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Identifying how greenspace impacts the temperature-mortality relationship in urban environments is crucial, especially given climate change and rapid urbanization. However, the effect modification of greenspace on heat-related mortality has been typically focused on a localized area or single country. This study examined the heat-mortality relationship among different greenspace levels in a global setting., This publication was developed under Assistance Agreement No. RD83587101 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. Research reported in this publication was also supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD012769. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Also, this work has been supported by the National Research Foundation of Korea (2021R1A6A3A03038675), Medical Research CouncilUK (MR/V034162/1 and MR/R013349/1), Natural Environment Research Council UK (Grant ID: NE/ R009384/1), Academy of Finland (Grant ID: 310372), European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655 and 874990), Czech Science Foundation (22-24920S), Emory University’s NIEHS-funded HERCULES Center (Grant ID: P30ES019776), and Grant CEX2018-000794-S funded by MCIN/AEI/ 10.13039/501100011033 The funders had no role in the design, data collection, analysis, interpretation of results, manuscript writing, or decision to publication.

Neural network based image reconstruction methods are becoming increasingly popular. However, limited training data and the lack of theoretical guarantees for generalizability raised concerns, especially in biomedical imaging applications. These challenges are known to lead to an unstable reconstruction process that poses significant problems in biomedical image reconstruction. In this paper, we present a new framework that uses untrained generator networks to tackle this challenge, leveraging the structure of deep networks for regularizing solutions based on a technique known as Deep Image Prior (DIP).To achieve a high reconstruction accuracy, we propose a framework optimizing both the latent vector and the weights of a generator network during the reconstruction process. We also propose the corresponding reconstruction strategies to improve the stability and convergent performance of the proposed framework. Furthermore, instead of calculating forward projection in each iteration, we propose implementing its normal operator as a convolutional kernel under parallel beam geometry, thus greatly accelerating the calculation.Our experiments show that the proposed framework has significant improvements over other state-of-the-art conventional, pre-trained, and untrained methods under sparse-view, limited-angle, and low-dose conditions.Applying to parallel beam X-ray imaging, our framework shows advantages in speed, accuracy, and stability of the reconstruction process. We also show that the proposed framework is compatible with all differentiable regularizations that are commonly used in biomedical image reconstruction literature. Our framework can also be used as a post-processing technique to further improve the reconstruction generated by any other reconstruction methods. Furthermore, the proposed framework requires no training data and can be adjusted on-demand to adapt to different conditions (e.g. noise level, geometry, and imaged object).

Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive technique to probe the complex micro-architecture of the tissue being imaged. The diffusional properties of the tissue at the imaged resolution are well captured by the ensemble average propagator (EAP), which is a probability density function characterizing the probability of water molecule diffusion. Many properties in the form of imaging 'stains' can then be computed from the EAP that can serve as bio-markers for a variety of diseases. This motivates the development of methods for the accurate estimation of the EAPs from dMRI, which is an actively researched area in dMRI analysis. To this end, in the recent past, dictionary learning (DL) techniques have been applied by many researchers for accurate reconstruction of the EAP fields from dMRI scans of the central nervous system (CNS). However, most of the DL-based methods did not exploit the geometry of the space of the EAPs, which are probability density functions. By exploiting the geometry of the space of probability density functions, it is possible to reconstruct EAPs that satisfy the mathematical properties of a density function and hence yield better accuracy in the EAP field reconstruction. Using a square root density parameterization, the EAPs can be mapped to a unit Hilbert sphere, which is a smooth manifold with well known geometry that we will exploit in our formulation of the DL problem. Thus, in this paper, we present a general formulation of the DL problem for data residing on smooth manifolds and in particular the manifold of EAPs, along with a numerical solution using an alternating minimization method. We then showcase the properties and the performance of our algorithm on the reconstruction of the EAP field in a patch-wise manner from the dMRI data. Through several synthetic, phantom and real data examples, we demonstrate that our non-linear DL-based approach produces accurate and spatially smooth estimates of the EAP field from dMRI in comparison to the state-of-the-art EAP reconstruction method called the MAPL method, as well as the linear DL-based EAP reconstruction approaches. To further demonstrate the accuracy and utility of our approach, we compute an entropic anisotropy measure (HA), that is a function of the well known Rényi entropy, from the EAP fields of control and injured rat spinal cords respectively. We demonstrate its utility as an imaging 'stain' via a quantitative comparison of HA maps computed from EAP fields estimated using our method and competing methods. The quantitative comparison is achieved using a two sample t-test and the results of significance are displayed for a visualization of regions of the spinal cord affected most by the injury.

Numerous methods such as adding different eccentric and concentric braces, steel or concrete shear walls, etc. are used to strengthen reinforced concrete (RC) frames. If the seismic characteristics of the hybrid seismic resistant system are not known, choosing the suitable system for strengthening would be difficult. In this paper, the behavior of a moment resisting reinforced concrete frame strengthened with partially attached steel infill plate subjected to cyclic lateral loads has been investigated. The assessment has been carried out through an experimental approach with conventional and perforated steel infill plates. Five moment resisting reinforced concrete frames with identical dimensions, steel content, and concrete strength were built with the scale of 1:3. Among the four samples, two incorporated perforated, partially attached infill plates and the other two were strengthened with conventional partially attached steel infill plates. Finally, the cracking pattern, effective stiffness, strength reduction factors, ductilities, ultimate strengths, and energy absorption capacities of all the samples were calculated and compared. The results show that using partially attached steel infill plate causes the cracking pattern of the strengthened frame to be almost similar to that of the initial frame. In addition, the perforated steel infill plate not only increases the stiffness and lateral strength of the frame, it also increases the strength reduction factor by 35%.

