Differenzierung aviärer Brachyspiren mit PCR-basierten Methoden und MALDI-TOF-MS

Observation of marine animals in their environment – whale-watching – has grown greatly in recent years, bringing risk to the animals. Of particular concern are harmful impacts on marine mammals, some of which are endangered. As a result, regulations have been developed for their protection, but these conservation measures require enforcement across a broad geographic region, which is difficult due to limited monitoring resources. A ship-borne information transmission system called AIS – Automatic Identification System – can provide information-rich marine vessel movement data that can be used to passively monitor vessels engaged in viewing wildlife, aiding regulatory bodies with compliance enforcement. Few studies explore the use of AIS data to determine when vessels are engaged in wildlife-viewing, and as such little guidance exists on how to implement classification models appropriately. The objective of this thesis is to use AIS data to evaluate the accuracy and utility of existing classification models to detect vessels engaged in observing wildlife, and determine whether information about species being observed can be extracted. Using a control set of observed cetacean encounter data, three classification models were statistically assessed. From this, a hidden Markov model was chosen for detailed analysis in the vicinity surrounding Vancouver Island, B.C., Canada. The resulting analysis concluded that a hidden Markov unsupervised classification approach was feasible for detecting vessel behaviours and differentiating species type. These findings suggest AIS can aid managers and the commercial whale-watching industry in making informed decisions regarding conservation regulations and their compliance. / Graduate / 2022-08-12

In the Canadian Arctic, ship traffic has been increasing at the same time as sea ice has been declining over the past decade-plus. The decrease in sea ice has been associated with trends in warm weather and atmospheric conditions in the summer seasons, which are expected to continue. Thus, it is anticipated that ship traffic will also continue to grow, as areas in the Arctic, including the Northwest Passage (NWP) and Northern Sea Route (NSR) experience less and less sea ice. The appeal of the opening of these major Arctic shipping routes, is that these routes are shorter in distance for commercial ships on international voyages in comparison to traditional routes that travel through the Panama and Suez Canals. Assuming safe and smooth sailing a shorter route can be beneficial monetarily for commercial shipping companies and as a result for other economic sectors reliant on the efficient shipment of goods. However, a major concern associated with the anticipated increase in Arctic ship traffic is the potential for an increase in the number and severity of maritime navigational related accidents. Thus, the overall aim of this thesis is to quantify recent historic links between ship traffic, sea ice, ship accidents, and accident rates within the Canadian Arctic as well as the global Arctic. There are three specific objectives including to: 1. Conduct a comparative statistical analysis between two ship traffic databases (NORDREG and AIS) within the Canadian Arctic to evaluate datasets strengths and weaknesses; 2. Use the best available data (see objective 1) to examine the statistical associations and trends for ship traffic, ship accidents, accident rates, and sea-ice extent within the Canadian Arctic during the shipping-season from 1990 to 2019; and 3. Derive and compare recent ship traffic accident rates to determine if statistical trends from 2012 to 2019 exist for ships across the global Arctic. Results of the study show that both NORDREG and AIS data is useful in understanding shipping traffic trends in Arctic Canada over time and that each dataset is effective depending on the temporal period of interest (Objective 1). NORDREG data is most effective for identifying ship positioning before 2012 (+106,811 more nm sailed per matched unique vessels and +9 overall unique vessels from NORDREG) and from 2012 onwards AIS is more accurate, highlighted by the year of 2018 (+84,149 more nm sailed and +169 unique vessels from AIS). Using available data sources from 1990 to 2019, it was revealed that although commercial and non-commercial ship traffic is increasing across in Arctic Canada, the total number of accidents and overall accident rate for commercial vessels has declined, whereas they have increased for non-commercial ships (Objective 2). There are significant positive trends in overall ship traffic for all ship types (+9,275 nm yr⁻¹), commercial ships (+5,011 nm yr⁻¹) and non-commercial ships (+4,658 nm yr⁻¹). Whereas there have been significant negative trends in ship accidents for commercial ships (-0.06 accidents yr⁻¹), ship accident rates, for all ship types (-6.31E-07 accidents/nm yr⁻¹). Sea ice extent at the monthly level during the shipping season has been significantly decreasing (-3,193 km² mo⁻¹). Results also indicate that there are significant negative correlations between monthly ship traffic and sea ice extent, for all ship types (-0.50), commercial ships (-0.49), and non-commercial ships (-0.48). At the global scale, ship traffic is increasing while ship accident rates are decreasing (Objective 3). For the global Arctic there are positive statistically significant trends for all ship traffic (+2.655 million nm yr⁻¹), commercial ships (+1.598 million nm yr⁻¹), and non-commercial ships (+1.446 million nm yr⁻¹); where there are statistically significant annual decreases in ship accident rates for all ships (-3.64E-07 ship accidents/nm yr⁻¹), commercial ships (-9.39E-07 ship accidents/nm yr⁻¹), and non-commercial ships (-1.19E-07 ship accidents/nm yr⁻¹). At the country level, ship traffic associated to Russia, Norway, and Iceland contributes the most to global increase for both commercial and non-commercial ships. Norway has the largest statistically significant negative trend for all ship and commercial ship accident rates. Future research should focus around expanding on the analytical approach taken for objective 3, as more years of AIS data become available, as currently, the focus is on a shorter time-period (2012 to 2019). Given that incident rates are low globally, for high impacts (i.e., large spills), working with a long time series allows for considering more incidents. It would also be beneficial to perform an analysis that determines if there are statistical associations between yearly accident rates and sea ice extent in the global Arctic, as well as for each country within the Arctic. This information can help to answer questions around ship safety in the global Arctic, specifically: (a) Has the shipping become safer (e.g., less accidents per distance sailed) for the global Arctic and the countries within the Arctic? (b) Are there statistical associations between sea ice extent and accident rates within the global Arctic and the countries within the Arctic? (c) Are there countries associated with a higher incident rate compared to others? This information would help target measures to specific country ships that may be less safe for navigation.

