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Exploring the Impact of Value Co-Creation Through AI-Driven Chatbbots on Customer Repeat Purchases
Get PDFDrawing on the Stimulus-Organism-Response (S-O-R) framework, this study explores how perceived value co-creation during chatbot interactions influences customer repeat purchase intentions through cognitive, emotional, and social responses to chatbots. A survey of 220 participants revealed that perceived value co-creation significantly affected repeat purchase intentions, with cognitive evaluations, emotional reactions, and social value serving as key mediators. However, the direct effect of value co-creation on purchase intentions was not significant. The findings suggest that while value co-creation enhances consumer engagement, repeat purchases occur only when consumers experience positive cognitive, emotional, and social outcomes. Therefore, it is crucial for retailers to incorporate features that promote functionality, emotion-evoking entertainment, and social presence into their chatbot services
Contemporary Caribbean-American Literature: Thoughts on Identity Constructions for Caribbean Diasporic Subjects in American Racial and Cultural Context.
No full textThis study includes six contemporary literary texts (novels and short stories) that offer exemplary representations of identification process of the Caribbean diaspora situated within American context. The texts included in this study are: Now Lila Knows by Elizabeth Nunez (2022), Disposable People by Ezekel Alan (2012), Brother, I’M Dying by Edwidge Danticat (2007), Ayiti by Roxane Gay (2011), Dominicana by Angie Cruz (2019), and How to Love a Jamaican by Alexia Arthurs (2018). By studying representations of identity formation throughout the Caribbean-American literature, a postcolonial analysis integrated with cultural studies such as critical race theory, decolonial approach, and diaspora studies leads to a discovery of differences in identity processes for Caribbean subjects in America based on personal and temporal experiences. Identity formation for Caribbean subjects show a level of struggle that is informed by alienation, critical emotions such as hate, fear, melancholic self, confusion over racial identity, liminality, lack of empowerment, hybridization, race-consciousness, triple marginalization, and exile. Through contemporary narrative – many considered realist in style– the authors offer representations of individuals taking on the process of identity negotiation while inscribing the character of the migrant/immigrant/foreigner as confused, weak, and passive. Through the act of literary production, Caribbean diasporic identity illustrates the potential values of literary studies in developing critical awareness for the United States and hemispheric racial politics. Literature that deals comparatively with identity formation in America about immigrants is an important component to cultural studies, research about immigration, and race theory
Estimating Depth Of Roadway Flooding Using Data From LiDAR And Surveillance Cameras
Get PDFIn this study, we fuse data from a 3D LIDAR mounted on a vehicle and images from an external traffic surveillance camera to create a 3D representation of a segment of a roadway that experiences frequent flooding. The point cloud data from the LiDAR in this study is collected from a road segment of W 49th Street in Norfolk, near the ODU campus. The traffic surveillance camera is mounted on a public parking building in the same area. The LiDAR collects continuous point cloud frames as the vehicle traverses the section. Multiple LIDAR frames related to the respective segment of the road, monitored by the external camera, are first merged into a unit point cloud using the ICP registration method, representing a local high-resolution digital elevation model (DEM) of the road segment. Then, the resulting DEM is projected onto the images of the flooded road captured by the surveillance camera. To this end, a camera calibration technique is employed to estimate the transformation parameters. The camera calibration method relies on a dataset comprising points and their corresponding pixels in the target image. A virtual grid of points and corresponding pixels was generated to run a camera calibration function. The mentioned dataset was generated with the aid of projecting point clouds on LiDAR’s internal camera, enabling us to identify objects and curbsides. The perspective geometry principles were also employed to create the DEM. The projection results show the successful performance of the employed technique for camera calibration. The depth estimation was carried out using the projected DEM model on a flood image recorded by the external camera
Exploring Differences in Cannabis Use and Harm Perceptions Among Sexual Minority and Heterosexual Females: A Brief Report
Get PDFObjective: Sexual minority women (SMW) have higher rates of cannabis use compared to heterosexual women, which may be partially attributed to lower harm perceptions. However, no study has examined if the association between harm perceptions and cannabis use is stronger for SMW than heterosexual women. This study examined if sexual identity (SMW vs. heterosexual woman) moderated the association between harm perceptions and past 30-day cannabis use among a sample of female young adults (18-25 years old). Method: Participants were 949 (29.8% SMW; Mean age = 24.33; 92.1% non-Hispanic White) females (99.3% cisgender) recruited from Amazon Mechanical Turk who reported weekly cannabis use. Participants reported how many days they used cannabis in the past 30-days and how harmful they perceived cannabis to be to their health (not at all/slightly/somewhat harmful vs. very/extremely harmful). An Analysis of Covariance examined the study aim. Results: A significantly larger percentage of heterosexual women perceived cannabis to be very/extremely harmful to their health than SMW (45.2% vs. 22.6%). Those who perceived cannabis to be very/extremely harmful reported more frequent cannabis use in the past-30 days. SMW who perceived cannabis to be very/extremely harmful reported more frequent cannabis use relative to those who held lower harm perceptions; there were no significant differences for heterosexual women. Conclusions: SMW may perceive cannabis as harmful because they may be experiencing health consequences from frequent use. It may be important for interventions and public health campaigns to be tailored specifically to SMW and include information about the potential harms of cannabis use
Learning and Belonging in a Museum-School Environmental Stewardship Partnership
Get PDFStudents in urban, low-income communities can lack access to environmental science outreach in natural settings. As a result, they may face barriers to applying course content to real-world contexts and developing an agentic, hopeful stance towards environmental stewardship. In this paper, we describe a partnership between a maritime museum and park, an urban school district, and ecology experts. The project aimed to increase students’ knowledge of aquatic ecosystems, develop awareness of stewardship-related actions and career pathways, and support a sense of belonging to the museum. Activities included classroom visits by experts, field trips to plant classroom-grown grasses in the museum’s lake, and teacher professional development. A single-group, pre-test/posttest design investigated changes in N=148 high school students’ knowledge, along with post-program environmental stewardship attitudes and sense of belonging to the site. A paired t-test revealed a significant increase in knowledge scores. Changes were evident in students’ ability to identify environmental problems and solutions. Post-program, environmental hope was significantly correlated with scientific knowledge and sense of belonging. Open-ended responses revealed elaborative processing of STEM content, students’ sense of agency towards environmental action, and self-perceptions as an environmental steward. Implications for the conceptualization and design of field-based STEM outreach are discussed
Cultures for Acid-Fast Bacilli: Are We Being Good Stewards?
