularis and Echinococcus granulosus sheep (G1) and camel (G6) strains. Fluorescent-PCR was subsequently performed on a panel of E. multilocularis isolates to assess intra-species polymorphism level. EmsB provided a multi-peak profile, characterized by tandemly repeated microsatellite sequences in the E. multilocularis genome. This "repetition of repeats" feature provided to EmsB a high discriminatory power in that eight clusters, supported by bootstrap p-values larger than 95%, could be defined among the tested E. multilocularis samples. We were able to differentiate not only the Alaskan from the European samples, but also to detect different European isolate clusters. In total, 25 genotypes were defined within 37 E. multilocularis samples. Despite its complexity, this tandem repeated multi-loci microsatellite possesses the three important features for a molecular marker, i.e. sensitivity, repetitiveness and discriminatory power. It will permit assessing the genetic polymorphism of E. multilocularis and to investigate its spatial distribution in detail.

Development of a Mathematical Model for the Calculation of the Tool Appropriation Delay depending on the Tool Inventory

Compliance with punctual delivery under the high pressure of costs can be implemented through the optimization of the in-house tool supply. Within the Transfer Project 13 of the Collaborative Research Centre 489 using the example of the forging industry, a mathematical model was developed which determines the minimum inventory of forging tools required for production, considering the tool appropriation delay.

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Design of SOA services: experiences from industry

Because of the unknown usage scenarios, designing the elementary services of a service-oriented architecture (SOA), which form the basis for later composition, is rather difficult. Various design guide lines have been proposed by academia, tool vendors and consulting companies, but they differ in the rigor of validation and are often biased toward some technology. For that reason a multiple-case study was conducted in five large organizations that successfully introduced SOA in their daily business. The observed approaches are contrasted with the findings from a literature review to derive some recommendations for SOA service design.

A new universal, standardized implant database for product identification: a unique tool for arthroplasty registries.

INTRODUCTION Every joint registry aims to improve patient care by identifying implants that have an inferior performance. For this reason, each registry records the implant name that has been used in the individual patient. In most registries, a paper-based approach has been utilized for this purpose. However, in addition to being time-consuming, this approach does not account for the fact that failure patterns are not necessarily implant specific but can be associated with design features that are used in a number of implants. Therefore, we aimed to develop and evaluate an implant product library that allows both time saving barcode scanning on site in the hospital for the registration of the implant components and a detailed description of implant specifications. MATERIALS AND METHODS A task force consisting of representatives of the German Arthroplasty Registry, industry, and computer specialists agreed on a solution that allows barcode scanning of implant components and that also uses a detailed standardized classification describing arthroplasty components. The manufacturers classified all their components that are sold in Germany according to this classification. The implant database was analyzed regarding the completeness of components by algorithms and real-time data. RESULTS The implant library could be set up successfully. At this point, the implant database includes more than 38,000 items, of which all were classified by the manufacturers according to the predefined scheme. Using patient data from the German Arthroplasty Registry, several errors in the database were detected, all of which were corrected by the respective implant manufacturers. CONCLUSIONS The implant library that was developed for the German Arthroplasty Registry allows not only on-site barcode scanning for the registration of the implant components but also its classification tree allows a sophisticated analysis regarding implant characteristics, regardless of brand or manufacturer. The database is maintained by the implant manufacturers, thereby allowing registries to focus their resources on other areas of research. The database might represent a possible global model, which might encourage harmonization between joint replacement registries enabling comparisons between joint replacement registries.

The Application of Digital Rights Management Systems in the Music Industry - An Empirical Investigation

Technology advances in hardware, software and IP-networks such as the Internet or peer-to-peer file sharing systems are threatening the music business. The result has been an increasing amount of illegal copies available on-line as well as off-line. With the emergence of digital rights management systems (DRMS), the music industry seems to have found the appropriate tool to simultaneously fight piracy and to monetize their assets. Although these systems are very powerful and include multiple technologies to prevent piracy, it is as of yet unknown to what extent such systems are currently being used by content providers. We provide empirical analyses, results, and conclusions related to digital rights management systems and the protection of digital content in the music industry. It shows that most content providers are protecting their digital content through a variety of technologies such as passwords or encryption. However, each protection technology has its own specific goal, and not all prevent piracy. The majority of the respondents are satisfied with their current protection but want to reinforce it for the future, due to fear of increasing piracy. Surprisingly, although encryption is seen as the core DRM technology, only few companies are currently using it. Finally, half of the respondents do not believe in the success of DRMS and their ability to reduce piracy.

Pricing and Policy Problems in the Northeast Fluid Milk Industry

This article documents the need for reform of milk pricing in the Northeast. The New York price gouging law can be recast as a fair share law. This new milk policy “kills two birds with one stone.” It corrects regional inequities in raw milk pricing by reforming the pricing of milk at retail by limiting and redistributing excessive retail margins to farmers and consumers. The fair share policy relieves allocative price inefficiency, improves the performance of the federal milk market order pool, and the general performance of the Northeast dairy farming and fluid milk industries.

