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PurposeMany waste-to-energy (WTE) plants are constructed and operated using the public-private partnership (PPP) mode in China. However, risk events of PPP WTE incineration projects sometimes occur. This study aims to clarify the relationship of risks in China's PPP WTE incineration projects and identify the key risks accordingly and risk transmission paths.Design/methodology/approachA risk list of PPP WTE incineration projects was obtained based on literature analysis. Moreover, a hybrid approach combining fuzzy sets, decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) was developed to analyze the causality of risks, explore critical risks and reveal the risk transmission paths. The quantitative analysis process was implemented in MATLAB.FindingsThe results show that government decision-making risk, government credit risk, government supervision behavior risk, legal and policy risk, revenue and cost risk and management capacity risk are the critical risks of PPP WTE incineration projects in China. These critical risks are at different levels in the risk hierarchy and often trigger other risks.Originality/valueCurrently, there is a lack of exploration on the interaction between the risks of PPP WTE incineration projects. This study fills this gap by examining the key risks and risk transfer pathways of PPP WTE incineration projects from the perspective of risk interactions. The findings can help the public and private sectors to systematically understand the risks in PPP WTE incineration projects, thus enabling them to identify the risks that need to be focused on when making decisions and to optimize risk prevention strategies. The proposed hybrid approach can offer methodological ideas for risk analysis of other types of PPP projects.

People-oriented landscape experiences have become the focus of park design with the increasing demand of outdoor activities from urban residents, whereas thermal-comfort-guided landscape design has attracted more attention in academic circles. Based on the investigation of the microclimate of typical subregions in riverside parks, this paper uses AI recognition to analyze the spatiotemporal distribution of camping crowds, and considers the correlation between landscape morphological parameters, microclimates, and crowd behavior. Finally, we built a model to raise the number of landscape optimization strategies for landscape design. The results show that landscape morphological parameters, such as tree height, crown canopy, and sky visibility factor (SVF), can significantly affect the air temperature (Ta), relative humidity (RH), and physiological temperature (PET) in an environment, while hardly affecting wind speed (WS). For microclimate parameters, Ta has a moderate correlation with camping behavior, with a correlation coefficient of −0.145 and a p-value of 0.040, while the corresponding correlation with PET was non-significant with a p-value of 0.622. The temporal distribution of the number of campers per day show a linear upward trend with a reasonable goodness-of-fit, with an adjusted R2 above 0.789 for all subregions. The model based on landscape morphological parameters has a good fit, with coefficients of tree height and crown canopy of −0.195 and 1.316, respectively. This study provides theoretical support and design suggestions for the design of riverside parks based on crowd behavior patterns.

One of the intelligent transportation system's critical tasks is to understand traffic signs and convey traffic information to humans. However, most related works are focused on the detection and recognition of traffic sign texts or symbols, which is not sufficient for understanding. Besides, there has been no public dataset for traffic sign understanding research. Our work takes the first step towards addressing this problem. First, we propose a "CASIA-Tencent Chinese Traffic Sign Understanding Dataset" (CTSU Dataset), which contains 5000 images of traffic signs with rich semantic descriptions. Second, we introduce a novel multi-task learning architecture that extracts text and symbol information from traffic signs, reasons the relationship between texts and symbols, classifies signs into different categories, and finally, composes the descriptions of the signs. Experiments show that the task of traffic sign understanding is achievable, and our architecture demonstrates state-of-the-art and superior performance. The CTSU Dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-Tencent%20CTSU/index.html.

A tremendous amount of research use questionnaires to obtain individuals’ fear of crime and aggregate it to the neighborhood level to measure the spatial distribution of fear of crime. However, the cost of using questionnaires to measure the large-scale spatial distribution of fear of crime is high. The built environment is known to influence people’s perceptions, including fear of crime. This study develops a machine learning model to link built environment extracted from street view images to fear of crime obtained from questionnaires, and then applies this model to extrapolate fear of crime for neighborhoods without the questionnaires. Using massive street view images and a survey among 1,741 residents in 80 neighborhoods in Guangzhou, China, this study developed a novel systematic approach to measuring large-scale spatial fear of crime at the neighborhood level for 1,753 neighborhoods. This is the first study to measure fear of crime at the neighborhood level for a metropolitan area of nearly 20 million people. The integration of survey data and street view images provides an opportunity to develop a more effective way to measure the spatial distribution of fear of crime. This approach could be applied to map other types of perceptions at a spatial resolution of the neighborhood level.

We design intelligent evaluation mechanism for cloud-edge-end based next generation ship simulator towards maritime pilot training in this paper. Firstly, we introduce the concept of “cloud-edge-end” and design the next generation ship simulator system architecture. In the proposed architecture, the edge node can perform artificial intelligence to achieve the distributed evaluation for maritime pilot training. The instructor station is set at the cloud nodes. Then, an intelligent evaluation scheme with Back Propagation (BP) neural networks is designed to decrease artificial evaluation error and improve the objectivity of evaluation according to Automatic Identification System (AIS) data. The experiment results show that the evaluation accuracy of the proposed scheme is higher than the traditional artificial evaluations.

