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The purpose of this paper is to inquiry dietary behavior and the physical and mental health status of food pantry users to better understand issues related to food insecurity and to explore predictors of intentions for self-sufficiency.

The authors randomly surveyed 12 food pantries (151 consumers) sponsored by the North Texas Food Bank in USA, regarding dietary behavior, health status, reasons for food pantry use, satisfaction with services provided, and self-sufficient behavior and support.

About 37 percent of survey participants would expect to continue using food pantry services for one or more years. Reasons for food pantry use included low job earnings, unemployment, poor health, and disability. Over 83 percent of them were either overweight or obese, and over half (57 percent) of them had moderate or severe mental disorder symptoms that warrant examination by healthcare practitioners. On average, their health-related quality of life was lower than the general population. Participants’ physical health was significantly correlated with work intention. The hierarchical regression model predicting work intention had a large effect size.

This research has highlighted the importance of improving food pantry consumers’ health and self-sufficiency in order to live sufficiently and healthily.

Community health practitioners need to help food banks address the needs beyond hunger to focus on the larger ramification of food insecurity such as self-sufficiency and health-related quality of life.

This work extends the existing studies focused on food insecurity, and it will enable the collaborations among food banks, social workers, vocational rehabilitation counselors, and public health practitioners.

This study aims to evaluate social media’s communication effectiveness on travel product promotion among college students in Hong Kong. As traveling has become a popular activity, promoting travel products via social network sites (SNSs) has become common. Thus, identifying factors that affect shopping decisions is vital to tourism businesses. While the number of people using social communication tools has increased quickly, social media marketing provides a new strategy for the local travel business to sell and promote their products online.

This study adopts the attention, interest, desire and action (AIDA) marketing communication model to explore the influence of Facebook on the marketing of travel products among youngsters. Because Facebook is the most widely used social media platform in Hong Kong, it was selected for this study. An online survey was conducted via Google Form to collect responses from students of different local universities.

The findings indicate that our respondents consider purchasing travel products according to brand, discount and customer comments. They generally perceived Facebook advertising as a platform that could deliver various updated travel promotions and discounts, which can be adequately explained based on the AIDA model. However, respondents were unwilling to recommend the travel company to their friends on Facebook, even if they were satisfied with the travel products after purchase. Also, Facebook promotion could positively influence, draw the attention and make travel desire of the customer, but no positive influence to arouse their interest.

Although there are many studies on the travel industry’s marketing and social media, scant studies have investigated the influence of social media on the promotion of travel products with the AIDA model. In particular, it is crucial to explore what factors can affect young people’s decision-making, their perception of social media advertising and how marketers can make good use of this channel.

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model has become popular because it needs only a few data points to construct a time-series model without statistical assumptions. Several methods have been developed to improve prediction accuracy of the original GM(1,1) model by only estimating the sign of each residual. This study aims to address that this is too tight a restriction for the modification range.

Based on the predicted residual, this study uses the functional-link net (FLN) with genetic-algorithm-based learning to estimate the modification range for its corresponding predicted value obtained from the original GM(1,1) model.

The forecasting ability of the proposed grey prediction model is verified using real energy demand cases from China. Experimental results show that the proposed prediction model performs well compared to other grey residual modification models with sign estimation.

The proposed FLNGM(1,1) model can improve prediction accuracy of the original GM(1,1) model using residual modification. The distinctive feature of the proposed model is to use an FLN to estimate sign and modification range simultaneously for the predicted value based on its corresponding predicted residual obtained from the residual GM(1,1) model.

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting accuracy. This paper aims to apply multiple attribute decision-making (MADM) methods to develop new combination forecasting methods.

Grey relational analysis (GRA) is applied to assess weights for individual constituents, and the Choquet fuzzy integral is employed to nonlinearly synthesize individual forecasts from single grey models, which are not required to follow any statistical property, into a composite forecast.

The empirical results indicate that the proposed method shows the superiority in mean accuracy over the other combination methods considered.

For tourism practitioners who have no experience of using grey prediction, the proposed methods can help them avoid the risk of forecasting failure arising from wrong selection of one single grey model. The experimental results demonstrated the high applicability of the proposed nonadditive combination method for tourism demand forecasting.

By treating both weight assessment and forecast combination as MADM problems in the tourism context, this research investigates the incorporation of MADM methods into combination forecasting by developing weighting schemes with GRA and nonadditive forecast combination with the fuzzy integral.