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Getting Undergraduates Ready for China’s Belt and Road Initiative (BRI) Through an Overseas Experiential Learning Project

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China and the World 2021年第3期4卷

作者： Andrew Min Han Chin Thomas Menkhoff Hans-Dieter Evers Hoong Hui Daniel Gn Kevin Koh Chester Wey Lee Patrick H. M. Loh Linda Low Sebastian Tan Teng Seng Teo Natalie Yap Lee Kong Chian School of Business Singapore Management University (SMU) 81 Victoria Street Singapore 188065 Singapore Andrew Chin Min Han graduated from Tsinghua University in Beijing China with a Tsinghua–MIT Sloan International Master of Business Administration degree under the Singapore Government’s Asian Business Fellowship (ABF) scholarship program. He is the Program Director of Singapore Management University (SMU) Executive Certificate in China Business and of the SMU Internationalisation Series. He teaches as an Adjunct Faculty of Strategy and Organisation at the SMU Lee Kong Chian School of Business since January 2017. He teaches effectively in English and Chinese. With over 23 years of experience in education and publishing industry in Asia Andrew Chin’s multi-sector career has enabled him to experience the full spectrum of the education industry in his roles as the Singapore EDB Officer handling the World-Class University portfolio and the Specialist Information and Publishing Industry portfolio Chief Representative and Country Manager (China) at Thomson Learning (Fortune 500 company) and Co-founder and CEO for an early childhood education business. Andrew was a Business Development Director (Universities) at Sodexo Group (Fortune 500 company) for the Asia-Pacific region. After working 10 years in Beijing China Andrew returned to Singapore in 2012 to work closely with the top leadership at Singapore Management University on the SMU China & East Asia Initiatives. Thomas Menkhoff is the Professor of Organisational Behaviour & Human Resources (Education) at the Lee Kong Chian School of Business Singapore Management University (SMU). Two of his recent publications include: (i) Menkhoff Thomas Ning Kan Siew Evers Hans-Dieter and Chay Yue Wah eds. Living in Smart Cities: Innovation and Sustainability (New Jersey: World Scientific Publishing 2018) and (ii) Chay Yue Wah Menkhoff Thomas and Low Linda eds. China’s Belt and Road Initiative — Understanding the Dynamics of a Global Transfor

In this paper, we explain how an experiential learning course and study tour to Gansu Province (People’s Republic of China) enabled undergraduates at the Singapore Management University (SMU) to acquire 21st-century competencies and higher-order thinking skills by analyzing and evaluating specific aspects of China’s Belt and Road Initiative (BRI) and China–Singapore (Chongqing) Connectivity Initiative — New international Land–Sea Trade Corridor (CCI-ILSTC) with emphasis on developing viable Go-To-Market (GTM) strategies aimed at selling Gansu produce in four Southeast Asian markets. We share how the course was designed to support the attainment of key learning goals and discuss how we turned pedagogical aspirations into concrete learning outcomes. We introduce key aspects of the so-called “SMU-XO” project that the students conducted in partnership with an industry partner, Pacific international Lines (PIL), and discuss how the project work helped learners to gain global competency by (i) examining critical issues related to BRI such as multi-modal infrastructure connectivity, (ii) appreciating the local perspectives of project stakeholders in Lanzhou and Shanghai and (iii) successfully interacting with people from different cultures, namely China, Indonesia, Vietnam, Malaysia and Thailand.

关键词： Experiential learning Singapore China Belt and Road Initiative (BRI)

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Dilute-nitride GaNP planar and core/shell microwire solar cells

Dilute-nitride GaNP planar and core/shell microwire solar ce...

