Novel Analytical Methodologies for the Monitoring of Traditional and Non-traditional Pollutants in different Environmental Compartments of South Florida

Routine monitoring of environmental pollution demands simplicity and speed without sacrificing sensitivity or accuracy. The development and application of sensitive, fast and easy to implement analytical methodologies for detecting emerging and traditional water and airborne contaminants in South Florida is presented. A novel method was developed for quantification of the herbicide glyphosate based on lyophilization followed by derivatization and simultaneous detection by fluorescence and mass spectrometry. Samples were analyzed from water canals that will hydrate estuarine wetlands of Biscayne National Park, detecting inputs of glyphosate from both aquatic usage and agricultural runoff from farms. A second study describes a set of fast, automated LC-MS/MS protocols for the analysis of dioctyl sulfosuccinate (DOSS) and 2-butoxyethanol, two components of Corexit®. Around 1.8 million gallons of those dispersant formulations were used in the response efforts for the Gulf of Mexico oil spill in 2010. The methods presented here allow the trace-level detection of these compounds in seawater, crude oil and commercial dispersants formulations. In addition, two methodologies were developed for the analysis of well-known pollutants, namely Polycyclic Aromatic Hydrocarbons (PAHs) and airborne particulate matter (APM). PAHs are ubiquitous environmental contaminants and some are potent carcinogens. Traditional GC-MS analysis is labor-intensive and consumes large amounts of toxic solvents. My study provides an alternative automated SPE-LC-APPI-MS/MS analysis with minimal sample preparation and a lower solvent consumption. The system can inject, extract, clean, separate and detect 28 PAHs and 15 families of alkylated PAHs in 28 minutes. The methodology was tested with environmental samples from Miami. Airborne Particulate Matter is a mixture of particles of chemical and biological origin. Assessment of its elemental composition is critical for the protection of sensitive ecosystems and public health. The APM collected from Port Everglades between 2005 and 2010 was analyzed by ICP-MS after acid digestion of filters. The most abundant elements were Fe and Al, followed by Cu, V and Zn. Enrichment factors show that hazardous elements (Cd, Pb, As, Co, Ni and Cr) are introduced by anthropogenic activities. Data suggest that the major sources of APM were an electricity plant, road dust, industrial emissions and marine vessels.

What to do when K-means clustering fails:a simple yet principled alternative algorithm

The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.

Tuberculosis epidemiology in islands: insularity, hosts and trade

Because of their relative simplicity and the barriers to gene flow, islands are ideal systems to study the distribution of biodiversity. However, the knowledge that can be extracted from this peculiar ecosystem regarding epidemiology of economically relevant diseases has not been widely addressed. We used information available in the scientific literature for 10 old world islands or archipelagos and original data on Sicily to gain new insights into the epidemiology of the Mycobacterium tuberculosis complex (MTC). We explored three nonexclusive working hypotheses on the processes modulating bovine tuberculosis (bTB) herd prevalence in cattle and MTC strain diversity: insularity, hosts and trade. Results suggest that bTB herd prevalence was positively correlated with island size, the presence of wild hosts, and the number of imported cattle, but neither with isolation nor with cattle density. MTC strain diversity was positively related with cattle bTB prevalence, presence of wild hosts and the number of imported cattle, but not with island size, isolation, and cattle density. The three most common spoligotype patterns coincided between Sicily and mainland Italy. However in Sicily, these common patterns showed a clearer dominance than on the Italian mainland, and seven of 19 patterns (37%) found in Sicily had not been reported from continental Italy. Strain patterns were not spatially clustered in Sicily. We were able to infer several aspects of MTC epidemiology and control in islands and thus in fragmented host and pathogen populations. Our results point out the relevance of the intensity of the cattle commercial networks in the epidemiology of MTC, and suggest that eradication will prove more difficult with increasing size of the island and its environmental complexity, mainly in terms of the diversity of suitable domestic and wild MTC hosts.

