11,056 research outputs found
Review of Epoicocladius Šulc et Zavřel from China (Diptera, Chironomidae)
No full textYan, Chuncai, Liu, Ting, Cao, Wei, Zhang, Xinghua, Liu, Wenbin (2019): Review of Epoicocladius Šulc et Zavřel from China (Diptera, Chironomidae). Zootaxa 4590 (2): 289-295, DOI: 10.11646/zootaxa.4590.2.
FIGURES 2–7 in Review of Epoicocladius Šulc et Zavřel from China (Diptera, Chironomidae)
No full textFIGURES 2–7. Epoicocladius ephemerae (Kieffer, 1924). Larvae. 2—mandible; 3—mentum; 4—antenna; 5—labro-epipha- ryngeal region. Pupae. 6—Thoracic horn and precorneals in Chinese specimen; 7—precorneals in European specimens.Published as part of Yan, Chuncai, Liu, Ting, Cao, Wei, Zhang, Xinghua & Liu, Wenbin, 2019, Review of Epoicocladius Šulc et Zavřel from China (Diptera, Chironomidae), pp. 289-295 in Zootaxa 4590 (2) on page 292, DOI: 10.11646/zootaxa.4590.2.6, http://zenodo.org/record/265202
sj-docx-1-tct-10.1177_15330338231212071 - Supplemental material for SGMS1-AS1/MicroRNA-106a-5p/CPT2 Axis as a Novel Target for Regulating Lactate Metabolism in Colon Cancer
No full textSupplemental material, sj-docx-1-tct-10.1177_15330338231212071 for SGMS1-AS1/MicroRNA-106a-5p/CPT2 Axis as a Novel Target for Regulating Lactate Metabolism in Colon Cancer by Yan Ruochen, Ji Wenbin, Gao Chao, Yuan Yuhua and Qi Feng in Technology in Cancer Research & Treatment</p
Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships
No full textIt is critical to have accurate ship trajectory prediction for collision avoidance and intelligent traffic management of manned ships and emerging Maritime Autonomous Surface Ships (MASS). Deep learning methods for accurate prediction based on AIS data have emerged as a contemporary maritime transportation research focus. However, concerns about its accuracy and computational efficiency widely exist across both academic and industrial sectors, necessitating the discovery of new solutions. This paper aims to develop a new prediction approach called Deep Bi-Directional Information-Empowered (DBDIE) by utilising integrated multiple networks and an attention mechanism to address the above issues. The new DBDIE model extracts valuable features by fusing the Bi-directional Long Short-Term Memory (Bi-LSTM) and the Bi-directional Gated Recurrent Unit (Bi-GRU) neural networks. Additionally, the weights of the two bi-directional units are optimised using an attention mechanism, and the final prediction results are obtained through a weight self-adjustment mechanism. The effectiveness of the proposed model is verified through comprehensive comparisons with state-of-the-art deep learning methods, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-LSTM, Bi-GRU, Sequence to Sequence (Seq2Seq), and Transformer neural networks. The experimental results demonstrate that the new DBDIE model achieves the most satisfactory prediction outcomes than all other classical methods, providing a new solution to improving the accuracy and effectiveness of predicting ship trajectories, which becomes increasingly important in the era of the safe navigation of mixed manned ships and MASS. As a result, the findings can aid the development and implementation of proactive preventive measures to avoid collisions, enhance maritime traffic management efficiency, and ensure maritime safety
Using performance assessment in secondary school mathematics: an empirical study in a Singapore classroom
No full textThis article reports an exploratory study on using performance assessment in mathematics instruction in a high-performing secondary school in Singapore. An intact mathematics class participated in the study, and received chapter-based performance tasks as intervention during regular mathematics lessons for about one and a half school years. The performance tasks used included authentic and/or open-ended tasks. The students’ academic achievements and attitudes in mathematics were compared with a comparison class that did not receive the intervention. Both quantitative and qualitative data were collected, mainly through questionnaire surveys, performance task tests, conventional school exams, and interviews with students and teachers. The results suggest that the students receiving the intervention performed significantly better than their counterparts in solving conventional exam problems, and in general they also showed more positive changes in attitudes towards mathematics and mathematics learning. The students from the experimental class also expressed positive views about the benefits of using performance tasks in promoting their ability in higher order thinking, though no statistically significant difference was detected between the two classes of students in solving unconventional tasks before and after intervention. Overall, the results appear to support teachers’ using contextualised problems in real life situations and open-ended investigations in students’ learning of mathematic
sj-docx-1-cpj-10.1177_00099228231219336 – Supplemental material for Newborn Screening of 6 Lysosomal Storage Disorders by Tandem Mass Spectrometry
No full textSupplemental material, sj-docx-1-cpj-10.1177_00099228231219336 for Newborn Screening of 6 Lysosomal Storage Disorders by Tandem Mass Spectrometry by Yao Chen, Yan Yang, Yinglin Zeng, Qingying Lin, Peiran Zhao, Bin Mao, Xiaolong Qiu, Ting Huang, Liangpu Xu and Wenbin Zhu in Clinical Pediatrics</p
A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping
