Wind turbine generator interaction with conventional diesel generators on Block Island, Rhode Island. Volume 2: Data analysis

Assessing the performance of a MOD-OA horizontal axis wind turbine connected to an isolated diesel utility, a comprehensive data measurement program was conducted on the Block Island Power Company installation on Block Island, Rhode Island. The detailed results of that program focusing on three principal areas of (1) fuel displacement (savings), (2) dynamic interaction between the diesel utility and the wind turbine, (3) effects of three models of wind turbine reactive power control are presented. The approximate two month duration of the data acquisition program conducted in the winter months (February into April 1982) revealed performance during periods of highest wind energy penetration and hence severity of operation. Even under such conditions fuel savings were significant resulting in a fuel reduction of 6.7% while the MOD-OA was generating 10.7% of the total electrical energy. Also, electrical disturbance and interactive effects were of an acceptable level

Wind turbine generator interaction with conventional diesel generators on Block Island, Rhode Island. Volume 1: Executive summary

Primary results are summarized for a three-part study involving the effects of connecting a MOD-OA wind turbine generator to an isolated diesel power system. The MOD-OA installation considered was the third of four experimental nominal 200 kW wind turbines connected to various utilities under the Federal Wind Energy Program and was characterized by the highest wind energy penetration levels of four sites. The study analyses address: fuel displacement, dynamic interaction, and three modes of reactive power control. These analyses all have as their basis the results of the data acquisition program conducted on Block Island, Rhode Island

Results of special mechanical analyses of Luna 16 material

The studies carried out on the Luna 16 regolith have confirmed the data that were already published internationally. By means of activation analysis under irradiation in the reactor, activation analysis with a 14 MeV U-generator, and mass spectroscopy on samples of 10 or 20 mg, six main and 63 trace elements were quantitatively determined and compared with known data

A Framework for the Planning and Management of Cybersecurity Projects in Small and Medium-sized Enterprises

Cybersecurity remains one of the key investments for companies that want to protect their business in a digital era. Therefore, it is essential to understand the different steps required to implement an adequate cybersecurity strategy, which can be viewed as a cybersecurity project to be developed, implemented, and operated. This article proposes SECProject, a practical framework that defines and organizes the technical and economics steps required for the planning and implementation of a cost-effective cybersecurity strategy in Small and Medium-sized Enterprises (SME). As novelty, the SECProject framework allows for a guided and organized cybersecurity planning that considers both technical and economical elements needed for an adequate protection. This helps even companies without technical expertise to optimize their cybersecurity investments while reducing their business risks due to cyberattacks. In order to show the feasibility of the proposed framework, a case study was conducted within a Swiss SME from the pharma sector, highlighting the information and artifacts required for the planning and deployment of cybersecurity strategies. The results show the benefits and effectiveness of risk and cost management as a key element during the planning of cybersecurity projects using the SECProject as a guideline

Resolution and Efficiency of the ATLAS Muon Drift-Tube Chambers at High Background Rates

The resolution and efficiency of a precision drift-tube chamber for the ATLAS muon spectrometer with final read-out electronics was tested at the Gamma Irradiation Facility at CERN in a 100 GeV muon beam and at photon irradiation rates of up to 990 Hz/square cm which corresponds to twice the highest background rate expected in ATLAS. A silicon strip detector telescope was used as external reference in the beam. The pulse-height measurement of the read-out electronics was used to perform time-slewing corrections which lead to an improvement of the average drift-tube resolution from 104 microns to 82 microns without irradiation and from 128 microns to 108 microns at the maximum expected rate. The measured drift-tube efficiency agrees with the expectation from the dead time of the read-out electronics up to the maximum expected rate

Deep learning image analysis for filamentous fungi taxonomic classification: Dealing with small datasets with class imbalance and hierarchical grouping

Deep learning applications in taxonomic classification for animals and plants from images have become popular, while those for microorganisms are still lagging behind. Our study investigated the potential of deep learning for the taxonomic classification of hundreds of filamentous fungi from colony images, which is typically a task that requires specialized knowledge. We isolated soil fungi, annotated their taxonomy using standard molecular barcode techniques, and took images of the fungal colonies grown in petri dishes (n = 606). We applied a convolutional neural network with multiple training approaches and model architectures to deal with some common issues in ecological datasets: small amounts of data, class imbalance, and hierarchically structured grouping. Model performance was overall low, mainly due to the relatively small dataset, class imbalance, and the high morphological plasticity exhibited by fungal colonies. However, our approach indicates that morphological features like color, patchiness, and colony extension rate could be used for the recognition of fungal colonies at higher taxonomic ranks (i.e. phylum, class, and order). Model explanation implies that image recognition characters appear at different positions within the colony (e.g. outer or inner hyphae) depending on the taxonomic resolution. Our study suggests the potential of deep learning applications for a better understanding of the taxonomy and ecology of filamentous fungi amenable to axenic culturing. Meanwhile, our study also highlights some technical challenges in deep learning image analysis in ecology, highlighting that the domain of applicability of these methods needs to be carefully considered