La construcción de sentido (‘sensemaking’) de un entorno estratégico turbulento por parte de los altos directivos: un estudio en una multinacional automotriz

Este artículo presenta los resultados de una investigación realizada al interior de dos contextos. Por un lado, el teórico, en el marco de uno de los discursos más relevantes en los campos de la estrategia organizacional, de la managerial and organizational cognition (MOC) y, en general, de los estudios organizacionales (organization studies): la construcción de sentido (sensemaking). Por el otro, el empírico, en una de las grandes compañías multinacionales del sector automotriz con presencia global. Esta corporación enfrenta una permanente tensión entre lo que dicta la casa matriz, en relación con el cumplimiento de metas y estándares específicos, considerando el mundo entero, y los retos que, teniendo en cuenta lo regional y lo local, experimentan los altos directivos encargados de hacer prosperar la empresa en estos lugares. La aproximación implementada fue cualitativa. Esto en atención a la naturaleza de la problemática abordada y la tradición del campo. Los resultados permiten ampliar el actual nivel de comprensión acerca de los procesos de sensemaking de los altos directivos al enfrentar un entorno estratégico turbulento.

Influence of free-radical functionalization on non-woven filtering media for medical application

Nowadays the leukodepletion is one of the most important processes done on the blood in order to reduce the risk of transfusion diseases. It can be performed through different techniques but the most popular one is the filtration due to its simplicity and efficiency. This work aims at improving a current commercial product, by developing a new filter based on Fenton-type reaction to cross-link a hydrogel on to the base material. The filters for leukodepletion are preferably made through the melt flow technique resulting in a non-woven tissue; the functionalization should increase the stability of the filter restricting the extraction of substances to minimum amount when in contact with blood. Through the modification the filters can acquire new properties including wettability, surface charge and good resistance to the extractions. The most important for leukodepletion is the surface charge due to the nature of the filtration process. All the modified samples results have been compared to the commercial product. Three different polymers (A, B and C) have been studied for the filter modifications and every modified filter has been tested in order to determine its properties.

Technologies for urban and rural internet of things

Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then, machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented.

Attenuation of thermal radiation from flare through water curtains on offshore platform.

Flaring has been widely used in the upstream operation of the oil and gas industry, both onshore and offshore. It is considered a safe and reliable way to protect assets from overpressure and the environment from toxic gas using combustion. However, there are drawbacks to using flares, such as vibration and thermal radiation. Excessive contact with thermal radiation is harmful to offshore personnel and equipment. Research organizations and companies have invested time and money to combat this. Many technologies have been developed so far to reduce the risk of thermal radiation, one of them being the water curtain system. Several tests were done to see the effectiveness of the water curtain system in mitigating thermal radiation in an offshore environment. Each test varied in the flare output, wind speed, and the size of water droplets size of the water curtain. Later, the results of each test were compared and analyzed. The results showed that a water curtain system could be a solution to excessive thermal radiation that comes from an offshore flare. Moreover, the water curtain with smaller water droplets diameter gives a more favorable result in reducing thermal radiation. These results suggest that, although it offers simplicity and efficiency, designing an efficient water curtain system requires deep study. Various conditions, such as wind speed, flare intensity, and the size of the water droplets, plays a vital role in the effectiveness of the water curtain system in attenuating thermal radiation.

Mitigating non-Lambertian surfaces issues in Stereo Matching with Neural Radiance Fields

Depth estimation from images has long been regarded as a preferable alternative compared to expensive and intrusive active sensors, such as LiDAR and ToF. The topic has attracted the attention of an increasingly wide audience thanks to the great amount of application domains, such as autonomous driving, robotic navigation and 3D reconstruction. Among the various techniques employed for depth estimation, stereo matching is one of the most widespread, owing to its robustness, speed and simplicity in setup. Recent developments has been aided by the abundance of annotated stereo images, which granted to deep learning the opportunity to thrive in a research area where deep networks can reach state-of-the-art sub-pixel precision in most cases. Despite the recent findings, stereo matching still begets many open challenges, two among them being finding pixel correspondences in presence of objects that exhibits a non-Lambertian behaviour and processing high-resolution images. Recently, a novel dataset named Booster, which contains high-resolution stereo pairs featuring a large collection of labeled non-Lambertian objects, has been released. The work shown that training state-of-the-art deep neural network on such data improves the generalization capabilities of these networks also in presence of non-Lambertian surfaces. Regardless being a further step to tackle the aforementioned challenge, Booster includes a rather small number of annotated images, and thus cannot satisfy the intensive training requirements of deep learning. This thesis work aims to investigate novel view synthesis techniques to augment the Booster dataset, with ultimate goal of improving stereo matching reliability in presence of high-resolution images that displays non-Lambertian surfaces.