Journal Staff

There are fewer longitudinal studies from China on symptoms as described for the sick building syndrome (SBS). Here, we performed a two-year prospective study and investigated associations between environmental parameters such as room temperature, relative air humidity (RH), carbon dioxide (CO2), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O-3), particulate matter (PM10), and health outcomes including prevalence, incidence and remission of SBS symptoms in junior high schools in Taiyuan, China. Totally 2134 pupils participated at baseline, and 1325 stayed in the same classrooms during the study period (2010-2012). The prevalence of mucosal symptoms, general symptoms and symptoms improved when away from school (school-related symptoms) was 22.7%, 20.4% and 39.2%, respectively, at baseline, and the prevalence increased during follow-up (P<0.001). At baseline, both indoor and outdoor SO2 were found positively associated with prevalence of school-related symptoms. Indoor O-3 was shown to be positively associated with prevalence of skin symptoms. At follow-up, indoor PM10 was found to be positively associated with new onset of skin, mucosal and general symptoms. CO2 and RH were positively associated with new onset of mucosal, general and school-related symptoms. Outdoor SO2 was positively associated with new onset of skin symptoms, while outdoor NO2 was positively associated with new onset of skin, general and mucosal symptoms. Outdoor PM10 was found to be positively associated with new onset of skin, general and mucosal symptoms as well as school-related symptoms. In conclusion, symptoms as described for SBS were commonly found in school children in Taiyuan City, China, and increased during the two-year follow-up period. Environmental pollution, including PM10, SO2 and NO2, could increase the prevalence and incidence of SBS and decrease the remission rate. Moreover, parental asthma and allergy (heredity) and pollen or pet allergy (atopy) can be risk factors for SBS

U-ACCESS & Phfeast – Food Security Partnership

The Office of Urban and Off-Campus Support Services, otherwise known as U-ACCESS, employs a multi-disciplinary approach to assist students who are dealing with a multitude of issues such as homelessness, emancipated from foster care, food insecurity and financial struggles. Phfeast, Inc. is a new start-up operating in the Venture Development Center and provides a restaurant loyalty program where customers earn dining gift cards for people in need

miR-146b suppresses LPS-induced M1 macrophage polarization via inhibiting the FGL2-activated NF-κB/MAPK signaling pathway in inflammatory bowel disease

Objectives: M1 macrophage polarization and phenotype in Inflammatory Bowel Disease (IBD) are common biological responses. Method: Herein, IBD mice models were constructed and macrophages were derived. Results: It was discovered that microRNA-146b (miR-146b) was downregulated in IBD mice and Lipopolysaccharide (LPS)-induced macrophages. Moreover, the inhibitory role of overexpressed miR-146b in reducing the inflammation level and blocking M1 macrophage polarization was confirmed. Further investigation indicated that Fibrinogen Like 2 (FGL2) acted as the target gene of miR-146b, and FGL2 mediated activation of NLRP3, NF-κB-p65, and p38-MAPK. More importantly, it was validated that miR-146b could ameliorate inflammatory phenotype and prevent M1 macrophage polarization via inhibiting FGL2 in vitro, and miR-146b overexpression alleviated the intestinal injury of IBD mice in vivo. Conclusions: Overall, it is potential to use miR-146b for the amelioration of IBD

Full-counting statistics of particle distribution on a digital quantum computer

Full-counting statistics (FCS) provides a powerful framework to access the statistical information of a system from the characteristic function. However, applications of FCS for generic interacting quantum systems often be hindered by the intrinsic difficulty of classical simulation of quantum many-body problems. Here, we propose a quantum algorithm for FCS that can obtain both the particle distribution and cumulants of interacting systems. The algorithm evaluates the characteristic functions by quantum computing and then extracts the distribution and cumulants with classical post-processing. With digital signal processing theory, we analyze the dependency of accuracy with the number of sampling points for the characteristic functions. We show that the desired number of sampling points for accurate FCS can be reduced by filtering some components of the quantum state that are not of interest. By numeral simulation, we demonstrate FCS of domain walls for the mixed Ising model. The algorithm suggests an avenue for studying full-counting statistics on quantum computers

A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

The frequent occurrence of major public emergencies in China has caused significant human and economic losses. To carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. Using earthquakes as an example of a public emergency, this paper combines the Deep Belief Network (DBN) and Case-Based Reasoning (CBR) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. The validity of the model is then verified by an example. The results of this study can be applied to maximize the efficiency of emergency rescue decisions

The effect of mandatory regulation on corporate social responsibility reporting quality: evidence from China

Corporate Social Responsibility (CSR) disclosure has attracted attention from regulatory bodies and academics over the past few decades. Due to the unreliability resulted from CSR voluntary disclosure, an increasing number of researchers are calling for more government regulation on CSR disclosure. Based on 1830 standalone CSR reports disclosed by the Chinese-listed firms during 2009-2012, we examine the effect of mandatory regulation on CSR reporting quality. We further hypothesize and test for the moderating effect of firm size and other characteristics on the link between government regulation on CSR reporting quality. Our results suggest that government mandatory regulation leads to an overall improvement in CSR reporting quality. We also find that this positive effect is greater when firms are larger and have better financial performance, but less when firms are controlled by government. Our study provides a direct answer to the recent calling for mandatory disclosure on CSR reports, and helps to understand why recent studies of social disclosure regulation suggest that government interventions do not seem to resolve the problems that are generally attributed to voluntary disclosures. Our findings should be of interest to the academics, regulators, and investors

Temporal knowledge discovery in big BAS data for building energy management

With the advances of information technologies, today's building automation systems (BASs) are capable of managing building operational performance in an efficient and convenient way. Meanwhile, the amount of real-time monitoring and control data in BASs grows continually in the building lifecycle, which stimulates an intense demand for powerful big data analysis tools in BASs. Existing big data analytics adopted in the building automation industry focus on mining cross-sectional relationships, whereas the temporal relationships, i.e., the relationships over time, are usually overlooked. However, building operations are typically dynamic and BAS data are essentially multivariate time series data. This paper presents a time series data mining methodology for temporal knowledge discovery in big BAS data. A number of time series data mining techniques are explored and carefully assembled, including the Symbolic Aggregate approXimation (SAX), motif discovery, and temporal association rule mining. This study also develops two methods for the efficient post-processing of knowledge discovered. The methodology has been applied to analyze the BAS data retrieved from a real building. The temporal knowledge discovered is valuable to identify dynamics, patterns and anomalies in building operations, derive temporal association rules within and between subsystems, assess building system performance and spot opportunities in energy conservation.Department of Building Services EngineeringDepartment of Computin