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Indium Phosphide and Related Materials Conference

作者： Supanee Sukrittanon Ren Liu Janet L Pan Katherine L Jungjohann Shadi A Dayeh Charles W Tu Graduate Program of Materials Science and Engineering University of California San Diego United States of America Department of Electrical and Computer Engineering University of California San Diego United States of America Center for Integrated Nanotechnologies Sandia National Laboratories United States of America

ISBN: (纸本)9781509019656

Dilute nitride GaNP is potentially useful as the top junction in dual-junction solar cells on Si because it has a tunable direct-bandgap and small lattice mismatch with Si. Investigations on GaP are the first step toward this goal. Our best GaNP solar cell on GaP ([N] = 1.8%, Eg = 2.05 eV) achieves an efficiency of 7.9%, which is 3 times higher than the most efficient GaP solar cell to date and higher than other solar cells with similar direct bandgap (InGaP, GaAsP). We also report GaP/GaNP core/shell microwire solar cells utilizing top-down reactiveion etching to create the GaP cores and molecular beam epitaxy to create the GaNP shells. Systematic studies of cell performance over a series of microwire lengths, array periods, and sidewall morphologies, examined by transmission electron microscopy, were conducted to shed light on performance-limiting factors and to optimize the cell efficiency.

关键词： Photovoltaic cells Silicon Compounds computers Laboratories Junctions Lattices

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An automatic calibration framework applied on a metaheuristic fuzzy model for the CIF competition

An automatic calibration framework applied on a metaheuristi...

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international Joint Conference on Neural Networks

作者： Vitor N. Coelho Igor M. Coelho Ivan R. Meneghini Marcone J. F. Souza Frederico G. Guimaraes Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Av. Ant?nio Carlos 6627 31270-901 Belo Horizonte Brazil Department of Computer Science State University of Rio de Janeiro Brazil Department of Computer Science Universidade Federal de Ouro Preto MG Brazil Departament of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (纸本)9781509006212

The challenging problem of forecasting a given time series as accurately as possible is reality in different areas of expertise. The requirement of achieving reliable forecasts, for assisting the new generation of soft sensors, requests the development of novel smart mechanisms to be integrated into the available forecasting models. This current paper improves the previous work of Coelho et al. [1], [2], introduced in the 2014 WCCI, which introduced a Hybrid Self-adaptive Forecasting Model (HFM), calibrated using an evolutionary metaheuristic. Here, an expert input selection assisted by the use of Neighborhood Structures is used for learning the dataset provide in the CIF 2016 forecasting competition. Two different metaheuristic algorithms are used for training the HFM, computational experiments are conducted for optimizing both models. By selecting the maximum lag to be used by our model and, consequently, defining a rate of training cycles, we calibrate the HFM in order to minimize Symmetric Mean Absolute Percentage Errors. Blind forecasts generated by the model will be submit to the competition.

关键词： Forecasting Time series analysis Predictive models Training Computational modeling Electronic mail Prediction algorithms

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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Erratum to: An approach to the research on ion and water properties in the interphase between the plasma membrane and bulk extracellular solution

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The journal of physiological sciences : JPS 2018年第1期68卷 101页

作者： Hiroshi Hibino Madoka Takai Hidenori Noguchi Seishiro Sawamura Yasufumi Takahashi Hideki Sakai Hitoshi Shiku Department of Molecular Physiology Niigata University School of Medicine 1-757 Asahimachi-dori Chuo-ku Niigata Niigata 951-8510 Japan. hibinoh@med.niigata-u.ac.jp. Center for Transdisciplinary Research Niigata University Niigata Niigata 950-2181 Japan. hibinoh@med.niigata-u.ac.jp. AMED-CREST AMED Niigata Japan. hibinoh@med.niigata-u.ac.jp. Department of Bioengineering School of Engineering The University of Tokyo Tokyo Japan. International Center for Materials Nanoarchitectonics and Global Research Center for Environment and Energy Based on Nanomaterials Science National Institute for Materials Science Tsukuba Ibaraki Japan. Department of Molecular Physiology Niigata University School of Medicine 1-757 Asahimachi-dori Chuo-ku Niigata Niigata 951-8510 Japan. Division of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Kanazawa 920-1192 Japan. Precursory Research for Embryonic Science and Technology (PRESTO) Japan Science and Technology Agency (JST) Saitama 332-0012 Japan. Department of Pharmaceutical Physiology Graduate School of Medicine and Pharmaceutical Sciences University of Toyama Toyama Japan. Graduate School of Engineering Tohoku University Sendai 980-8579 Japan.

The article An approach to the research on ion and water properties in the interphase between the plasma membrane and bulk extracellular solution, written by Hiroshi Hibino, Madoka Takai, Hidenori Noguchi, Seishiro Sawamura, Yasufumi Takahashi, Hideki Sakai and Hitoshi Shiku, was originally published Online First without open access.

