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,

Functional renormalization group and variational Monte Carlo studies of the electronic instabilities in graphene near 1/4 doping

We study the electronic instabilities of near 1/4 electron doped graphene using the functional renormalization group (FRG) and variational Monte-Carlo method. A modified FRG implementation is utilized to improve the treatment of the von Hove singularity. At 1/4 doping the system is a chiral spin density wave state exhibiting the anomalous quantized Hall effect, or equivalently a Chern insulator. When the doping deviates from 1/4, the $d_{x^2-y^2}+i d_{xy}$ Cooper pairing becomes the leading instability. Our results suggest near 1/4 electron or hole doped graphene is a fertile playground for the search of Chern insulators and superconductors.Comment: 7 pages, 8 figures, with technical details, published versio

Exposure of the Hidden Anti-Ferromagnetism in Paramagnetic CdSe:Mn Nanocrystals

We present theoretical and experimental investigations of the magnetism of paramagnetic semiconductor CdSe:Mn nanocrystals and propose an efficient approach to the exposure and analysis of the underlying anti-ferromagnetic interactions between magnetic ions therein. A key advance made here is the build-up of an analysis method with the exploitation of group theory technique that allows us to distinguish the anti-ferromagnetic interactions between aggregative Mn2+ ions from the overall pronounced paramagnetism of magnetic ion doped semiconductor nanocrystals. By using the method, we clearly reveal and identify the signatures of anti-ferromagnetism from the measured temperature dependent magnetisms, and furthermore determine the average number of Mn2+ ions and the fraction of aggregative ones in the measured CdSe:Mn nanocrystals.Comment: 26 pages, 5 figure

Manipulation of heat current by the interface between graphene and white graphene

We investigate the heat current flowing across the interface between graphene and hexagonal boron nitride (so-called white graphene) using both molecular dynamics simulation and nonequilibrium Green's function approaches. These two distinct methods discover the same phenomena that the heat current is reduced linearly with increasing interface length, and the zigzag interface causes stronger reduction of heat current than the armchair interface. These phenomena are interpreted by both the lattice dynamics analysis and the transmission function explanation, which both reveal that the localized phonon modes at interfaces are responsible for the heat management. The room temperature interface thermal resistance is about $7\times10^{-10}$m$^{2}$K/W in zigzag interface and $3.5\times10^{-10}$m$^{2}$K/W in armchair interface, which directly results in stronger heat reduction in zigzag interface. Our theoretical results provide a specific route for experimentalists to control the heat transport in the graphene and hexagonal boron nitride compound through shaping the interface between these two materials.Comment: accepted by EP

Poly[aqua­[μ2-cis-1,2-bis­(4-pyrid­yl)ethyl­ene-κ2 N:N′](μ2-5-nitro­isophthalato-κ3 O:O′,O′′)nickel(II)]

In the title compound, [Ni(C8H3NO6)(C12H10N2)(H2O)]n, the NiII atom is octa­hedrally coordinated by two cis N atoms from two different 1,2-bis­(4-pyrid­yl)ethyl­ene (bpe) ligands, two O atoms from one chelating carboxyl group of the 5-nitro­isophthalic acid (nip) ligand, one O atom from another monodentate nip ligand and one O atom from a water mol­ecule, forming a three-dimensional network structure. Inter­molecular O—H⋯O hydrogen bonding stabilizes this arrangement. The asymmetric unit of the structure contains one NiII atom, one water mol­ecule, one nip ligand and two half-mol­ecules of the bpe ligand with an inversion centre at the mid-point of the central C=C bond

Existence of Positive Solutions for Nonlinear Eigenvalue Problems

We use a fixed point theorem in a cone to obtain the existence of positive solutions of the differential equation, u″+λf(t,u)=0, 0<t<1, with some suitable boundary conditions, where λ is a parameter

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table