On the nature, occurance and origin of cold low salinity water along the edge of the Gulf Stream

In the left margin of the Gulf Stream, looking in the direction of flow, a narrow discontinuous band of relatively cold water is often found. This haa been attributed to upwelling from the colder depths of the ocean, but recent investigations throw doubt on this explanation, since this water, which forms a thin layer close to the surface, may have a lower salinity than any water in the depths. Evidence is given that the only cold low salinity source is the so-called Shelf water, which is found in a broad band between· the shore and a boundary off the Continental Shelf...

Complex relationships : the state, privateers, & organized crime

1 online resource (68 pages)Includes abstract and appendices.Includes bibliographical references (pages 57-61).The purpose of this research is to examine the history and nature of privateers during the 17th through the 19th centuries with the aim in answering this question: Using contemporary definitions, can the business of privateering can be categorized as organized crime? Privateering has long been considered, not only a legal course of reprisal for wartime losses, but also a heroic action that was celebrated, at least on the side of the privateer. The reality is more complex. In order to explain why privateers were employed despite the harm they perpetrated throughout the Maritimes during the seventeenth to nineteenth centuries, this paper incorporates a blend of sociology, criminology, Atlantic Canadian history, and political economy to show the connection between privateering and organized crime. It draws on a combination of sources to gather data on the complex history and nature of privateering. It also applies a combination of definitions to show that this class of mercenary/merchant marine were not only necessary in establishing the interests of foreign powers in Canada, but were also instrumental in the foundation and development of the early government in the Maritimes; shaping the course Canada would rise to or take in the coming two centuries. Finally, Stephen Schneider’s 23-point comprehensive taxonomy of the characteristics of an organized crime conspiracy is applied, along with the historical and contemporary evidence to point to a classification of privateering as organized crime

Drawing Out Inner Feelings: The Visual Expression and Recognition of Emotions in the Art of Children with Autism

This pilot research was undertaken in order to explore the visual expression and recognition of emotions in the art of high-functioning early school-aged children with autism. Children with autism have social and communication impairments which limit their ability to express and recognize emotions. The current study explored the expression and recognition of emotions in the drawings of children with and without autism in two stages. Firstly, a small sample of eight children with and without autism created drawings of four emotions: happiness, sadness, anger, and fear. Secondly, a larger group of thirty-two children with and without autism looked at these emotional drawings and chose which of the four emotions they recognized in each of the thirty-two drawings. Interestingly, drawings made by children with autism received significantly higher ratings of agreement than drawings made by typically developing children. Additionally, there was no significant difference between the performance of children with and without autism in their ability to recognize the intended emotion in the drawings. This offers some preliminary evidence that some children with autism may be able to recognize the intended emotion in drawings just as well as their typically developing peers. Findings from this preliminary study suggest that artistic expression and recognition of emotion may be potential strengths in some children with autism. This paper suggests avenues for further research and discusses some important applications to art therapy

Towards a Data-driven Performance Management in Digital Shop Floor Management

Key performance indicators (KPIs) are crucial for measuring and managing the performance of industrial processes. They are used to detect deviations in processes, enabling opportunities to improve manufacturing processes within the three dimensions time, quality, and cost. In this context, the timeliness of information plays a decisive role in the success of measures since delayed information availability can leave decision makers with no time to react. With the introduction of digitization and industry 4.0, increasing amounts of data become available. They can be used to accelerate problem detection and shortening reaction times to define appropriate actions. This paper presents a data-driven performance management approach integrated in digital shop floor management (dSFM). If a deviation is detected in one process, KPIs of subsequent processes (horizontal level) as well as subordinate levels (vertical level) are checked for correlations and, if present, the associated team is notified by an automatic warning through the dSFM system. Based on the identified correlations, the team discusses the deviations and defines suitable countermeasures. The aim of this approach is to identify deviations more quickly and to quantify their impacts, thus giving shop floor managers the ability to react in time

Beyond Pareto Analysis: A Decision Support Model for the Prioritization of Deviations with Natural Language Processing

In the manufacturing domain, the systematic problem-solving (SPS) process is essential to eliminate the root causes of deviations from expected performance. The major goal of SPS is to prevent the recurrence of known deviations. However, due to time and resource limitations, the deviations that occur on the shop floor should be prioritized before applying SPS. Therefore, a method to support the decision-making process for prioritization of deviations is required. Traditional methods, such as the Pareto analysis, are widely accepted and applied for easy use. But their performance is no more sufficient for the production environment with large fluctuations nowadays. Therefore, this paper proposes a decision support model - the error score - to prioritize deviations on the shop floor. The error score is calculated based on the process data as well as textual data found in the deviation documentation. As the quality of textual data in the deviation documentation has great effects on the performance of the model, Natural Language Processing (NLP) methods are developed to pre-process the unstructured text. To validate the model, it is applied to a real-world use case in the automotive industry to demonstrate and evaluate the performance. The study shows that the proposed model can effectively support the decision-making process on the shop floor and is superior to traditional methods

View of Purcell's Cove

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View of Portuguese Cove

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View of Purcell's Cove Road, Purcell's Cove, from above

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View of Purcell's Cove showing islands.

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