For a long time, ships have relied on navigators that could figure out their course andlocation based on seeing objects around them. However, this approach is limited to thenavigators’ ability, and with the increasing number of ships, this job becomes harder andharder. With these aspects in mind, the new system, the Automatic identification system(AIS), was created as a tool to help navigators to navigate and increase safety on the sea.AIS is an automatic identification system and is designed to send out information aboutthe vessel and its location. This thesis looks at the state of the art of Automatic identifica-tion systems and Digital selective calling systems to evaluate the security aspects of thesesystems. The thesis aims to investigate if these two systems are susceptible to spoofingattacks and what resources are required for creating successful attacks. Two experimentswere used to achieve this aim and answer the research questions. The first one was to eval-uate the Automatic identification system and test different spoofing attacks on that system.The second experiment was to test different spoofing attacks on the Digital selective callingsystem. Both of these experiments used two software-defined radios for the experiments.The experiment results show that some of the attacks tested on the systems were success-ful, and the attacks tested were successfully executed against the created system. Theseattacks were created and performed using two software-defined radios to send and receivemessages. To conclude, the two systems are susceptible to spoofing attacks. However, anattacker can gain the necessary information to create spoofing attacks on the systems, withvarying consequences and some limitations.

Machine learning methods are increasingly being used in the maritime domain to predict traffic anomalies and to mitigate risk, for example avoiding collision and groundingaccidents. However, most machine learning systems used for detecting such issues hasbeen trained predominately on single data sources such as vessel positioning data. Hence,it is desirable to support the means to combine different sources of data - in the trainingphase - to allow more complex models to be built. In this thesis, we propose a multi-data pipeline for accumulating, decoding, preprocessing, and merging Automatic Identification System (AIS) data with weather datato train time series based deep learning models. The pipeline comprises several REST APIsto connect and listen to the data sources, and storing and merging them using StructuredQuery Language (SQL). Specifically, the training pipeline consists of an AIS NMEA message decoder, weather data receiver, and a Postgres database for merging and storing thedata sources. Moreover, the pipeline was assessed by training a TensorFlow vRNN model.The proposed pipeline approach allows flexibility in the inclusion of new data sources toeffectively build models for the maritime domain as well as other traffic domains that usespositioning data.

