MATHEMATICAL MODELING AND PREDICTION METHOD OF CONCRETE CARBONATION AND ITS APPLICATIONS

ABSTRACT The carbonation process of concrete is principally a diffusion phenomenon. The penetration rate of carbon dioxide depends mainly on the concrete quality and the exposure condition. Based on both Fick's first and second laws of linear diffusion equations, the threedimensional equation of conservation of mass is expressed. This equation can be reduced to two-and one-dimensional equations of conservation of mass which can predict the carbonation depth beneath corners and the general surface of concrete structures, respectively. The objectives of this investigation are to measure the depth of carbonation from the free surface of a concrete member and to predict the carbonation depth using a statistical method. The result of the present study indicates that the maximum carbonation depth of concrete at corners is larger than 2 times that of the general surface under the conditions of homogeneous, isotropic and uncracked concrete subjected to static load. The carbonation depths of ten existing reinforced concrete (RC) bridges in Taiwan were predicted by the statistical method. The predicted results can be offered as a reference basis of repair, reinforcement or demolition for the existing concrete structures

Scaling of global input–output networks

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input–output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input–output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input–output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input–output networks to support economic policymaking

Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households

The U.S. household consumption, a key engine for the global economy, has significant carbon footprints across the world. Understanding how the U.S. household consumption on specific goods or services drives global greenhouse gas (GHG) emissions is important to guide consumption-side strategies for climate mitigation. Here we examined global GHG emissions driven by the U.S. household consumption from 1995 to 2014 using an environmentally extended multi-regional input-output model and detailed U.S. consumer expenditure survey data. The results show that the annual carbon footprint of the U.S. households ranged from 17.7 to 20.6 tCO2eq/capita with an expanding proportion occurring overseas. Housing and transportation contributed 53–66% of the domestic carbon footprint. Overseas carbon footprint shows an overall increasing trajectory, from 16.4% of the total carbon footprint in 1995 to the peak of 20.4% in 2006. These findings provide valuable insights on the scale, distribution, and variations of the global GHG emissions driven by the U.S. household consumption for developing consumption-side strategies in the U.S. for climate mitigation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150690/1/Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households.pdfDescription of Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households.pdf : Main articl

Correlation of caveolin-1 expression with microlymphatic vessel density in colorectal adenocarcinoma tissues and its correlation with prognosis

AbstractObjectiveTo study the expression of caveolin-1 in colorectal adenocarcinoma tissues and its correlation with microlymphatic vessel density (LMVD), and to investigate the clinical pathological prognostic significance of caveolin-1 and LMVD in patients with colorectal cancer.MethodsThe expression of caveolin-1 and LMVD in 45 specimens of normal colorectal tissues, and 90 specimens of colorectal adenocarcinoma tissues were detected by immunohistochemistry technique. The correlation between their expression and the clinicopathologic features was analyzed. Multivariable Cox regression was used to analyze the association between the laboratory indices and overall survival time.ResultsThe positive rates of caveolin-1 in colorectal adenocarcinoma tissues were significantly higher than those in normal colorectal tissues (P < 0.01). LMVD in colorectal adenocarcinoma tissues were significantly higher than those in normal colorectal tissues (P < 0.01). Mean LMVD in group with caveolin-1 positive was significantly higher than in that with caveolin-1 negative. The median survival time was 26.7 months. Cox regression analysis showed that the caveolin-1 expression, invation depth, lymph node metastasis, TNM stage, liver metastasis and LMVD were independent risk factors of overall survival time of patients with colorectal carcinoma.ConclusionsCaveolin-1 may contribute to the lymphangiogenesis in the tumor. During the occurrence and development of colorectal adenocarcinoma, there is a close relationship between the expression of caveolin-1 and lymphatic microvessel of tumor. Caveolin-1 expression and microlymphatic vessel density are significant prognostic value of colorectal carcinoma

Income-based greenhouse gas emissions of nations

Accounting for greenhouse gas (GHG) emissions of nations is essential to understanding their importance to global climate change and help inform the policymaking on global GHG mitigation. Previous studies have made efforts to evaluate direct GHG emissions of nations (a.k.a. production-based accounting method) and GHG emissions caused by the final consumption of nations (a.k.a. consumption-based accounting method), but overlooked downstream GHG emissions enabled by primary inputs of individual nations and sectors (a.k.a. income-based accounting method). Here we show that the income-based accounting method reveals new GHG emission profiles for nations and sectors. The rapid development of mining industries drives income-based GHG emissions of resource-exporting nations (e.g., Australia, Canada, and Russia) during 1995–2009. Moreover, the rapid development of sectors producing basic materials and providing financial intermediation services drives income-based GHG emissions of developing nations (e.g., China, Indonesia, India, and Brazil) during this period. The income-based accounting can support supply side policy decisions and provide additional information for determining GHG emission quotas based on cumulative emissions of nations and designing policies for shared responsibilities

Determination of QPO properties in the presence of strong broad-band noise: a case study on the data of MAXI J1820+070

Accurate calculation of the phase lags of quasi-periodic oscillations (QPOs) will provide insight into their origin. In this paper we investigate the phase lag correction method which has been applied to calculate the intrinsic phase lags of the QPOs in MAXI J1820+070. We find that the traditional additive model between BBN and QPOs in the time domain is rejected, but the convolution model is accepted. By introducing a convolution mechanism in the time domain, the Fourier cross-spectrum analysis shows that the phase lags between QPOs components in different energy bands will have a simple linear relationship with the phase lags between the total signals, so that the intrinsic phase lags of the QPOs can be obtained by linear correction. The power density spectrum (PDS) thus requires a multiplicative model to interpret the data. We briefly discuss a physical scenario for interpreting the convolution. In this scenario, the corona acts as a low-pass filter, the Green's function containing the noise is convolved with the QPOs to form the low-frequency part of the PDS, while the high-frequency part requires an additive component. We use a multiplicative PDS model to fit the data observed by Insight-HXMT. The overall fitting results are similar compared to the traditional additive PDS model. Neither the width nor the centroid frequency of the QPOs obtained from each of the two PDS models were significantly different, except for the r.m.s. of the QPOs. Our work thus provides a new perspective on the coupling of noise and QPOs.Comment: 13 pages, 8 figure

Experimental performance analysis of a dual source heat pump integrated with thermal energy storage

To mitigate disturbances to the electric grid resulting from the growing penetration of intermittent and decentralized renewable generation, a dual-source (air source and ground source) heat pump (DSHP) integrated with thermal energy storage (TES) was developed. The DSHP can use either ambient air or the shallow subsurface of the ground to provide space heating or space cooling to the building as the conventional heat pump and produce hot/cold water for charging TES. Using dual sources (air and ground) can reduce the required size of the expensive ground heat exchangers while retaining high energy efficiency. During the off-peak period, the DSHP cools/heats the TES with low-cost electricity or overproduced renewable power. The stored cooling/heating energy in the TES is released during peak hours of the electric grid to meet the thermal demands of the building without consuming electricity to run the DSHP. A 2-ton (7 kW) prototype DSHP was developed and integrated with a 50-gallon (189 L) TES tank filled with a phase change material. Field tests were conducted to characterize the performance of the integrated system operating in various operation modes