366 research outputs found
sj-docx-1-hpq-10.1177_13591053241240735 – Supplemental material for How dyadic appraisal moderate the association between dyadic coping and diabetes management efficacy
No full textSupplemental material, sj-docx-1-hpq-10.1177_13591053241240735 for How dyadic appraisal moderate the association between dyadic coping and diabetes management efficacy by Huiqiong Zheng, Xinyu Fan, Yuyang Liu, Yanjuan Wu, Yixuan Liu, Yingxin Xu, Jingyi Zhi, Conghui Yang and Jing Liao in Journal of Health Psychology</p
sj-docx-2-hpq-10.1177_13591053241240735 – Supplemental material for How dyadic appraisal moderate the association between dyadic coping and diabetes management efficacy
No full textSupplemental material, sj-docx-2-hpq-10.1177_13591053241240735 for How dyadic appraisal moderate the association between dyadic coping and diabetes management efficacy by Huiqiong Zheng, Xinyu Fan, Yuyang Liu, Yanjuan Wu, Yixuan Liu, Yingxin Xu, Jingyi Zhi, Conghui Yang and Jing Liao in Journal of Health Psychology</p
Simulation on particle adhesion on simulated and modified drinking water biofilms
No full textLimited Restriction Lifted for Item 91442 on 2018-03-03T10:15:30Z.Biofilms, commonly found in drinking water distribution system (DWDS), play an important role in pathogens transportation and persistent and raise concern on drinking water safety. They can harbor opportunistic pathogen from disinfectants added to control pathogen. Since bacterial adhesion is the prerequisite for further propagation, understanding the mechanisms of bacterial adhesion on biofilm surface is important to prevent pathogen adhesion and reduce the risk to exposure in DWDS. In this study, bacterial size particles were used to model bacterial adhesion on simulated drinking water biofilms surfaces. Simulations on effects of Brownian motion and drag force on adhesion mechanism were conducted using COMSOL Multiphysics. The role of surface topography and roughness on particle deposition were determined through simulations on biofilm surfaces and artificial surfaces maintaining roughness or topography similar to biofilms. The simulation results showed that surface topography instead of roughness and associated hydrodynamic condition can affect particle adhesion tendency. Spatial analysis through semivariogram showed that the deposition location was not dominated by surface structure.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-12-01The student, Conghui Huang, accepted the attached license on 2015-12-10 at 16:26.The student, Conghui Huang, submitted this Thesis for approval on 2015-12-10 at 16:36.This Thesis was approved for publication on 2015-12-11 at 13:03.DSpace SAF Submission Ingestion Package generated from Vireo submission #9003 on 2016-03-02 at 14:14:26Made available in DSpace on 2016-03-02T21:07:12Z (GMT). No. of bitstreams: 2
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Previous issue date: 2015-12-11Embargo set by: Seth Robbins for item 91442
Lift date: 2018-03-02T21:07:27Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD syste
Emerg Infect Dis
No full textWe explored the secondary attack rate in different types of contact with persons presymptomatic for coronavirus disease (COVID-19). Close contacts who lived with or had frequent contact with an index case-patient had a higher risk for COVID-19. Our findings provide population-based evidence for transmission from persons with presymptomatic COVID-19 infections
Rhodium-mediated Activation and Borylation Reactions of Fluorinated Olefins
No full textDie Dissertation beinhaltet Studien zur Reaktivität von Rhodiumkomplexen gegenüber unterschiedlichen ungesättigten fluorierten Olefinen mit einem Fokus auf C–F Aktivierungs- und Borylierungsreaktionen.
Der Rhodium(I)hydridokomplex [Rh(H)(PEt3)3] (1) wurde als Katalysator in den Reaktionen von HFO-1234yf, HFO-1234ze, HFO-1225zc bzw. HFO-1225ye (Z) mit HBpin verwendet. Dabei wurden Produktgemische bestehend aus Borylierungsprodukten erhalten. Die selektive Mono- und Dihydroborierung von 3,3,3-Trifluorpropin konnte durch Verwendung von Komplex 1 als Katalysator erreicht werden.
Trifluorethylen konnte durch die Reaktion mit HBpin und Komplex 1 als Katalysator in ein Produktgemisch überführt werden. Stöchiometrische Reaktion zeigen, dass Komplex 1 sowohl unter C–F-Bindungsaktivierung reagiert als auch die Koordination von Trifluorethylen, unter Bildung des Komplexes trans-[Rh(F)(ƞ2-CF2CFH)(PEt3)2], stattfindet. Im Falle von 1,1,2-Trifluorbuten wurde ebenfalls eine C–F-Bindungsaktivierung durch Komplex 1 beobachtet. Mechanistische Untersuchungen der Reaktion von Komplex 1 und 1,1,2-Trifluorbuten bei unterschiedlichen Temperaturen zeigten Hinweise für Koordination & Insertion des Alkens, sowie anschließende β-H-Eliminierung und oxidative C–F-Bindungsadditions- und reduktive HF-Eliminierungsschritte.
