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DIA-MS as a means to examine bacteriophage infection proteomics
No full textBacteriophages such as SPN3US are viruses that infect bacterial cells and use their resources and machinery to produce progeny. In this study, data-independent mass spectrometry (DIA-MS) was used to track changes in the SPN3US proteome during an infection of S. enterica. DIA-MS was found to be a viable method of obtaining proteomic data as compared to data-dependent techniques and will prove useful in future studies of bacteriophage infections. Major findings include further information on the two phage RNAPs and the speed of the infection process, plus candidate topics for future research such as the involvement of bacterial flagella and the possibility of a second round of infection
Proactive Approach to Identify and Mitigate Cybersecurity Threats in Smart Building using AI
No full textSmart buildings form a cornerstone of modern urban development. The integration of Internet of Things (IoT) devices in buildings has revolutionized facility management, improving system intelligence and user experience. However, these smart devices also introduce cybersecurity challenges and risks. The initiative Identifying and Mitigating Cybersecurity Threats in Smart Building using AI focuses on leveraging advanced artificial intelligence (AI) to address and mitigate cybersecurity threats within smart buildings. This study integrates predictive analytics, deep learning, and machine learning to identify and prevent cyberattacks, protecting sensitive data and networked IoT devices. By analyzing previous attack patterns, network behaviors, and potential vulnerabilities, this research aims to develop proactive security mechanisms that enhance threat detection and automate incident response. The main objective is to strengthen smart building security frameworks against the growing cyber threat landscape, improving system resilience and safeguarding critical data, including biometric information, image and voice recordings, and surveillance footage. This study will utilize the TON_IoT dataset and deploy AI models to evaluate the effectiveness of AI-driven cybersecurity solutions in real-world smart building environments. The expected outcomes include a robust intrusion detection system, improved threat detection accuracy, and enhanced cybersecurity strategies tailored for smart infrastructure
Consumer Behavior of Gen Z in Kosovo: Purchasing Preferences and Brand Engagement
Get PDFThis capstone project examines the consumer behavior of Generation Z in Kosovo by analyzing their purchasing preferences and brand engagement. The Generation Z demographic, which includes individuals born between 1996 and 2010, is the focus of this study. This generation is characterized by digital savviness and a significant influence from social media platforms, shaping their purchasing decisions and brand engagement. Generation Z’s behavior is often marked by a preference for online shopping, collaborative marketing approaches, and sustainability, making it essential to understand these factors in the context of Kosovo. The data for analyzing Generation Z’s consumer behavior were collected through an online survey, which gathered responses from 142 participants. This primary data collection was supplemented by existing research and literature. Secondary data played an important role in developing the research framework and understanding broader trends in digital engagement, social media influence, and consumer decision-making. These insights guided the interpretation of the primary data and improved the overall research. The findings from this study reveal important factors influencing Kosovo’s Generation Z consumer behavior, such as digital engagement, the overall impact of social media, and a strong preference for brands that align with social and environmental values. These findings provide insights for businesses seeking to modify their marketing strategies for this demographic, highlighting the importance of authenticity, social responsibility, and online presence. Based on these findings, recommendations are provided for marketers aiming to reach and engage with Generation Z. The study acknowledges certain limitations, primarily related to the online survey format. As a result, there is the possibility of overrepresenting more digitally-savvy respondents, which may lead to overlooking segments of Kosovo\u27s Generation Z that are less engaged with online platforms. Despite these limitations, the study provides a foundation for understanding the consumer behavior of Kosovo\u27s Generation Z
The Bandages Problem
Get PDFA new probability problem, named the Bandages Problem, is described and solved. The problem involves repeatedly selecting and removing an item at random from a finite population that initially consists of a known configuration of single and paired items. For each selection, the probability that the chosen item is single is found. Generalizations are suggested
Meta-Learning Coordinated Eye–Hand Dynamics
Get PDFThe analysis of fine-grained behavioral time-series, such as eye-hand coordination, offers a powerful lens for understanding neurodevelopmental disorders like Autism Spectrum Disorder (ASD). Traditional assessments often miss subtle dynamic patterns that are critical for characterizing individual differences. This thesis investigates the temporal dynamics of coordinated eye and hand behavior in adolescents with and without ASD during a structured ”maze painting” tablet task. The goal is not to propose a new predictive model, but to use a state-of-the-art meta-learning framework based on latent Neural Ordinary Differential Equations (Neural ODEs) as a ”computational microscope” to explore how these behavioral dynamics vary within and across individuals and task contexts. By mapping high-dimensional, irregular eye-gaze and touch data into a low-dimensional latent space, we analyze the resulting trajectories to uncover patterns related to individual identity and temporal evolution. The results, visualized using t-SNE, demonstrate that the model successfully learns to encode stable, individual-specific behavioral signatures. Notably, the clarity of these individual signatures is timescale-dependent, becoming significantly more pronounced in longer-duration windows, which suggests that unique behavioral traits emerge over multi-second intervals. This work validates the use of la- tent dynamics models for scientific inquiry, revealing nuanced, person-specific patterns in sensorimotor behavior that could inform future digital biomarker development for ASD
The Case for Mass Media Communication Theories in the Study of Artificial Intelligence: A Uses and Gratifications & Parasocial Interaction Theory Approach
