A Bayesian-Based Approach for Public Sentiment Modeling

Public sentiment is a direct public-centric indicator for the success of effective action planning. Despite its importance, systematic modeling of public sentiment remains untapped in previous studies. This research aims to develop a Bayesian-based approach for quantitative public sentiment modeling, which is capable of incorporating uncertainty and guiding the selection of public sentiment measures. This study comprises three steps: (1) quantifying prior sentiment information and new sentiment observations with Dirichlet distribution and multinomial distribution respectively; (2) deriving the posterior distribution of sentiment probabilities through incorporating the Dirichlet distribution and multinomial distribution via Bayesian inference; and (3) measuring public sentiment through aggregating sampled sets of sentiment probabilities with an application-based measure. A case study on Hurricane Harvey is provided to demonstrate the feasibility and applicability of the proposed approach. The developed approach also has the potential to be generalized to model various types of probability-based measures

The Impact of Technical Progress and Fuel Switching on Building Sector's Decarbonization in China

AbstractDuring the recent decades, China's building energy consumption has been growing rapidly. And the energy structure also changes quickly with more natural gas & electricity and less coal. Meanwhile, the technology used in building sector is improving towards higher energy efficiency. In this paper, the impact of technical progress and fuel switching in building sector are analyzed. China TIMES model is used to model the future energy consumption in building sector. The modelled results indicate that energy consumption grows up to around 39EJ in 2050 while the energy intensity still stays in a reasonable level in building sector. And with a stricter policy on fuel switching, building sector can reach a relatively low-carbon future with more clean and low-carbon fuel used in this sector, but it's still very hard to access the emission peak before 2050

Projection of Cement Demand and Analysis of the Impacts of Carbon Tax on Cement Industry in China

AbstractCement industry plays a vital role in the process of urbanization and industrialization in China. This paper disaggregates cement consumptions into five large subsectors: building, railway, highway, rural infrastructure and others. We suggest that cement demand will reach the peak of 2.5 billion tons in 2017, followed by a slowly reduction in the next 10 years and a gradually decrease from 2.3 billion tons in 2030 to 1.5 billion tons in 2050. Based on the scenarios analysis of China TIMES model, this paper shows that carbon tax doesn’t work significantly on the technology choice and CO2 emission reduction in the short term. However, in a long run, high carbon tax may increase the application of production with CCS or wasted heat recovery and cut down the small- and medium-sized plants. Moreover, tax on all industries acts more effectively than that only on the cement industry

Are environmentally friendly firms more innovation-oriented?

This study aims to explore the relationship between corporate environmental responsibility and firm’s innovation decision. It proposes that firms investing in environmental protection are more likely to have strong dynamic capabilities to proactively respond to the growing environmental awareness and movement, which in turn can trigger their innovation intention. Moreover, building on stakeholder theory, this study also hypothesizes an indirect relationship between firms’ environmental responsibility and innovation decision mediated by government support. The hypotheses are tested by analyzing the Chinese Private Enterprise Survey data collected in 2010 and are supported. The theoretical and practical implications of the findings are discussed

Mass Spectrometry-based Methods for Phosphorylation Site Mapping of Hyperphosphorylated Proteins Applied to Net1, a Regulator of Exit from Mitosis in Yeast

Prior to anaphase in Saccharomyces cerevisiae, Cdc14 protein phosphatase is sequestered within the nucleolus and inhibited by Net1, a component of the RENT complex in budding yeast. During anaphase the RENT complex disassembles, allowing Cdc14 to migrate to the nucleus and cytoplasm where it catalyzes exit from mitosis. The mechanism of Cdc14 release appears to involve the polo-like kinase Cdc5, which is capable of promoting the dissociation of a recombinant Net1·Cdc14 complex in vitro by phosphorylation of Net1. We report here the phosphorylation site mapping of recombinant Net1 (Net1N) and a mutant Net1N allele (Net1N-19m) with 19 serines or threonines mutated to alanine. A variety of chromatographic and mass spectrometric-based strategies were used, including immobilized metal-affinity chromatography, alkaline phosphatase treatment, matrix-assisted laser-desorption post-source decay, and a multidimensional electrospray mass spectrometry-based approach. No one approach was able to identify all phosphopeptides in the tryptic digests of these proteins. Most notably, the presence of a basic residue near the phosphorylated residue significantly hampered the ability of alkaline phosphatase to hydrolyze the phosphate moiety. A major goal of research in proteomics is to identify all proteins and their interactions and post-translational modification states. The failure of any single method to identify all sites in highly phosphorylated Net1N, however, raises significant concerns about how feasible it is to map phosphorylation sites throughout the proteome using existing technologies

Dynamic Placement of Virtual Machines with Both Deterministic and Stochastic Demands for Green Cloud Computing

Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide. However, data centers hosting cloud applications consume huge amounts of energy, leading to high operational cost and greenhouse gas emission. Therefore, green cloud computing solutions are needed not only to achieve high level service performance but also to minimize energy consumption. This paper studies the dynamic placement of virtual machines (VMs) with deterministic and stochastic demands. In order to ensure a quick response to VM requests and improve the energy efficiency, a two-phase optimization strategy has been proposed, in which VMs are deployed in runtime and consolidated into servers periodically. Based on an improved multidimensional space partition model, a modified energy efficient algorithm with balanced resource utilization (MEAGLE) and a live migration algorithm based on the basic set (LMABBS) are, respectively, developed for each phase. Experimental results have shown that under different VMs’ stochastic demand variations, MEAGLE guarantees the availability of stochastic resources with a defined probability and reduces the number of required servers by 2.49% to 20.40% compared with the benchmark algorithms. Also, the difference between the LMABBS solution and Gurobi solution is fairly small, but LMABBS significantly excels in computational efficiency

Surgical treatment of giant plexiform neurofibroma associated with pectus excavatum

Plexiform neurofibromas are benign tumors originating from subcutaneous or visceral peripheral nerves, which are usually associated with neurofibromatosis type 1. They are almost always congenital lesions and often cause the surrounding soft tissue and bone to grow aberrantly. We treated a 12-year-old boy who presented with asymmetric pectus excavaum and an anterior chest wall plexiform neurofibroma. The pectus excavaum was corrected by modified Nuss procedure, followed by simultaneous resection of the giant mass. The patient is doing well at the 4 years follow-up visit