DO HEALTH CLAIMS MATTER FOR CONSUMER PREFERENCE ON TEA BEVERAGE? EXPERIMENTAL EVIDENCE FROM TAIWAN

This paper aims to identify consumer preference for tea drinking products in Taiwan by applying conjoint analysis and investigate whether health claims as attributes would influence consumer’s choice behavior. From 1 July to 31 August 2005, 620 consumers of tea drinking products participated in the choice-based conjoint experiment, which conducted in the city of Taipei, Taichung, Tainan, and Kaohsiung in Taiwan. The data were collected in supermarket using questionnaire for personal interviews. Overall, the estimated individual models fit the data well using Conditional Logit Model. Regarding the result of “Original Tea”, consumer’s order ranking of tea category is green tea, oolong tea, and black tea. The most importance on the standard that health claims have positive influence on higher likelihood of purchasing tea drinks. In addition, consumer prefers to tea drinks with Catechins, processing technology using cold extraction, and paper package. However, it could be seen that as the price increases the utility for the consumer decreases. Also, we report the negative relationship between price and purchasing intention. It is found that respondents preferred to tea drinking products with health claims. This result stands for consumer’s concern on their health status by intaking additives like Catechins. Our results also suggest that respondents prefer that tea drinks include less sugar that implies that the product is produced “light”.Tea Drinking Products, Consumer Preference, Health Claims, Conjoint Analysis, Conditional Logit Model, Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Health Economics and Policy,

Super-resolution image transfer by a vortex-like metamaterial

We propose a vortex-like metamaterial device that is capable of transferring image along a spiral route without losing subwavelength information of the image. The super-resolution image can be guided and magnified at the same time with one single design. Our design may provide insights in manipulating super-resolution image in a more flexible manner. Examples are given and illustrated with numerical simulations.Comment: 7 pages, 6 figure

Implementing the 3E assessment model of sustainable development to investigate coastal pollution management: using PET recycling (bottle-to-fiber) as a case study

Recently, plastic pollution of the ocean has been garnering increasing attention. The United Nations considers the problem as a major issue, and the UN Environment Programme (UNEP) has launched a global "Clean Ocean" campaign. An estimated 51 trillion plastic particles can be found in our oceans, and the pollution has caused plastics to enter the food chain. This study investigates the life cycle of recycling waste PET (polyethylene terephthalate) bottles in the ocean to the regeneration of recycled raw materials in the process of producing blankets made from such materials. First, the activity data of the relevant literature was collected, and the life cycle assessment software Open LCA was used as the assessment tool. We assume that the functional unit is 1 kg of recycled PET bottles. Secondly, with the ILCD 2011 Midpoint impact assessment method for environmental impact analysis, we identify the impact of pollutants generated during the recycling process on the environment as follows: Photochemical ozone formation 7872256.41218/ kg NMVOC eq; Fresh-water ecotoxicity 240566129.10051/ CTUe; Human toxicity, cancer effects 120.28305/ CTUh; Human toxicity, non-cancer effects 1.53496/ CTUh. Finally, we conduct risk assessment using the 3E (Engineering, Environment and Economic) assessment model, and propose an overall recovery treatment optimization assessment model

Estimating systemic fibrosis by combining galectin-3 and ST2 provides powerful risk stratification value for patients after acute decompensated heart failure

Background: Two fibrosis biomarkers, galectin-3 (Gal-3) and suppression of tumorigenicity 2 (ST2), provide prognostic value additive to natriuretic peptides and traditional risk factors in patients with heart failure (HF). However, it is to be investigated whether their combined measurement before discharge provides incremental risk stratification for patients after acute HF. Methods: A total of 344 patients with acute HF were analyzed with Gal-3, and ST2 measured. Patients were prospectively followed for 3.7 ± 1.3 years for deaths, and composite events (death/HF-related re-hospitalizations). Results: The levels of Gal-3 and ST2 were only slightly related (r = 0.20, p < 0.001). The medians of Gal-3 and ST2 were 18 ng/mL and 32.4 ng/mL, respectively. These biomarkers compensated each other and characterized patients with different risk factors. According to the cutoff at median values, patients were separated into four subgroups based on high and low Gal-3 (HG and LG, respectively) and ST2 levels (HS and LS, respectively). Kaplan-Meier survival curves showed that HGHS powerfully identified patients at risk of mortality (Log rank = 21.27, p < 0.001). In multivariable analysis, combined log(Gal-3) and log(ST2) was an in­dependent predictor. For composite events, Kaplan-Meier survival curves showed a lower event- -free survival rate in the HGHS subgroup compared to others (Log rank = 34.62, p < 0.001; HGHS vs. HGLS, Log rank = 4.00, p = 0.045). In multivariable analysis, combined log(Gal-3) and log(ST2) was also an independent predictor. Conclusions: Combination of biomarkers involving heterogeneous fibrosis pathways may identify patients with high systemic fibrosis, providing powerful risk stratification value

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page