Get PDFThis study analyzes communication and relationship building between textual generative Artificial Intelligence programs and their users through the lens of parasocial interactions and uses and gratifications. It measures loneliness, knowledge gaining, and task completion as the primary reasons motivating perceived relationship development with AI applications. Through a quantitative survey, it explores the connections between time spent using AI, as well as the previously mentioned factors and seeks to determine what influences a user’s perceived relationship with an AI chatbot program. Comparisons are also drawn between the relationships with AI and the user’s relationships with celebrities, further expanding the justification for utilizing a parasocial framework for analyzing human-AI interactions. This thesis looks at determining a correlation between time spent using AI, one’s attitude towards AI, and one’s reasons for using AI and one’s relationship with AI. As AI software such as ChatGPT is further integrated into our lives, this study focuses on critically examining the ways in which human-AI interactions are developing. It suggests that parasocial interaction scales can be applied to human-AI communication as AI responses become increasingly better at mimicking human behavior. The study found a significant positive relationship between the time one has spent using AI and their perceived relationship with AI, the total time since one has first used AI and their perceived relationship with AI, how lonely one is and their perceived relationship with AI, and one’s attitudes towards AI and their perceived relationship with AI, and the reasons why one uses AI
Automated Prioritization and Routing of IT Support Tickets Using Machine Learning
Get PDFThe increasing complexity of IT service management underscores the need for efficient and scalable solutions to manage IT tickets. This thesis explores the application of machine learning techniques for automated prioritization & routing of IT tickets. So by leveraging publicly available dataset from Kaggle containing IT ticket the study aims to reduce manual intervention, improve resolution times, & optimize resource allocation within IT environments. The study will evaluate various machine learning algorithms, including SVM, Random Forest, & ensemble models, for their ability to classify and prioritize tickets accurately. to accurately classify and prioritize tickets. Advanced data preprocessing techniques like TF-IDF vectorization and class balancing are used to handle data inconsistencies and imbalances. Additionally, the study also explores hybrid approaches that combine machine learning with rule-based systems are investigated to enhance classification performance for low-frequency and ambiguous ticket categories. Moreover, incorporating feedback loops & real-time data updates ensures model adaptability to evolving IT environments. The expected outcomes include significant improvements in classification accuracy and the development of a scalable framework for real-time IT ticket management. By using a publicly available dataset, this research aims to provide a framework for organizations looking to enhance service efficiency and comply service-level agreements standards
Enhanced 3D Sub-Canopy Mapping via Airborne Full-Waveform LiDAR
Get PDFAirborne light detection and ranging (LiDAR) systems have been used to gather information about forests, their canopies and what lies beneath them for many decades. Recent advances in LiDAR sensor technology have enabled higher sampling rates, leading to increased point densities for discrete point clouds. However, vast portions of the forest sub-canopy still remain either unsampled or occluded. We contend, that waveform LiDAR, as a specific type of structural modality that digitizes the intensity of the laser backscatter as a function of time (range), contains additional information that can be extracted using modern artificial intelligence (AI) and machine learning (ML) methods. In this study we developed a geometrically, radiometrically, and structurally accurate 3D model of a 700 m x 500 m plot within the Harvard Forest to generate realistic waveform LiDAR. The Harvard Forest scene was validated by comparing simulated remote sensing data to field data that had been collected in 2019 and 2021. Simulated hyperspectral data produced realistic reflectance values across the entire spectrum for all tree species. When compared to hyperspectral data captured by the National Ecological Observatory Network’s (NEON) Airborne Observation Platform (AOP), the simulated data showed strong correlations across the spectrum with a RMSE under 5.5%. Leaf area index (LAI) values were generated from simulated ceptometer measurements across the scene and compared to real field data, in order to validate the structure of the scene’s canopy. The average LAI for the scene was within 6% of the real values and well within a standard deviation of the range. Simulated NEON Optech LiDAR point clouds were also compared to real data and produced extremely realistic duplicates that accurately modeled point density and canopy penetration rates to within 1%. Once the scene and simulated datasets were validated, we used the data to train a convolution neural network (CNN) to classify portions of the waveform previously unused. We used a modified CNN, originally intended for classifying discrete point clouds, to classify real NEON waveform LiDAR data into five classes (background, leaf, bark, ground, and man-made objects). This process yielded mixed results. It failed to correctly classify ground and sub-canopy object voxels due to high variability within the limited training data set. However, the CNN produced accurate canopy models filled with leaf and bark voxels that were four times greater than the point density (PD) of discrete systems. The scene and processes developed during this research effort will help expand our knowledge of discrete LiDAR systems and will provide a foundation for future iterations of AI/ML efforts to unlock the true potential of waveform data
The DX7 of Literature? Creative Writing in the Age of GenAI
Get PDFAbstract:
This paper examines the impact of artificial intelligence on contemporary literature, focusing on how writers are incorporating AI into their creative processes. Through case studies of avant-garde artists, mainstream literary authors, and genre fiction writers, the study explores various approaches to AI collaboration in writing. The analysis reveals that while AI offers new possibilities for artistic expression, it also presents ethical challenges and potential threats to traditional notions of authorship. The paper concludes by discussing the implications of AI in creative writing education, emphasizing the need for a balanced approach that embraces technological advancements while preserving the human element in literary creation