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Dieser Beitrag entwickelt ein Konzept zur praktischen Umsetzung eines Machine- Learning Verfahrens zum Lösen von Vehicle Routing Problemen im Kontext einer nachhaltigen “Letzte-Meile”-Logistik, welches durch einen Prototyp umgesetzt und getestet wurde. Der Prototyp basiert auf dem “Reinforcement Learning”-System und verwendet als Algorithmus “REINFORCE mit Baseline”. In einer Vergleichsanalyse wurde der Prototyp mit dem bekannten Vertreter Google-OR, anhand von zwei Anwendungsszenarien, verglichen. Der Prototyp überzeugt dabei in der Laufzeit und dem Automatismus. Es konnte festgestellt werden, dass eine Verwendung von Lernenden-Systemen für das Vehicle Routing Problem sich bei nur bei einem größeren Stoppvolumen und einer erweiterten IT-Infrastruktur empfiehlt.
A unified understanding of terms and their applicability is essential for every scientific discipline: steganography is no exception. Being divided into several domains (for instance, text steganography, digital media steganography, and network steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to some specific applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction of a pattern-based taxonomy for network steganography. Six years later, in 2021, the first work towards a pattern-based taxonomy for steganography was proposed. However, this initial attempt still faced several shortcomings, e.g., the lack of patterns for several steganography domains (the work mainly focused on network steganography and covert channels), various terminology issues, and the need of providing a tutorial on how the taxonomy can be used during engineering and scientific tasks, including the paper-writing process.</p>
<p>As the consortium who published this initial 2021-study on steganography patterns, in this paper we present the first comprehensive pattern-based taxonomy tailored to fit all known domains of steganography, including smaller and emerging areas, such as filesystem steganography and cyber-physical systems steganography. Besides, to make our contribution more effective and promote the use of the taxonomy to advance research on steganography, we also provide a thorough tutorial on its utilization. </p>
<p>Our pattern collection is available at <a href="https://patterns.ztt.hs-worms.de/" target="_blank">https://patterns.ztt.hs-worms.de</a>.</p>
Fast protein and metabolite liquid chromatography (FPLMC) was introduced years ago to enable the easy separation of high-molecular compounds such as proteins from small molecules and the identification of the low-molecular substances. In this paper, the method is applied for the rapid evaluation of freshness and monitoring the aging of animal meat and fish. A novel chromatographic sensor was developed with a deep UV LED-based photometric detection unit (255–265 nm), an original flow cuvette and registration scheme; the processing of a chromatogram with the sensor takes approximately 15 min. Strict isochronism between the elution of ATP metabolites, mainly hypoxanthine (Hx) and inosine monophosphate (IMP), and the time of maturation of meat or fish, was discovered. A new freshness index H* = [Hx]/[IMP] was introduced, which is proportional to the instrumental delay time in the FPMLC chromatograms: the H* index < 0.5 indicates the presence of inosine monophosphate (IMP) and the high quality of the meat or fish. Reasonably strong correlations were revealed between data obtained by FPMLC and total volatile basic nitrogen TVB-N (for fish) or volatile fatty acids VFA (for meat) content. Moreover, putative nucleotide salvage and an increase in the concentration of IMP were observed in fish after heat treatment using the FPMLC sensor and NMR technique.