Southern Arkansas University

Autism Spectrum Disorder (ASD) is defined by ongoing difficulties in social communication, flexibility in behavior, and adaptive learning skills. Interventions that utilize robots have demonstrated potential in providing organized training for children with ASD; however, there is a lack of controlled studies that specifically examine the effects of reinforcement strategies. This research introduces a systematic interaction policy based on reinforcement, founded on the principles of Applied Behavior Analysis (ABA), and assesses its effectiveness through a randomized controlled experimental design with observation. The humanoid robot NAO was used in two different interaction scenarios, one involving a reinforcement condition (RC) and the other a non-reinforcement condition (RC), ensuring that the instructional material and environment were maintained, while only the availability of contingent positive feedback was altered. A total of 50 participants diagnosed with ASD Level 2 engaged in structured word-learning sessions. Learning outcomes were assessed using institutional performance criteria, average response time, and emotion analysis derived from a CNN-based facial expression model. Independent samples t-tests revealed statistically significant improvements in both performance scores (t(48) = 3.779, p < 0.05) and response times (t(48) = 3.758, p < 0.05) in the reinforcement condition compared to the non-reinforcement condition. The findings demonstrate that structured ABA-based reinforcement within robotic interaction significantly enhances learning efficiency and task engagement, contributing methodologically rigorous evidence to robot-assisted ASD intervention research.

BackgroundOral nicotine pouches (ONPs), such as Zyn, have gained popularity among young people; however, their portrayal on social media remains under-studied. Instagram memes, a widely shared form of digital communication, may shape young people’s perceptions about ONPs and contribute to the widespread acceptance of ONP use.ObjectiveThis study examines the thematic content of Instagram memes related to ONPs to understand how these products are represented online.MethodsThe content of Instagram memes tagged with ONP-related hashtags—#oralnicotinepouch, #zyn, #on, #velo, and #nicotinepouch—was systematically analyzed. After screening, a total of 244 photo- and text-based memes were included in the final dataset. Using a structured coding framework, 3 researchers categorized the memes into key themes using NVivo software.ResultsThree dominant themes emerged: (1) the Zyn community (35.6%)—memes fostered a sense of belonging among users; (2) marketing and branding (27.8%)—humorous critiques of product advertising and accessibility; and (3) perceived consequences of use (13.9%)—memes highlighted perceived positive or negative consequences of ONP use. Engagement metrics revealed high levels of interaction, with the Zyn community theme garnering the most user engagement.ConclusionsONP-related Instagram memes are primarily focused on community identity, humor, and marketing, with community-centered content receiving the highest engagement. These findings indicate that social belonging and humor are central to the online representation of ONPs.

AI-based recommendation engines increasingly shape digital commerce, yet their specific behavioral and profitability effects remain insufficiently quantified within controlled academic settings, requiring rigorous empirical evaluation. This study aims to evaluate how AI-driven recommendation engines influence customer engagement, conversion behavior, and firm-level profitability metrics using multidimensional behavioral analytics and financial indicators within a defined academic research environment. A prospective analytical study was conducted in the Department of Business Analytics, Southern Arkansas University, from June–December 2024, involving 46 participants interacting with an AI-enabled e-commerce simulation platform. Behavioral logs, engagement time, click-through rate, purchase probability, and profitability indicators were analyzed using multivariate regression, paired t-testing, and effect-size estimation. Statistical significance was defined at p<0.05 with 95% confidence intervals. AI-generated recommendations increased mean engagement duration from 142.3±38.6 seconds to 198.7±42.1 seconds (mean difference 56.4 seconds; p=0.003). Click-through rate improved from 22.8% to 37.5% (+14.7%; Cohen’s d=0.81). Purchase likelihood rose from 18.4% to 29.6% (+11.2%; p=0.021). Average order value increased from USD 32.7±9.5 to 41.2±10.3 (p=0.017). Customer satisfaction (measured on a 10-point scale) improved from 6.1±1.4 to 7.8±1.2 (p=0.004). Profitability indicators demonstrated a 19.4% rise in simulated gross margin, while retention probability increased by 23.7% based on a logistic model (OR=1.42; CI: 1.06–1.95). All calculations represent study-generated estimates rather than market-level metrics. AI-driven recommendation systems substantially enhance customer engagement, behavioral conversion, and simulated profitability metrics. Future research should validate these findings using larger datasets, cross-industry samples, and real-time commercial deployment environments.

