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This paper aims to study the removal of wide-stripe noise in hyperspectral remote sensing images. There is a great deal of stripe noises in short-wave infrared hyperspectral remote sensing image, especially wide-stripe noise, which brings great challenge to the interpretation and application of hyperspectral images.

To remove the noise and to reduce the impact based on in-depth study of the mechanism of the stripe noise generation and its distribution characteristics, this paper proposed two statistical local processing and moment matching algorithms for the elimination of wide-stripe noise, namely, the gradient mean moment matching (GMMM) algorithm and the gradient interpolation moment matching (GIMM) algorithm.

The experiments were carried out with the practical short-wave infrared hyperspectral image data and good experiment results were obtained. Experiments show that both can reduce the impact of wide-stripe noise, and the filtering effect and the application range of the GIMM algorithm is better than that of the GMMM algorithm.

Using new methods to deal with the hyperspectral remote sensing images, it can effectively improve the quality of hyperspectral images and improve their utilization efficiency and value.

The paper research objectives are: to investigate into the classification of special interest tour preferences in terms of their types and to compare whether consumers with different demographic attributes result in discrepancies in special interest tour preferences.

Those collected questionnaires that had incomplete answers and that had a significant response tendency or were left blank with no answers were eliminated. The required statistical methods are explained thus: this study conducts analysis on special interest tour preferences by factor analysis to distinguish between the categories of special interest tour preferences; this study adopts correlation analysis to examine the ratio scale of the study's demographic variables, including age and education level; this study adopts one‐way ANOVA to examine the variables of categorical or nominal scale, such as gender, marital status, and occupation.

After collecting the questionnaire data, factor analysis is used to conduct classification of the types and a total of four types emerged: recreation type, nature/ecology type, physical adventure type, historical/artistic activity type. Furthermore, in the verification of the demographic variables of each type preferences: age and nature‐eco type preferences constitute a significant positive correlation, and age has also formed a significant negative correlation with physical adventure type; gender differences result in a significant difference in recreation type preferences and a significant difference in physical adventure type preferences; marital status has a significant variation regarding physical adventure preferences.

Special interest tours are gradually on the rise and the previous literature is still lacking a systematic method for investigative analysis. Accordingly, conducting a systematic categorization of special interest tour preferences and to examining the background of the consumers of each type of special interest tour preference is essential.

The necessity for special interest tours to conform to consumer interests, and the existence of special interests, require that those in the travel industry conduct market segmentation, prior to designing travel itineraries, so as to have an understanding of the target market. Furthermore, the types of special interest tour preference this study provides can offer the basis for discussion of relevant issues for those travel business industry operators in the industry and future researchers.

Measuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a major role in similarity measurement methodology, the lack of semantic insight on the data may leave these approaches imperfect. The purpose of this paper is to incorporate data semantics into the measuring process.

The emerged linked open data (LOD) provide a practical solution to tackle such difficulty. Common methodologies consuming LOD mainly focused on using link attributes that provide some sort of semantic relations between data. In this work, methods for measuring semantic distances between resources using information gathered from LOD were proposed. Such distances were then applied to music recommendation, focusing on the effect of various weight and level settings.

This work conducted experiments using the MusicBrainz dataset and evaluated the proposed schemes for the plausibility of LOD on music recommendation. The experimental result shows that the proposed methods electively improved classic approaches for both linked data semantic distance (LDSD) and PathSim methods by 47 and 9.7%, respectively.

The main contribution of this work is to develop novel schemes for incorporating knowledge from LOD. Two types of knowledge, namely attribute and path, were derived and incorporated into similarity measurements. Such knowledge may reflect the relationships between resources in a semantic manner since the links in LOD carry much semantic information regarding connecting resources.

External knowledge should not be limited in one zone or level. Researchers have paid more attention to the perspective of multilevel cluster networks. However, little research has empirically studied the various dimensions of external knowledge. The purpose of this paper is to study different levels of external knowledge, their relation with trade and non-trade interdependence and their relation with different kinds of innovations, namely, exploitation and exploration.

Both quantitative and qualitative research methods were adopted in this study. In terms of the quantitative research method, data were collected from 168 companies in the Shenzhen Hi-Tech Industrial Park of China using convenience sampling. As for the qualitative research method, a total of 35 interviews were carried out in this study.

The quantitative results indicate that different levels of external knowledge in the Shenzhen Hi-Tech Park have different effects. First of all, the results indicate that cross-national connections have a positive influence on trade interdependence, which helps firms to produce exploration. Second, cross-regional connections have a positive influence on both trade and non-trade interdependence, which further help firms to create innovative exploitation and exploration. Third, inter-regional connections have a positive influence on non-trade interdependence, which helps firms to increase innovative exploitation. The qualitative result makes a plausible explanation for the quantitative results. The interview results indicate that as the telecommunications industry has so much to do with China’s national security, there are several initiatives of market protection strategies and political interventions, which help firms to form different levels of knowledge flow in Shenzhen.

There are several limitations of this study which primarily relate to the case study method. The results can be contextually generalized to the domestic-oriented cluster in developing countries.

This study has several managerial implications. First, this research ensures that it is important to consider the multilevel nature of external knowledge before starting with the decision-making process of a firm in a cluster. Second, all levels of administrators and managers in a company should investigate what kinds of involvement and innovation are needed and most highly valued for organizational development. Third, the research framework of this study can be applied to understand which level of external knowledge influences organizational performance.

This study is an initial attempt to provide an examination of external knowledge, organizational involvement and innovation performance of an industrial cluster via a mixed method.