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Commercial titanium dioxide is successfully plasma‐treated under ambient conditions for different periods, leading to reduced crystallite size and the creation of oxygen vacancies. Density functional theory‐based calculations reveal the emergence of additional localized states close to the conduction band, primarily associated with under‐coordinated titanium atoms in non‐stoichiometric titanium‐oxide systems. The plasma‐treated samples exhibit improved photocatalytic performance in the degradation of methylene blue compared to untreated samples. Moreover, the 4‐hour plasma‐treated photocatalyst demonstrates commendable stability and reusability. This work highlights the potential of cost‐effective plasma treatment as a simple modification technique to significantly enhance the photocatalytic capabilities of titanium‐oxide materials.

 

We investigate the state-of-the-art Lanczos eigensolvers available in the Grid and QUDA libraries. They include Implicitly Restarted Lanczos, Thick-Restart Lanczos, and Block Lanczos. We measure and analyze their performance for the Highly Improved Staggered Quark (HISQ) Dirac operator. We also discuss optimization of Chebyshev acceleration. 

Abstract. We investigated preservice teachers’ (PSTs) (N=13) experiences in a science teaching inquiry group professional learning experience on integrating computational thinking (CT) into elementary science. A subgroup of PSTs (n=6) participated alongside their mentor teachers. The others (n=7) participated independently. Our research question was: To what extent, if any, did participating in a professional learning experience on CT along with their mentor teachers appear to enhance PSTs’ learning and practice related to CT integration? We analyzed evaluation feedback, interviews, participant-developed lesson plans, surveys, and attendance data. Findings suggested that participants in both groups reacted positively to the learning experience’s content and approach, and expressed similar perceptions of their CT integration knowledge. PSTs participating with their mentor teachers felt slightly more successful in their CT integration efforts, and perceived CT integration as more feasible in their teaching contexts. However, differences between the groups were minimal. We also noted possible of influence of PSTs’ perceptions of the districts in which they were teaching. Our findings underscore the importance of PSTs’ perceptions of their teaching contexts when bringing a new innovation to the classroom - namely, perceptions of their mentors and curricula as supportive of the innovation. Through this ongoing work, we seek to identify empirically-supported strategies for preparing PSTs to integrate CT into their future classrooms. 

Recent advances in data and information technologies have enabled extensive digital datasets to be available to decision makers throughout the life cycle of a transportation project. However, most of these data are not yet fully reused due to the challenging and time-consuming process of extracting the desired data for a specific purpose. Digital datasets are presented only in computer-readable formats and they are mostly complicated. Extracting data from complex and large data sources is significantly time-consuming and requires considerable expertise. Thus, there is a need for a user-friendly data exploration framework that allows users to present their data interests in human language. To fulfill that demand, this study employs natural language processing (NLP) techniques to develop a natural language interface (NLI) which can understand users’ intent and automatically convert their inputs in the human language into formal queries. This paper presents the results of an important task of the development of such a NLI that is to establish a method for classifying the tokens of an ad-hoc query in accordance with their semantic contribution to the corresponding formal query. The method was validated on a small test set of 30 plain English questions manually annotated by an expert. The result shows an impressive accuracy of over 95%. The token classification presented in this paper is expected to provide a fundamental means for developing an effective NLI to transportation asset databases.