Juan Tao, Wu Yingying and Zhang Jingyi
The purpose of this paper is to re-examine the effectiveness of price limits on stock volatilities in China over a more recent time period spanning from 2007 to 2012. The…
Abstract
Purpose
The purpose of this paper is to re-examine the effectiveness of price limits on stock volatilities in China over a more recent time period spanning from 2007 to 2012. The motivation stems from the fact that very high stock market volatilities are observed in China and we are sceptical of the volatility mitigating effect claimed by advocates of price limits.
Design/methodology/approach
The effectiveness of price limits on volatilities is examined using an event study methodology and within an expanded framework of volatility-volume relationships. The sample stocks include the 300 component stocks of the CSI300 Index.
Findings
Both event study and regression analysis suggest that price limits exaggerate market volatilities by causing volatility spillovers. The destabilising effect is much more pronounced for small firm stocks and when the market falls. In addition to the informational source of volatilities (represented by volume), price limits create another non-trivial frictional source of volatilities in China’s stock market.
Originality/value
This research is the first to re-examine the price limit effect in China’s stock market in an expanded framework of volatility-volume relationships. It identifies price limits, in addition to information, as another non-trivial frictional source of volatilities. The findings derived from a recent sample period confirm the conventional view of inefficiency of price limits raised by Fama (1989) and provide evidence in support of the pervasive trend of stock market deregulations.
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Gebeyehu Belay Gebremeskel, Chai Yi, Chengliang Wang and Zhongshi He
Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is…
Abstract
Purpose
Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.
Design/methodology/approach
Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.
Findings
The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.
Originality/value
The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.
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Jeremy Harris Lipschultz, Karen Freberg and Regina Luttrell
Zhen Li, Xianwei Liu, Yiwei Lian, Juan Xie, Xiaorui Gao and Tao Chang
This paper aims to report the conductivity measurement of ten different surfactant-free microemulsions (SFMEs)
Abstract
Purpose
This paper aims to report the conductivity measurement of ten different surfactant-free microemulsions (SFMEs)
Design/methodology/approach
The variations of electrical conductivity as a function of water volume fraction are examined at one constant alcohol (or DMF, ethyl lactate, γ-valerolactone)/water, alcohol (or DMF, ethyl lactate, γ-valerolactone)/oil volume ratios for each sample.
Findings
Most of the results are consistent with percolation character. The conductive mechanism of these SFMEs is discussed by the percolation model, and it is found that it might be described with the static percolation model below the percolation threshold.
Originality/value
Our report gives a systematic research on the percolation mechanism of as many species of SFMEs as possible by the theoretical models
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Ma Juan, Chen Jian‐jun, Zhang Jian‐guo and Jiang Tao
The uncertainty of the interval variable is represented by interval factor, and the interval variable is described as its mean value multiplied by its interval factor. Based on…
Abstract
The uncertainty of the interval variable is represented by interval factor, and the interval variable is described as its mean value multiplied by its interval factor. Based on interval arithmetic rules, an analytical method of interval finite element for uncertain structures but not probabilistic structure or fuzzy structure is presented by combining the interval analysis with finite element method. The static analysis of truss with interval parameters under interval load is studied and the expressions of structural interval displacement response and stress response are deduced. The influences of uncertainty of one of structural parameters or load on the displacement and stress of the structure are examined through examples and some significant conclusions are obtained.
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Nima Gerami Seresht, Rodolfo Lourenzutti, Ahmad Salah and Aminah Robinson Fayek
Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and…
Abstract
Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.
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Juan Ignacio Vazquez, Diego López de Ipiña and Iñigo Sedano
Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in…
Abstract
Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge‐rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web‐powered context‐aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.
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Hyejin Kim, Tao (Tony) Deng, Juan Mundel and Jennifer Honeycutt
Ismael Gómez-Talal, Lydia González-Serrano, José Luis Rojo-Álvarez and Pilar Talón-Ballestero
This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into…
Abstract
Purpose
This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.
Design/methodology/approach
A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.
Findings
The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.
Research limitations/implications
This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.
Originality/value
The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.