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Optimal impression materials for implant-supported fixed complete dentures: A systematic review and meta-analysis

Journal of Prosthetic Dentistry 2023年第5期133卷 1172-1182页

作者： Guo, Yong-qing Ma, Yun Cai, Shu-ning Yu, Hao Postgraduate student Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University School and Hospital of Stomatology Fujian Medical University Fujian Fuzhou China PhD Candidate Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University School and Hospital of Stomatology Fujian Medical University Fujian Fuzhou China Postgraduate student Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University School and Hospital of Stomatology Fujian Medical University Fujian Fuzhou China Associate Professor and Associate Dean School and Hospital of Stomatology Fujian Medical University Fuzhou Fujian PR China and Adjunct Associate Professor Department of Applied Prosthodontics Graduate School of Biomedical Sciences Nagasaki University Nagasaki Japan

Statement of problem: Although polyvinyl siloxane (PVS) materials and polyether (PE) materials have been the recommended materials for making impressions for implant-supported fixed complete dentures (IFCDs), a consensus regarding the optimal impression materials has yet to be established. Purpose: The purpose of this systematic review and meta-analysis was to evaluate the effect of impression materials on the accuracy of conventional impressions for IFCDs and to provide guidance for selecting the optimal impression material. Material and methods: The PubMed, Web of Science, and Embase databases were searched and supplemented via hand searches. Studies comparing the accuracy of conventional impressions for IFCDs by using PVS and PE materials with either direct (open-tray) or indirect (closed-tray) techniques were included. Linear distance deviations and angular deviations between adjacent implants were evaluated. The mean difference (MD) with a 95% confidence interval (CI) was calculated for continuous data. A subgroup analysis was conducted to evaluate the impact of implant angulation (α=.05). Results: Among the 597 publications identified, 27 in vitro studies were included for qualitative analysis, and 12 were included for quantitative analysis. The general analysis revealed no significant differences in linear distance and angular deviations between the 2 impression materials with the direct or indirect technique. The subgroup analysis found that a statistically significant difference in linear distance deviations was found when implants were placed at an angle greater than 15 degrees, favoring PE materials when using the direct technique (P=.010, MD: 32.54 µm;95% CI: 6.83 to 58.24) and indirect technique (P=.020, MD: 138.15 µm, 95% CI: 19.17 to 257.13). However, only 2 relevant studies assessed the indirect technique. Conclusions: When providing IFCDs, conventional impressions obtained by using PVS and PE materials were found to have similar accuracy in most sce

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Modeling Decision Making to Control the Allocation of Virtual Machines in a Cloud Computing System with Reserve Machines

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2021年第2期48卷 1-9页

作者： Ouammou, Abdellah Zaaloul, Abdellah Hanini, Mohamed Bentahar, Abdelghani A PhD student at Hassan First University Faculty of Sciences and Techniques With Computer Networks Mobility and Modeling laboratory IR2M Settat Morocco A professor at the department of Mathematics Ibn Zohr University Is with Complex Systems and Interactions Research Team F.S.J.E.S Ait melloul Morocco A professor at the department of Mathematics and Computer science Hassan First University Faculty of Sciences and Techniques Is with Computer Networks Mobility and Modeling laboratory IR2M Settat Morocco

Due to the complex and dynamic nature of virtual machine allocation in the cloud, it is difficult to manage and control the resources or to choose the best allocation of these resources. In this paper, we propose an optimal model for allocating computing resources to assess the best management of system resources where a group of physical machines is defined as "reserves". The controller activates them one by one when the system has a high number of tasks. The objective is to maximize the reward of the cloud computing system. This reward is calculated based on the energy and execution time of each customer and the characteristics of the system. Finding the best allocation for such a complex system is a challenge. For this, we used a heuristic algorithm and dynamic programming approach. The results analysis showed the advantage of using our model to control the use of reserve machines to get high quality of service and low energy consumption. © 2021. All Rights Reserved.

