An Ancient Mosque in Ningbo, China “Historical and Architectural Study”

With the rise of Tang dynasty (618–907), Ningbo was an important commercial city on the Chinese eastern coast. Arab merchants had an important role in trade relations between China and the West. Ningbo mosque was initially built in 1003 during Northern Song period by Muslims traders who had migrated from Arab lands to settle in China. Through ongoing research of representative Muslim architecture, such as Chinese Mosques, this paper seeks to shed light on the artistic features of this mosque. Many of the key characteristics of this distinctive ethnic heritage are based on commonly held religious beliefs and on the relationship between culture and religion. This paper aims to study the characteristics of Chinese mosques architecture, through studying one of the most important planning patterns of the traditional courtyards plan Known as Siheyuan, and it will also make a practical study on Ningbo Yuehu Mosque. The result of this study shows that the Ningbo Yuehu mosque is like Chinese mosques which follows essentially the norms of Chinese planning, layout design, and wooden structures

Relationship between Speed Characteristics and Traffic Safety on Freeways in Mainland China

Based on the traffic accidents statistical data of 10 typical freeways in mainland China, by using of some kinds of regression model, the influences of the average vehicle speed and the speed standard deviation on the traffic safety are studied. According to the regression results, the accidents show an increasing trend with the increase of the vehicle average speed and the speed standard deviation. On this basis, in view of the regression results, the strategy is put forward for controlling the vehicle average speed and the speed standard deviation, which has important theoretical and practical significance for improving highway safety. After a comprehensive comparison among these regression methods, it is found that the nonlinear regression method of user-defined model expression has the best fitting effect, and it can also more accurately describe the objective reality. It has high practicality and popularized value

Development of the ASHRAE Global Thermal Comfort Database II

Recognizing the value of open-source research databases in advancing the art and science of HVAC, in 2014 the ASHRAE Global Thermal Comfort Database II project was launched under the leadership of University of California at Berkeley’s Center for the Built Environment and The University of Sydney’s Indoor Environmental Quality (IEQ) Laboratory. The exercise began with a systematic collection and harmonization of raw data from the last two decades of thermal comfort field studies around the world. The ASHRAE Global Thermal Comfort Database II (Comfort Database), now an online, open-source database, includes approximately 81,846 complete sets of objective indoor climatic observations with accompanying “right-here-right now” subjective evaluations by the building occupants who were exposed to them. The database is intended to support diverse inquiries about thermal comfort in field settings. A simple web based interface to the database enables filtering on multiple criteria, including building typology, occupancy type, subjects’ demographic variables, subjective thermal comfort states, indoor thermal environmental criteria, calculated comfort indices, environmental control criteria and outdoor meteorological information. Furthermore, a web-based interactive thermal comfort visualization tool has been developed that allows end-users to quickly and interactively explore the data

Prevailing construction and demolition waste management practices : a China study

The construction industry is regarded as one of the largest generators of solid waste in the world. These large amounts of construction and demolition waste generated by continuous construction activities have harmful effects on the environment. China is the largest producer of construction and demolition waste, with an annual generation of 2.4 billion tonnes. Moreover, with the continued development of their construction industry, the total volume of construction and demolition waste generated in China is forecasted to reach 3.96 billion tonnes by 2020. However, without proper management, a significant amount of construction and demolition waste has inevitably occupied land resources. In the current age of enhanced environmental awareness, transformation to sustainable management in the construction sector is needed. Despite the increasing awareness of recycling in industry, the average recovery rate of construction and demolition waste in China is only approximately 5%. Few developed countries have a sustainable society with an orderly, reasonable, and effective construction and demolition waste management system. However, as most of the waste in China is directly dumped or landfilled, it lags far behind these high-performance countries. The objectives of this study were to obtain information about the current status of construction and demolition waste management in China from comparative analyses and case studies, and to identify the obstacles to the promotion of recycling. Recovery rates of construction and demolition waste were collected from 35 countries, and the existing policies, standards, and official documents at a national level in China and seven selected countries with high performance (including Japan, South Korea, Germany, Austria, the Netherlands, Italy, and the United Kingdom) were reviewed. The comparative analysis revealed the reasons for China’s unsatisfactory performance. These reasons included the following: (1) inadequate guidance on recycling, (2) an underdeveloped recycling market, (3) incomplete knowledge of stakeholders’ responsibilities, (4) ineffective cooperation among governing bodies, (5) a lack of penalty for other stakeholders, and (6) an ineffective supervision system. Few studies have been performed to evaluate construction and demolition waste management performance of different Chinese cities, although case studies have been performed on specific cities. In the present study, site visits to 10 different Chinese cities were conducted: Shanghai (Direct-administered municipalities), Hangzhou (Zhejiang Province), Suzhou (Jiangsu Province), Chongqing (Direct-administered municipalities), Chengdu (Sichuan Province), Xi’an (Shaanxi Province), Changsha (Hunan Province), Shenzhen (Guangdong Province), Nanjing (Jiangsu Province), and Zhoukou (Henan Province). Construction and demolition waste management performance, as well as the obstacles for waste management, varies among these Chinese cities. These obstacles included the following: (1) a lack of sources of construction and demolition waste, (2) a lack of guidance on the application of building materials recycled from waste, (3) a lack of land use for fixed industries, (4) a lack of precise estimations of the waste amount and distribution, (5) a lack of guidance on the classification of construction and demolition waste, (6) unrestrictive landfilling, (7) inconsistent cooperation among different official governments, (8) inadequate research on the use of recycled products and waste separation, and (9) an incomplete waste traceability system. If these obstacles are more fully understood, recommendations can be provided to enhance construction and demolition waste management in China. Policymakers in China can consider the following suggestions: (1) ensuring the stability of construction and demolition waste sources; (2) precise estimation of waste generation and distribution; (3) an effective waste tracing system; (4) improvements in related technology; (5) expanding financial incentives; (6) prioritising the recycling of waste and promoting waste reduction; (7) completion of waste classification and relevant standards; (8) strict landfill bans; (9) consistent cooperation among governmental departments; and (10) expansion of the recycling market

Dominant Influence of Biomass Combustion and Cross-Border Transport on Nitrogen-Containing Organic Compound Levels in the Southeastern Tibetan Plateau

The Tibetan Plateau (TP) is highly susceptible to climate change and the nitrogen-containing organic compounds (NOCs) in fine particulate matter (PM2.5) represent one of the large uncertainties in affecting the climate in high-altitude areas. Previous studies have shown that NOCs play a vital role in the nitrogen budget of PM2.5. However, our understanding of the composition and sources of NOCs in PM2.5, particularly in TP, is limited. Here, we aim to enhance our understanding of NOCs in the TP region by examining their identification, concentration levels, sources, and origins. We conducted field sampling at a regional background sampling site in Gaomeigu, in the southeastern margin of TP from March 11th to May 13th in 2017. The daily mass concentrations of NOCs ranged from 714.4 to 3887.1 ng m-3, with an average of (2119.4 ± 875.0 ng m-3) during the campaign. This average concentration was approximately 40 % higher than that reported at a typical regional site in the North China Plain (NCP), highlighting a more significant presence of NOCs in the Tibetan area. Biomass burning and secondary sources were identified as the major contributors to total NOCs. This was further substantiated by a regional air quality model, which indicated that over 80 % of the aerosol in the southeast of TP originated from neighboring countries. This study enhances our understanding of NOCs’ contribution to PM2.5 in TP and their potential impacts on the climate stability in high-altitude areas

Linguistically-driven framework for computationally efficient and scalable sign recognition

We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. This parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL)