面向医院建筑项目的IPD模式应用研究

医院建筑项目是非常复杂且具有挑战性的建设项目。传统的交付模式无法满足人们对医院建筑建设、运营方面的高要求,为此开展面向医院建筑项目的IPD模式应用研究。在对IPD模式发展现状整理的基础上,阐述医院建筑项目IPD模式的适用性。分析采用IPD模式的医院建筑项目特点,并以上海市某公立三甲专科医院项目为例,对IPD模式在国内的应用进行探索性研究。     

Reversible data hiding based on RSBEMD coding and adaptive multi-segment left and right histogram shifting

By leveraging the secret data coding using the remainder storage based exploiting modification direction (RSBEMD), and the pixel change operation recording based on multi-segment left and right histogram shifting, a novel reversible data hiding (RHD) scheme is proposed in this paper. The secret data are first encoded by some specific pixel change operations to the pixels in groups. After that, multi-segment left and right histogram shifting based on threshold manipulation is implemented for recording the pixel change operations. Furthermore, a multiple embedding policy based on chess board prediction (CBP) and threshold manipulation is put forward, and the threshold can be adjusted to achieve adaptive data hiding. Experimental results and analysis show that it is reversible and can achieve good performance in capacity and imperceptibility compared with the existing methods.     

Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag,spatial error and GWR

Many methods can be used to construct geographical cellular automata (CA) models of urban land use, but most do not adequately capture spatial heterogeneity in urban dynamics. Spatial regression is particularly appropriate to address the problem to reproduce urban patterns. To examine the advantages and disadvantages of spatial regression, we compare a spatial lag CA model (SLM-CA), a spatial error CA model (SEM-CA) and a geographically-weighted regression CA model (GWR-CA) by simulating urban growth at Nanjing, China. Each CA model is calibrated from 1995 to 2005 and validated from 2005 to 2015. Among these, SLM and SEM are spatial autoregressive (SAR) models that consider spatial autocorrelation of urban growth and yield highly similar land transition probability maps. Both SAR-CA and GWR-CA accurately reproduce urban growth at Nanjing during the calibration and validation phases, yielding overall accuracies (OAs) exceeding 94% and 85%, respectively. SAR-CA is superior in simulating urban growth when measured by OA and figure-of-merit (FOM) while GWR-CA is superior regarding the ability to address spatial heterogeneity. A concentric ring buffer-based assessment shows OA valleys that correspond to FOM peaks, where the ranges of valleys and peaks indicate the areas with active urban development. By comparison, SAR-CA captures more newly-urbanized patches in highly-dense urban areas and shows better performance in terms of simulation accuracy; whereas, GWR-CA captures more in the suburbs and shows better ability to address spatial heterogeneity. Our results demonstrate that spatial regression can help produce accurate simulations of urban dynamics featured by spatial heterogeneity, either implicitly or explicitly. Our work should help select appropriate CA models of urban growth in different terrain and socioeconomic contexts.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Damage identification in concrete using impact non-linear reverberation spectroscopy

High amplitude non-linear acoustic methods have shown potential for the identification of micro damage in brittle materials such as concrete. Commonly, these methods evaluate a non-linearity parameter from the relative change in frequency and attenuation with strain amplitude. Here, a novel attenuation model is introduced to describe the free reverberation from a standard impact resonance frequency test, together with an algorithm for estimating the unknown model coefficients. The non-linear variation can hereby by analyzed over a wider dynamic range as compared to conventional methods. The experimental measurement is simple and fully compatible with the standardized free-free linear impact frequency test.     

