Stressors, mediators, and health care outcomes of home enteral nutrition in family caregivers and their older adult care recipients

Family caregivers manage home enteral nutrition (HEN) for over 77% of an estimated 1 of every 400 Medicare recipients. Increasing usage of HEN in older adults combined with reliance on family caregivers raises concerns for the quality, outcomes, and costs of care. These concerns are relevant in light of Medicare limitations on nursing assistance and non-reimbursement for nutrition services, despite annual costs of over $600 million. This study applied stress process theories to assess stressor, mediator, and outcome variables salient to HEN and caregiving. In-home structured interviews occurred with a multi-ethnic sample of 30 caregiving dyads at 1–3 months after discharge on HEN. Care recipients were aged ≥60 (M = 68.4 years) and did not have dementia. Caregivers were aged ≥21, unpaid, and lived within 45 minutes of care recipients. Caregivers performed an average of 19.7 tasks daily for 61.9 hours weekly. Training needs were identified for 33 functional, care management, technical, and nutritional tasks. Preparedness scores were low (M = 1.73/4.0), and positively correlated with competence, self-rated quality of care and positive feelings, and negatively with overload, role captivity, and negative feelings (Ps < .05). Caregivers had multiple changes in lifestyle and dietary behaviors. Lifestyle changes positively correlated with overload, and negatively with preparedness and positive feelings. Dietary changes positively correlated with number of tasks, overload, role captivity and negative feelings, and negatively with preparedness (Ps < .01). Fifty-seven percent of caregivers aged >50 were at nutrition risk. Care recipients fared worse. Average weight change was −4.35 pounds (P < .001). Physical complications interrupted daily enteral infusions. Water intake was half of fluid need and associated with signs of dehydration (P < .001). Physical and social function was poor, with older subjects more impaired ( P < .04). Those with better prepared or less overloaded caregivers had higher functionality and QOL (P < .002). Complications, type of feeding tube, and caregiver preparedness correlated with frequency of health care utilization (Ps < .05). Efficacy of HEN in older adults requires specialized caregiver training, attention to caregivers' needs, and frequent monitoring from a highly skilled multidisciplinary team including dietitians. ^

Vocabulary learning through use of the picture-word inductive model for young English learners in China: A mixed methods examination using Cognitive Load Theory

English has been taught as a core and compulsory subject in China for decades. Recently, the demand for English in China has increased dramatically. China now has the world's largest English-learning population. The traditional English-teaching method cannot continue to be the only approach because it merely focuses on reading, grammar and translation, which cannot meet English learners and users' needs (i.e., communicative competence and skills in speaking and writing). ^ This study was conducted to investigate if the Picture-Word Inductive Model (PWIM), a new pedagogical method using pictures and inductive thinking, would benefit English learners in China in terms of potential higher output in speaking and writing. With the gauge of Cognitive Load Theory (CLT), specifically, its redundancy effect, I investigated whether processing words and a picture concurrently would present a cognitive overload for English learners in China. ^ I conducted a mixed methods research study. A quasi-experiment (pretest, intervention for seven weeks, and posttest) was conducted using 234 students in four groups in Lianyungang, China (58 fourth graders and 57 seventh graders as an experimental group with PWIM and 59 fourth graders and 60 seventh graders as a control group with the traditional method). No significant difference in the effects of PWIM was found on vocabulary acquisition based on grade levels. Observations, questionnaires with open-ended questions, and interviews were deployed to answer the three remaining research questions. A few students felt cognitively overloaded when they encountered too many writing samples, too many new words at one time, repeated words, mismatches between words and pictures, and so on. Many students listed and exemplified numerous strengths of PWIM, but a few mentioned weaknesses of PWIM. The students expressed the idea that PWIM had a positive effect on their English teaching. ^ As integrated inferences, qualitative findings were used to explain the quantitative results that there were no significant differences of the effects of the PWIM between the experimental and control groups in both grade levels, from four contextual aspects: time constraints on PWIM implementation, teachers' resistance, how to use PWIM and PWIM implemented in a classroom over 55 students.^