Bank Performance: Market Power or Efficient Structure?

Regulatory change not seen since the Great Depression swept the U.S. banking industry beginning in the early 1980s, culminating with the Interstate Banking and Branching Efficiency Act of 1994. Significant consolidations have occurred in the banking industry. This paper considers the market-power versus the efficient-structure theories of the positive correlation between banking concentration and performance on a state-by-state basis. Temporal causality tests imply that bank concentration leads bank profitability, supporting the market-power, rather than the efficient-structure, theory of that positive correlation. Our finding suggests that bank regulators, by focusing on local banking markets, missed the initial stages of an important structural change at the state level.

Workers and respirators: What factors influence medical clearance? Experience in the petrochemical industry 2004--2008

This retrospective cohort study analyzed data from more than 2200 OSHA-mandated respirator medical evaluations performed between 2004 and 2008, with information initially obtained using an online questionnaire, to determine what factors influence medical clearance and the ability to safely wear respiratory protection in a large petrochemical company.^ The employees were mostly white males with a high school education, ranging in age from 25 to 60 years of age, who had been employed with the company an average of eight years. Their work was typically performed outdoors in a rural or offshore setting. Respirators were typically required for emergency response – escape or rescue only – and/or limited to less than four hours per month.^ Approximately 90% of the population achieved medical clearance by utilizing the online questionnaire. Of the remaining 10%, 66% were cleared after additional "hands-on" medical examination exam; 28% of the individuals' jobs were modified by their supervisor in order to not use a respirator, and 6% of the individuals (n=13) were excluded from wearing a respirator on the basis of the medical examination. The primary causes for exclusion from respirator use were cardiovascular (37.5%) and respiratory (31.3%) issues, followed by psychological (18.8%) and musculoskeletal (12.5%) concerns. Ultimately, over 99% of workers evaluated under this system were found capable of using respiratory protection safely. This questionnaire has proven to be an excellent health screening tool capable of initiating early detection and further investigation of potentially serious medical conditions within a large and diverse population in multiple locations. ^

The development of poultry farms risk assessment tool for avian influenza in Imo State, Nigeria

Background: Nigeria was one of the 13 countries where avian influenza outbreak in poultry farms was reported during the 2006 avian influenza pandemic threat and was also the first country in Africa to report the presence of H5N1influenza among its poultry population. There are multiple hypotheses on how the avian influenza outbreak of 2006 was introduced to Nigeria, but the consensus is that once introduced, poultry farms and their workers were responsible for 70% of the spread of avian influenza virus to other poultry farms and the population. ^ The spread of avian influenza has been attributed to lack of compliance by poultry farms and their workers with poultry farm biosecurity measures. When poultry farms fail to adhere to biosecurity measures and there is an outbreak of infectious diseases like in 2006, epidemiological investigations usually assess poultry farm biosecurity—often with the aid of a questionnaire. Despite the importance of questionnaires in determining farm compliance with biosecurity measures, there have been few efforts to determine the validity of questionnaires designed to assess poultry farms risk factors. Hence, this study developed and validated a tool (questionnaire) that can be used for poultry farm risk stratification in Imo State, Nigeria. ^ Methods: Risk domains were generated using literature and recommendations from agricultural organizations and the Nigeria government for poultry farms. The risk domains were then used to develop a questionnaire. Both the risk domain and questionnaire were verified and modified by a group of five experts with a research interest in Nigeria's poultry industry and/or avian influenza prevention. Once a consensus was reached by the experts, the questionnaire was distributed to 30 selected poultry farms in Imo State, Nigeria that participated in this study. Survey responses were received for all the 30 poultry farms that were selected. The same poultry farms were visited one week after they completed the questionnaires for on-site observation. Agreement among survey and observation results were analyzed using a kappa test and rated as poor, fair, moderate, substantial, or nearly perfect; and internal consistency of the survey was also computed. ^ Result: Out of the 43 items on the questionnaire, 32 items were validated by this study. The agreement between the survey result and onsite observation was analyzed using kappa test and ranged from poor to nearly perfect. Most poultry farms had their best agreements in the contact section of the survey. The least agreement was noted in the farm management section of the survey. Thirty-two questions on the survey had a coefficient alpha > 0.70, which is a robust internal consistency for the survey. ^ Conclusion: This study developed 14 risk domains for poultry farms in Nigeria and validated 32 items from the original questionnaire that contained 43 items. The validated items can be used to determine the risk of introduction and spread of avian influenza virus in poultry farms in Imo State, Nigeria. After further validations in other states, regions and poultry farm sectors in Nigeria; this risk assessment tool can then be used to determine the risk profile of poultry farms across Nigeria.^

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.