Platooning has the potential to be a breakthrough in increasing road capacity and reducing fuel consumption, as it allows a chain of vehicles to closely follow each other on the road. When the number of vehicles increases, platoons will follow one another in what is referred to as multiplatooning. Many Cooperative Intelligent Transportation Systems (C-ITS) applications rely on periodically exchanged beacons among vehicles to improve traffic safety. However, as the number of connected vehicles increases, the network may become congested due to periodically exchanged beacons. Therefore, without some congestion control method, safety critical messages such as Cooperative Awareness Messages (CAMs) may not be delivered on time in high vehicle density scenarios. Both the European Telecommunications Standards Institute (ETSI) and the Institute of Electrical and Electronics Engineers (IEEE) have been working on different standards to support vehicular communication. ETSI dened the Decentralized Congestion Control (DCC) mechanism which adapts transmission parameters (message rate, transmit data rate, and transmit power, etc.) to keep channel load under control. ETSI DCC utilizes a three-state machine with RELAXED, ACTIVE, and RESTRICTIVE states. In this thesis, we implemented this three-state machine by adapting the message rate based on the channel busy ratio (CBR). We name this message-rate based three-state machine DCC-3. DCC-3 has the ability to control channel load; however, it has unfairness and instability problems due to the dramatic parameter changes between states. Therefore, we divided the ACTIVE state of DCC-3 into ve sub-states, and refer to this as DCC-7. We benchmarked DCC-3 against static beaconing (STB), dynamic beaconing (DynB), LInear MEssage Rate Integrated Control (LIMERIC), and DCC-7 using different evaluation metrics with different numbers of platoons. Our results from the Plexe simulator demonstrate that DCC-7 has the best performance when considering all evaluation metrics, including CBR, Inter-reception time (IRT), collisions, safe time ratio, and fairness. Furthermore, we found using transmit power control could greatly improve the performance of CBR and collision rates. / Platooning (fordonskonvojer) har potential att bli ett genombrott i öka vägkapaciteten och minska bränsleförbrukning, eftersom det tillåter en kedja av fordon att noga följa varandra på vägen. När antalet fordon ökar, kommer att plutoner följa varandra i vad som benämns multiplatooning (konvoj av fordonskonvojer). Många kooperativ intelligenta transportsystem (C-ITS) tillämpningar förlitar sig på regelbundet utbytte beacons bland fordon att förbättra traffiksäkerheten. Dock som antalet uppkopplade fordon ökar, kan nätverket bli överbelastat på grund av regelbundet utbytte beacons. Utan någon trängsel kontrollmetod, får därför säkerhet kritiska meddelanden såsom kooperativ medvetenhet meddelanden (CAMs) inte levereras i tid i höga fordon densitet scenarier. Både Europeiska institutet för telekommunikationsstandarder (ETSI) och Institute el och elektroniska tekniker (IEEE) har arbetat på olika standarder för att stödja vehicular kommunikation. ETSI definieras den decentraliserade överbelastning kontroll (DCC) mekanism som anpassar överföring parametrar (meddelande hastighet, överföra datahastighet och sändningseffekt, etc.) för att hålla kanalen belastningen under kontroll. ETSI DCC använder en tre-state maskin med RELAXED, ACTIVE och RESTRICTIVE stater. I denna avhandling har genomfört vi denna tre-state maskin genom att anpassa meddelande hastighet baserat på kanal upptagen förhållandet (CBR). Vi nämna detta meddelande-hastighet baserat tre-state machine DCC-3. DCC-3 har förmågan att kontrollera kanal belastning; Det har dock otillbörlighet och instabilitet problem på grund av de dramatiska parameterändringar mellan stater. Därför vi indelat det ACTIVE tillståndet för DCC-3 i fem undertillstånd och hänvisar till detta som DCC-7. Vi benchmarkade DCC-3 mot statiska leda (STB), dynamisk leda (DynB), linjära MEssage Rate integrerad kontroll (LIMERIC) och DCC-7 med olika utvärdering statistik med olika antal plutoner. Våra resultat från Plexe simulator visar att DCC-7 har bästa prestanda när man överväger alla utvärdering statistik, inklusive CBR, mellan receptionen tid (IRT), kollisioner, säker tid baserat och rättvisa. Vi fann dessutom använda Sändareffektstyrning kan avsevärt förbättra prestanda för CBR och kollision priser.

