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International IEEE Conference on Intelligent systems, IS

作者： Petar Mrazovic Elif Eser Hakan Ferhatosmanoglu Josep L. Larriba-Pey Mihhail Matskin Dept. of Software and Computer System Royal Institute of Technology Stockholm Sweden Dept. of Computer Engineering Bilkent University Ankara Turkey Dept. of Computer Science University of Warwick Coventry United Kingdom Dept. of Computer Architecture Polytechnic University of Catalonia Barcelona Spain

ISBN: (纸本)9781538670989;9781538670972

The urban parking spaces for loading/unloading are typically over-occupied, which shifts delivery operations to traffic lanes and pavements, increases traffic, generates noise, and causes pollution. We present a data analytics based routing optimization that improves the circulation of vehicles and utilization of parking spaces. We formalize this new problem and develop a novel multivehicle route planner that avoids congestions at loading/unloading areas and minimizes the total duration. We present the developed tool with an illustration and analysis for the urban freight in the city of Barcelona, which monitors tens of thousands of deliveries every day. Our system includes an effective evaluation of candidate routes by considering the waiting times and further delays of other deliverers as a first class citizen in the optimization. A two-layer local search is proposed with a greedy randomized adaptive method for variable neighborhood search. Our approach is applied and validated over data collected across Barcelona's urban freight transport network, which contains 3,704,034 parking activities. Our solution is shown to significantly improve the use of available parking spaces and the circulation of vehicles, as evidenced by the results. The analysis also provides useful insights on how to manage delivery routes and parking spaces for sustainable urban freight transport and city logistics.

关键词： Loading Optimization Planning Routing Urban areas Roads Load modeling

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Author Correction: Barriers and opportunities for resilient and sustainable urban forests

Nature Cities

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Nature Cities 2025年 1页

作者： Manuel Esperon-Rodriguez Rachael Gallagher Renee M. Marchin Mark G. Tjoelker Carlo Calfapietra Paloma Cariñanos Cynnamon Dobbs Ana Alice Eleuterio Diego Esperon Rodriguez Ali Jahani Elizaveta Litvak Stephen J. Livesley Gabriele Manoli Timon McPhearson Christian Messier Johan Östberg Henrik Sjöman Björn Wiström Lara A. Roman Alessio Russo Maryam Saffariha Charlie Shackleton Ingjerd Solfjeld Jonah Susskind Jens-Christian Svenning Natalie van Doorn Jun Yang Hawkesbury Institute for the Environment Western Sydney University Penrith New South Wales Australia School of Science Western Sydney University Penrith New South Wales Australia Research Institute on Terrestrial Ecosystems National Research Council of Italy Porano Italy National Biodiversity Future Centre Palermo Italy Department of Botany Faculty of Pharmacy University of Granada Granada Spain Andalusian Institute for Earth System Research University of Granada (IISTA-CEAMA) Granada Spain Department of Natural Resources and the Environment University of Connecticut Storrs CT USA Instituto Latino-Americano de Economia Sociedade e Política Universidade Federal da Integração Latino-Americana Foz do Iguaçu Brazil Centro Universitario de Ciencias Sociales y Humanidades Universidad de Guadalajara Guadalajara Mexico Faculty of Assessment and Environment Risks Department Research Center of Environment and Sustainable Development Tehran Iran School of Sustainability Arizona State University Tempe AZ USA School of Agriculture Food and Ecosystem Sciences Faculty of Science The University of Melbourne Richmond Victoria Australia School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland Urban Systems Lab The New School New York NY USA Cary Institute of Ecosystem Studies Millbrook NY USA Stockholm Resilience Centre Stockholm University Stockholm Sweden Département des Sciences Naturelles Institut des Sciences de la Forêt Tempérée Université du Québec en Outaouais Ripon Quebec Canada Centre d’Étude de la Forêt Université du Québec à Montréal Montreal Quebec Canada Department of Landscape Architecture Planning and Management Swedish University of Agricultural Sciences Alnarp Sweden Gothenburg Botanical Garden Gothenburg Sweden Royal Botanic Gardens Richmond UK Pacific Southwest Research Station USDA Forest Service Riverside CA USA School of Architecture and Built Environment Queensland

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Thermal Stress and Reliability Analysis of TSV-Based 3-D ICs With a Novel Adaptive Strategy Finite Element Method

Thermal Stress and Reliability Analysis of TSV-Based 3-D ICs...