Außerdem konnte durch Verwendung von Komplex 1 oder [Rh(Bpin)(PEt3)3] (3) als Katalysator eine stöchiometrische und katalytische Hydroborierung von Pentafluorstyren mit HBpin erreicht werden. Die Rhodium(I)komplexe 1 und 3 sind in der Lage das Olefin zu koordinieren und die C–F-Bindung zu aktivieren, während die Verwendung der Verbindung [Rh(Me)(PEt3)3] die C–H-Bindungsaktivierung fördert. Bei 333 K findet die Aktivierung des fluorierten Aromaten in der 4-Stellung statt, während bei Raumtemperatur die Aktivierung in der 2-Stellung bevorzugt ist.The dissertation reports on studies on the reactivity of rhodium complexes towards different fluorinated olefins with a focus on C–F activation steps and borylation reactions.
The rhodium(I) hydrido complex [Rh(H)(PEt3)3] (1) was employed as catalyst in the reactions of HFO-1234yf, HFO-1234ze, HFO-1225zc and HFO-1225ye with HBpin. A product mixture consisting of borylation products was obtained. Selective mono and dihydroboration reactions of 3,3,3-trifluoropropyne were achieved by employing complex 1 as the catalyst.
Similarly, trifluoroethylene was also converted into a mixture of products by the reaction with HBpin with complex 1 as the catalyst. A stoichiometric reaction of complex 1 resulted in the C–F bond activation as well as a coordination of trifluoroethylene to give complex trans-[Rh(F)(ƞ2-CF2CFH)(PEt3)2]. Furthermore, the C–F bond activation was also realized with complex 1 and 1,1,2-trifluorobutene. Mechanistic investigations of the reaction of complex 1 towards 1,1,2-trifluorobutene at variable temperatures indicated the formation of products of coordination, insertion of the olefin and subsequent β-H elimination, C–F oxidative addition as well as HF reductive elimination steps.
Furthermore, when utilizing complex 1 or [Rh(Bpin)(PEt3)3] (3) as catalysts, stoichiometric and catalytic hydroboration reactions of pentafluorostyrene occurred with HBpin. The rhodium(I) complexes 1 and 3 were capable of the coordination of the olefin and a C–F bond activation reaction with pentafluorostyrene, while complex [Rh(Me)(PEt3)3] promoted the C–H bond activation. At 333 K, the activation of the fluorinated aromatic ring occurred at the 4-position, while at room temperature, an activation at the 2-position was preferred
Emerg Infect Dis
No full textDuring January 26-February 10, 2020, an outbreak of 2019 novel coronavirus disease in an air-conditioned restaurant in Guangzhou, China, involved 3 family clusters. The airflow direction was consistent with droplet transmission. To prevent the spread of the virus in restaurants, we recommend increasing the distance between tables and improving ventilation
Human-Machine Trust Interaction
No full textImproving user’s trust appropriately could help in designing an intelligent system and make it work effectively, especially with the fast growth of Web-base technology. This chapter introduces the solutions of improving user’s trust in human-machine interaction (HMI), especially for electronic commerce (e-commerce). The author firstly reviews the concept of trust and the main factors that affects the appropriateness of user’s trust in human-machine interaction, such as the properties of machine systems, the properties of human, and context. On the basis of these, the author further discusses the current state, challenges, problems and limitations of establishing and improving the user’s trust in human-machine interaction. Finally, the author summarizes and evaluates the existing solutions for improving the user’s trust appropriately in e-commerce environment.</p
Correction: Novel azobenzene-based amphiphilic copolymers: synthesis, self-assembly behavior and multiple-stimuli-responsive properties
No full textCorrection for ‘Novel azobenzene-based amphiphilic copolymers: synthesis, self-assembly behavior and multiple-stimuli-responsive properties’ by Yiting Xu et al., RSC Adv., 2018, 8, 16103–16113.</p
Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning
No full textRefusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as I don\u27t know , RAIT enhances the reliability of LLMs and reduces their hallucination. Generally, RAIT modifies training samples based on the correctness of the initial LLM\u27s response. However, this crude approach can cause LLMs to excessively refuse answering questions they could have correctly answered, the problem we call over-refusal. In this paper, we explore two primary causes of over-refusal: Static conflict occurs when similar samples within the LLM\u27s feature space receive differing supervision signals (original vs. modified I don\u27t know ). Dynamic conflict arises as the LLM\u27s evolving knowledge during SFT enables it to answer previously unanswerable questions, but the now-answerable training samples still retain the original I don\u27t know supervision signals from the initial LLM state, leading to inconsistencies. These conflicts cause the trained LLM to misclassify known questions as unknown, resulting in over-refusal. To address this issue, we introduce Certainty Represented Knowledge Flow for Refusal-Aware Instructions Tuning (CRaFT). CRaFT centers on two main contributions: First, we additionally incorporate response certainty to selectively filter and modify data, reducing static conflicts. Second, we implement preliminary rehearsal training to characterize changes in the LLM\u27s knowledge state, which helps mitigate dynamic conflicts during the fine-tuning process. We conducted extensive experiments on open-ended question answering and multiple-choice question task. Experiment results show that CRaFT can improve LLM\u27s overall performance during the RAIT process. Code and data will be released at https://github.com/opendatalab/CRaFT .Equal contribution: Runchuan Zhu, Zhipeng Ma, Jiang Wu; Corresponding author: Conghui H
A vector-valued estimate of multilinear Calderón-Zygmund operators in Herz-Morrey spaces with variable exponents
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