Next generation of recommender systems: Algorithms and applications

Personalized recommender systems aim to assist users in retrieving and accessing interesting items by automatically acquiring user preferences from the historical data and matching items with the preferences. In the last decade, recommendation services have gained great attention due to the problem of information overload. However, despite recent advances of personalization techniques, several critical issues in modern recommender systems have not been well studied. These issues include: (1) understanding the accessing patterns of users (i.e., how to effectively model users' accessing behaviors); (2) understanding the relations between users and other objects (i.e., how to comprehensively assess the complex correlations between users and entities in recommender systems); and (3) understanding the interest change of users (i.e., how to adaptively capture users' preference drift over time). To meet the needs of users in modern recommender systems, it is imperative to provide solutions to address the aforementioned issues and apply the solutions to real-world applications. ^ The major goal of this dissertation is to provide integrated recommendation approaches to tackle the challenges of the current generation of recommender systems. In particular, three user-oriented aspects of recommendation techniques were studied, including understanding accessing patterns, understanding complex relations and understanding temporal dynamics. To this end, we made three research contributions. First, we presented various personalized user profiling algorithms to capture click behaviors of users from both coarse- and fine-grained granularities; second, we proposed graph-based recommendation models to describe the complex correlations in a recommender system; third, we studied temporal recommendation approaches in order to capture the preference changes of users, by considering both long-term and short-term user profiles. In addition, a versatile recommendation framework was proposed, in which the proposed recommendation techniques were seamlessly integrated. Different evaluation criteria were implemented in this framework for evaluating recommendation techniques in real-world recommendation applications. ^ In summary, the frequent changes of user interests and item repository lead to a series of user-centric challenges that are not well addressed in the current generation of recommender systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective recommendation framework.^

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Vocabulary Learning through Use of the Picture-Word Inductive Model for Young English Learners in China: A Mixed Methods Examination Using Cognitive Load Theory

English has been taught as a core and compulsory subject in China for decades. Recently, the demand for English in China has increased dramatically. China now has the world’s largest English-learning population. The traditional English-teaching method cannot continue to be the only approach because it merely focuses on reading, grammar and translation, which cannot meet English learners and users’ needs (i.e., communicative competence and skills in speaking and writing). This study was conducted to investigate if the Picture-Word Inductive Model (PWIM), a new pedagogical method using pictures and inductive thinking, would benefit English learners in China in terms of potential higher output in speaking and writing. With the gauge of Cognitive Load Theory (CLT), specifically, its redundancy effect, I investigated whether processing words and a picture concurrently would present a cognitive overload for English learners in China. I conducted a mixed methods research study. A quasi-experiment (pretest, intervention for seven weeks, and posttest) was conducted using 234 students in four groups in Lianyungang, China (58 fourth graders and 57 seventh graders as an experimental group with PWIM and 59 fourth graders and 60 seventh graders as a control group with the traditional method). No significant difference in the effects of PWIM was found on vocabulary acquisition based on grade levels. Observations, questionnaires with open-ended questions, and interviews were deployed to answer the three remaining research questions. A few students felt cognitively overloaded when they encountered too many writing samples, too many new words at one time, repeated words, mismatches between words and pictures, and so on. Many students listed and exemplified numerous strengths of PWIM, but a few mentioned weaknesses of PWIM. The students expressed the idea that PWIM had a positive effect on their English teaching. As integrated inferences, qualitative findings were used to explain the quantitative results that there were no significant differences of the effects of the PWIM between the experimental and control groups in both grade levels, from four contextual aspects: time constraints on PWIM implementation, teachers’ resistance, how to use PWIM and PWIM implemented in a classroom over 55 students.

Use of Fiber Reinforced Polymer Composite Cable for Post-tensioning Application

Corrosion of steel tendons is a major problem for post-tensioned concrete, especially because corrosion of the steel strands is often hard to detect inside grouted ducts. Non-metallic tendons can serve as an alternative material to steel for post-tensioning applications. Carbon fiber reinforced polymer (CFRP), given its higher strength and elastic modulus, as well as excellent durability and fatigue strength, is the most practical option for post-tensioning applications. The primary objective of this research project was to assess the feasibility of the use of innovative carbon fiber reinforced polymer (CFRP) tendons and to develop guidelines for CFRP in post-tensioned bridge applications, including segmental bridges and pier caps. An experimental investigation and a numerical simulation were conducted to compare the performance of a scaled segmental bridge model, post-tensioned with two types of carbon fiber strands and steel strands. The model was tested at different prestress levels and at different loading configurations. While the study confirms feasibility of both types of carbon fiber strands for segmental bridge applications, and their similar serviceability behavior, strands with higher elastic modulus could improve structural performance and minimize displacements beyond service loads. As the second component of the project, a side-by-side comparison of two types of carbon fiber strands against steel strands was conducted in a scaled pier cap model. Two different strand arrangements were used for post-tensioning, with eight and six strands, respectively representing an over-design and a slight under-design relative to the factored demand. The model was tested under service and factored loads. The investigation confirmed the feasibility of using carbon fiber strands in unbonded post-tensioning of pier caps. Considering both serviceability and overload conditions, the general performance of the pier cap model was deemed acceptable using either type of carbon fiber strands and quite comparable to that of steel strands. In another component of this research, creep stress tests were conducted with carbon fiber composite cable (CFCC). The anchorages for all the specimens were prepared using a commercially available expansive grout. Specimens withstood 95% of the guaranteed capacity provided by the manufacturer for a period of five months, without any sign of rupture.