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作者： Zhou, Hao Zhu, Hengliang Cui, Tao Pan, David Z. Zhou, Dian Zeng, Xuan State Key Laboratory of ASIC and Systems Department of Microelectronics Fudan University Shanghai200433 China State Key Laboratory of Scientific and Engineering Computing National Center for Mathematics and Interdisciplinary Sciences Institute of Computational Mathematics Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Department of Electrical Engineering University of Texas at Dallas RichardsonTX75080 United States

Thermomechanical stress is one of the most important issues in performance and reliability analysis of through silicon via-based 3-D integrated circuits (3-D ICs), where an accurate numerical approach is generally needed to produce stress models and identify weak points in the structure. In this paper, we propose a knowledge-oriented nonuniform (KONU) refinement strategy for 3-D IC stress simulation under the framework of a parallel adaptive finite element method (FEM), and apply it in 3-D IC stress and reliability analysis. Parallel adaptive FEM is promising for solving large-scale problems due to its high accuracy and parallel efficiency. It produces refined meshes based on the a posteriori error analysis, which has the quasi-optimal convergence rate for solving the problem. It has high parallel efficiency, which makes it suitable for handling large and complex structures in 3-D ICs. The KONU refinement strategy developed in this paper can efficiently reduce the number of refinement iterations in parallel adaptive FEM for 3-D IC thermomechanical stress simulation and improves the computational efficiency. It is demonstrated in this paper through several examples that parallel adaptive FEM for thermomechanical stress evaluation can be widely applied in 3-D IC reliability analysis, where accurate stress simulation and modeling is exceptionally important to produce accurate results. © 2018 IEEE.

关键词： Reliability analysis

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Barriers and opportunities for resilient and sustainable urban forests

Nature Cities

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Nature Cities 2025年 1-9页

作者： Manuel Esperon-Rodriguez Rachael Gallagher Renee M. Marchin Mark G. Tjoelker Carlo Calfapietra Paloma Cariñanos Cynnamon Dobbs Ana Alice Eleuterio Diego Esperon Rodriguez Ali Jahani Elizaveta Litvak Stephen J. Livesley Gabriel Manoli Timon McPhearson Christian Messier Johan Östberg Henrik Sjöman Björn Wiström Lara A. Roman Alessio Russo Maryam Saffariha Charlie Shackleton Ingjerd Solfjeld Jonah Susskind Jens-Christian Svenning Natalie van Doorn Jun Yang Hawkesbury Institute for the Environment Western Sydney University Penrith New South Wales Australia School of Science Western Sydney University Penrith New South Wales Australia Research Institute on Terrestrial Ecosystems National Research Council of Italy Porano Italy National Biodiversity Future Centre Palermo Italy Department of Botany Faculty of Pharmacy University of Granada Granada Spain Andalusian Institute for Earth System Research University of Granada (IISTA-CEAMA) Granada Spain Department of Natural Resources and the Environment University of Connecticut Storrs CT USA Instituto Latino-Americano de Economia Sociedade e Política Universidade Federal da Integração Latino-Americana Foz do Iguaçu Brazil Centro Universitario de Ciencias Sociales y Humanidades Universidad de Guadalajara Guadalajara Mexico Faculty of Assessment and Environment Risks Department Research Center of Environment and Sustainable Development Tehran Iran School of Sustainability Arizona State University Tempe AZ USA School of Agriculture Food and Ecosystem Sciences Faculty of Science The University of Melbourne Richmond Victoria Australia School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland Urban Systems Lab The New School New York NY USA Cary Institute of Ecosystem Studies Millbrook NY USA Stockholm Resilience Centre Stockholm University Stockholm Sweden Département des Sciences Naturelles Institut des Sciences de la Forêt Tempérée Université du Québec en Outaouais Ripon Quebec Canada Centre d’Étude de la Forêt Université du Québec à Montréal Montreal Quebec Canada Department of Landscape Architecture Planning and Management Swedish University of Agricultural Sciences Alnarp Sweden Gothenburg Botanical Garden Gothenburg Sweden Royal Botanic Gardens Richmond UK Pacific Southwest Research Station USDA Forest Service Riverside CA USA School of Architecture and Built Environment Queensland

As cities heat up and expand in area and population, urban forests offer a nature-based solution to enhance liveability and reduce rising temperatures in cities. However, urban forests are vulnerable to climate change and face costly establishment and maintenance challenges. Here we explore four key ecological and socioeconomic barriers to achieving resilient urban forests: species selection, tree supply, tree life cycle (establishment and maintenance, including irrigation) and community engagement. We discuss how integrating traditional urban forestry practices with emerging technology offers a holistic approach to creating resilient, sustainable urban forests that can adapt to climate change while meeting community needs.

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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A Modified Polynomial Chaos Modeling Approach for Uncertainty Quantification

A Modified Polynomial Chaos Modeling Approach for Uncertaint...

