A relational exploratory study of business incubation and smart cities - Findings from Europe

This is a pioneering research linking two areas of knowledge which attract great attention from academia, industry, and governments: business incubation and smart cities. It is a quantitative exploratory study, whose purpose is to investigate the relationship between incubation mechanisms and local urban development according to the concept of Smart City. Methodologically, canonical correlation analysis was applied between two sets of elements: variables associated to business incubation and variables related to smart city characteristics. The research was concentrated on 157 medium- and large-sized cities from 25 European countries. Based on the findings, clusters analysis was also performed. The article presents, among the main results: (1) evidence of correlation between business incubation and smart urban development, and (2) visual classification of these cities into four clusters, according to their urban development and incubation activity.     

Why do individuals engage in collective actions against major construction projects? —An empirical analysis based on Chinese data

Collective actions against major construction projects are becoming common because of the public’s increasing civil rights awareness and environmental consciousness. Thus, it is important to explore the critical factors responsible for collective actions against major construction projects. This study initially developed 25 indicators inducing collective actions based on a comprehensive literature review. An empirical study with 127 samples was conducted using questionnaire surveys and semi-structured interviews from projects located in Central China. Furthermore, ranking analysis and factor analyses were implemented to conclude that collective actions in major construction projects can be explained by a six-dimension critical factor system: benefits to the public, characteristics of project performers, layout of projects, living quality of the public, perceptions of the public, and influence from the authority. This study contributes to government administration for collective actions against major construction projects and serves as a useful reference for further studies of this type.     

Agent-based monitoring service for management of disruptive events in supply chains

Schedules of supply chains are generated with buffers to absorb the effect of disruptive events that could occur during their execution. Schedules can be systematically repaired through specific modifications within buffers by using appropriate decision models that consider the distributed nature of a supply chain. To this aim, information of disruptive events at occurrence or in advance allows decision models to make better decisions. To detect and predict disruptive events along a schedule execution, a service-oriented monitoring subsystem that uses a reference model for defining monitoring models was proposed. This subsystem offers services for collecting execution data of a schedule and environment data, and assessing them to detect/anticipate disruptive events. Because of the distributed nature and the complexity of these services functionalities, this paper presents an agent-based approach for their implementation. This technology allows dealing with supply chain monitoring by structuring monitoring subsystem functionalities as a set of autonomous entities. These entities are able to perform tailored plans created at execution time to concurrently monitor different schedules. A case study is described to try out the implemented prototype system.     

Bis(arylquinoxalinyl)carbazole derivatives as saturated blue emitters for electroluminescent devices

Bis(arylquinoxalinyl)carbazole derivatives 9-phenyl-3,6-bis-(3-phenyl-quinoxalin-2-yl)-9H-carbazole (PPQC), 9-phenyl-3,6-bis-[3-(4-methoxy-phenyl)-quinoxalin-2-yl]-9H-carbazole (PMQC) and 9-phenyl-3,6-bis-(3-p-tolyl-quinoxalin-2-yl)-9H-carbazole (PTQC) were conveniently synthesized from the corresponding tetraone and o-phenylenediamine. Electroluminescent devices using PTQC as the dopant emitters were fabricated. These devices all emit saturated blue light from the PTQC doped layer. Device C that consists of CuPc (10 nm)/TCTA (30 nm)/PTQC:TPBI (7%, 30 nm)/TPBI (40 nm) shows the highest performance. The device emits blue light at 446 nm with CIE values of (0.15, 0.08) and shows a maximum external quantum efficiency of 1.31%, current efficiency of 1.00 cd/A, and brightness of 5067 cd/m².     

