Would the reputation and behaviour of the Chinese stock exchange be a disincentive to investors considering a Chinese REIT?

China has drawn the world’s attention with the emergence, rapid growth and increasing maturity of its real estate market in the past twenty years. Currently the world’s third largest economy, China was the second largest Asian country for commercial property transaction capital flows in 2006 (JLL, 2007). International investors have recently shown considerable interest regarding property investment in China, via both direct and indirect property and changes to the rules governing internal funds are likely to initiate high levels of effective demand from domestic institutions too. China is yet to develop a Real Estate Investment trust (REIT) market; despite this investment demand encouragement for development of pilot REITs by the PRC government has waxed and waned with political imperatives to manage market and economic volatility. Chinese REITs would theoretically provide the opportunity for investors to access Chinese “property” returns with liquidity and flexibility and might further play a significant role in stabilising the Chinese capital market in the medium and long term. The purpose of this paper is to examine whether the reputation and behaviour of the Chinese stock exchanges is a disincentive to investors considering a Chinese REIT. This is addressed firstly by assessing Chinese stock market volatility compared to that of the Hong Kong and Singapore stock exchanges. Secondly, a survey was used to explore Chinese domestic investors’ attitudes to investment in Chinese property REITs and their preferences amongst the three main Asian stock exchanges where Chinese REITs might potentially be available

Microwave Breast Imaging Techniques and Measurement Systems

Electromagnetic waves at microwave frequencies allow penetration into many optically non-transparent mediums such as biological tissues. Over the past 30 years, researchers have extensively investigated microwave imaging (MI) approaches including imaging algorithms, measurement systems and applications in biomedical fields, such as breast tumor detection, brain stroke detection, heart imaging and bone imaging. Successful clinical trials of MI for breast imaging brought worldwide excitation, and this achievement further confirmed that the MI has potential to become a low-risk and cost-effective alternative to existing medical imaging tools such as X-ray mammography for early breast cancer detection. This chapter offers comprehensive descriptions of the most important MI approaches for early breast cancer detection, including reconstruction procedures and measurement systems as well as apparatus

Determination of Optimal Cell and Plasmid Concentration for Transfection of I-SceI by DR-GFP Reporter

https://openworks.mdanderson.org/sumexp21/1195/thumbnail.jp

Using Software Dependency to Bug Prediction

Software maintenance, especially bug prediction, plays an important role in evaluating software quality and balancing development costs. This study attempts to use several quantitative network metrics to explore their relationships with bug prediction in terms of software dependency. Our work consists of four main steps. First, we constructed software dependency networks regarding five dependency scenes at the class-level granularity. Second, we used a set of nine representative and commonly used metrics—namely, centrality, degree, PageRank, and HITS, as well as modularity—to quantify the importance of each class. Third, we identified how these metrics were related to the proneness and severity of fixed bugs in Tomcat and Ant and determined the extent to which they were related. Finally, the significant metrics were considered as predictors for bug proneness and severity. The result suggests that there is a statistically significant relationship between class’s importance and bug prediction. Furthermore, betweenness centrality and out-degree metric yield an impressive accuracy for bug prediction and test prioritization. The best accuracy of our prediction for bug proneness and bug severity is up to 54.7% and 66.7% (top 50, Tomcat) and 63.8% and 48.7% (top 100, Ant), respectively, within these two cases

Night shifts, insomnia, anxiety, and depression among Chinese nurses during the COVID-19 pandemic remission period: A network approach

BackgroundThe outbreak of the COVID-19 pandemic imposed a heavy workload on nurses with more frequent night shifts, which led to higher levels of insomnia, depression, and anxiety among nurses. The study aimed to describe the symptom-symptom interaction of depression, anxiety, and insomnia among nurses and to evaluate the impact of night shifts on mental distress via a network model.MethodsWe recruited 4,188 nurses from six hospitals in December 2020. We used the Insomnia Severity Index, Patient Health Questionnaire-9, and Generalized Anxiety Disorder Scale-7 to assess insomnia, depression, and anxiety, respectively. We used the gaussian graphical model to estimate the network. Index expected influence and bridge expected influence was adapted to identify the central and bridge symptoms within the network. We assessed the impact of night shifts on mental distress and compared the network structure based on COVID-19 frontline experience.ResultsThe prevalence of depression, anxiety, and insomnia was 59, 46, and 55%, respectively. Nurses with night shifts were at a higher risk for the three mental disorders. “Sleep maintenance” was the central symptom. “Fatigue,” “Motor,” “Restlessness,” and “Feeling afraid” were bridge symptoms. Night shifts were strongly associated with sleep onset trouble. COVID-19 frontline experience did not affect the network structure.Conclusion“Sleep maintenance,” “Fatigue,” “Motor,” and “Restlessness” were important in maintaining the symptom network of anxiety, depression, and insomnia in nurses. Further interventions should prioritize these symptoms

The mTOR Pathway Independent Function of NPRL2 in the Regulation of S-Phase DNA Damage Response

https://openworks.mdanderson.org/sumexp22/1070/thumbnail.jp

Reaction And Characterization Of Low-Temperature Effect Of Transition Nanostructure Metal Codoped Scr Catalyst

Typical p-type semiconductor MnO codoped with n-type semiconductors such as CeO2 and V2O5 was reported to achieve high efficiency in catalytic NO removal by NH3. In this paper, we present novel Mn-Ce codoped V2O5/TiO2 catalyst which exhibited an excellent NO conversion efficiency of 90% at 140°C. By using this codoped catalyst, the best low-temperature activity was greatly decreased when compared with single Mn- or Ce-doped catalyst. According to the characterization results from BET, XRD, and XPS, the codoped catalyst was composed of both CeO2 and amorphous Mn. The electron circulation formed between doping elements is believed to promote the electron transfer, which may be one of the reasons for excellent low-temperature denitration performance

Reaction and Characterization of Low-Temperature Effect of Transition Nanostructure Metal Codoped SCR Catalyst

Typical p-type semiconductor MnOx codoped with n-type semiconductors such as CeO2 and V2O5 was reported to achieve high efficiency in catalytic NOx removal by NH3. In this paper, we present novel Mn-Ce codoped V2O5/TiO2 catalyst which exhibited an excellent NO conversion efficiency of 90% at 140°C. By using this codoped catalyst, the best low-temperature activity was greatly decreased when compared with single Mn- or Ce-doped catalyst. According to the characterization results from BET, XRD, and XPS, the codoped catalyst was composed of both CeO2 and amorphous Mn. The electron circulation formed between doping elements is believed to promote the electron transfer, which may be one of the reasons for excellent low-temperature denitration performance