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International Applied Computational Electromagnetics Society Symposium

作者： Majid Ahadi Dolatsara Ambrish Varma Kumar Keshavan Madhavan Swaminathan School of Electrical and Computer Engineering Center for Co-Design of Chip Package System (C3PS) Georgia Institute of Technology Atlanta Ga Cadence Design Systems Inc. Chelmsford MA

ISBN: (纸本)9781728115184;9780996007887

Uncertainty quantification is a key element for modeling high speed circuits, which is often done with Monte Carlo analysis. However, because this method is computationally expensive, new approaches with higher efficiency have been developed. Many popular methods are based on the surrogate models developed with Polynomial Chaos theory. However, size of these models can be prohibitively large for realistic examples. Hence, this paper provides a novel methodology to use an ensemble of weaker models to improve efficiency. Finally, a numerical example with a DDR4 topology is provided.

关键词： DDR4 Design space exploration Polynomial chaos theory Uncertainty quantification

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Impact of Aggregation Interval on Harmonic Phase Angle Measurements

Impact of Aggregation Interval on Harmonic Phase Angle Measu...

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IEEE International Workshop on Applied Measurements for Power systems (AMPS)

作者： Brandon Peterson Ana Maria Blanco Johan Rens Jan Meyer Gerhard Botha Jan Desmet Faculty of Engineering: School of Electrical Electronic and Computer Engineering North West University Potchefstroom South Africa Institute of Electrical Power Systems Technische Universität Dresden Dresden Germany Faculty of Engineering and Architecture Ghent University Kortrijk Belguim

The aggregation principles of IEC 61000-4-30 is a well-known concept implemented in Power Quality (PQ) recorders. Only rms data is retained. Harmonic emission assessment requires the analysis of harmonic phasors. Continuous measurement of harmonic phasors is constrained by the volume of data resulting if the measurement period is longer. An innovative approach to also aggregate harmonic phase angles has been introduced and this paper evaluates how the aggregation interval affect the application of this concept in harmonic emission. Two different types of harmonic emission sources are evaluated in a distribution network. Results from this field data are used to demonstrate that although it is not possible to use a standard aggregation interval as in the IEC 61000-4-30, that the concept of aggregating harmonic phasors present an improved understanding of harmonic emission.

关键词： Harmonic analysis Current measurement Power system harmonics IEC Standards Phase measurement Voltage measurement

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Electromagnetic Time Reversal Applied to Fault Location: On the Properties of Back-Injected Signals

Electromagnetic Time Reversal Applied to Fault Location: On ...

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Power systems Computation Conference

作者： Zhaoyang Wang Reza Razzaghi Mario Paolone Farhad Rachidi Electromagnetic Compatibility (EMC) Laboratory Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia Distributed Energy System Laboratory (DESL) Swiss Federal Institute of Technology EPFL Lausanne Switzerland

By using the electromagnetic time reversal (EMTR) theory, the paper studies its properties in order to derive a new fault location method to be used in power networks. It is shown that, in the reversed-time stage, the current signal observed at the true fault location can be clearly distinguished since it appears as a time-delayed copy of the back-injected fault-originated transient signal. Then, based on this similarity property, a fault location method is derived. Finally, the method is numerically validated with reference to a reproducible simulated power network composed of an inhomogeneous multi-conductor transmission-line system.

关键词： Cross-correlation sequence Electromagnetic time reversal Electromagnetic transients Fault location Power system

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Improvements on Interpolation Techniques based on Linear Programming for Constrained Control

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IFAC-PapersOnLine 2017年第1期50卷 1403-1408页

作者： Rubin D. Mercader P. Nguyen H.-N. Gutman P.-O. Baños A. Autonomous Systems Program Technion – Israel Institute of Technology Haifa Israel Department of Computer and System Engineering University of Murcia Murcia Spain Control Signal and System Department IFP Energies Nouvelles Lyon France Faculty of Civil and Environmental Engineering Technion – Israel Institute of Technology Haifa Israel

We present some improvements on interpolation based control (IC) for linear discrete-time systems with polyhedral constraints on the control and the states variables. The plant may be uncertain, time-varying, and subject to bounded disturbances. Roughly speaking, IC approach is based on the interpolation between an inner and an outer controller. The extension presented here is twofold. On the one hand, the IC approach is extended to deal with higher order inner controllers, making it e.g. possible to incorporate integral action in a vicinity of the origin. On the other hand, this paper presents a modification that better exploits the control signal range. As with previous IC strategies, the presented method demands relatively low online computational resources, and is applicable also to uncertain plants. The benefits of the improved scheme are illustrated in some examples. © 2017

关键词： Constrained control Linear programing Model predictive optimization-based control Robust control Time-varying systems

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Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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