Examining issues influencing green building technologies adoption : the United States green building experts' perspectives

Green building (GB) has been viewed as an effective means to implement environmental, economic, and social sustainability in the construction industry. For the adoption of GB technologies (GBTs) to continue to succeed and gain popularity, a better understanding of the key issues influencing its progress is crucial. While numerous studies have examined the issues influencing green innovations adoption in general, few have specifically done so in the context of GBTs. This study aims to investigate the underpinnings of GBTs adoption in the following areas: (1) the critical barriers inhibiting the adoption of GBTs, (2) major drivers for adopting GBTs, and (3) important strategies to promote GBTs adoption. To achieve these objectives, a questionnaire survey was carried out with 33 GB experts from the United States. Ranking analysis was used to identify the significant issues associated with GBTs adoption. Resistance to change, a lack of knowledge and awareness, and higher cost have been the most critical barriers. The major drivers for adopting GBTs are greater energy- and water-efficiency, and company image and reputation. The analysis results also indicate that the most important strategies to promote the adoption of GBTs are financial and further market-based incentives, availability of better information on cost and benefits of GBTs, and green labelling and information dissemination. The findings provide a valuable reference for industry practitioners and researchers to deepen their understanding of the major issues that influence GB decision-making, and for policy makers aiming at promoting the adoption of GBTs in the construction industry to develop suitable policies and incentives. This study contributes to expanding the body of knowledge about the influences that hinder and those that foster GBTs implementation

Non-receptor tyrosine kinase Src is required for ischemia-stimulated neuronal cell proliferation via Raf/ERK/CREB activation in the dentate gyrus

<p>Abstract</p> <p>Background</p> <p>Neurogenesis in the adult mammalian hippocampus may contribute to repairing the brain after injury. However, Molecular mechanisms that regulate neuronal cell proliferation in the dentate gyrus (DG) following ischemic stroke insult are poorly understood. This study was designed to investigate the potential regulatory capacity of non-receptor tyrosine kinase Src on ischemia-stimulated cell proliferation in the adult DG and its underlying mechanism.</p> <p>Results</p> <p>Src kinase activated continuously in the DG 24 h and 72 h after transient global ischemia, while SU6656, the Src kinase inhibitor significantly decreased the number of bromodeoxyuridine (BrdU) labeling-positive cells of rats 7 days after cerebral ischemia in the DG, as well as down-regulated Raf phosphorylation at Tyr(340/341) site, and its down-stream signaling molecules ERK and CREB expression followed by 24 h and 72 h of reperfusion, suggesting a role of Src kinase as an enhancer on neuronal cell proliferation in the DG via modifying the Raf/ERK/CREB cascade. This hypothesis is supported by further findings that U0126, the ERK inhibitor, induced a reduction of adult hippocampal progenitor cells in DG after cerebral ischemia and down-regulated phospho-ERK and phospho-CREB expression, but no effect was detected on the activities of Src and Raf.</p> <p>Conclusion</p> <p>Src kinase increase numbers of newborn neuronal cells in the DG via the activation of Raf/ERK/CREB signaling cascade after cerebral ischemia.</p

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.Comment: Please feel free to email He-Liang Huang with any comments, questions, suggestions or concern

Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis

Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities

Assessing heat transfer characteristics of building envelope deployed BIPV and resultant building energy consumption in a tropical climate

Building-Integrated Photovoltaic (BIPV) is a viable technology towards increasing renewable energy production and achieving low carbon footprints for buildings. Mauritius, with a daily average of 5.6 kWh/m2 of solar radiation over 2350 h annually, has been targeting at achieving its low carbon goals by focusing on photovoltaic technology including the uptake of BIPV. However, BIPV has not been well researched in terms of its overall thermal impact especially overheating on the building envelope and the resultant energy performance for buildings for the tropical climatic condition in Mauritius. This research, by means of validated simulation modelling, adopted a novel approach of coupling thermal finite element analysis (FEA) with whole building dynamic simulations to assess the heat transfer characteristics of BIPV either on facades or roof and the resultant energy consumptions of a typical office building in Mauritius. The façade scenario had two options, namely BIPV curtain wall and BIPV double-skin façade (BIPV-DSF), while the roof scenario also had two options, namely uninsulated and insulated roof BIPV membranes. Results show that roof BIPV membrane options had a better thermal performance in reducing overheating for the building compared to the BIPV façade options, with a reduction in cooling load of 8% and 15% for the uninsulated and insulated BIPV membranes, respectively. In terms of energy performance, both BIPV façade options were not capable of reducing the energy consumption of the building, as the BIPV curtain wall resulted in 1.66% more net energy consumption on a yearly basis. This shows an ineffectiveness of using vertical BIPV glazing for typical office buildings in Mauritius. Although the BIPV-DSF achieved an annual net energy saving of 5.16% benefited from the BIPV energy production, it was not as good as the net savings of 160% and 172% from the respective uninsulated and insulated roof BIPV membrane options.</p