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The relationship between daylight and happiness for women in residential districts of Isfahan, Iran

Environmental Quality Management 2019年第3期28卷 103-110页

作者： Javani, Zahra Madani, Ramin Hojat, Isa Zadeh, Rana Department of Architecture Art University Isfahan Iran Department of Architecture Tehran University Tehran Iran Health Design Innovations Lab Department of Design and Environmental Analysis Cornell University Ithaca NY United States

This study explores the relationship between daylight exposure in urban residential units and the mood/happiness of nonworking female residents (housewives) in Isfahan, Iran. A growing body of evidence points to the physiological and psychological benefits of daylight, yet few studies have focused on the relationship between residential daylight and women's mental health. A cross-sectional study was conducted using two-stage random cluster sampling of nonworking female occupants of buildings in four residential districts. Mood/happiness was determined using the Oxford Happiness Inventory and the Fordyce Happiness Inventory. Spearman's rank correlation analysis was used to determine the relationship between the variables. A significant relationship was found between residents’ happiness and the amount of daylight in their residential units (Oxford Inventory, r = 0.68, P < 0.05;Fordyce Inventory, r = 0.53, P < 0.05). The findings suggest that features of physical residential environments, such as daylight, may be linked to the happiness of housewives. Persons belonging to various demographic groups who spend the majority of their time indoors (e.g., housewives, children, elderly individuals, and people with disabilities) are most dependent on architecture and environmental design for their well-being, and therefore, are most affected by design decisions. © 2019 Wiley Periodicals, Inc.

关键词： architecture daylight environmental design happiness Iran mental health physical environment residential buildings women

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A methodological framework for estimating ambient PM_(2.5)particulate matter concentrations in the UK

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Journal of Environmental Sciences 2025年第4期150卷 676-691页

作者： David Galán-Madruga Parya Broomandi Alfrendo Satyanaga Ali Jahanbakhshi Mehdi Bagheri Aram Fathian Rasoul Sarvestan J.Cárdenas-Escudero J.O.Cáceres Prashant Kumar Jong Ryeol Kim Department of Atmospheric Pollution National Centre for Environment HealthHealth Institute Carlos III.Ctra.Majadahonda a Pozuelo km 2.2Madrid 28220Spain Department of Civil and Environmental Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Department of Electrical and Computer Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan School of Architecture Building and Civil EngineeringLoughborough UniversityLeicestershire LE113TUUnited Kingdom Neotectonics and Natural Hazards Institute RWTH Aachen UniversityAachen 52056Germany UNESCO Chair on Coastal Geo-Hazard Analysis Research Institute for Earth SciencesTehran 13185-1494Iran Water SedimentHazardsand Earth-surface Dynamics(waterSHED)LabDepartment of GeoscienceUniversity of CalgaryCalgary Alberta T2N 1N4Canada Department of Climatology Faculty of Geography and Environmental SciencesHakim Sabzevari UniversitySabzevar ***Iran Laser Chemistry Research Group Department of Analytical ChemistryFaculty of ChemistryComplutense University of MadridPlaza de Ciencias 1Madrid 28040Spain Analytical Chemistry Department FCNETUniversity of PanamaUniversity CityUniversity MailPanama 4Panama City 3366Panama Global Centre for Clean Air Research(GCARE) School of SustainabilityCivil and Environmental EngineeringFaculty of Engineering and Physical SciencesUniversity of SurreyGuildford GU27XHUnited Kingdom Institute for Sustainability University of SurreyGuildford GU27XHUnited Kingdom

Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public *** to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its *** this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate *** PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested *** from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the *** indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to *** highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.

关键词： Air quality PM_(2.5)particles Meteorological variables Prediction model Long-term trend

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Variable stiffness design optimization of fiber-reinforced composite laminates with regular and irregular holes considering fiber continuity for additive manufacturing

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Chinese Journal of Aeronautics 2025年第3期 338-358页

作者： Yi LIU Zunyi DUAN Chunping ZHOU Yuan SI Chenxi GUAN Yi XIONG Bin XU Jun YAN Jihong ZHU School of Mechanics Civil Engineering & ArchitectureNorthwestern Polytechnical University Research & Development Institute of Northwestern Polytechnical University in Shenzhen Key Lab for Airborne Hi-performance Electro-magnetic Window RISAC School of System Design and Intelligent Manufacturing Southern University of Science and Technology State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of Technology State IJR Center of Aerospace Design and Additive Manufacturing MIIT Lab of Metal Additive Manufacturing and Innovative DesignNorthwestern Polytechnical University

Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel,and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization(NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples consid

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Crowdsourcing Geospatial Data for Earth and Human Observations:A Review

Journal of Remote Sensing

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Journal of Remote Sensing 2024年第1期4卷 766-790页

作者： Xiao Huang Siqin Wang Di Yang Tao Hu Meixu Chen Mengxi Zhang Guiming Zhang Filip Biljecki Tianjun Lu Lei Zou Connor Y.H.Wu Yoo Min Park Xiao Li Yunzhe Liu Hongchao Fan Jessica Mitchell Zhenlong Li Alexer Hohl Department of Environmental Sciences Emory UniversityAtlantaGAUSA Spatial Sciences Institute University of Southern CaliforniaLos AngelesCAUSA Wyoming Geographic Information Science Center University of WyomingLaramieWYUSA Department of Geography Oklahoma State UniversityStillwaterOKUSA Department of Geography and Planning University of LiverpoolLiverpoolUK Carilion School of Medicine Virginia TechBlacksburgVAUSA Department of Geography&the Environment University of DenverDenverCOUSA Department of Architecture National University of SingaporeSingaporeSingapore Department of Epidemiology and Environmental Health College of Public HealthUniversity of KentuckyLexingtonKYUSA Department of Geography Texas A&M UniversityCollege StationTXUSA Department of Management Science and Information Systems Oklahoma State UniversityStillwaterOKUSA Department of Geography University of ConnecticutStorrsCTUSA Transport Studies Unit University of OxfordOxfordUK The MRC Centre for Environment and Health Imperial College LondonLondonUK Department of Civil and Environmental Engineering Norwegian University of Science and TechnologyTrondheimNorway Spatial Analysis Lab University of MontanaMissoulaMTUSA Geoinformation and Big Data Research Laboratory Department of GeographyThe Pennsylvania State UniversityUniversity ParkPAUSA Department of Geography The University of UtahSalt Lake CityUTUSA.

The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial *** by advancements in digital technology and high-speed communication,this paradigm shift has democratized data collection,obliterating traditional barriers between data producers and *** previous literature has compartmentalized this subject into distinct platforms and application domains,this review offers a holistic examination of crowdsourced geospatial *** a narrative review approach due to the interdisciplinary nature of the topic,we investigate both human and Earth observations through crowdsourced *** review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data ***,it addresses salient challenges,encompassing data quality,inherent biases,and ethical *** contend that this thorough analysis will serve as an invaluable scholarly resource,encapsulating the current state-of-the-art in crowdsourced geospatial data,and offering strategic directions for future interdisciplinary research and applications across various sectors.

关键词： crowdsourced geospatial datafacilitated crowdsourcing human observations geospatial data high speed communication paradigm shift earth observations digital technology

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How Urban Green Spaces Need to Change to Address the Public Post-Covid Expectations

SSRN

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SSRN 2022年

作者： Sikorska, Daria Wojnowska-Heciak, Magdalena Heciak, Jakub Bukowska, Joanna Laszkiewicz, Edyta Sikorski, Piotr Warsaw University of Life Sciences - SGGW Institute of Environmental Engineering Department of Remote Sensing and Environmental Assessment Nowoursynowska 166 Warsaw02-776 Poland Warsaw University of Life Sciences - SGGW Institute of Environmental Engineering Department of Landscape Architecture Nowoursynowska 166 Warsaw02-776 Poland Kielce University of Technology Department of Civil Engineering and Architecture Al. 1000-lecia Państwa Polskiego 5 Kielce25-541 Poland University of Lodz Social-Ecological Systems Analysis Lab Faculty of Economics and Sociology Rewolucji 1905 r. 39 Lódz90-214 Poland

