Everglades Online Thesaurus: A Standard Vocabulary for the South Florida Ecosystem

The Everglades Online Thesaurus is a structured vocabulary of concepts and terms relating to the south Florida environment. Designed as an information management tool for both researchers and metadata creators, the Thesaurus is intended to improve information retrieval across the many disparate information systems, databases, and web sites that provide Everglades-related information. The vocabulary provided by the Everglades Online Thesaurus expresses each relevant concept using a single ‘preferred term’, whereas in natural language many terms may exist to express that same concept. In this way, the Thesaurus offers the possibility of standardizing the terminology used to describe Everglades-related information — an important factor in predictable and successful resource discovery.

Online solid phase extraction liquid chromatography tandem mass spectrometry (SPE-LC-MS/MS) method for the determination of sucralose in reclaimed and drinking waters and its photo degradation in natural waters from South Florida

Background Sucralose has gained popularity as a low calorie artificial sweetener worldwide. Due to its high stability and persistence, sucralose has shown widespread occurrence in environmental waters, at concentrations that could reach up to several μg/L. Previous studies have used time consuming sample preparation methods (offline solid phase extraction/derivatization) or methods with rather high detection limits (direct injection) for sucralose analysis. This study described a faster and sensitive analytical method for the determination of sucralose in environmental samples. Results An online SPE-LC–MS/MS method was developed, being capable to quantify sucralose in 12 minutes using only 10 mL of sample, with method detection limits (MDLs) of 4.5 ng/L, 8.5 ng/L and 45 ng/L for deionized water, drinking and reclaimed waters (1:10 diluted with deionized water), respectively. Sucralose was detected in 82% of the reclaimed water samples at concentrations reaching up to 18 μg/L. The monthly average for a period of one year was 9.1 ± 2.9 μg/L. The calculated mass loads per capita of sucralose discharged through WWTP effluents based on the concentrations detected in wastewaters in the U. S. is 5.0 mg/day/person. As expected, the concentrations observed in drinking water were much lower but still relevant reaching as high as 465 ng/L. In order to evaluate the stability of sucralose, photodegradation experiments were performed in natural waters. Significant photodegradation of sucralose was observed only in freshwater at 254 nm. Minimal degradation (<20%) was observed for all matrices under more natural conditions (350 nm or solar simulator). The only photolysis product of sucralose identified by high resolution mass spectrometry was a de-chlorinated molecule at m/z 362.0535, with molecular formula C12H20Cl2O8. Conclusions Online SPE LC-APCI/MS/MS developed in the study was applied to more than 100 environmental samples. Sucralose was frequently detected (>80%) indicating that the conventional treatment process employed in the sewage treatment plants is not efficient for its removal. Detection of sucralose in drinking waters suggests potential contamination of surface and ground waters sources with anthropogenic wastewater streams. Its high resistance to photodegradation, minimal sorption and high solubility indicate that sucralose could be a good tracer of anthropogenic wastewater intrusion into the environment.

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

The Relationship Between Online and In Person GED Preparatory Courses

Courses are taken in order to prepare for the General Educational Development Test. These courses are offered traditionally and virtually. The actual test must be taken in-person regardless of whether an individual took preparatory courses virtually or traditionally. This paper will explore the benefits and obstacles that each method of delivering instruction has.

A Basic Interpretive Study of the Experiences of University Students Who Have Dropped or Failed an Online Course

Online courses have increased in enrollments over the past few decades. As the number of students taking online courses have increased, so has the number of students who have dropped or failed an online course. According to the literature, online courses may have higher drop rates than traditional, face-to-face courses. The number of students who fail an online course is, also, of concern. As online courses may continue to grow over the next few decades, studies on persistence in online courses may benefit students, administrators, instructional designers, educators, and researchers. Although previous research studies have addressed persistence in online courses, very few examine it from the perspectives of students who were unsuccessful in their courses. These students may have unique insights about the online experience that may have related to their lack of success. The purpose of this study was to understand the experiences of university students who have failed or dropped an online course through the lenses of transactional distance theory and Kember’s model of dropout in distance education. Transactional distance theory discusses the dialog, structure, and learner autonomy involved in an online course, while, Kember’s model presents categories that may relate to dropping an online course. Together, the theory and model may help in understanding the experiences of students who have dropped or failed an online course. In this study, semi-structured interviews were conducted with 20 participants from a large Southeastern university in the United States. Based on the participants’ responses, the data was sorted and ranked according to the amount of transactional distance in their courses, as well as the categories of Kember’s model. Many of the participants who experienced low or high transactional distance have, also, expressed an issue with the goal commitment category of Kember’s model. Additionally, there were important differences in the student characteristics of those who dropped or failed an online course. Furthermore, suggestions for improving online courses were given by the participants. Some of these suggestions included more student-instructor interactions, the use of more technology tools in their online course, and for orientations to the online environment to be offered.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

A basic interpretive study of the experiences of university students who have dropped or failed an online course

Online courses have increased in enrollments over the past few decades. As the number of students taking online courses have increased, so has the number of students who have dropped or failed an online course. According to the literature, online courses may have higher drop rates than traditional, face-to-face courses. The number of students who fail an online course is, also, of concern. As online courses may continue to grow over the next few decades, studies on persistence in online courses may benefit students, administrators, instructional designers, educators, and researchers. Although previous research studies have addressed persistence in online courses, very few examine it from the perspectives of students who were unsuccessful in their courses. These students may have unique insights about the online experience that may have related to their lack of success. The purpose of this study was to understand the experiences of university students who have failed or dropped an online course through the lenses of transactional distance theory and Kember’s model of dropout in distance education. Transactional distance theory discusses the dialog, structure, and learner autonomy involved in an online course, while, Kember’s model presents categories that may relate to dropping an online course. Together, the theory and model may help in understanding the experiences of students who have dropped or failed an online course. In this study, semi-structured interviews were conducted with 20 participants from a large Southeastern university in the United States. Based on the participants’ responses, the data was sorted and ranked according to the amount of transactional distance in their courses, as well as the categories of Kember’s model. Many of the participants who experienced low or high transactional distance have, also, expressed an issue with the goal commitment category of Kember’s model. Additionally, there were important differences in the student characteristics of those who dropped or failed an online course. Furthermore, suggestions for improving online courses were given by the participants. Some of these suggestions included more student-instructor interactions, the use of more technology tools in their online course, and for orientations to the online environment to be offered.^