The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

American Society for Testing and Materials

The purpose of this paper is to provide a source of information on thermal energy use in buildings, its drivers, and their past, present and future trends on a global and regional basis. Energy use in buildings forms a large part of global and regional energy demand. The importance of heating and cooling in total building energy use is very diverse with this share varying between 18% and 73%. Biomass is still far the dominant fuel when a global picture is considered; the role of electricity is substantially growing, and the direct use of coal is disappearing from this sector, largely replaced by electricity and natural gas in the most developed regions. This paper identifies the different drivers of heating and cooling energy demand, and decomposes this energy demand into key drivers based on a Kaya identity approach: number of households, persons per household, floor space per capita and specific energy consumption for residential heating and cooling; and GDP, floor space per GDP, and specific energy consumption for commercial buildings. This paper also reviews the trends in the development of these drivers for the present, future – and for which data were available, for the past – in 11 world regions as well as globally. Results show that in a business-as-usual scenario, total residential heating and cooling energy use is expected to more or less stagnate, or slightly decrease, in the developed parts of the world. In contrast, commercial heating and cooling energy use will grow in each world region. Finally, the results show that per capita total final residential building energy use has been stagnating in the vast majority of world regions for the past three decades, despite the very significant increases in energy service levels in each of these regions.

Heating and cooling energy trends and drivers in buildings

Composites. Part A, Applied science and manufacturing

In past decades researchers found many difficulties while providing environmentally supportive materials for product making. Natural fibers possess many advantages over synthetic fibers such as eas...

Effect of natural filler materials on fiber reinforced hybrid polymer composites: An Overview

ポリウレタン/ptfeナノ粒子のコンポジットとナノコンポジットの表面特性,熱特性,および機械的特性 | 文献情報 | j-global 科学技術総合リンクセンター

In this paper, new and non-invasive imaging methods to assess interstitial fluid transport parameters in tumors in vivo are developed, analyzed and experimentally validated. These parameters include extracellular volume fraction (EVF), interstitial fluid volume fraction (IFVF) and interstitial hydraulic conductivity (IHC), and they are known to have a critical role in cancer progression and drug delivery effectiveness. EVF is defined as the volume of extracellular matrix per unit volume of the tumor, while IFVF refers to the volume of interstitial fluid per unit bulk volume of the tumor. There are currently no established imaging methods to assess interstitial fluid transport parameters in cancers in vivo. We develop and test new theoretical models and imaging techniques to assess fluid transport parameters in cancers using non-invasive ultrasound methods. EVF is estimated via the composite/mixture theory with the tumor being modeled as a biphasic (cellular phase and extracellular phase) composite material. IFVF is estimated by modeling the tumor as a biphasic poroelastic material with fully saturated solid phase. Finally, IHC is estimated from IFVF using the well-known Kozeny-Carman method inspired by soil mechanics theory. The proposed methods are tested using both controlled experiments and in vivo experiments on cancers. The controlled experiments were performed on tissue mimic polyacrylamide samples and validated using scanning electron microscopy (SEM). In vivo applicability of the proposed methods was demonstrated using a breast cancer model implanted in mice. Based on the controlled experimental validation, the proposed methods can estimate interstitial fluid transport parameters with an error below 10% with respect to benchmark SEM data. In vivo results demonstrate that EVF, IFVF and IHC increase in untreated tumors whereas these parameters are observed to decrease over time in treated tumors. The proposed non-invasive imaging methods may provide new and cost-effective diagnostic and prognostic tools to assess clinically relevant fluid transport parameters in cancers in vivo. 

/pdf/non-invasive-imaging-of-interstitial-fluid-transport-1b8p2hrs.pdf

Non-invasive imaging of interstitial fluid transport parameters in solid tumors in vivo

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. The available methods, including state-of-the-art deep learning techniques, are incapable of imputing the gene expressions reliably because of the lack of a mechanism to explicitly consider the underlying biological knowledge of the system. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system: (i) we reposition the genes in such a way that their spatial configuration reflects their interactive relationships; and (ii) we use a self-supervised 2D convolutional neural network to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

/pdf/self-supervised-deep-learning-of-gene-gene-interactions-for-290159ex.pdf

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery

Pathologists need to combine information from differently stained pathology slices for accurate diagnosis. Deformable image registration is a necessary technique for fusing multi-modal pathology slices. This paper proposes a hybrid deep feature-based deformable image registration framework for stained pathology samples. We first extract dense feature points via the detector-based and detector-free deep learning feature networks and perform points matching. Then, to further reduce false matches, an outlier detection method combining the isolation forest statistical model and the local affine correction model is proposed. Finally, the interpolation method generates the deformable vector field for pathology image registration based on the above matching points. We evaluate our method on the dataset of the Non-rigid Histology Image Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019 conference. Our technique outperforms the traditional approaches by 17% with the Average-Average registration target error (rTRE) reaching 0.0034. The proposed method achieved state-of-the-art performance and ranked 1st in evaluating the test dataset. The proposed hybrid deep feature-based registration method can potentially become a reliable method for pathology image registration.

A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathological Images

Objective. Compression-induced solid stress (SSc) and fluid pressure (FPc) during ultrasound poroelastography (USPE) experiments are correlated with two markers of cancer growth and treatment effectiveness: growth-induced solid stress (SSg) and interstitial fluid pressure (IFP). The spatio-temporal distributions of SSg and IFP are determined by the transport properties of the vessels and interstitium in the tumor microenvironment. Approach. We propose a new USPE method for the non-invasive imaging of the local cancer mechanical parameters and dynamics of fluid flow. When performing poroelastography experiments, it may be difficult to implement a typical creep compression protocol, which requires to maintain a constant normally applied force. In this paper, we investigate the use of a stress relaxation protocol, which might be a more convenient choice for clinical poroelastography applications. Main results. Based on our finite element and ultrasound simulations study, we demonstrate that the SSc, FPc and their spatio-temporal distribution related parameters, interstitial permeability and vascular permeability, can be determined from stress relaxation experiments with errors below 10% as compared to the ground truth and accuracy similar to that of corresponding creep tests, respectively. We also demonstrate the feasibility of the new methodology for in vivo experiments using a small animal cancer model. Significance. The proposed non-invasive USPE imaging methods may become an effective tool to assess local tumor pressure and mechanopathological parameters in cancers.

Assessment of compression-induced solid stress, fluid pressure and mechanopathological parameters in cancers in vivo using poroelastography

Objective: Knowledge based planning (KBP) typically involves training an end-to-end deep learning model to predict dose distributions. However, training end-to-end KBP methods may be associated with practical limitations due to the limited size of medical datasets that are often used. To address these limitations, we propose a content based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Approach: Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient’s anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. Summary metrics (e.g. dose-volume histogram, conformity index, homogeneity index, etc.) are computed and can then be utilized in subsequent automated planning. All source code for this project is available on github. Main Results: The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available plans and clinical plans from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network. Significance: Applying CBIR to inform subsequent treatment planning potentially addresses many limitations associated with end-to-end KBP. Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works, potentially through methods like the MetaPlanner framework.

M. Tauhidul Islam

Papers

Non-invasive imaging of interstitial fluid transport parameters in solid tumors in vivo

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery

A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathological Images

Assessment of compression-induced solid stress, fluid pressure and mechanopathological parameters in cancers in vivo using poroelastography

Learning Treatment Plan Representations for Content Based Image Retrieval