Search results

Urban water bodies play an important role in reducing summertime urban heat island (UHI) effects. Previous studies focused mainly on the impact of water bodies of large areas, and there is no analysis of the efficacy and scale effect of how small and medium-sized water bodies reduce the UHI effects. Hence, these studies could not provide theoretical support for the scientific planning and design of urban water bodies. This study aims to confirm, within different scale ranges, the efficacy of a water body in reducing the summertime UHI effects. We propose a scale sensitivity method to investigate the temporal and spatial relationship between urban water bodies and UHI. Based on the scale theory and geostatistical analysis method in landscape ecology, this study used the platforms of 3S, MATLAB, and SPSS to analyze the distance-decay law of water bodies in reducing summertime UHI effects, as well as the scale response at different water surface ratios. The results show that the influence of water surfaces on UHIs gradually decreases with increasing distance, and the temperature rises by 0.78 °C for every 100 m away from the water body. During daytime, there is a scaled sensitivity of how much water surfaces reduce the summertime UHI effects. The most sensitive radius from the water was found at the core water surface ratio of 200 m. A reduction of UHI intensity by 2.3 °C was observed for every 10% increase of the average core water surface ratio. This study provides a theoretical reference to the control of heat islands for the planning and design of urban water bodies.

This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.

The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.

The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.

The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

The goal of making buy-in decisions is to purchase materials at the right time with the required quantity and a minimum material cost (MC). To help achieve this goal, the purpose of this paper is to find a way of optimizing the buy-in decision with the consideration of flexible starting date of non-critical activities which makes daily demand adjustable.

First, a specific algorithm is developed to calculate a series of demand combinations modeling daily material demand for all the possible start dates. Second, future material prices are predicted by applying artificial neural network. Third, the demand combinations and predicted prices are used to generate an optimal buy-in decision.

By comparing MC in situation when non-critical activities always start at the earliest date to that in situations when the starting date is flexible, it is found that making material buy-in decision with the consideration of the flexibility usually helps reduce MC.

In this paper, a material buy-in decision-making method that accounts non-critical activities’ flexible starting date is proposed. A ternary cycle algorithm is developed to calculate demand combinations. The results that making material buy-in decision considering non-critical activities’ flexible starting date can reduce MC in most times indicates that contractors may consider non-critical activities’ flexibility a part of the buy-in decision-making process, so as to achieve an MC decrease and profit increase.

In this letter, based on the infinitesimal transformations with respect to the generalized coordinates and generalized momentums, we obtain the definition, determining equations and structure equation of the momentum‐dependent symmetry for the systems. This study directly leads to the non‐ Noether type conserved quantity for the systems. Further we also give the inverse issue of the momentum‐dependent symmetries of the systems. However, a theory of momentum‐dependent symmetries of the nonconservative Hamiltonian systems is established. Finally, an example is discussed to illustrate the results.

The purpose of this paper is to study the influence of moisture on corrosion behaviour of steel ground rods in mildly desertified soil and the mechanism behind it.

The specimens were used for weight loss corrosion experiments and polarization scans were taken at different moisture levels. Specimen surfaces were characterized using a scanning electron microscope, energy dispersive spectrometer, and using X‐ray diffraction.

The results indicated that the moisture content of the soil influenced steel corrosion considerably. The maximum corrosion of 20G and Q235 galvanised steels occurred at 10 per cent and 12.5 per cent soil moisture, respectively. The corrosion products of 20G steel were mostly Fe₂O₃ and Fe₃O₄, whereas that of Q235 galvanised steel was Zn₅(OH)₈Cl₂ · H₂O.

The paper provides information regarding the relationship between moisture and corrosion of steel ground rods, which is useful for understanding the mechanism of soil corrosion. The research results can provide theoretical guidelines for preventing the corrosion of steel ground rods buried in mildly desertified soil.

The purpose of this paper is to take a holistic view of the body of knowledge of the second wave of Chinese outbound tourists so as to develop a strategic framework towards positioning Greece in the China outbound tourism market.

The survey adopted a perspective from knowledge management, investigating a deeper understanding of the knowledge about, from and for the Chinese tourists, and Greece was taken as a case study. An online survey with quantitative aspects was conducted from October 2013 to January 2014.

The knowledge about the Chinese tourists depicts a young, well-educated segment with a predominance of female and western China residents. The knowledge from the Chinese tourists reveals that they prefer: visiting islands and heritage sites; safety while travelling; a faster visa process; a direct flight between China and Greece; and making a trip combined with other Schengen countries. The knowledge for the Chinese tourists indicates that purchasing tourism products/services online provides better price, is enjoyable, convenient and time-saving; and the top five digital platforms often used are Ctrip, Qunar, QQ, Weibo and WeChat, which could be effective tools for the promotion of Greek tourism in China.

This research offers a first step to investigate the knowledge of Chinese tourists for the destination of Greece. The findings help to propose a strategic knowledge framework for the Greek tourism authorities so that Greece can become a more active player in the Chinese outbound tourism market.

This study seeks to employ the capability‐based view to investigate the direct effect of an organization's development of technological and design capabilities on technology commercialization. It aims to use two indicators to test the claim of ambidexterity, i.e. that synchronizing the development of technological capabilities and design capabilities can enhance the performance of technology commercialization.

The research subjects consisted of R&D teams in Taiwan developing new high‐tech information and communications products; a total of 109 valid questionnaires were recovered. A hierarchical multiple regression model was used to perform hypothesis testing.

The findings of this study indicate that both technological and design capabilities have a positive effect on technology commercialization results, and the contribution of design capabilities is greater than that of technological capabilities. The interaction of technological capabilities and design capabilities has a positive influence on the results of technology commercialization. A relative imbalance between technological and design capabilities has a negative effect on technology commercialization.

This study specifies that there are tensions between technological and design capabilities. However, an ambidextrous strategy involving the concurrent development of technological and design capabilities is suggested. Also the ambidexterity hypothesis is verified. This study consequently suggests that enterprises should simultaneously develop their technological and design capabilities, and seek to balance the allocation of management attention and resources between these two types of capabilities, if they wish to obtain optimal technology commercialization results.