Due to the lack of sufficient concrete strength or change in design codes, some RC structures are in need of retrofitting. Retrofitting and reevaluating a building is only possible if the seismic characteristics of the new hybrid seismic system are specified. This study attempts to experimentally investigate the effect of using the Chevron brace with ADAS and TADAS yielding dampers in retrofitting RC moment resisting frames. Seven RC moment resisting frames were constructed and six of which were retrofitted with Chevron braces and a different number of ADAS or TADAS yielding dampers. The frames were subjected to cyclic loading and strength, crack expansion, stiffness, ductility, energy dissipation, and strength reduction factor of all the frames were evaluated. The results show that the yielding dampers not only increase the strength of the RC frame, they also elevate its strength reduction factor and ductility. The effect of the ADAS damper is better than the TADAS damper and in both cases, pinching in the hysteresis diagram has considerably decreased.

Due to lack of sufficient concrete strength or change in design guidelines, some RC structures are in need of retrofitting. In the past few decades, using steel braces as a means with which to retrofit RC structures has become the subject of more attention and the reason can be attributed to fast implementation of the system as well as a significant increase in the stiffness and the strength of the structure. By adding different types of braces to moment resisting RC frames, the seismic properties of the structure including its ductility, strength reduction factor, stiffness, and strength undergo change. Retrofitting a building and designing it is only possible if the behavioral properties of the new hybrid seismic resisting system are known. This study experimentally investigates the effect of adding different types of steel braces on the behavioral properties of RC moment resisting frames. Eight RC moment resisting frames with identical steel bar configuration and concrete strength were built and seven of which were retrofitted with different braces such as the X, the knee, the chevron, the eccentric brace and the chevron brace with a vertical link. All the frames were subject to cyclic loading and their hysteresis load-displacement diagrams were plotted. Strength, stiffness, crack expansion, ductility, energy dissipation, and the strength reduction factor of all the frames were assessed. From the ductility and strength reduction factor viewpoints, the results indicate that the eccentric brace has a better performance compared to the other specimens. However, from the stiffness, strength, and cracking control standpoints, the behavior of the X brace is more desirable.

We provide an interactive tool for extracting exemplar isosurfaces from a 3D scalareld using a novel isovalue classication process.We propose a structural VQA metric that uses representative isosurfaces as benchmark structures to assess the visual quality of compressed 3D scalarelds.Our experiments on a number of benchmark data sets suggest that, compared to existing methods, the proposed isovalue classication approach provides a more distinct set of isosurfaces that are more representative of the complexity of the datasets.We examine a number of widely-used compression techniques (i.e.,discrete wavelet transform, discrete cosine transform, and tensor approximation) to establish the utility of our VQA approach. Display Omitted Visualization of volumetric data has been widely used for exploration of data from scientific simulations and biomedical imaging. Despite advances of GPU-assisted rendering, which has become the state-of-art in direct volume rendering, still many volumetric data sets are too large to be visualized interactively. Therefore, compression-domain rendering approaches are used in visualization processes in order to reduce the amount of data sent to the GPU and thus speed up interactive visualization. Hence, reliable tools to assess the quality of the reconstructed 3D data are of great importance, influencing the effectiveness of the visualization. However, numerical error analysis approaches such as mean-squared-based metrics are often inconsistent with perceived visual quality. We propose a structural volume quality assessment approach for 3D scalar volume based on the human visual system (HVS). Our approach consists of two stages: First, we provide an interactive tool for extracting significant volume features via isosurfaces from a 3D scalar field using an isovalue classification process. Second, we propose a structural volume quality assessment (VQA) metric that employs representative isosurfaces as benchmark structures. For this purpose, we use a recently developed perceptual-based mesh quality metric [1] to assess the visual quality of compressed 3D scalar fields. Our experiments on a number of benchmark data sets suggest that, compared to existing methods, the proposed isovalue classification approach provides a more distinct set of isosurfaces that are more representative of the complexity of the data sets. We examine a number of widely used compression approaches, namely, discrete wavelet transform, discrete cosine transform, and tensor approximation, to establish the utility of our volume quality assessment approach.

We demonstrate that non-separable box splines deployed on body centered cubic lattices (BCC) are suitable for fast evaluation on present graphics hardware. Therefore, we develop the linear and quintic box splines using a piecewise polynomial (pp)-form as opposed to their currently known basis (B)-form. The pp-form lends itself to efficient evaluation methods such as de Boor's algorithm for splines in box splines basis. Further on, we offer a comparison of quintic box splines with the only other interactive rendering available on BCC lattices that is based on separable kernels for interleaved Cartesian cubic (CC) lattices. While quintic box splines result in superior quality, interleaved CC lattices are still faster, since they can take advantage of the highly optimized circuitry for CC lattices, as it is the case in graphics hardware nowadays. This result is valid with and without prefiltering. Experimental results are shown for both a synthetic phantom and data from optical projection tomography. We provide shader code to ease the adaptation of box splines for the practitioner. (C) 2010 Elsevier Ltd. All rights reserved.