Negative stereotypes of aging, such as dependency, tend to paint a picture of older adults as frail or a burden on society. This study aimed to explore the relationship between the Fear of Dependency Scale and anxiety and depression, evaluating gender, age, and physical health as moderators. Findings suggest that age moderated the relationship between fear of dependency and depression and anxiety in women, with middle-aged women reporting the highest levels of depression and anxiety. Similarly, poor physical health in women with high fear of dependency being related to higher levels of depression and anxiety. Fear of dependency was related to higher levels of depression, but not anxiety in men. Age did not moderate the relationship between fear of dependency and mental health measures, but physical health did moderate the relationship. Men with poor perceived health and a high fear of dependency reported higher levels of depression.

This thesis studies impact, sustainability, and dependency in the nonprofit sector. Empower Playgrounds, Inc. (EPI) is a nonprofit organization working to improve educational conditions in Africa through educational recreation, lighting for education, and hands-on science labs. A social impact assessment I performed for EPI in 2008 creates the context for discussion of EPI's sustainability. Dependency results from a lack of sufficient conditions for sustainability—resources, capability, and opportunity—which I elucidate in a framework of corresponding theories of capital. Using this sufficiency framework I analyze the resources, capabilities, and opportunities required for EPI program sustainability, determine unmet conditions, and propose solutions to improve sustainability. This thesis contributes to the nonprofit sustainability literature by combining problem-driven (as opposed to solution-driven) design processes with theories of capital to assess nonprofit program sustainability. This framework is a powerful planning and assessment tool for both business model generation and program design.

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With the prevalence of large data stored in the cloud, including unstructured information in the form of text, there is now an increased emphasis on text mining. A broad range of techniques are now used for text mining, including algorithms adapted from machine learning, NLP, computational linguistics, and data mining. Applications are also multi-fold, including classification, clustering, segmentation, relationship discovery, and practically any task that discovers latent information from written natural language. Classical mining algorithms have traditionally focused on shallow representations such as bag-of-words and similar feature-based models. With the advent of modern high performance computing, deep sentence level linguistic analysis of large scale text corpora has become practical. In this dissertation, we evaluate the utility of dependency parses as textual features for different text mining applications. Dependency parsing is one form of syntactic parsing, based on the dependency grammar implicit in sentences. While dependency parsing has traditionally been used for text understanding, we investigate here its application to supply features for text mining applications. We specifically focus on three methods to construct textual features from dependency parses. First, we consider a dependency parse as a general feature akin to a traditional bag-of-words model. Second, we consider the dependency parse as the basis to build a feature graph representation. Finally, we use dependency parses in a supervised collocation mining method for feature selection. To investigate these three methods, several applications are studied, including: (i) movie spoiler detection, (ii) text segmentation, (iii) query expansion, and (iv) recommender systems. / Ph. D.

Renewable Portfolio Standards (RPS) have been a staple energy policy in the state of Colorado since 2004. While there has been a steady increase to the RPS percentages over the years, there have been several points where this policy has been significantly contested. This paper explores the political decision making of the two largest electricity producers in the state, Xcel Energy and Tri-State Generation and Transmission Association, to determine at what points that the costs of legislation exceeded the costs of opposition for those two organizations. Using this information, the paper explores the implications these political battles have on characterizing Colorado as a state with renewable path dependency and renewable policy feedback. / Master of Arts / Since 2004, Colorado has enacted several successful laws to increase renewable energy use within the state. However, in recent years these laws have failed to achieve their goals. By analyzing the two largest producers of electricity in the state, Xcel Energy and Tri-State Generation and Transmission Association, this paper tries to answer what parts of the policies were too costly for the organizations to accept. Using this information, this project concludes that the current renewable energy policies are unlikely to be repealed in the future, but there is some evidence to suggest that the rate of renewable energy policy enactment may decrease.

