The traditional landings with large warships near the coastline have been replaced by means of vessels moving beyond the horizon from the coast where the landing will take place. The old landing crafts have been replaced by transport helicopters and close air support, a threat that the amphibious battalion lacks resources to meet. The Swedish Armed Forces faces a possible acquisition of MANPADS and therefore the main purpose with this study was to examine the systems: FIM-92 Stinger RMP, Mistral and RBS70 NG, in order to assess which one of them who had the greatest potential to be effective when used by the amphibious battalion in a coastal defense operation. The study was conducted as a multiple criteria decision analysis, based on the concept of military utility. The result indicated that the RBS70 NG was the system that best met the requirements and demonstrated the greatest potential to be military efficient, although Mistral with minor exceptions fulfilled the requirements. As the study was delimited to a theoretical study based on three specific scenarios, further studies are recommended with simulations, as well as field trials before the result can be given a higher validity. The result, however, already helps us to understand how MANPADS contributes with military utility in coastal defense operations. / De traditionella landstigningarna med stora fartyg uppträdandes kustnära har ersatts med metoder innebärandes att fartygen rör sig bortom horisonten från den kust där landsättningen/landstigningen ska ske. De gamla landstigningsbåtarna har i hög grad ersatts med helikoptrar som understödda av attackhelikoptrar och flygplan utgör den nya dimensionerande hotbilden. En hotbild som amfibiebataljonen saknar resurser för att möta. Då Försvarsmakten står inför en eventuell anskaffning av ett buret luftvärnssystem till amfibiebataljonen har denna undersöknings främsta syfte varit att bedöma vilket av systemen FIM-92 Stinger RMP, Mistral eller RBS70 NG som uppvisat störst potential att vara militärt effektivt när de nyttjas av en amfibiebataljon i kustförsvarsoperationer. Undersökningen genomfördes som en komparativ analys med multimålmetod och tre olika typfall som grund. Jämförelsen tog sin utgångspunkt kring teoribildningen om militär nytta och då mer specifikt militär effektivitet. Resultatet indikerade att RBS70 NG var det system som bäst mötte amfibiebataljonens krav och därmed uppvisade störst potential att vara militärt effektivt, även om Mistral med enstaka undantag också uppfyllde kraven till synes utan begränsningar. Då studien avgränsats till en teoretisk jämförelse i tre specifika typfall, rekommenderas fortsatta studier med såväl simuleringar som praktiska prov innan resultatet kan ges en högre validitet. Resultatet bidrar däremot redan nu till en förståelse för vad som ökar den militära effektivitetenvid genomförande av kustförsvarsoperationer.

<p dir="ltr">Recent advancements in large language models such as ChatGPT and AU Large allow for the effective integration and application of LLMs into network devices such as switches and routers in terms of the ability to play a role in configuration and management. The given devices are an essential part of every network infrastructure, and the nature of physical networking topologies is complex, which leads to the need to ensure optimal network efficiency and security via meticulous and precise configurations.</p><p dir="ltr">The research explores the potential of an AI-driven interface that utilizes AU Large to streamline, enhance, and automate the configuration process of network devices while ensuring that the security of the whole process is guaranteed by running the entire system on-premise. Three core areas are of primary concern in the given study: the effectiveness of integrating the AU Large into network management systems, the impact on efficiency, accuracy, and error rates in network configurations, and the scalability and adaptability to more complex requirements and growing network environments.</p><p dir="ltr">The key performance metrics evaluated are the error rate in the generated configurations, scalability by looking at the performance as more network devices are added, and the ability to generate incredibly complex configurations accurately. The high-level results of the critical performance metrics show an evident correlation between increased device count and increased prompt complexity with a degradation in the performance of the AU Large model from Mistral AI.</p><p dir="ltr">This research has significant potential to alter preset network management practices by applying AI to make network configuration more efficient, reduce the scope for human error, and create an adaptable tool for diverse and complex networking environments. This research contributes to both AI and network management fields by highlighting a path toward the “future of network management.”</p>