The number of students enrolling in Advanced Placement (AP) classes has been increasing in Florida and across the nation over the last decade. However, this trend is not happening for traditionally underserved groups of students such as African Americans, Hispanics, and students from low socioeconomic backgrounds. These minority groups are underrepresented in AP classes, while Asian and White students are overrepresented. This trend is alarming because there are qualified minority students who have a great chance of being successful in AP classes according to AP Potential data. For some reason though, these qualified minority and low income students are nevertheless not enrolling in AP classes. The purpose of this research was to investigate whether or not the extension of inviting messages to enroll in AP courses was dependent upon students' ethnic and/or socioeconomic background. A mixture of quantitative and qualitative research methods were employed to examine how the role of invitational theory affects minority student enrollment in AP classes. Mean scale scores from a survey were used to measure student attitudes about how welcoming and inviting schools were when it came to student recruitment into AP classes. T-tests and an analysis of variance were used to determine if there were differences in attitudes among students currently enrolled in AP classes, students of various economic means, and students of various ethnicities. Results from this study found that students already in AP classes felt very invited to challenge themselves in AP classes by teachers, administrators, parents, and peers. Richer students, Whites, and Asians also felt more invited to join AP classes than did poorer, African American, and Hispanic students, though these results were not statistically significant. To increase enrollment in AP classes, the overwhelming response from students was that schools should advertise the pros and cons of taking an AP class. Future research should examine students' perspectives regarding inviting school cultures in regions outside of the southeastern United States. Researchers should also focus on students in urban high schools as previous research has only examined student attitudes in rural and suburban high schools. Finally, future research should examine inviting school cultures from other stakeholders' perspectives such as parents and teachers.

Communication with customers is a major part of customer experience as well as a great source of data mining. More businesses are engaging with consumers via text messages. Before 2020, 39% of businesses already use some form of text messaging to communicate with their consumers. Many more were expected to adopt the technology after 2020[1]. Email response rates are merely 8%, compared to a response rate of 45% for text messaging[2]. A significant portion of this communication involves customer enquiries or support messages sent in both directions. According to estimates, more than 80% of today’s data is stored in an unstructured format (suchas text, image, audio, or video) [3], with a significant portion of it being stated in ambiguous natural language. When analyzing such data, qualitative data analysis techniques are usually employed. In order to facilitate the automated examination of huge corpora of textual material, researchers have turned to natural language processing techniques[4]. Under the light of shared statistics above, Billogram[5] has decided that support messages between creditors and recipients can be mined for predictive maintenance purposes, such as early identification of an outlier like a bug, defect, or wrongly built feature. As one sentence goal definition, Billogram is looking for an answer to ”why are people reaching out to begin with?” This thesis project discusses implementing unsupervised clustering of support messages by benefiting from natural language processing methods as well as performance metrics of results to answer Billogram’s question. The research also contains intent recognition of clustered messages in two different ways, one automatic and one semi-manual, the results have been discussed and compared. LDA and manual intent assignment approach of the first research has 100 topics and a 0.293 coherence score. On the other hand, the second approach produced 158 clusters with UMAP and HDBSCAN while intent recognition was automatic. Creating clusters will help identifying issues which can be subjects of increased focus, automation, or even down-prioritizing. Therefore, this research lands in the predictive maintenance[9] area. This study, which will get better over time with more iterations in the company, also contains the preliminary work for ”labeling” or ”describing”clusters and their intents.

I ett modernt industrisammanhang spelar kommunikation mellan inbyggda komponenter en viktig roll. Svetsmaskins företaget ESAB (Elektriska Svetsar AB) producerar och utvecklar svetsmaskiner med ett flertal olika inbyggda komponenter som kommunicerar sinsemellan för att konfigurera operationen för svetsning. Inom ramen för projektet på ESAB analyseras nätverkspaketen som skickas inom svetsmaskiner och som hämtas ut med hjälp av Wireshark. Nätverkspaketen används för systemkommunikations analys och hämtas ut i Pcapng-filformatet. Pcapng-filernagranskats manuellt med hjälp av Wireshark och deras skräddarsydda plugin, vilket kräver att varje paket inspekteras för att identifiera innehåll och potentiella felmeddelanden. Pcapng Analysatorn är ett program för att automatisera denna process och framhäva enbart de meddelanden och data som är av relevans för företaget. Denna rapport fokuserar på tekniker för manipulering och hantering av Pcapng-filer, samt metoder för extrahering av felmeddelanden från specialanpassade Pcapng-filer. Projektet representerar ett viktigt steg mot att effektivisera analysen av Pcapng-filer och att förse ESAB med en mer automatiserad och produktiv lösning för dataanalys inom sina verksamheter. / In a modern industrial context, communication between embedded components plays a crucial role. The welding machine company ESAB (Electric Welders AB) produces and develops welding machines with various embedded components that communicate with each other to configure the welding operation. Within the framework of the project at ESAB, network packets sent within welding machines are analysed and retrieved using Wireshark. These network packets are used for system communication analysis and extracted in Pcapng file format. The Pcapng files have been manually reviewed using Wireshark and a custom-built plugin, requiring each packet to be inspected to identify content and potential error messages. The Pcapng Analyzer is a program designed to automate this process and highlight only the messages and data relevant to the company. This report focuses on techniques for manipulating and managing Pcapng files, as well as methods for extracting error messages from customized Pcapng files. The project represents a significant step towards streamlining the analysis of Pcapng files and providing ESAB with a more automated and productive solution for data analysis in its operations.

