Since 2013, China has become the largest emitter of CO2 in the world. Among all emission sources, the building and construction industry contributes significant amounts due to its massive use of ...materials and equipment. However, emissions quantity, growth trends, and influencing factors have yet to be fully investigated. This study aims to calculate construction carbon emissions in China from 1994 to 2012 by identifying the longitudinal impact of seven key driving factors and evaluating the effectiveness of construction emissions policy. The data were collected from publicly accessible statistical yearbooks in China, and analyzed by the Logarithmic Mean Divisia Index (LMDI) to decompose incremental emission changes. Key findings include: (1) carbon emissions of China's building and construction industry reached 115 billion kg in 2012 and contributed 3.4% to the country's emissions; (2) on average, the annual emissions increase for the last 19 years was 6.9%, during which time “building materials consumption” contributed the most (63%) to the total increase of carbon emissions, while “energy intensity” offset the largest amount (54%) of total emissions mitigation; (3) in 2012, construction carbon intensity was far less (only 13.1%) than that of the national intensity level; and (4) the construction industry has met or surpassed most of the domestic emission-reduction targets in both the short- and mid-term, but there is uncertainty on whether long-term targets can be achieved. This research provides new scientific evidence of carbon emissions in China's building and construction industry from a decomposition method and raises new challenges for industry-specific emission regulations.
•Carbon emissions from China's building and construction industry from 1994 to 2012 was calculated.•Total construction emissions annually increased by 6.9%, but the emission intensity annually decreased by 4.7%.•Key influential factors and their quantitative impacts on the changes in emissions are discussed.•Building materials contributed most to increased emissions, while the energy intensity mitigated the most of emissions.•The construction industry accomplished or will mostly fulfill reduction targets in both the short- and mid-term periods.
Construction is responsible for 50% of carbon emissions, 40% of energy consumption, and half of the landfill waste globally. To confront those environmental issues, stipulating appropriate strategies ...to drive construction firms towards corporate environmentally responsible (CER) activities is essential. However, the impact of CER on construction firms' financial performance (FP) is still inconclusive. This study aims to investigate the relationship between CER and FP in the architecture, engineering, and construction (AEC) industry. 141 publicly listed AEC companies were selected worldwide, and the results showed that CER increased the firms’ Return of Equity (ROE) and Economic Added Value (EVA) margin by 2.62% and 0.10%, respectively. Asset turnover contributed most to the increase of ROE. The spatial-temporal comparison results indicated that green listed firms' EVA margin had a promising trend while ROE fluctuated in different economic stages. The findings revealed decisive implications for AEC organizations to implement CER while maintaining the competitiveness of corporate profitability.
•An assessment of CER on construction firms' profitability was provided.•CER enhanced short- and long-term profitability by 2.62% and 0.10%.•Increased profitability was attributed by factors including asset turnover.•CER is more recognized in growth economic conditions than recessions.
This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships ...between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment.
•A systemic Bayesian network based approach for safety risk analysis is developed.•An expert confidence indicator for probability fuzzification is proposed.•Safety risk analysis progress is extended to entire life cycle of risk-prone events.•A typical hazard concerning tunnel leakage in a tunnel case in China is presented.•A comparison between fuzzy Bayesian network and fuzzy fault tree analysis is conduced.
Objectives
The purpose of this study was to compare the image quality of coronary computed tomography angiography (CTA) subjected to deep learning–based image restoration (DLR) method with images ...subjected to hybrid iterative reconstruction (IR).
Methods
We enrolled 30 patients (22 men, 8 women) who underwent coronary CTA on a 320-slice CT scanner. The images were reconstructed with hybrid IR and with DLR. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured on all images and the contrast-to-noise ratio (CNR) in the proximal coronary arteries was calculated. We also generated CT attenuation profiles across the proximal coronary arteries and measured the width of the edge rise distance (ERD) and the edge rise slope (ERS). Two observers visually evaluated the overall image quality using a 4-point scale (1 = poor, 4 = excellent).
Results
On DLR images, the mean image noise was lower than that on hybrid IR images (18.5 ± 2.8 HU vs. 23.0 ± 4.6 HU,
p
< 0.01) and the CNR was significantly higher (
p
< 0.01). The mean ERD was significantly shorter on DLR than on hybrid IR images, whereas the mean ERS was steeper on DLR than on hybrid IR images. The mean image quality score for hybrid IR and DLR images was 2.96 and 3.58, respectively (
p
< 0.01).