关键词： Dynamic programming

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MatterChat: A Multi-Modal LLM for Material Science

arXiv

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arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Gao, Weilu Farrell, Steven Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Structure (composition)

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Earth’s CO2battle: a view from space

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Significance 2023年第1期20卷 14-19页

作者： Cressie, Noel Zammit-Mangion, Andrew Jacobson, Josh Bertolacci, Michael Noel Cressie is distinguished professor at the University of Wollongong Australia and director of its Centre for Environmental Informatics he is also affiliate at NASA’s Jet Propulsion Laboratory and adjunct professor at the University of Missouri Andrew Zammit-Mangion is associate professor at the University of Wollongong Australia with expertise in spatio-temporal modelling computational statistics and their application to the environmental sciences Josh Jacobson is a PhD candidate in the School of Mathematics and Applied Statistics at the University of Wollongong Australia doing research on multivariate spatial statistical modelling with application to remote sensing of environmental processes Michael Bertolacci is a senior research fellow at the University of Wollongong Australia working on problems at the intersection of environmental spatio- temporal and computational statistics

Our environment is undergoing rapid change as greenhouse gases warm the *** Cressie, Andrew Zammit-Mangion, Josh Jacobson, andMichael Bertolacciuse WOMBAT, a Bayesian hierarchical statistical framework, to infer the s...

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Correction to: modelling sediment transport in three-phase surface water systems

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Journal of Hydraulic research 2023年第1期61卷 168-171页

作者： Cass T. Miller William G. Gray Christopher E. Kees Iryna V. Rybak Brittany J. Shepherd a Okun Distinguished Professor Department of Environmental Sciences and Engineering University of North Carolina Chapel Hill North Carolina 27599-7431 USA b Professor Department of Environmental Sciences and Engineering University of North Carolina Chapel Hill North Carolina 27599-7431 USA Email: graywg@*** c CSRS Distinguished Professor Louisiana State University Baton Rouge Louisiana 70803 USA Email: cekees@lsu.edu d Research Engineer Institute of Applied Analysis and Numerical Simulation University of Stuttgart Germany Email: rybak@ians.uni-stuttgart.de e PhD Student Department of Mathematics University of North Carolina Chapel Hill North Carolina 27599-3250 USA Email: breizh@live.unc.edu

ABSTRACTABSTRACTThis note corrects an error made in Miller et al. (2019). Modelling sediment transport in three-phase surface water *** of Hydraulic research,57(4), 439–463,https://***/10.1080/00221686.2019.1581... 详细信息

关键词： Averaging theory continuum mechanics entropy production rate mechanistic modeling porous medium flow TCAT

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Scoring in microfinance: credit risk management tool –Case of Morocco-

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Procedia Computer Science 2019年 148卷 522-531页

作者： Ghita Bennouna Mohamed Tkiouat PhD student in the Laboratory of Research in Applied Mathematics Mohammed V School of Engineers University. Rabat Morocco” Professor of Industrial Economics and Game theory Mohammed V School of Engineers University. Rabat Morocco

The aim of this research lies in the development of a credit-scoring model using logistic regression and multivariate discriminant analysis applied to 1500 individual loans from Moroccan MFIs. This model combines both behavioral and descriptive data related to the borrowers (age, activity, level of education, number of unpaid debts, number of loans, etc.) and others related to the institution (amount of credit, duration of credit, number of concluded loans per portfolio manager etc.). The results showed the importance of having a larger sample, a deep enough history on the behavior of the customer, and also more information about variables related to the client’s activity and its performance to better predict the default.

关键词： Credit scoring microfinance Morocco credit risk logistic regression multivariate discriminant analysis probability of default behavioral model descriptive model

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Fuzzy logic approach applied to credit scoring for microfinance in Morocco

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Procedia Computer Science 2018年 127卷 274-283页

作者： Ghita Bennouna Mohamed Tkiouat PhD student in the Laboratory of Research in Applied Mathematics Mohammed V School of Engineers University. Rabat Morocco Professor of Industrial Economics and Game theory Mohammed V School of Engineers University. Rabat Morocco

Microfinance institutions in Morocco target clients who are excluded from the traditional financial systems and have no guarantees, exposing them to the risks of default and therefore bankruptcy. Also the microfinance sector is experiencing information asymmetry which makes it difficult to apply classical theory given the low databases quality and the lack of depth in the behavioral history of bad customers. The aim of this research is to use fuzzy logic approach that allows an individual and dynamic treatment of the customers by integrating the opinion of the experts in order to evaluate customer behavior to reduce loan default and ensure the viability and the sustainability of the microfinance institutions. The research used three composed Fuzzy Input variables with their trapezoidal membership function, an output variable and seventy eight fuzzy rules in the development of an evaluation model.