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Civil infrastructure serves as the driving force behind the evolution of a safe, sustainable, and efficient environment. However, the way information about civil infrastructure has been communicated to the public has been insufficient. Since every human is intrinsically different, designing, and dispersing information about civil infrastructure that accommodates everyone, while also being direct and concise has been a challenge for policymakers and other federal, state, local, and tribal civil engineering stakeholders. Though there has been a plethora of research conducted on message design and communication in other disciplines, little research has been done in the US that focuses on designing more accessible, actionable civil infrastructure messages. The objective of this research was to investigate how to improve the accessibility of civil infrastructure messages and communication infrastructure to enhance the public's ability to make daily infrastructure decisions. This research study utilized quantitative and qualitative methods to analyze and discuss various ways that civil infrastructure messages can be improved. Results from this study are based on the exploration of three different ways in which civil infrastructure messaging can be improved: policy, transportation/roadway safety, and emergency response. Data sources include eight publicly accessible energy policies from 1978-2022, a publicly available dataset of more than 75 thousand WEAs, and a dataset retrieved from Shealy et al. (2020), which collected data on 300 Virginia drivers in both rural and urban areas. A descriptive policy analysis and Flesch-Kincaid readability test were conducted to historically analyze energy policies and understand their accessibility impacts for research question 1; a brain activation network analysis was conducted and nodal network measures (i.e., network density, degree centrality) were used to investigate the cognitive response Virginia drivers had for various types of non-traditional traffic safety messages for research question 2; and sentiment analysis, emotion detection analysis, as well as a two-phased qualitative coding analysis (i.e., in-vivo coding, focused coding) were conducted to investigate how WEAs can be better designed to increase public attention and engagement for research question 3. The findings from this study demonstrate how emotional content that is present in tweets authored by community members affected by the natural disaster event can be incorporated into the WEA template. The findings from research question 1 identified potential issues with accessibility and energy policy. Also, the findings from this study describe the content included in the parallel documents that federal agencies use to communicate the most important information of a policy. The findings from research question 2 demonstrate that while the various types of non-traditional traffic safety messages produced variances in cognitive response, messages that included negative emotional content or statistics should be further explored on their impact on evoking safer driving behaviors. The findings from research question 3 reported on how emotional content could be incorporated into the template design of WEAs. The implications from this dissertation provide valuable insights for policymakers, civil engineers, transportation engineers, and emergency response stakeholders and the conclusions set the stage for future research to improve the design of more accessible civil infrastructure messages. / Doctor of Philosophy / Civil infrastructure messages are used daily, but improper design can make them difficult to understand or to continue to use over long periods of time. Also, every human is different and interprets information about civil infrastructure, which adds a level of difficulty to designing effective civil infrastructure messages. Though there has been a lot of research on the effectiveness of civil infrastructure, little research has used a human-centered design approach to improve civil infrastructure messages. This study analyzes three different ways to improve civil infrastructure messages: policy, traffic safety, and emergency response. We used publicly available energy policies from 1978-2022, data collected by co-authors from Shealy et al. (2020) to analyze the cognitive response of 300 Virginia drivers to various types of non-traditional traffic safety messages, a publicly available dataset of more than 75 thousand Wireless Emergency Alerts sent by FEMA, and a publicly available data set of more than 9.1 thousand tweets about Hurricane Harvey. To analyze this data, this research study utilized various methods to understand how easy policies are to read, to understand how the brains of Virginia drivers respond to different types of non-traditional traffic safety messages and to identify the differences between tweets and WEAs. Results from this study suggest that parallel documents should be published alongside energy policies to help the public understand the main points of the policy, establish a readability metric to use for all energy policies, continue to investigate non-traditional traffic safety messages that included negative emotional content or statistics, measure the brain activation and observe long-term driving behaviors, use more negative emotional content in templated WEAs, and use social media data to better design templated WEAs. The findings reported from this study can be beneficial for various types of civil infrastructure stakeholders such as policymakers, utilities, US State Departments of Transportation, FEMA, alerting officials, and the public to further explore ways in which the language of civil infrastructure messages can be improved to address accessibility issues with energy policy, traffic safety, and emergency response to the public.