Conclusions
DLR reduces the image noise and improves the image quality at coronary CTA.
Key Points
• Deep learning–based image restoration is a new technique that employs the deep convolutional neural network for image quality improvement.
• Deep learning–based restoration reduces the image noise and improves image quality at coronary CT angiography.
• This method may allow for a reduction in radiation exposure.
The chemical vapor deposition (CVD) synthesis using the solid/liquid carbon sources provides important alternative to economical and large-scale production of graphene-like materials. Herein, we ...applied the reactive molecular dynamics simulation to study the formation and growth of graphene on nickel surfaces using naphthalene/fluorene as carbon sources. The kinetic CVD process has been demonstrated. A series of fundamental mechanism steps were revealed and identified, where surface-assisted dehydrogenation reaction occurs at first stage, followed by coalescence reaction of active molecular species, which includes complicated multi-step processes. This unique behavior is different from the nucleation and growth mechanisms in the conventional graphene CVD process. The effect of annealing temperature, precursor concentration, and surface types was systematically investigated. Our result suggests that there exist optimal temperature and concentration in the CVD process. The moderate surface interaction on Ni (111) promotes the formation and growth of large and continuous graphene-like carbon network structure. Finally, we evaluate the self-healing function of surface graphene structures by extending the annealing time. Our simulation provides a new insight into the graphene surface growth and will be valuable to further develop the CVD process.
► We proposes a model for projecting C&D waste reduction of construction projects. ► The model can simulate effects of various management strategies on waste reduction. ► The model integrates all ...essential variables that affect C&D waste reduction. ► By using the model, best strategies could be identified before being implemented.
During the past few decades, construction and demolition (C&D) waste has received increasing attention from construction practitioners and researchers worldwide. A plethora of research regarding C&D waste management has been published in various academic journals. However, it has been determined that existing studies with respect to C&D waste reduction are mainly carried out from a static perspective, without considering the dynamic and interdependent nature of the whole waste reduction system. This might lead to misunderstanding about the actual effect of implementing any waste reduction strategies. Therefore, this research proposes a model that can serve as a decision support tool for projecting C&D waste reduction in line with the waste management situation of a given construction project, and more importantly, as a platform for simulating effects of various management strategies on C&D waste reduction. The research is conducted using system dynamics methodology, which is a systematic approach that deals with the complexity – interrelationships and dynamics – of any social, economic and managerial system. The dynamic model integrates major variables that affect C&D waste reduction. In this paper, seven causal loop diagrams that can deepen understanding about the feedback relationships underlying C&D waste reduction system are firstly presented. Then a stock-flow diagram is formulated by using software for system dynamics modeling. Finally, a case study is used to illustrate the validation and application of the proposed model. Results of the case study not only built confidence in the model so that it can be used for quantitative analysis, but also assessed and compared the effect of three designed policy scenarios on C&D waste reduction. One major contribution of this study is the development of a dynamic model for evaluating C&D waste reduction strategies under various scenarios, so that best management strategies could be identified before being implemented in practice.
Biomarker discrepancy between primary and recurrent/metastatic breast cancer is well known, however its impact on prognosis and treatment after relapse is still unclear. Current study aims to ...evaluate biomarkers discrepancy between primary and recurrent/metastatic lesions as well as to investigate its association with following treatment pattern and disease outcome.
We retrospectively included consecutive breast cancer patients undergoing surgery in our center from Jan. 2009 to Dec. 2016 and reported disease recurrence. Patients with re-biopsy and paired biomarkers statuses on primary and recurrent/metastatic lesions were further analyzed. Kappa test was used to analyze the concordance rate of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) status. Post-recurrence survival (PRS) was compared between subgroups by Kaplan-Meier curve. Cox regression model was applied to identify impact factors for PRS.
A total of 156 patients were finally included, of whom 70 and 86 had loco-regional and distant recurrence, respectively. Concordance rates of ER, PR and HER2 were 83.3%, 66.7%, and 97.1%, respectively, which was similarly distributed among different recurrent sites (all
0.05). Primary ER-positivity (
ER-negativity,
= 0.014) and loco-regional recurrence (
distant metastasis,
= 0.001) were independently associated with superior PRS, while patients with visceral metastasis (
< 0.001) had the worst disease outcome. Hormone receptor/HER2 status discrepancy was observed in 28 patients. Fifteen of them changed systemic treatment based on biomarker statuses of recurrent lesion, however, their PRS was not improved compared to those 13 patients who continued the same treatment according to primary biomarkers statuses (
= 0.298).