Based on a social preference survey, performed in Warsaw, we assessed the preferences toward alterations in UGS that should take place to ensure safe recreation. We identified how peoples’ personal characteristics are linked to the preferred changes to formulate recommendations for UGS alterations addressing post-covid challenges while keeping the resident’s preferences in mind. We used 1-7 scorings of images of different types of urban landscapes, characterized by low/medium and high share of e.g. built-up structures, the extent of the view and number of visitors present, followed by questions regarding recreational behaviour, anxiety towards COVID and preferred changes. There was a clear respondents’ need for more vegetation, both wild and cultivated, and more places for spending time in UGS. People perceived various types of UGS comparably, being significantly less likely to spend type in urban built-up landscapes compared to parks or natural forests. The respondents declared that vegetation density and share of vegetation were of the highest importance to them in the assessment of images and claimed to neglect crowding, which was the opposite of their scorings of those sceneries that favoured the possibility of isolation: freely straying off the track and no other users. Any repressive actions by distancing people spatially in UGS, isolating seniors or introducing entry limits did not meet social approval. We also identified two major behavioural patterns: people who were largely driven by fear of COVID-19, visiting green spaces closer to their homes and having less need for UGS alterations. The second group was those frequently visiting UGS who did not reveal fear of COVID-19, those who did not favour wide paths or the importance of UGS located nearby, while opted for more wilderness. These two approaches to recreation are likely to persist after the pandemic, which supports the need to increase urban greenspace diversity. © 2022, The Authors. All rights reserv

关键词： Vegetation

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Implications of circular strategies on energy, water, and GHG emissions in informal housing in Lima

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Energy and Buildings 2025年 344卷

作者： Sarmiento-Pastor, Jaime Lira-Chirif, Andrés Rondinel-Oviedo, Daniel R. Keena, Naomi Dyson, Anna Raugei, Marco Acevedo-De-los-Ríos, Alejandra Scientific Research Institute University of Lima Lima Peru Peter Guo-Hua Fu School of Architecture Faculty of Engineering McGill University Montreal Canada Yale Center for Ecosystems + Architecture 180 York Street New Haven 06511 CT United States School of Engineering Computing and Mathematics Oxford Brookes University United Kingdom Center for Life Cycle Analysis Columbia University NY United States Louvain Research Institute for Landscape Architecture and Built Environment (LAB) University of Louvain Louvain-la-Neuve Belgium

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Why Sponge Planet?Discussions on Land-Based,Water-Driven Solutions

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景观设计学(中英文) 2025年第1期13卷 2-12页

作者： Anne Whiston SPIRN Hans Joachim SCHELLNHUBER Glen T.DAIGGER Jun FU Peter CHILDS David SEDLAK Peter HEAD Timothy RAVASI Bruno De MEULDER Kelly SHANNON Daniel DULTZIN Fabiola SOSA Arthur KAY Humberto R.da ROCHA Sin Chan CHOU Marcos BUCKERIDGE Carlos A.NOBRE Makoto YOKOHARI Shang-Ping XIE Kazi Khaleed ASHRAF Kenneth STRZEPEK Franco MONTALTO Department of Architecture School of Architecture and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA International Institute for Applied Systems Analysis(IIASA) Laxenburg A-2361Austria National Academy of Engineering(USA) WashingtonD.C.20001USA Civil&Environmental Engineering University of MichiganAnn ArborMI 48109USA Chinese Academy of Engineering Beijing 100088China Accademia Nazionale dei Lincei Rome 00165Italy National School of Development Peking UniversityBeijing 100080China Royal Academy of Engineering London SW1Y 5DGUK Dyson School of Design Engineering Imperial College LondonLondon SW72AZUK Department of Civil and Environmental Engineering University of CaliforniaBerkeleyBerkeleyCA 94720USA Berkeley Water Center University of CaliforniaBerkeleyBerkeleyCA 94720USA National Alliance for Water Innovation(NAWI) BerkeleyCA 94720USA Marine Climate Change Unit Okinawa Institute of Science and Technology(OIST)Okinawa 904-0495Japan Department of Architecture Faculty of Engineering ScienceKU LeuvenHeverlee B-3001Belgium Climate Change Adaptation Strategies NPO Mexico City 06600Mexico Department of Environmental Economy Metropolitan Autonomous UniversityMexico City 02200Mexico University College London London WC1E 6BTUK Department of Atmospheric Sciences Institute of AstronomyGeophysics and Atmospheric SciencesUniversity of São PauloSão Paulo 05508-090Brazil National Institute for Space Research(INPE) São Paulo 12227-010Brazil Department of Botany Institute of BiosciencesUniversity of São PauloSão Paulo 05508-050Brazil International Geosphere-Biosphere Programme(IGBP) Stockholm S-10405Sweden ARISE City Research Unit The University of TokyoTokyo 113-8654Japan Scripps Institution of Oceanography University of CaliforniaSan DiegoSan DiegoCA 92093USA Bengal Institute for Architecture Landscapes and SettlementsDhaka 1213Bangladesh International Food Policy Research Institute WashingtonD.C.20005USA Oxford Martin School University of OxfordOxford O