Computational Notebook platforms are very commonly used by programmers and data scientists. However, due to the interactive development environment of notebooks, developers struggle to maintain effective code organization which has an adverse effect on their productivity. In this thesis, we research and develop techniques to help solve issues with code organization that developers face in an effort to improve productivity. Notebooks are often executed out of order which adversely effects their portability. To determine cell execution orders in computational notebooks, we develop a technique that determines the execution order for a given cell and if need be, attempt to rearrange the cells to match the intended execution order. With such a tool, users would not need to manually determine the execution orders themselves. In a user study with 9 participants, our approach on average saves users about 95% of the time required to determine execution orders manually. We also developed a technique to support insertion of cells in rows in addition to the standard column insertion to help better represent multiple contexts. In a user study with 9 participants, this technique on a scale of one to ten on average was judged as a 8.44 in terms of representing multiple contexts as opposed to standard view which was judged as 4.77. / Master of Science / In the field of data science computational notebooks are a very commonly used tool. They allow users to create programs to perform computations and to display graphs, tables and other visualizations to supplement their analysis. Computational Notebooks have some limitations in the development environment which can make it difficult for users to organize their code. This can make it very difficult to read through and analyze the code to find or fix any errors which in turn can have a very negative effect on developer productivity. In this thesis, we research methods to improve the development environment and increase developer productivity. We achieve this by offering tools to the user that can help organize and cleanup their code making it easier to comprehend the code and make any necessary changes.

No / Person-centred care can improve the well-being of patients and is therefore a key driver in healthcare developments in the UK. The current study aims to investigate the complex relationship between cognitive impairment, dependency and well-being in people with a wide range of acquired brain and spinal injuries. Sixty-five participants, with varied acquired brain and spinal injuries, were selected by convenience sampling from six inpatient clinical neuroscience settings. Participants were observed using Dementia Care Mapping – Neurorehabilitation (DCM-NR) and categorised based on severity of cognitive impairment. A significant difference in the behaviours participants engaged in, their well-being and dependency was found between the severe cognitive impairment group and the mild, moderate or no cognitive impairment groups. Dependency and cognitive impairment accounted for 23.9% of the variance in well–ill-being scores and 17.2% of the variance in potential for positive engagement. The current study highlights the impact of severe cognitive impairment and dependency on the behaviours patients engaged in and their well-being. It also affirms the utility of DCM-NR in providing insights into patient experience. Consideration is given to developing DCM-NR as a process that may improve person-centred care in neuroscience settings.

<p>This licentiate thesis deals with automatic syntactic analysis, or parsing, of natural languages. A parser constructs the syntactic analysis, which it learns by looking at correctly analyzed sentences, known as training data. The general topic concerns manipulations of the training data in order to improve the parsing accuracy.</p><p>Several studies using constituency-based theories for natural languages in such automatic and data-driven syntactic parsing have shown that training data, annotated according to a linguistic theory, often needs to be adapted in various ways in order to achieve an adequate, automatic analysis. A linguistically sound constituent structure is not necessarily well-suited for learning and parsing using existing data-driven methods. Modifications to the constituency-based trees in the training data, and corresponding modifications to the parser output, have successfully been applied to increase the parser accuracy. The topic of this thesis is to investigate whether similar modifications in the form of tree transformations to training data, annotated with dependency-based structures, can improve accuracy for data-driven dependency parsers. In order to do this, two types of tree transformations are in focus in this thesis.</p><p>%This is a topic that so far has been less studied.</p><p>The first one concerns non-projectivity. The full potential of dependency parsing can only be realized if non-projective constructions are allowed, which pose a problem for projective dependency parsers. On the other hand, non-projective parsers tend, among other things, to be slower. In order to maintain the benefits of projective parsing, a tree transformation technique to recover non-projectivity while using a projective parser is presented here.</p><p>The second type of transformation concerns linguistic phenomena that are possible but hard for a parser to learn, given a certain choice of dependency analysis. This study has concentrated on two such phenomena, coordination and verb groups, for which tree transformations are applied in order to improve parsing accuracy, in case the original structure does not coincide with a structure that is easy to learn.</p><p>Empirical evaluations are performed using treebank data from various languages, and using more than one dependency parser. The results show that the benefit of these tree transformations used in preprocessing and postprocessing to a large extent is language, treebank and parser independent.</p>