关键词： Fuzzy logic credit scoring microfinance Morocco credit risk

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Contributions from Pilot Projects in Quantum Technology Education as Support Action to Quantum Flagship

arXiv

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arXiv 2023年

作者： Faletic, Sergej Bitzenbauer, Philipp Bondani, Maria Chiofalo, Marilu Goorney, Simon R. Krijtenburg-Lewerissa, Kim Mishina, Oxana Müller, Rainer Pospiech, Gesche Ercan, Ilke Malgieri, Massimiliano Merzel, Avraham Michelini, Marisa Onorato, Pasquale Pol, Henk Santi, Lorenzo Seskir, Zeki Can Sherson, Jacob Kirsten E Stadermann, H. Stefanel, Alberto Surer, Elif Tóth, Kristof Malo, Jorge Yago Zabello, Olgas University of Ljubljana Faculty of Mathematics and Physics Jadranska ulica 19 Ljubljana1000 Slovenia Friedrich-Alexander-Universität Erlangen-Nürnberg Department of Physics Physics Education Research Staudstr. 7 Erlangen91058 Germany CNR-Institute for Photonics and Nanotechnology via Valleggio 11 Como Italy Department of Physics "Enrico Fermi" University of Pisa INFN-Pisa Italy Department of Management School of Business and Social Sciences Aarhus University Denmark Niels Bohr Institute Copenhagen University Denmark Freudenthal Institute Utrecht University P.O. Box 85170 Utrecht3508 AD Netherlands Area Science Park Basovizza TriesteI-34149 Italy Physikdidaktik TU Braunschweig Bienroder Weg 82 Braunschweig38106 Germany TU Dresden Department of Physics Research Group Physics Education Helmholtzstr. 10 Dresden01069 Germany Department of Microelectronics Mekelweg 4 Delft2628 CD Netherlands Department of Physics University of Pavia Via Bassi 6 Pavia27100 Italy The Seymour Fox School of Education The Hebrew University of Jerusalem Mount Scopus Jerusalem9190501 Israel PUnità di Ricerca in Didattica della Fisica Dipartimento di Scienze Matematiche Informatiche e Fisiche University of Udine via delle Scienze 206 Udine33100 Italy Physical Science Communication Laboratory Department of Physics University of Trento TN Povo38123 Italy ELAN Department of Teacher Development University of Twente P.O.Box 217 EnschedeNL-7500 AE Netherlands Karlsruhe Germany Department of Modeling and Simulation Graduate School of Informatics Middle East Technical University Ankara06800 Turkey Physics Education PhD Programme Eötvös Loránd University Budapest Hungary Department of Physics 'Enrico Fermi' INFN University of Pisa Largo B. Pontecorvo 3 PisaI-56127 Italy Offenburg University of Applied Sciences Badstraße 24 Offenburg77652 Germany

The GIREP community on teaching and learning quantum physics and the Education section of the Quantum flagship project of the European Union (QTEdu) have brought together different stakeholders in the field of teaching quantum physics on all levels, including outreach. The goal of QTEdu is to pave the way for the training of the future quantum workforce. To this end, it is necessary to understand the needs of the quantum technology (QT) field, make the general public aware of the existence and importance of QT, and introduce quantum physics already in high school, so that high school students can choose QT as their field of study and career. Finally, new university courses need to be established to support emerging specific profiles such as a "quantum engineer". In this symposium, four QTEdu pilot projects were brought together to demonstrate how their complementary approaches have worked towards realising the above goals. © 2023, CC BY.

关键词： Economic and social effects

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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