The recent Nature Water article,“To Solve Climate Change,We Need to Restore Our Sponge Planet,”by Kongjian Yu,Erica Gies,and Warren ***[1],makes a compelling case for recalibrating climate strategies to prioritize the water cycle alongside reducing carbon *** authors highlight how human activities-agriculture,urbanization,and industrialization-have degraded 75%of the earth’s land,severely disrupting natural water *** degradation diminishes the planet’s capacity to regulate temperature through water vapor,cloud formation,and the hydrological cycle,further accelerating climate instability.

关键词： water cycle urbanization land degradation recalibrating climate strategies agriculture climate change industrialization climate changewe

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ODT Flow: A scalable platform for extracting, analyzing, and sharing multi-source multi-scale human mobility

arXiv

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arXiv 2021年

作者： Li, Zhenlong Huang, Xiao Hu, Tao Ning, Huan Ye, Xinyue Li, Xiaoming Geoinformation and Big Data Research Lab Department of Geography University of South Carolina ColumbiaSC United States Department of Geosciences University of Arkansas FayettevilleAR United States Center for Geographic Analysis Harvard University CambridgeMA United States Department of Landscape Architecture & Urban Planning Texas A&M University TX United States Department of Health Promotion Education and Behavior University of South Carolina ColumbiaSC United States

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics. © 2021, CC BY.

关键词： Social networking (online)

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Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE) 23rd

Discovering Functional Brain Networks with 3D Residual Autoe...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

Functional MRI has attracted increasing attention in cognitive neuroscience and clinical mental health research. Towards understanding how brain give rises to mental phenomena, deep learning has been applied to functional MRI (fMRI) dataset to discover the physiological basis of cognitive process. Considering the unsupervised nature of fMRI due to the complex intrinsic brain activities, an encoder-decoder structure is promising to model hidden structure of latent signal sources. Inspired by the success of deep residual learning, we propose a 68-layer 3D residual autoencoder (3D ResAE) to model deep representations of fMRI in this paper. The proposed model is evaluated on the fMRI data under 3 cognitive tasks in Human Connectome Project (HCP). The experimental results have shown that the temporal representations learned by the encoder matches the task design and the spatial representations can be interpreted to be meaningful functional brain networks (FBNs), which not only include tasks based FBNs, but also intrinsic FBNs. The proposed model also outperforms a 3-layer autoencoder, showing the key factor for the performance improvement is depth. Our work demonstrates the feasibility and success of adopting 2D advanced deep residual networks in computer vision into 3D fMRI volume modeling. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification 23rd

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classi...

引用

23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

ISBN: (纸本)9783030597276

It has been of great interest in the neuroimaging community to model spatiotemporal brain function and disorders based on resting state functional magnetic resonance imaging (rfMRI). A variety of spatiotemporal methods have been proposed for rfMRI so far, including deep learning models such as convolution networks (CNN) and recurrent networks (RNN). However, the dominant models fail to capture the long-distance dependency (LDD) due to their sequential nature, which becomes critical at longer sequence lengths due to memory limit. Inspired by human brains extraordinary ability of long-term memory and attention, the attention mechanism is designed for machine translation to draw global dependencies and achieved state-of-the-art. In this paper, we propose a spatiotemporal attention autoencoder (STAAE) to discover global features that address LDDs in rfMRI. STAAE encodes the information throughout the rfMRI sequence and reveals resting state networks (RSNs) that characterize spatial and temporal properties of the data. Considering that the rfMRI is measured without external tasks, an unsupervised classification framework is developed based on the connectome generated with STAAE. This framework has been evaluated on 281 children with ADHD and 266 normal control children from 4 sites of ADHD200 datasets. The proposed STAAE reveals the global functional interaction in the brain and achieves a state-of-the-art classification accuracy from 59.5% to 77.2% on multiple sites. It is evident that the proposed attention-based model provides a novel approach towards better understanding of human brain. © 2020, Springer Nature Switzerland AG.

关键词： Brain

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