Value chain analysis of bio-coal business in Finland: Perspectives from multiple value chain members

The purpose of this study is to provide a comprehensive outline of the Value Chain (VC)¹ of bio-coal in Finland, define the proper business models of bio-coal business, and investigate the interrelationships between VC activities. The major findings are: 1) the study found significant interrelationships between support and primary activities in the bio-coal VC which suggest that we should not emphasis only the core value/primary activities of the bio-coal business, but pay more attentions to the “side values”/support activities; 2) The study identified political and environmental factors as the effective drivers, while technology and cost-profit uncertainty as major restrictions for the Finnish bio-coal business. The authors would like to suggest that instead of purely “environmental concerns”, the term “sustainability credentials” should be emphasized, which means value creating and adding items should not limit to monetary values, but consider social and environmental value propositions. 3) The study identified “low competition” in the current bio-coal business in Finland, which implies opportunities of market entrance for Finnish and international companies; 4) The study identified that the current Finnish bio-coal model is the decentralized model at the local level. However, the future Finnish bio-coal model will be a large scaled model, with a focus on export sales.     

Recent progress of the gas diffusion layer in proton exchange membrane fuel cells: Material and structure designs of microporous layer

Proton exchange membrane fuel cells (PEMFCs) have become the most attractive power supply units for stationary and mobile applications. The operation, design characteristics, as well as performance of PEMFCs, are closely related to the multiphase transport of mass, heat, and electricity in the cell, a critical of which is the gas diffusion layer (GDL). It is very important to guarantee the transmission of water and gasses under high current density, and which is the weakness of PEMFCs at present. Microporous layer (MPL) is considered to be the key variable for mass transfer, so varieties of works focus on modification of MPL materials and its structure design. However, there is still a lack of special review to summarize and prospect the progress of MPL in recent years. This review article therefore focuses on the insights and comprehensive understanding of four critical issues of the MPL, the porosity, pore size distribution, wettability, structural design and the durability of MPL. At last, the conclusion and recommendations section summarized the future prospects and recommendations for possible research opportunities.     

Simplified anode architecture for PEMFC systems based on alternative fuel feeding: Experimental characterization and optimization for automotive applications

The need to reduce PEMFC systems cost as well as to increase their durability is crucial for their integration in various applications and especially for transport applications. A new simplified architecture of the anode circuit called Alternating Fuel Feeding (AFF) offers to reduce the development costs. Requiring a new stack concept, it combines the simplicity of Dead-End Anode (DEA) with the operation advantages of the hydrogen recirculation. The three architectures (DEA, recirculation and AFF) are compared in terms of performance on a 5-kW test bench in automotive conditions, through a sensitivity analysis. A gain of 17% on the system efficiency is observed when switching from DEA to AFF. Moreover, similar performances are obtained both for AFF and for recirculation after an accurate optimization of the AFF tuning parameters. Based on DoE data, a gain of 25% on the weight of the anodic line has been identified compared to pulsed ejector architecture and 43% with the classic recirculation architecture with blower only (Miraï).     

基于双离合变速器电控单元的Volcano CAN开发

Volcano 是集汽车电子网络设计、实现和测试于一体的完整工具链。它包括整车网络规划及优化设计工具VNA,网络节点(TCU等)实现工具VTP以及网络测试工具Tellus。本文着重介绍了基于TCU的VTP。     

镁改性活性炭强化活性污泥处理混合污水研究

基于序批式活性污泥法(SBR)工艺,将镁盐改性活性炭(MgO-PAC)与传统活性炭(PAC)混合而成MPAC材料,用于处理生活与工业混合污水。通过连续30 d的运行实验,探讨了MPAC材料对生活与工业混合污水中COD、NH4^+-N和TP的去除效果以及对污泥的比耗氧速率、沉降性能和微生物多样性的影响。结果表明,投加MPAC材料对污水中COD的去除率提升了12.7百分点,对TP的去除率提升了17.5百分点,对NH4^+-N的去除率超过86.4%。投加MPAC后处理效果更好的重要原因,在于MPAC使得活性污泥的沉降性能和比耗氧速率得到明显改善,也提升了污泥的微生物丰度。MPAC对活性污泥处理生活与工业混合污水具有强化作用。