Weed Management in Organic Farming in the New EU Member States and the Acceding Countries - Status Quo and Main Limitations

Under the EU Specific Support Action (SSA) Project CHANNEL (“Opening Channels of Communication between the Associated Candidate Countries and the EU in Ecological Farming”), a survey on the status quo in weed management was conducted in 15 new member and acceding EU states. The focus of the data collection was on three main aspects relevant for the understanding of the current situation, they are: i.) legal and administrative framework, ii.) status quo in practice and iii.) scientific basis. These aspects were covered by separate questionnaires, addressed to different authorities and target groups. The results of the weed management questionnaire for the target group “experts engaged in practical farming” are presented in this paper. The analysis allowed the identification of a small group of weeds as the main target species of organic weed management. Alien species were reported by almost all countries as an upcoming problem in organic farming. There was conformity among the different countries in regards to the choice of prevention tools, whereas an obvious lack of modern equipment available for practicing mechanical weed control was noted. Within the new EU member states and candidate countries, the economic constraints (lack of available machinery, lack of capital and high economic costs) were stated as the main limitations of weed management success. The economic costs of weed management in general, could not yet be covered by the market prices of organic products. Therefore, the economic pressure and the scope for improving weed management in practice are small. In most countries, the scientific sector (facilities, projects and advisory services) working on weed management issues in organic farming is relatively small. The lack of project funding in this area limits the research profoundly

Cost of Czochralski wafers as a function of diameter

The impact of diameter in the range of 10 to 15 cm on the cost of wafers sliced from Czochralski ingots was analyzed. Increasing silicon waste and decreasing ingot cost with increasing ingot size were estimated along with projected costs. Results indicate a small but continuous decrease in sheet cost with increasing ingot size in this size range. Sheet costs including silicon are projected to be $50 to$60/sq m (1980 $) depending upon technique used

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

Antihydrogen is at the forefront of antimatter research at the CERN Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry and antigravity effects require the efficient detection of antihydrogen annihilation events, which is performed using highly granular tracking detectors installed around an antimatter trap. Improving the efficiency of the antihydrogen annihilation detection plays a central role in the final sensitivity of the experiments. We propose deep learning as a novel technique to analyze antihydrogen annihilation data, and compare its performance with a traditional track and vertex reconstruction method. We report that the deep learning approach yields significant improvement, tripling event coverage while simultaneously improving performance by over 5% in terms of Area Under Curve (AUC)