Get PDFObjective: To describe the use of cultures for acid-fast bacilli (AFB) and situations in which AFB cultures are unlikely to be of clinical benefit.
Design: Retrospective descriptive study of AFB cultures submitted to Sentara Health microbiology laboratory from December 1, 2021, to December 1, 2023. Data were collected from the electronic medical record and included patient demographics, the service line that ordered the culture, specimen source, and culture results.
Setting: Sentara Healthcare System.
Patients: All patients who had specimens submitted to the microbiology laboratory during the study period were included.
Results: A total of 13,944 AFB cultures from 8,243 patients were collected during the study period. Of these, 4.72% (n = 389) patients had a positive result, and 40 of 680 positive cultures were likely contaminants or non-mycobacterial. The average number of days between culture collection and positive results was 84.32 days (SD = 49.64) and 56.25 days (SD = 8.32) for negative results. Most cultures were ordered by medical subspecialties (44.06%, n = 6,144), followed by orthopedic providers (23.34%, n = 3,254) and surgical subspecialty providers (16.11%, n = 2,246). Most specimens were pulmonary (n = 6,620) with 619 (9.35%) positive cultures. Of 3,561 AFB cultures ordered from bone specimens, only 17 were positive (0.48%). The number of specimens processed by the microbiology laboratory required 2 full-time microbiology technicians to process specimens.
Conclusions: Many AFB cultures were sent from patients who did not have clinical scenarios consistent with mycobacterial disease and cultures were not clinically indicated. Implementation of testing criteria could decrease AFB cultures and healthcare costs
Exploring VR User Experiences
Get PDFVirtual reality (VR) has unique potential for simulating environments and situations beyond real-life experiences. This study investigates the limitations of VR equipment design for learning spaces, specifically online environments. Presented are the findings of an exploratory study that examined the immersion, ease of use, and physiological and emotional experiences of seven university students participating in a 30–60 min VR session. Given the small sample size, the findings were analyzed with descriptive statistics and qualitatively. The results revealed that participants recognized the potential of VR as a teaching and learning tool, while also identifying certain limitations associated with health and safety. To supplement this analysis, a post-hoc Wilcoxon rank test was conducted which suggested a critical t yielding significance
Optimizing AI Language Models: A Study of ChatGPT-4 vs. ChatGPT-4o
Get PDFThis paper presents a comparative analysis of OpenAI\u27s GPT-4 and its optimized variant, GPT-4o, focusing on their architectural differences, performance, and real-world applications. GPT-4, built upon the Transformer architecture, has set new standards in natural language processing (NLP) with its capacity to generate coherent and contextually relevant text across a wide range of tasks. However, its computational demands, requiring substantial hardware resources, make it less accessible for smaller organizations and real-time applications. In contrast, GPT-4o addresses these challenges by incorporating optimizations such as model compression, parameter pruning, and memory-efficient computation, allowing it to deliver similar performance with significantly lower computational requirements. This paper examines the trade-offs between raw performance and computational efficiency, evaluating both models on standard NLP benchmarks and across diverse sectors such as healthcare, education, and customer service. Our analysis aims to provide insights into the practical deployment of these models, particularly in resource-constrained environments
A Comprehensive Survey of Prompt Engineering Techniques in Large Language Models
Get PDFPrompt engineering has arisen as a pivotal discipline in optimizing the performance of Large Language Models (LLMs) by structuring inputs to enhance coherence, accuracy, and task alignment. This paper comprehensively surveys various prompting techniques, systematically categorizing them according to their application domains and methodological foundations. Fundamental approaches like zero-shot and few-shot prompting are examined along with advanced strategies, including chain-of-thought reasoning, retrieval-augmented generation, and self-consistency mechanisms. A rigorous qualitative analysis is conducted to evaluate each technique\u27s strengths, limitations, and optimal use cases, offering a structured framework for selecting the most effective prompting strategies. Theoretical insights and empirical findings are consolidated to provide researchers and practitioners with advanced methodologies for refining prompt design and enhancing LLM capabilities in complex reasoning, decision-making, and knowledge synthesis while improving reliability and factual accuracy in generated outputs
Security Enhancement in UAV Swarms: A Case Study Using Federated Learning and SHAP Analysis
Get PDFAs cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging federated learning and FedAvg for weight averaging, combined with SHAP analysis to identify key contributing features. This AI-based system requires less human intervention and is more effective in detecting novel attacks than traditional intrusion detection systems (IDS). Using the IEEE DataPort UAV Attack Dataset, this study aims to develop a robust distributed ML security solution for UAV swarms, significantly advancing the cybersecurity landscape for CPSs