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The purpose of this paper is to describe a strap‐down image stable strategy for multi‐load optoelectronic imaging platform hung below a tethered aerostat.

Four two dimension pods, each with a visible light camera, are fixed on the optoelectronic platform. A POS (Position and Orientation System) is used to acquire the attitude rate data of optoelectronic platform, while the data can be coupled to the pods' servo systems through corresponding coordinate rotation, then the motors of pods will adjust the line of sight to the opposite way to keep the stabilization of image exported by visible light camcorders. Simultaneously, two rate gyros are installed at the inner frame of each pod, which are used as a backup to avoid the failure of POS.

Using one attitude and position measurement system can realize the stabilization of multi optoelectronic pods, which is same as or even better than the ratio gyro stabilization.

As the tethered aerostat is a flexible body, it is affected a lot by the wind speed and wind direction at the low height (<1,000 m), which leads to the motors of pods always adjust the line of sight to the mechanical limiting of pods.

Strap‐down stabilization technology has been successfully used in the tethered aerostat monitoring platform to surveillance Shanghai World EXPO site. Long time experiments verify the feasible and effective of the multi‐load stabilization technology. The impact on the image by the adjustment of servos is less than 10 percent of the whole view of sight.

The paper introduces a strap‐down stabilization technology for multi‐load tethered aerostat platform, which is more suitable to be applied in the platform of relatively minor attitude change, like the airborne multi‐load platform and multi‐load UAV (unmanned aerial vehicle) platform.

Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.

In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.

The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.

This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.

The purpose of this study is to explore the antecedents of consumers’ online review intention in e-commerce platforms from a unique perspective of consumer commitment and platform design. Meanwhile, for the dual-platform strategy, i.e. providing both the web and mobile platforms simultaneously, which is widely adopted in the industry but lacks theoretical concerns, this study aims to examine the differences that platform design influences consumer commitment, consequently contributing to online review intention, between the web and mobile contexts.

A cross-sectional online survey is employed, and a structural equation model-based approach is utilized to analyze the data collected from both the website-preferred consumers (N = 167) and the mobile app-preferred consumers (N = 247).

The results indicate that instrumental support design factors and socio-emotional support factors positively influence consumer commitment, which further affect online review intention positively. Furthermore, design factors in different use contexts generate different impacts, and consumer commitment generates a greater effect on online review intention in the mobile than in the web context. Empathy is found to be an important motivator of consumer commitment in both contexts.

To the best of the authors’ knowledge, as one of the first attempts to capture the differences in the relationship between platform design on consumer commitment and online review intention in different use contexts within the dual-platform e-commerce, this study provides insights for e-commerce platform managers and designers to promote consumer commitment and online review engagement by prioritizing the platform design.

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods.

First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data.

The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R² is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R² are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties.

This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

The purpose of this paper is to provide further insight into Chinese wine consumers’ preference, grasp the regional sensory preference differences of China and build up a predictive model for wine consumers’ sensory preferences.

The study involved 3,421 Chinese wine consumers in the survey. Classified statistics were conducted to excavate regional differences of wine consumers’ sensory preferences. By analyzing influencing factors, prediction models for consumers’ sensory attribute preferences were constructed on the basis of multivariate disorder logistic regression method.

Empirical research showed that the wine with the following sensory attributes was the most preferred by Chinese consumers: dry red, refreshing and soft taste, still type, moderate aroma degree and mellow aroma, and sweet wine was also popular. Consumers’ preference varied from region to region. The proposed predicting method of the study realized more than 70 percent accuracy when conducting prediction for color, sweetness, aroma type and flavor preferences.

By shedding light on the latest sensory attribute preferences of Chinese wine consumers, this study will help wine industry participants conduct market segmentation based on the diversification of consumers’ preferences. The wine enterprises can gain guidance from the results to conduct market positioning, adjust strategies and provide specific production for target wine consumers.

Based on the actual situation of Chinese wine market, this study defines sensory attribute indexes of wine from the perspective of wine consumers and presents the most recent comprehensive research on the sensory preferences of Chinese wine consumers through a nationwide survey.

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

The purpose of this paper is to analyze the financial structure of Italian cooperatives in the period before and during the crisis (2004-2013), in relation to two discriminating factors. At this end, it focuses on two research questions: What financial dynamics the Italian cooperatives have involved before, during and after the 2008 crisis, that is, in the decade 2004/2013? Are there statistically differences between business sectors and geographic area?

Secondary data on AIDA database have been used. The financial structure is assessed using two ratios: the financial leverage ratio and quick ratio. The final sample consists of 1,446 cooperatives. The trend and exploratory analysis, analysis of variance and Tukey-Kramer post-hoc test have been used.

The financial structure of cooperatives has not been substantially affected by the crisis in any geographic area and business sector, by virtue of resilience of their business model. Moreover, these two factors produce statistically significant differences in the financial structure of cooperatives.

The study takes into account only the cooperatives that survived the crisis, so, presumably, the strongest. Moreover, another and more ratios should be considered at the end to have a more complete view on the financial dynamics.

The literature on resilience of cooperatives is still not very rich. Moreover, this work analyses and integrates aspects and approaches that are not usually considered together.

The purpose of the studies was to test if extent of prior social media activity could predict likelihood that a consumer would post reviews on a new review website.

Two online surveys were conducted presenting scenarios in which users were asked about prior social media activity, the number of social networks they belonged to in study one, and how actively they had posted reviews in study two. These questions were followed by descriptions of new review websites, a general local merchant review website in study one and a local restaurant review website in study two.

Although demographics did a poor job of predicting who would post reviews on the new review websites, prior active social media use and review posting did modestly predict intention to post reviews on the new review websites.

This is not an experimental study and so causality cannot be claimed. Descriptively, although the results were consistent in two studies using different stimuli, other factors might prove to be better predictors of active user‐generated content for other types of sites.

The findings suggest a simple and effective way for two‐sided platform managers to identify potential active reviewers so that they can target them through marketing strategies to encourage their essential participation and less‐active users can be similarly targeted to encourage modest use.

No other studies can be found that focus on this aspect of managing two‐sided platforms. The results might be important for managers of other similar websites that depend on user‐generated content for their value.