No full textAccurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic efficiency. To address the challenge of the inherently complex and dynamic growth of the VTF time series, a new hierarchical methodology for VTF prediction is proposed. Firstly, the original VTF data is reconfigured as a three-dimensional tensor by a modified Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) tensor decomposition model. Secondly, the VTF matrix (hour ✕ day) of each week is decomposed into high- and low-frequency matrices using a Bidimensional Empirical Mode Decomposition (BEMD) model to address the non-stationary signals affecting prediction results. Thirdly, the self-similarities between VTF matrices of each week within the high-frequency tensor are utilised to rearrange the matrices as different one-dimensional time series to solve the weak mathematical regularity in the high-frequency matrix. Then, a Dynamic Time Warping (DTW) model is employed to identify grouped segments with high similarities to generate more suitable high-frequency tensors. The experimental results verify that the proposed methodology outperforms the state-of-the-art VTF prediction methods using real Automatic Identification System (AIS) datasets collected from two areas. The methodology can potentially optimise relation operations and manage vessel traffic, benefiting stakeholders such as port authorities, ship operators, and freight forwarders
Systematic investigation of gastrointestinal diseases in China (SILC): validation of survey methodology
No full textBackground: Symptom-based surveys suggest that the prevalence of gastrointestinal diseases is lower in China than in Western countries. The aim of this study was to validate a methodology for the epidemiological investigation of gastrointestinal symptoms and endoscopic findings in China. Methods: A randomized, stratified, multi-stage sampling methodology was used to select 18 000 adults aged 18-80 years from Shanghai, Beijing, Xi'an, Wuhan and Guangzhou. Participants from Shanghai were invited to provide blood samples and undergo upper gastrointestinal endoscopy. All participants completed Chinese versions of the Reflux Disease Questionnaire (RDQ) and the modified Rome II questionnaire; 20% were also invited to complete the 36-item Short Form Health Survey (SF-36) and Epworth Sleepiness Scale (ESS). The psychometric properties of the questionnaires were evaluated statistically. Results: The study was completed by 16 091 individuals (response rate: 89.4%), with 3219 (89.4% of those invited) completing the SF-36 and ESS. All 3153 participants in Shanghai provided blood samples and 1030 (32.7%) underwent endoscopy. Cronbach's alpha coefficients were 0.89, 0.89, 0.80 and 0.91, respectively, for the RDQ, modified Rome II questionnaire, ESS and SF-36, supporting internal consistency. Factor analysis supported construct validity of all questionnaire dimensions except SF-36 psychosocial dimensions. Conclusion: This population-based study has great potential to characterize the relationship between gastrointestinal symptoms and endoscopic findings in China.Xiaoyan Yan, Rui Wang, Yanfang Zhao, Xiuqiang Ma, Jiqian Fang, Hong Yan, Xiaoping Kang, Ping Yin, Yuantao Hao, Qiang Li, John Dent, Joseph Sung, Duowu Zou, Saga Johansson, Katarina Halling, Wenbin Liu and Jia H
Why hedge? Extent, nature, and determinants of derivative usage in U.S. municipalities
No full textUsing a hand-collected dataset of over 300 observations of large U.S. cities and counties, this paper investigates the extent, nature and determinants of derivatives usage in the municipal sector.Over half of our sample entities engage in derivative transactions and a vast majority of these transactions are intended to manage interest rate risk. Swaps, by far, are the most popular derivative instrument. In terms of the determinants of derivative usage,we find that the propensity to use derivatives as well as the extent of derivative usage is higher for municipalities that are larger and more financially constrained. We do not find growth to be related to municipal derivative usage. Contrary to suggestions made in the popular press, we fail to find managerial opportunism to be a significant factor in municipal derivative usage. We also find that more sophisticated managers of large municipalities and less sophisticated managers of small municipalities are more likely to engage in derivative transactions.Peer reviewe
A study on the reliability of consecutive k-Out-of-n: G systems based on Copula
No full textThe computation of reliability characteristics of a system that consists of dependent components is sometimes difficult especially when the type of dependence is not known. This article introduces the copula method to calculate the reliability of dependent consecutive k-out-of-n: G systems. The components in these systems are dependent on each other and the dependency may be either linear or nonlinear. The copula is a popular tool for modeling the dependence structure of data. It contains the information about the dependency structure of a vector of random variables and can capture nonlinear dependence. Based on the copula theory, the article analyzes the consecutive k-out-of-n: G systems and gets the reliability indexes. Finally, some numerical examples are presented to illustrate the results obtained in this article.Peer reviewe
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