Biomarker discrepancy was observed between primary and recurrent/metastatic breast cancer lesions and had certain influence on treatment strategies after relapse. However, its impact on disease outcome wasn't established in the current study, which deserves further evaluation.
This paper presents an innovative approach of integrating Building Information Modeling (BIM) and expert systems to address deficiencies in traditional safety risk identification process in tunnel ...construction. A BIM-based Risk Identification Expert System (B-RIES) composed of three main built-in subsystems: BIM extraction, knowledge base management, and risk identification subsystems, is proposed. The engineering parameter information related to risk factors is first extracted from BIM of a specific project where the Industry Foundation Classes (IFC) standard plays a bridge role between the BIM data and tunnel construction safety risks. An integrated knowledge base, consisting of fact base, rule base and case base, is then established to systematize the fragmented explicit and tacit knowledge. Finally, a hybrid inference approach, with case-based reasoning and rule-based reasoning combined, is developed to improve the flexibility and comprehensiveness of the system reasoning capacity. B-RIES is used to overcome low-efficiency in traditional information extraction, reduce the dependence on domain experts, and facilitate knowledge sharing and communication among dispersed clients and domain experts. The identification of a safety hazard regarding the water gushing in one metro station of China is presented in a case study. The results demonstrate the feasibility of B-RIES and its application effectiveness.
To trace the linkage between Japanese healthcare-associated methicillin-resistant Staphylococcus aureus (HA-MRSA) strains in the early 1980s and the 2000s onward, we performed molecular ...characterizations using mainly whole-genome sequencing. Among the 194 S. aureus strains isolated, 20 mecA-positive MRSA (10.3%), 8 mecA-negative MRSA (4.1%) and 3 mecA-positive methicillin-susceptible S. aureus (MSSA) (1.5%) strains were identified. The most frequent sequence type (ST) was ST30 (n = 11), followed by ST5 (n = 8), ST81 (n = 4), and ST247 (n = 3). Rates of staphylococcal cassette chromosome mec (SCCmec) types I, II, and IV composed 65.2%, 13.0%, and 17.4% of isolates, respectively. Notably, 73.3% of SCCmec type I strains were susceptible to imipenem unlike SCCmec type II strains (0%). ST30-SCCmec I (n = 7) and ST5-SCCmec I (n = 5) predominated, whereas only two strains exhibited imipenem-resistance and were tst-positive ST5-SCCmec II, which is the current Japanese HA-MRSA genotype. All ST30 strains shared the common ancestor strain 55/2053, which caused the global pandemic of Panton-Valentine leukocidin-positive MSSA in Europe and the United States in the 1950s. Conspicuously more heterogeneous, the population of HA-MRSA clones observed in the 1980s, including the ST30-SCCmec I clone, has shifted to the current homogeneous population of imipenem-resistant ST5-SCCmec II clones, probably due to the introduction of new antimicrobials.
The
(maize weevil) and
(rice weevil) are two insect pests that have caused huge economic losses to stored grains worldwide. It is urgent to develop an environmentally friendly strategy for the ...control of these destructive pests. Here, the olfactory-mediated selection preference of the two weevil species to three stored grains was analyzed, which should help establish a pull-push system in managing them. Bioassays showed that maize weevil adults prefer to select maize, followed by paddy and wheat, while rice weevil adults mainly migrate towards wheat. Volatile analyses revealed that 2-ethylhexanol, piperitone, and (+)-Δ-cadiene are the major components in volatiles from both maize and wheat, but the abundance of these chemicals is much lower in maize than that in wheat. The volatile limonene was only detected in paddy. Y-tube bioassays suggest that 2-ethylhexanol, piperitone, and (+)-Δ-cadiene were all attractive to both weevils, whereas limonene was attractive only to rice weevils. Overall, maize weevil appeared more sensitive to the tested volatiles based on having much lower effective concentrations of these volatiles needed to attract them. The differences in volatile profiles among the grains and the sensitivity of the two species towards these volatiles may explain the behavioral differences between maize and rice weevils in selecting host grains. The differences in sensitivity of maize and rice weevils towards host volatile components with abundance differences are likely determinants driving the two insect species to migrate towards different host grains.
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