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Surface plasmon polaritons at lossy metamaterial-air interfaces

Surface plasmon polaritons at lossy metamaterial-air interfa...

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Signal Processing in Photonic Communications, SPPCom 2016

作者： Sang-Nourpour, Nafiseh Lavoie, Benjamin Kheradmand, R. Sanders, Barry Institute for Quantum Science and Technology University of Calgary Alberta Canada Photonics group Research Institute for Applied Physics and Astronomy University of Tabriz Iran Photonics group Aras International Campus University of Tabriz Iran Department of Electrical and Computer Engineering University of Calgary Alberta Canada Hefei National Laboratory for Physical Sciences at the Microscale University of Science and Technology of China Hefei Anhui China Shanghai Branch CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai China Program in Quantum Information Science Canadian Institute for Advanced Research TorontoON Canada

We derive rigorous criteria for the existence of surface-plasmon polaritons (SPPs) at lossy interfaces by considering properties of SPPs and employing the requirement of causality. These criteria impose limits on susc... 详细信息

ISBN: (纸本)9781943580149

关键词： Photons

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A Smart Pool Search Matheuristic for Solving a Multi-objective Microgrid Storage Planning Problem

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Energy Procedia 2016年 103卷 292-297页

作者： Vitor N. Coelho Igor M. Coelho Bruno N. Coelho Marcone J.F. Souza Haroldo Gambini N. Mladenović Frederico G. Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto de Pesquisa e Desenvolvimento de Tecnologias Ouro Preto Brazil Department of Computer Science State University of Rio de Janeiro Rio de Janeiro Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil Mathematical Institute Serbian Academy of Science and Arts Serbia LAMIH Université de Valenciennes et du Hainaut Cambrésis Valenciennes France Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil

In this paper, a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units is considered. The problem involves several PEVs and a microgrid community, composed of small houses, residential areas, and different Renewable Energy Resources. Three different objectives are considered: microgrid total costs; usage of PEV batteries and maximum grid peak load. In order to find sets of non-dominated solutions, a matheuristic black box solves several Mixed Integer Linear programming (MILP) subproblems. We improve a previously developed MILP model and design a new multi-objective matheuristic including new problem initialization mechanisms.

关键词： Microgrids Power dispatching Energy storage management Matheuristic Mixed-Integer Linear programming Multi-objetive optimization

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A hybrid evolutionary probabilistic forecasting model applied for rainfall and wind power forecast

A hybrid evolutionary probabilistic forecasting model applie...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Guilherme G. Netto Alexandre C. Barbosa Mateus N. Coelho Arthur R. L. Miranda Vitor N. Coelho Marcone J. F. Souza Frederico G. Guimarães Agnaldo J. R. Reis Department of Control and Automation Engineering Universidade Federal de Ouro Preto Ouro Preto MG Brazil Graduate Program of Electrical Engineering Univesidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto MG Brazil Departament of Electrical Engineering Univesidade Federal de Minas Gerais Belo Horizonte MG Brazil

Several works in the literature so far have been focused on deterministic point forecasts, which, usually, indicates the conditional mean of future observations. An increasing need for generating the entire conditional distribution of future observations has been required for the new generation of soft sensors. This study aims the probabilistic forecasts, reporting the use of a hybrid fuzzy forecasting model applied in two different forecasting problems. Our adapted model is applied to predict the rain of the city of Vitoria, in the state of Espírito Santo, Brazil. Real data from a wind farm, provided by the Irish EirGrid institute, was used for analyzing the proposal over a real time series with high fluctuations. Due to the stochasticity of the the hybrid model, which is calibrated through the use of an evolutionary metaheuristic, we adapted it in order to generate future using quantile regression. Computational experiments indicated the ability of the model in finding useful probabilistic quantiles, which were flexible enough in order to limit the lower and upper bounds of the historical datasets. While the probabilistic quantiles suggested the probability of rain and its magnitude, they were also able to predict expected ranges of the amount of energy generated from the wind farm.

关键词： Forecasting Predictive models Wind forecasting Probabilistic logic Wind power generation Adaptation models Time series analysis

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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