Models to predict sunlight-induced photodegradation rates of contaminants in wastewater stabilisation ponds and clarifiers

Two kinetic models were established for conservative estimates of photodegradation rates of contaminants under sunlight irradiation, in particular for wastewater stabilisation ponds and clarifiers in conventional wastewater treatment plants. These two models were designated for (1) contaminants with high photolytic rates or high photolytic quantum yields, whose photodegradation is unlikely to be enhanced by aquatic photosensitisers; and (2) contaminants withstanding direct photolysis in sunlit waters but subjected to indirect photolysis. The effortlessly intelligible prediction procedure involves sampling and analysis of real water samples, simulated solar experiments in the laboratory, and transfer of the laboratory results to realise water treatment using the prediction models. Although similar models have been widely used for laboratory studies, this paper provides a preliminary example of translating laboratory results to the photochemical fate of contaminants in real waters. (C) 2019 Hohai University. Production and hosting by Elsevier B.V.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Transformation of Selected Antibiotics and Dissolved Organic Matter under Simulated Sunlight

This dissertation presents the phototransformation of selected antibiotics (fluoroquinolone and sulphonamide) and dissolved natural organic matter in sunlit waters under controlled laboratory experimental conditions. It contributes to a better understanding of the photochemical fate of these antibiotics in the aquatic environment, the transient nature of fluoroquinolones, characterisation of dissolved organic matter and its chemical transformation under sunlight irradiation. The results of this work have significant implications for water quality and biogeochemistry of aquatic systems

Should I Stay or Should I Go: Two Features to Help People Stop An Exploratory Search Wisely

poster abstractAs information becomes more ubiquitously available, many information users tend to experience a sense of anxiety due to the “information overload”. Few studies have systematically examined searchers’ stopping behavior, i.e., how users recognize how much information is enough to terminate a search. Bad decisions on a stopping point will lead to either insufficient information or unnecessary waste of time and effort without much additional information gain. Understanding searchers’ stopping behavior is extremely important to assist in thorough search result evaluation and to prevent a premature or a too-late search stopping. In this study, we present the design and implementation of two search techniques: Result Preview (RP) and History Review (HR), to help people make right decisions about when to terminate a search and how to consume information efficiently when facing an overwhelming amount of information. The basic idea of RP is to visualize the distribution of newly retrieved and re-retrieved documents to users, and that of HR is to display the previous search activities for searchers to review what has been done to help define the next steps. Both features are aiming at guiding searchers through the process of problem solving and decision making about whether to stay or leave during the search process. To implement the two techniques, we developed the search system on Bing Search API. The Bing search results were brought back to the search interface using AJAX and PHP. A formal user experiment with 24 participants is also proposed to evaluate the benefits and limitations, and also inform the future RP and HR design

Correcting for the solar wind in pulsar timing observations: the role of simultaneous a nd l ow-frequency observations

The primary goal of the pulsar timing array projects is to detect ultra-low-frequency gravitational waves. The pulsar data sets are affected by numerous noise processes including varying dispersive delays in the interstellar medium and from the solar wind. The solar wind can lead to rapidly changing variations that, with existing telescopes, can be hard to measure and then remove. In this paper we study the possibility of using a low frequency telescope to aid in such correction for the Parkes Pulsar Timing Array (PPTA) and also discuss whether the ultra-wide-bandwidth receiver for the FAST telescope is sufficient to model the solar wind variations. Our key result is that a single wide-bandwidth receiver can be used to model and remove the effect of the solar wind. However, for pulsars that pass close to the Sun such as PSR J1022+1022, the solar wind is so variable that observations at two telescopes separated by a day are insufficient to correct the solar wind effect.Comment: accepted by RA

Final Report for 2015 ER&L + EBSCO Library Fellowship Research Project

We report findings from a comprehensive assessment of e-book user experience (search and information seeking) from transaction logs, e-book usage data, and user tests. There are differences between e-book and general searches in terms of query length, number of queries and actions per session. There are also distinctive reading patterns from e-book usage data. The user tests showed that experience levels with e-books and features of e-book platforms influenced users’ information seeking behavior. Results of the assessment have significant implications for the design of e-book features to support users’ reading strategies and help libraries create a consistent e-book user experience

Predictive Analytics of E-Commerce Search Behavior for Conversion

This study explores online customer search behavior on a large e-commerce website—Walmart.com. In order to more accurately predict customer purchase conversion based on their search behavior, we adopt a modern machine-learning technique, random forest, as well as logistic regression to develop two computational models. We also integrate information retrieval literature to propose metrics to quantify online consumers’ search behavior. Results show that the random forest model performs better with a very high accuracy rate (76%) in predicting customers who will purchase the item they browsed. Among all the predictors, page and session dwell time, user type, click entropy, and click position are the strongest influential factors for the conversion behavior. The findings suggest that, with the enhanced metrics and modeling approaches, search behavior could offer strong cues about customers’ purchasing decision. Additionally, the findings also suggest operational implications about how to accommodate and induce the desired search behavior with the e-commerce website