The goal of the current study was to describe the organizational and institutional foundations within traditional rural communities that facilitate implementation of participatory community poverty reduction programs. The focus was on communities within or adjacent to protected areas. A case study research approach was used to assess community mobilization, participation and analytical capacity and also to evaluate community groups and organizations for their competence to be local implementing agents of poverty reduction programs. The research findings showed that inadequate skills and organizational levels limited the ability of communities to fully utilize protected areas as poverty reduction initiatives. Key determinants of community participation were the ability to mobilize and also to undertake detailed analysis of local situations. Community mobilization depended on the relationship between the mobilizing agent and the community, social cohesion and gender. Analytical capacity was influenced mostly by the level of education, prior experience and gender. Interestingly, community groups that had the highest potential to be implementing agents, had strong ties to traditional institutions, suggesting that groups with well recognized power and legitimacy within the community are better positioned to facilitate implementation of community poverty eradication initiatives. A number of indicators of community competence were identified and these were used to develop an analytical framework that can be used as a diagnostic tool for determining community competence. / Protected Areas and Poverty Reduction - Canada-Africa Research and Learning Alliance Project

A cross-media analysis framework is an integrated multi-modal platform where a media resource containing different types of data such as text, images, audio and video is analyzed with metadata extractors, working jointly to contextualize the media resource. It generally provides cross-media analysis and automatic annotation, metadata publication and storage, searches and recommendation services. For on-line content providers, such services allow them to semantically enhance a media resource with the extracted metadata representing the hidden meanings and make it more efficiently searchable. Within the architecture of such frameworks, Natural Language Processing (NLP) infrastructures cover a substantial part. The NLP infrastructures include text analysis components such as a parser, named entity extraction and linking, sentiment analysis and automatic speech recognition. Since NLP tools and techniques are originally designed to operate in isolation, integrating them in cross-media frameworks and analyzing textual data extracted from multimedia sources is very challenging. Especially, the text extracted from audio-visual content lack linguistic features that potentially provide important clues for text analysis components. Thus, there is a need to develop various techniques to meet the requirements and design principles of the frameworks. In our thesis, we explore developing various methods and models satisfying text and speech analysis requirements posed by cross-media analysis frameworks. The developed methods allow the frameworks to extract linguistic knowledge of various types and predict various information such as sentiment and competence. We also attempt to enhance the multilingualism of the frameworks by designing an analysis pipeline that includes speech recognition, transliteration and named entity recognition for Amharic, that also enables the accessibility of Amharic contents on the web more efficiently. The method can potentially be extended to support other under-resourced languages.

Indiana University-Purdue University Indianapolis (IUPUI) / Gastric residual volume (GRV) is a clinical assessment to evaluate gastric emptying and enteral feeding tolerance. Factors such as the tube size, tube material, tube port configuration, placement of the tube in the gastric fluid, the amount of fluid and person completing the assessment may influence the accuracy of residual volume assessment. Little attention has been paid to assessing the accuracy of GRV measurement when the actual volume being aspirated is known, and no studies have compared the accuracy in obtaining RV using the three different techniques reported in the literature that are used to obtain aspirate in practice (syringe, suction, and gravity drainage). This in vitro study evaluated three different methods for aspirating feeding formula through two different tube sizes (10 Fr [small] and 18 Fr [large]), tube materials (polyvinyl chloride and polyurethane), using four levels of nursing experience (student, novice, experienced and expert) blinded to the five fixed fluid volumes of feeding formula in a simulated stomach, to determine if the RV can be accurately obtained. The study design consisted of a 3x2x2x4x5 completely randomized factorial ANOVA (with a total of 240 cells) and 479 RV assessments were made by the four nurse participants. All three methods (syringe, suction and gravity) used to aspirate RV did not perform substantially well in aspirating fluid, and on average, the methods were able to aspirate about 50% of the volume available. The syringe and suction techniques were comparable and produced higher proportions of RVs, although the interrater reliability of RV assessment was better with the syringe method. The gravity technique generally performed poorly. Overall, the polyvinyl chloride material and smaller tubes were associated with higher RV assessments. RV assessment is a variable assessment and the three methods did not perform well in this in vitro study. These findings should be further explored and confirmed using larger samples. This knowledge will be important in establishing the best technique for assessing RV to maximize EN delivery in practice and will contribute to future research to test strategies to optimize EN intake in critically ill patients.