Artificial intelligence based ensemble approach for intrusion detection systems

Internet attacks pose a severe threat to most of the online resources and are a prime concern of security administrators these days. In spite of many efforts, the security techniques are unable to detect the intrusions accurately. Most of the methods suffer from the limitations of a high false positive rate, low detection rate and provide one solution which lacks the classification trade-offs. In this work, an effective two-stage method is proposed to produce a pool of non-dominating solutions or Pareto optimal solutions as base models and their ensembles for detecting the intrusions accurately. It generates Pareto optimal solutions to a chromosome structure in stage 1 formulating Pareto front. Whereas, another approximation to the Pareto front of optimal solutions is made to obtain non-dominating ensembles in the second stage. The final prediction ensemble solutions are computed from individual predictions using majority voting approach. Applicability of the suggested method is validated using benchmark dataset NSL-KDD dataset. The experimental results show that the recommended method provides better results than conventional ensemble techniques. The recommended method is also adequate to generate Pareto optimal solutions that address the issue of improving detection accuracy for minority as well as majority attack classes along with handling classification tradeoff problem. The proposed method resulted detection accuracy of 97% with FPR of 2% for KDD dataset respectively. The most attractive feature of the proposed method is that both generation of base classifier and their ensemble thereof are multi-objective in nature addressing the issue of low detection accuracy and classification tradeoffs.     

Quality-related English text classification based on recurrent neural network

With the rapid development of artificial intelligence technology, text categorization technology is becoming more and more mature. However, text categorization in real situations still faces various unconstrained conditions. English text is an important part of text information, it is also an important way for people to get information from abroad. How can everyone get the desired content from the massive data quickly and accurately, it has become a hot issue in current research. This paper improves the current text categorization algorithm based on English quality-related text categorization. The design and implementation of text categorization system are illustrated with an example of English quality-related text categorization system, complete the research work of text categorization algorithm. The core work of this paper is to mine, classify and analyze large amounts of data in English text by using the method of combining cyclic neural network with quality. Finally, the essential features of high quality English texts are obtained. Traditional English text categorization algorithm if the amount of training data is large, it is easy to show some defects such as unclear feature items. In view of these problems, in order to improve the accuracy and flexibility of English text categorization, this paper proposes a quality-related English text categorization method based on cyclic neural network. A mechanism combining attention is proposed to improve the problem of label disorder and make the structure of the model more flexible. The model proposed in this paper is compared and optimized. Experiments show that the accuracy of neural text classification based on quality classification can reach about 96%.     

地铁车辆-轨道耦合振动响应分析及轨道结构参数优化

针对在建杭州地铁3号线下穿某文教区工程,根据车辆-轨道耦合动力学理论,分别建立车辆-普通整体道床轨道耦合动力学模型和车辆-钢弹簧浮置板轨道耦合动力学模型。基于赫兹非线性接触关系,实现轮轨间的力平衡和位移协调。利用有限元软件ABAQUS,对两种轨道结构模型的动力响应进行计算,研究轮轨耦合动力相互作用机理和轨道振动源强特性,分析浮置板长度、轨道不平顺、扣件刚度、钢弹簧刚度和行车速度对钢弹簧浮置板轨道动力特性的影响,并对轨道结构参数进行优化。研究成果可为该地铁轨道的减振设计提供科学依据。     

Evaluating the use of supplementary cementitious materials to mitigate damage in cementitious materials exposed to calcium chloride deicing salt

This paper discusses the role of supplementary cementitious materials (SCM) in reducing damage caused by calcium oxychloride formation. Calcium oxychloride is a destructive product of a reaction between calcium hydroxide (CH) that exists in a cementitious matrix and CaCl₂ that can enter the pores of the matrix when it is used as a deicing salt. Paste samples were prepared where a percentage of ordinary portland cement was replaced with various types of SCM (including fly ash, slag, and silica fume). This paper examined the amount of calcium oxychloride that formed using low-temperature differential scanning calorimetry, and damage development detected using acoustic emission. Thermogravimetric analysis was also performed to determine the relationship between the amount of CH in cementitious materials and the amount of calcium oxychloride formation. The results show that the use of SCM is effective in reducing the calcium oxychloride formation and resulting damage when cementitious materials are exposed to various compositions of solution containing CaCl₂. The explanation of the benefit of using SCM is that it can reduce the calcium oxychloride formation due to a reduction in the amount of CH in the cementitious materials through pozzolanic reaction and dilution of cement. As a result, cementitious materials with SCM exposed to CaCl₂ may experience less damage and have a longer service life.