Determinants of Choice Regarding Food with Nutrition and Health Claims

Health is an increasingly important topic in the food market. The regulation (EC) No 1924/2006 on nutrition and health claims is meant to facilitate healthy food choices of consumers. However, research studies about claim perception and choice behaviour are scarce in Europe up to this point, especially those focusing on revealed preferences or a close-to-realistic study design. This contribution reports findings of realistically designed choice-tests accompanied by video-observation and followed by a face-to-face questionnaire. Logistic regression analysis was applied in order to determine the influencing factors on purchase behaviour of food products with claims. Perception of relative healthiness of the product with a claim, credibility of the claim and extent of information acquisition were found to influence choice positively, while claim format and product category were of no importance.Consumer behaviour, health claims, choice tests, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

The European Market for Organic Products: Growth and Development

The European Market for organic food has been growing rapidly in terms of both supply and demand during the 1990s. However, national markets develop in many different directions. In some countries the market share ist quiet high while in others a market for organic farming products nearly does not exist. This book detects and compares the national markets of the main organic products in 18 European countries - the 15 EU countries plus Switzerland, Norway and the Czech Republic - on the basis of the most comprehensive collection of data ever presented covering the period 1993 - 1997/1998. It is shown that European demand is far from being satisfied and the major efforts in organising a transparent international market and developing marketing strategies is necessary to realise this potential. This book is aimed at policy makers, the private sector, researchers and students in the field of economics and politics of organic farming

PASCAL/48 reference manual

PASCAL/48 is a programming language for the Intel MCS-48 series of microcomputers. In particular, it can be used with the Intel 8748. It is designed to allow the programmer to control most of the instructions being generated and the allocation of storage. The language can be used instead of ASSEMBLY language in most applications while allowing the user the necessary degree of control over hardware resources. Although it is called PASCAL/48, the language differs in many ways from PASCAL. The program structure and statements of the two languages are similar, but the expression mechanism and data types are different. The PASCAL/48 cross-compiler is written in PASCAL and runs on the CDC CYBER NOS system. It generates object code in Intel hexadecimal format that can be used to program the MCS-48 series of microcomputers. This reference manual defines the language, describes the predeclared procedures, lists error messages, illustrates use, and includes language syntax diagrams

SALES RESPONSES TO RECALLS FOR LISTERIA MONOCYTOGENES: EVIDENCE FROM BRANDED READY-TO-EAT MEATS

Empirical models are used to measure sales losses experienced by frankfurter brands following a recall for a foodborne pathogen. Recalled brands experience a 22 to 27 percent sales decline after a recall. Brand recovery occurs within 4 to 5 months after a recall. Non-recalled brands do not experience sales losses.Food Consumption/Nutrition/Food Safety,

Maintaining Topological Properties on the Brink of Destruction

Throughout the paper we consider the setting where f is a continuous function (a mapping) whose domain X and range Y are both Hausdorff spaces. Our object is to determine conditions on the map f which insure that when X has a certain topological property Q, then Y will also have property Q. For example, if X is metrizable, then it does not necessarily follow that Y is a metric space; but if f is a perfect map, then metrizability is preserved. Chapter III is devoted to the study of this metrizability problem. In particular, we present Frink\u27s [ 2] characterization of metrizable spaces, and we use it to show that a closed map f preserves metrizability provided Y is either first countable or for each p∈Y, f-1(p)n has a compact frontier. This was apparently first observed by Stone [ 6]. From Stone\u27s result and from the result that first countable is preserved by open mappings, it follows easily that metrizability is preserved when f is both open and closed. In this case we can even describe a familiar metric for Y; namely, if p, q∈Y then the metric σ for Y is given by σ(p, q) = d (f-1(p), f-1(q) ) where d denotes the Hausdorff distance. This result is due to Balanchandran [ 1]. The proof we present, however, differs from Balanchandran\u27s since ours depends heavily on a previous theorem due to Wallace [ 7] where necessary and sufficient conditions are given for a decomposition G of a metric space into disjoint, nonempty closed sets to be continuous

The Economic Potential of Composting Breeder and Pullet Litter with Eggshell Waste

Expansion of the wastes coordinated by the Ozark Poultry Litter Bank is needed. This study examined a method of combining low value poultry wastes to produce compost. Analyses of four compost blends and two hypothetical production systems provide entrepreneurs with the production and financial information to make informed decisions.composting, poultry industry, waste management, product development, Environmental Economics and Policy, Livestock Production/Industries, Q53, Q13, Q16,

On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201