454 research outputs found
Sylvia Yunjun Chen, violin, BM recital
No full textProgram for recital offered in partial fulfillment of the requirements for the degree of Bachelor of Music. With Min Young Park, piano
Induction and preliminary characterization of a novel halophage SNJ1 from lysogenic Natrinema sp F5
No full textHalophage SNJ1 was induced with mitomycin C from Natrinema sp. strain F5. The phage produces plaques on Natrinema sp. strain J7 only. The phage has a head of about 67 nm in diameter and a tail of 570 nm in length and belongs morphologically to the family Siphoviridae. The phage is strongly salt dependent; NaCl concentration affects the integrity of SNJ1, phage adsorption, and plaque formation. The optimal NaCl concentration for phage adsorption and plaque formation is 30% and 25%, respectively.Halophage SNJ1 was induced with mitomycin C from Natrinema sp. strain F5. The phage produces plaques on Natrinema sp. strain J7 only. The phage has a head of about 67 nm in diameter and a tail of 570 nm in length and belongs morphologically to the family Siphoviridae. The phage is strongly salt dependent; NaCl concentration affects the integrity of SNJ1, phage adsorption, and plaque formation. The optimal NaCl concentration for phage adsorption and plaque formation is 30% and 25%, respectively
Group visible nearest neighbor queries in spatial databases
No full textTraditional nearest neighbor queries and its variants, such as Group Nearest Neighbor Query (GNN), have been widely studied by many researchers. Recently obstacles are involved in spatial queries. The existence of obstacles may affect the query results due to the visibility of query point. In this paper, we propose a new type of query, Group Visible Nearest Neighbor Query (GVNN), which considers both visibility and distance as constraints. Multiple Traversing Obstacles (MTO) Algorithm and Traversing Obstacles Once (TOO) Algorithm are proposed to efficiently solve GVNN problem. TOO resolves GVNN by defining the invisible region of MBR of query set to prune both data set and obstacle set, and traverses obstacle R*-tree only once. The experiments with different settings show that TOO is more efficient and scalable than MTO
Assessment of Computed Tomography-Defined Muscle and Adipose Tissue Features in Relation to Length of Hospital Stay and Recurrence of Hypertriglyceridemic Pancreatitis [Corrigendum]
No full textYu H, Huang Y, Chen L, Shi L, Yang Y, Xia W. Int J Gen Med. 2021;14:1709–1717.
The authors have advised the author list, affiliations and correspondence section on page 1709 is incorrect. The correct details are as follows.
Weizhi Xia1
Yingbao Huang2
Lifang Chen2
Liuzhi Shi3
Yunjun Yang2
Huajun Yu4
1Department of Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China; 2Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China; 3Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China; 4Department of Pancreatitis Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China
Correspondence: Huajun Yu
Department of Pancreatitis Center, The First Affiliated Hospital of Wenzhou Medical University, Nan Bai Xiang Road, Quhai District, Wenzhou 325000, People’s Republic of China
Email [email protected]
The authors apologize for these errors.
Read the original articl
Integrating MOOCs in Blended-Learning Courses: Perspectives of Teachers and Students
No full textBeing recognized by Chinese universities, the MOOC has been currently applied in higher education institutions, and has been widely believed as an important opportunity for educational practices. However, only a few teachers have quickly adapted toward MOOC teaching in China. This research highlights the experiences perceived by students’ blended learning in their computer courses, as well as the specific challenges that teachers encounter in blended teaching. This study adopts a mixed-method approach. Relevant data include those generated from email interviews with university teachers (n = 12) and from online questionnaires with students (n = 96) in a Chinese university. The results suggest that both from teachers and students’ perspectives, the effect of application of blended teaching is satisfactory, as it improves classroom teaching in general. Students’ learning interests and their self-study and analytical abilities are likewise improved. Moreover, this research highlights the challenges of teachers based on several aspects – educational technology, teaching content design, students’ participation, and integration of online and offline course contents. The paper concludes with pertinent suggestions for blended teaching, such as finding a balance amongst the three aspects of technology, science, and humanities amidst a constant improvement of the teaching methods
Predicting Potential Difficulties in Second Language Lexical Tone Learning with Support Vector Machine Models
No full textSecond language speech learning is affected by learners’ native language backgrounds. Teachers can facilitate learning by tailoring their pedagogy to cater for unique difficulties induced by native language interference. The present study employed Support Vector Machine (SVM) models to simulate how naïve listeners of diverse tone languages will assimilate non-native lexical
tone categories into their native categories. Based on these simulated assimilation patterns and extrapolating basic principles from the Perceptual Assimilation Model (Best 1995), we predicted potential learning difficulties for each group. The results offer teachers guidance concerning which tone(s) to
emphasize when instructing students from particular language backgrounds
Connecting Targets to Tweets: Semantic Attention-Based Model for Target-Specific Stance Detection
No full textUnderstanding what people say and really mean in tweets is still a wide open research question. In particular, understanding the stance of a tweet, which is determined not only by its content, but also by the given target, is a very recent research aim of the community. It still remains a challenge to construct a tweet’s vector representation with respect to the target, especially when the target is only implicitly mentioned, or not mentioned at all in the tweet. We believe that better performance can be obtained by incorporating the information of the target into the tweet’s vector representation. In this paper, we thus propose to embed a novel attention mechanism at the semantic level in the bi-directional GRU-CNN structure, which is more fine-grained than the existing token-level attention mechanism. This novel attention mechanism allows the model to automatically attend to useful semantic features of informative tokens in deciding the target-specific stance, which further results in a conditional vector representation of the tweet, with respect to the given target. We evaluate our proposed model on a recent, widely applied benchmark Stance Detection dataset from Twitter for the SemEval-2016 Task 6.A. Experimental results demonstrate that the proposed model substantially outperforms several strong baselines, which include the state-of-the-art token-level attention mechanism on bi-directional GRU outputs and the SVM classifier
Time-Aware Boolean Spatial Keyword Queries
No full textWith advances in geo-positioning technologies and mobile internet, location-based services have attracted much attention, and spatial keyword queries are catching on fast. However, as far as we aware, no prior work considers the temporal information of geo-tagged objects. Temporal information is important in the spatial keyword query because many objects are not always valid. For example, visitors may plan their trips according to the opening time of attractions. In this paper, we identify and solve a novel problem, i.e., the time-aware Boolean spatial keyword query (TABSKQ), which returns the k objects that satisfy users' spatio-temporal description and textual constraint. We first present pruning strategies and algorithm based on the CIR+-tree (i.e., the CIR-tree with temporal information). Then, we propose an efficient index structure, called the TA-tree, and its corresponding algorithms, which can prune the search space using both spatio-temporal and textual information. Furthermore, we study an interesting TABSKQ variant, i.e., Joint TABSKQ (JTABSKQ), which aims to process a set of TABSKQs jointly, and extend our techniques to tackle it. Extensive experiments with real datasets offer insight into the performance of our proposed indices and algorithms.</p
- …