Diet change over time is assessed for a Malabar II period (900 C.E. to 1565 C.E.) Ais indigenous community in Cape Canaveral, Florida, at the Penny Plot site (8BR158). To this end, 7,760 faunal fragments were examined, with 1,876 identified at the species, genus, or family level. Through identification and analysis of faunal remains, it can be concluded that, while the amounts of overall remains left behind as a whole increased, there were no significant changes in the types of fauna utilized or patterns of consumption. This suggests that the indigenous people who occupied this site managed their resources very effectively for a long period of time and likely passed on management strategies through generations to allow for plentiful food for years. This area of Florida has received little serious attention from scholars in the past, and through studying sites such as the Penny Plot site we are starting to paint a picture of precontact and colonial era Indigenous life in coastal Central Florida. Thus, we are better educating ourselves about the people who came first to Florida and their complex relationship with their surrounding environment, paralleling that of modern Floridians.'

The natural immune system embodies a wealth of information processing capabilities that can be exploited as a metaphor for the development of artificial immune systems. Chief among these features is the ability to recognize previously encountered substances and to generalize beyond recognition in order to provide appropriate responses to pathogens not seen before. This thesis presents a new supervised learning paradigm, resource limited artificial immune classifiers, inspired by mechanisms exhibited in natural and artificial immune systems. The key abstractions gleaned from these immune systems include resource competition, clonal selection, affinity maturation, and memory cell retention. A discussion of the progenitors of this work is offered. This work provides a thorough explication of a resource limited artifical immune classification algorithm, named AIRS (Artificial Immune Recognition System). Experimental results on both simulated data sets and real world machine learning benchmarks demonstrate the effectiveness of the AIRS algorithm as a classification technique.

No description available.

This thesis describes a method to generate new trajectories based on historic positiondata for a given geographical area. The thesis uses AIS-data from fishing boats to first describe a method that uses DBSCAN and OPTICS algorithms to cluster the data into clustersbased on routes where the boats travel and areas where the boats fish.Here bayesian optimization has been utilized to search for parameters for the clusteringalgorithms. In this given scenario it was shown DBSCAN is better in all fields, but it hasmany points where OPTICS has the potential to become better if it was modified a bit.This is followed by a method describing how to take the clusters and build a nodenetwork that then can be traversed using a path finding algorithm combined with internalrules to generate new routes that can be used in simulations to give a realistic enoughsituation picture. Finally a method to evaluate these generated routes are described andused to compare the routes to each other

Air intake system (AIS) plays an important role on affecting the performance of the engine and the vehicle. The design and optimization of the structures and materials of AIS contributes to producing a quality reliable system, reducing noise, cost and weight at the same time, which are significant to improve the performance of engine and vehicle. Fresh air hoses/pipes are intended to transport, in air cleaner purified, air from the air cleaner to the throttle fitted on the inlet manifold or to the compressor fitted to the exhaust manifold. Air cleaner box is responsible for filtering out the dust and impurities in the air. The charge air pipes are intended to transport purified compressed air from the compressor to the charge air cooler and then to the throttle fitted on the inlet pipe. For the air cleaner box optimization, through the benchmarking and the innovation ideas from the daily life, two alternative optimized designs were introduced to reduce the cost and weight. The first is four clips connectors and the other is spring clamps. For the hose clamps, another innovation design was introduced to replace the previous hose clamps, which is called friction connector on the inner side of the bellow hose, the outer side of the air cleaner box lid and the clean side duct. For the material of the charge air ducts, TPEE was selected to replace the previous EACM rubber hoses. Further tests and prototypes should be conducted and produced to verify the effect of the optimization.