Standards and Regulations

Contents 1 International Standards 1.1 IFOAM Standards 1.2The Codex Alimentarius 2 National and Supranational Regulations 2.1 The EU Regulation on Organic Production 2.2 Other National Regulations 2.3 US and EU Import Procedures 3 Private Standards 4 Relationship to Fair Trade 5 Literatur

Standards and Regulations

Contents 5.1 International Standards 5.1.1 IFOAM Standards 5.1.2 The Codex Alimentarius 5.2 National and Supranational Regulations 5.2.1 The EU Regulation on Organic Production 5.2.2 Other National Regulations 5.2.3 US and EU Import Procedures 5.3 Private Standards 5.4 Relationship to Fair Trade 5.5 Literatur

Superfood in Austria : analysis of customer perception and market dynamics in Austrian retail, using the example of the Avocado

The purpose of this thesis is the academic study of customer perceptions of the avocado and buying behavior in the Austrian retail market. By using the framework of the Total Food Quality Model (Brunsø et al, 2002), perceptional aspects like search attributes, experience attributes, credence attributes, beliefs and familiarity, but also social and socio demographic aspects were analyzed. Results from collected quantitative and qualitative data show overlaps but also significant differences from conventional literature and market studies from similar markets. Furthermore, results show important aspects to consider for retail and connecting factors for future research.A tese que se apresenta tem como objetivo o estudo das perceções do cliente sobre o abacate e qual o comportamento de compra no comércio de retalho Austríaco. Analisaram-se, com base no Modelo de Qualidade Alimentar Total (Total Food Quality Model), aspetos percetuais tais como, atributos de procura, de experiência, de credibilidade, de convicção, familiaridade, assim como aspetos sociodemográficos. Com recurso a técnicas de pesquisa quantitativa e qualitativa, obtiveram-se resultados que, apesar de indicarem existência de algumas sobreposições, mostram diferenças significativas relativamente à literatura e estudos de mercados existentes sobre mercados semelhantes. Conclui-se que os resultados obtidos expõem aspetos importantes a ter em conta no mercado de retalho, gerando ainda alguns fatores a considerar para investigações futuras

The Organic Market in Switzerland and the EU - Overview and market access information for producers and international trading companies

The organic market is highly diverse. Production standards and certification and trade rules place considerable demands upon market participants. At the same time, this market holds out excellent opportunities for creative and circumspect producers, processors and traders. To operate successfully in organic import and export business alike, it is crucial to have accurate information on the potential of the specific organic market and on the conditions governing market access. The attractively designed handbook offers the very latest market information for producers an international trading companies, organized both by product group an by country on 80 pages. This second edition is updated in all chapters and has in addition three new markets: Austria, Italy and Sweden. In addition, the handbook provides an easily accessible overview of the - in some instances - quite complicates - import requirements applicable in Switzerland and the EU. In a further 50-page appendix, the handbook contains an extensive collection of addresses (trading companies, authorities, certification bodies, organizations etc.) and Internet Websites. The second edition of this handbook has been produced in English

Earthworm sampling

Earthworms are key organisms and indicators of soil quality, which also links to the productivity of an ecosystem. The higher the earthworm density and diversity, the better the earthworm’s living conditions and the more fertile is the soil. They are relatively easy to detect and determine into three ecological groups. The number and distribution of the three ecological categories and the numerous earthworm species depends on soil management, soil type, the Vegetation and climate conditions. In this technical note, various methods how to sample, measure and interpret earthworms are shown

Avoidance of Concave Obstacles through Rotation of Nonlinear Dynamics

Controlling complex tasks in robotic systems, such as circular motion for cleaning or following curvy lines, can be dealt with using nonlinear vector fields. In this paper, we introduce a novel approach called rotational obstacle avoidance method (ROAM) for adapting the initial dynamics when the workspace is partially occluded by obstacles. ROAM presents a closed-form solution that effectively avoids star-shaped obstacles in spaces of arbitrary dimensions by rotating the initial dynamics towards the tangent space. The algorithm enables navigation within obstacle hulls and can be customized to actively move away from surfaces, while guaranteeing the presence of only a single saddle point on the boundary of each obstacle. We introduce a sequence of mappings to extend the approach for general nonlinear dynamics. Moreover, ROAM extends its capabilities to handle multi-obstacle environments and provides the ability to constrain dynamics within a safe tube. By utilizing weighted vector-tree summation, we successfully navigate around general concave obstacles represented as a tree-of-stars. Through experimental evaluation, ROAM demonstrates superior performance in terms of minimizing occurrences of local minima and maintaining similarity to the initial dynamics, outperforming existing approaches in multi-obstacle simulations. The proposed method is highly reactive, owing to its simplicity, and can be applied effectively in dynamic environments. This was demonstrated during the collision-free navigation of a 7 degree-of-freedom robot arm around dynamic obstaclesComment: 20 pages, 19 figure

A modular implementation of an effective interaction approach for harmonically trapped fermions in 1D

We introduce a generic and accessible implementation of an exact diagonalization method for studying few-fermion models. Our aim is to provide a testbed for the newcomers to the field as well as a stepping stone for trying out novel optimizations and approximations. This userguide consists of a description of the algorithm, and several examples in varying orders of sophistication. In particular, we exemplify our routine using an effective-interaction approach that fixes the low-energy physics. We benchmark this approach against the existing data, and show that it is able to deliver state-of-the-art numerical results at a significantly reduced computational cost.Comment: 22 + 4 page

The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks.

In laboratory object recognition tasks based on undistorted photographs, both adult humans and deep neural networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1.3M images) perform poorly on distorted images. However, the last 2 years have seen impressive gains in DNN distortion robustness, predominantly achieved through ever-increasing large-scale datasets-orders of magnitude larger than ImageNet. Although this simple brute-force approach is very effective in achieving human-level robustness in DNNs, it raises the question of whether human robustness, too, is simply due to extensive experience with (distorted) visual input during childhood and beyond. Here we investigate this question by comparing the core object recognition performance of 146 children (aged 4-15 years) against adults and against DNNs. We find, first, that already 4- to 6-year-olds show remarkable robustness to image distortions and outperform DNNs trained on ImageNet. Second, we estimated the number of images children had been exposed to during their lifetime. Compared with various DNNs, children's high robustness requires relatively little data. Third, when recognizing objects, children-like adults but unlike DNNs-rely heavily on shape but not on texture cues. Together our results suggest that the remarkable robustness to distortions emerges early in the developmental trajectory of human object recognition and is unlikely the result of a mere accumulation of experience with distorted visual input. Even though current DNNs match human performance regarding robustness, they seem to rely on different and more data-hungry strategies to do so