Excellent neural network architecture is built on the specific target task and device. As the target task or device is different, the neural architecture we need will be different, too. Rather than redesigning or searching a brand new one, adjusting the existing architecture automatically is an alternative yet efficient way. To this end, we propose a method to Shape the existing Neural Architectures Progressively (SNAP) to adapt the target task and device better. Inspired by the streamline of water drop shaped by air resistance, we define an information density criterion (play the role of resistance) to drive the network architecture reducing the size of the part with the lowest information density. Iteratively, a more adaptive architecture will be obtained progressively in a greedy way. Theoretically, we prove that the greedy strategy is reasonable and can shape a better architecture. Because of the small adjustment of architecture each time, new architecture can inherit the parameters in old architecture to avoid retraining it from scratch. So the proposed method is very efficient in no need of high computation cost. Experimental results show that proposed method can effectively improve the given network by adjusting its architecture. And it can generate different architectures for different tasks and devices to adapt them well. Compared with search-based auto-generated neural architectures, our approach can achieve comparable or even better performance in no need of tremendous computation resources.

This study conducted hourly dynamic simulations of the thermal climate and thermal comfort of 120 blocks within the complex district of the International Low Carbon City (ILCC) by applying an urban...

The technological expression of contemporary architecture is developed basing on modernistic architecture. As one of the most influential architectural design methods, it is being paid more and more attention to by contemporary architects. Technological expression has made lots of changes in the design of architectural forms, space and environment. With the rapid development of science, more and more fantastic technology is being applied, which makes it beyond just a method and forms an architectural design theory itself. This paper makes some analysis on the relationship between technological expression and regional culture, ecological concept and natural environment. Since digital information technology is penetrating into architectural designing field, I hope this paper can give some inspiration to architects on their architectural study and practice.

Based on the necessity and feasibility of implementation of Energy Control Center in colleges and universities, taking Beijing Normal University as an example, it introduces the design concept and technical architecture of the Energy Control Center, describes function modules, construction contents and social, economic and environmental benefits of the Energy Management System in this paper. Adopting automation, information technology and centralized management model, the Energy Control Center carries on refined energy saving reform according to the actual situation of campus buildings, which has made great efforts to enhance the level of energy management and remote monitoring, producing guiding significance to the construction of resource-efficient campuses at home.

With the emergence of cloud computing, network teaching platforms are built an open, scalable platform for teaching, sharing resources, which can be help to provide the new development direction of network teaching and make it more efficiently. This article states functions and features of the online education platform, and the concept about AppScale and Eucalyptus. In order to propose a reasonable architecture of AppScale-based online education platform, and explore the existing network teaching platform migrating to under the cloud, providing support to set up and research the network teaching platform in the cloud computing environment.

Software Systems running in the Internet are facing an ever-changing environment, which requires constant monitoring and adaptation facilities to be in place, so that user's needs and system requirements are satisfied on-demand. This paper summarizes our general observation and understanding to how user and system goals can drive the adaptation mechanisms of individual software service modules, implemented as internetware components. In particular, a theoretical typology of different levels of adaptation capability is proposed. Their correspondence with the key characteristics of internetware is analyzed. A common architecture to support the run-time adaptation of internetware is also suggested in company with a development method.

The technological background of smart planet generating is discussed in informationized surveying and mapping stage. The concept of smart planet and differences between smart planet and Digital Earth is generalizes. Architecture and a solution of smart planet based on wireless networks are constructed, and figures of physical frame and logic frame of Smart Planet are presented. Technological component of smart planet is analyzed, six key technologies to achieve smart planet is proposed including sensors technology, internet of things, cloud computing, data mining technology, information security technology and relevant laws and regulations. The prospect of smart planet is forecasted and the significance of smart planet is stated. This provides references for the theory and implement mode of the smart planet.

This paper presents a global SHM architecture and an example of conceptual design study of a structural health monitoring system.

We performed numerical simulations to study the secular orbital evolution and dynamical structure of the quintuplet planetary system 55 Cancri using the self-consistent orbital solutions of Fischer et al. (2008).

In this paper, we propose a formalism for service requirements and capability modelling. It adopts concepts from the agent-oriented requirements modelling framework i*, which can be used as a means of studying the requirements and architecture for distributed agent systems. We argue that a social modelling framework such as i*, extended with the necessary service-related concepts and formal reasoning mechanisms, offers a better understanding of the social/organisational relationship in an open services world. By explicitly representing the underlying assumptions and the essential factors of services, a semiformal requirements model in i* can automatically evolve and be refined into a service requirements and capability reasoning framework. Eventually, it will assist intelligent agents with certain knowledge and intentions to make intelligent, rational decisions during service discovery, publication, selection and binding within an open services community.