Hochschule Bonn-Rhein-Sieg
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Geschäftsordnung der Hochschulwahlversammlung der Hochschule Bonn-Rhein-Sieg (GO HSW) in der Fassung der zweiten Änderungsordnung vom 21.01.2025
No full textdCTIDH: Fast & Deterministic CTIDH
No full textThis paper presents dCTIDH, a CSIDH implementation that combines two recent developments into a novel state-of-the-art deterministic implementation. We combine the approach of deterministic variants of CSIDH with the batching strategy of CTIDH, which shows that the full potential of this key space has not yet been explored. This high-level adjustment in itself leads to a significant speed-up. To achieve an effective deterministic evaluation in constant time, we introduce Wombats, a new approach to performing isogenies in batches, specifically tailored to the behavior required for deterministic CSIDH using CTIDH batching.
Furthermore, we explore the two-dimensional space of optimal primes for dCTIDH, with regard to both the performance of dCTIDH in terms of finite-field operations per prime and the efficiency of finite-field operations, determined by the prime shape, in terms of cycles. This allows us to optimize both for choice of prime and scheme parameters simultaneously. Lastly, we implement and benchmark constant-time, deterministic dCTIDH. Our results show that dCTIDH not only outperforms state-of-the-art deterministic CSIDH, but even non-deterministic CTIDH: dCTIDH-2048 is faster than CTIDH-2048 by 17 percent, and is almost five times faster than dCSIDH-2048
Modelling of a Solar Photovoltaic Power Supply for a Wireless Access Point in a Rural Area
No full textStructure and function of the geldanamycin amide synthase from Streptomyces hygroscopicus
No full textAmide synthases catalyze the formation of macrolactam rings from aniline-containing polyketide-derived seco-acids as found in the important class of ansamycin antibiotics. One of these amide synthases is the geldanamycin amide synthase GdmF, which we recombinantly expressed, purified and studied in detail both functionally as well as structurally. Here we show that purified GdmF catalyzes the amide formation using synthetically derived substrates. The atomic structures of the ligand-free enzyme and in complex with simplified substrates reveal distinct structural features of the substrate binding site and a putative role of the flexible interdomain region for the catalysis reaction
You Can't Touch This: Detecting Typosquatting Packages for Enhanced Malware Prevention in Software Supply Chains
No full textThis repository includes the following datasets:
Own Dataset: A collection of 394 with source code typosquatting packages we have collected based on SonaType, Phylum.io and Snyk listings.
Backstabbers Knife Collection: A snapshot of Backstabbers Knife Collection during our analysis for reproduction purposes
MalOSS: A snapshot of the MalOSS dataset during our analysis for reproduction purposes.
Source code: The source code of our programs and algorithms, mainly the Random Forest models, and the Extended Damerau-Levenshtein MetricHowever, the source code of the packages provided by MalOSS and Backstabbers Knife Collection must be retrieved by the corresponding owner/maintainer
A Probabilistic Model Predictive Control Approach for PV-Diesel Hybrid Systems in Ghana’s Health Sector Using Seamless State Prediction Methods
Get PDFIn Ghana, unreliable public grid infrastructure greatly impacts rural healthcare, where diesel generators are commonly used despite their high financial and environmental costs. Photovoltaic (PV)-hybrid systems offer a sustainable alternative, but require robust, predictive control strategies to ensure reliability. This study proposes a sector-specific Model Predictive Control (MPC) approach, integrating advanced load and meteorological forecasting for optimal energy dispatch. The methodology includes a long-short-term memory (LSTM)-based load forecasting model with probabilistic Monte Carlo dropout, a customized Numerical Weather Prediction (NWP) model based on the Weather Research and Forecasting (WRF) framework, and deep learning-based All-Sky Imager (ASI) nowcasting to improve short-term solar predictions. By combining these forecasting methods into a seamless prediction framework, the proposed MPC optimizes system performance while reducing reliance on fossil fuels. This study benchmarks the MPC against a traditional rule-based dispatch system, using data collected from a rural health facility in Kologo, Ghana. Results demonstrate that predictive control greatly reduces both economic and ecological costs. Compared to rule-based dispatch, diesel generator operation and fuel consumption are reduced by up to 61.62% and 47.17%, leading to economical and ecological cost savings of up to 20.7% and 31.78%. Additionally, system reliability improves, with battery depletion events during blackouts decreasing by up to 99.42%, while wear and tear on the diesel generator and battery are reduced by up to 54.93% and 37.34%, respectively. Furthermore, hyperparameter tuning enhances MPC performance, introducing further optimization potential. These findings highlight the effectiveness of predictive control in improving energy resilience for critical healthcare applications in rural settings