Lung cancer screening (LCS) with low-dose computed tomography is an effective strategy for early detection and improved survival. Despite its clinical benefits, public engagement with LCS topic remains unclear, particularly in the digital health communities. This study examines the thematic landscape of lung cancer-related discussions on Reddit. Using Python's Reddit API Wrapper, we collected 109,868 posts from six lung cancer-related subreddits between January 2019 and December 2024. After preprocessing, 105,118 unique posts were analyzed using Latent Dirichlet Allocation topic modeling to identify emergent themes. Topics were qualitatively reviewed and categorized into four high-level themes: treatment, mental health, smoking, and LCS. Mental health (71.82%) and treatment (16.84%) dominated the discourse, followed by smoking (8.30%), while LCS remained underrepresented (3.04%). Despite an increase in overall engagement from 2022 onward, LCS-related posts remained sparse, with no sustained upward trend. Reddit users frequently discuss treatment and mental health concerns related to lung cancer but rarely engage with LCS as a topic, revealing a critical gap in public awareness. These findings highlight the need for targeted public health strategies to promote LCS awareness on social media platforms, leveraging the platforms' growing role in health communication.

Though justice, injustice and cruelty receive significant attention in political theory, mercy is not commonly associated with politics or modern political theory. Yet there is space where a discussion of mercy may be useful. While it seems obvious that justice is opposed to injustice, and by extension justice corrects or addresses injustice; mercy can also be opposed to injustice.

Internet-of-Things (IoT) is playing a key role in modern society by offering enhanced functionalities and services. As IoT devices may introduce new security risks to the network, network administrators profile the behavior of IoT devices using device fingerprinting. Device fingerprinting typically involves training a machine learning model using the network behavioral data of existing devices. If a new device is added, the network becomes vulnerable to attacks until the time that the machine learning model is trained and updated to integrate the new device. Furthermore, if many devices are regularly added to the network, the cost of adapting the machine learning model can be significant. To address the challenges of security and scalability in fingerprinting, we create a collection of observed behaviors of IoT devices from existing devices and use this collection to construct a fingerprint for a new device. In our approach, we design a bi-component neural network architecture consisting of a transformer-based behavior-extractor (BE) and a fingerprinting interpreter.We perform a one-time training of the BE to extract behaviors from known devices. We use the generated BE for (a) fingerprinting existing devices and (b) adapting the existing fingerprinting model to new device data. In our experiments on 22 diverse IoT devices, we show that our model can identify newly introduced devices as well as known devices with a high identification rate. Our approach improves the time to adapt a model by a factor of 78.3× with no loss of accuracy, achieving recall over 98%.

Impact of a Jet" experiment is a commonly offered laboratory exercise in most undergraduate engineering programs.The equipment available in the market for this experiment can be expensive despite the fact that no sophisticated technologies are involved.This paper presents an attempt to develop a low cost prototype equipment for "Impact of a Jet" experiment through senior design course at Southern Arkansas University.

Considerable percentage of greenhouse gas emissions comes from industry, and needs to be managed to avoid climate crisis around the world.Manufacturing as a key sector has a share to contribute in solving climate change problems.These challenges redefine the skillset of 21 st century workforce and it is increasing demand on the education to integrate sustainability principles into engineering and technology programs in a response to environmental challenges and future jobs skillset.This research emphasizes the central role of engineers in shaping the future of industries, highlighting the need to integrate sustainability into education, particularly in manufacturing engineering and technology.It advocates for mentoring independent studies as another approach beside developed curriculum with sustainability to foster a culture of sustainability excellence in manufacturing engineering and technology, supporting the development of sustainability education in both teaching and research.From sustainability principles integration, and environmentally friendly designs to optimizing production processes to leveraging Industry 4.0 technologies, this array is seen as key to reshaping the future of manufacturing.The approach of this work focuses on an independent research-based study to experimentally test the impact of main operational conditions on Carbon Dioxide (CO 2 ) production to optimize turning manufacturing processes.Three critical factors-rotational speed, feed rate, and lubrication-are examined for their influence on CO 2 .The work used a lathe machine and a set of three sensors strategically placed around the machine along with tools and workpiece samples.Full factorial design of experimentation is used to test and analyze the relationships among factors and the response function of CO 2 production.The technical results reveal significant main effects for the factors as follows: lubrication, rotational speed, and feed rate, along with interaction effects.Lowering rotational speed and high feed rate decrease CO 2 production supporting the hypothesis.Surprisingly, within-oil lubrication decreases CO 2 production, not as expected.Interaction effects emphasize the additive and subtractive influences of the factors on CO 2 production.While the full factorial design of experimentation applied allows for comprehensive factor effects analysis, it acknowledges limitations in the specific selected levels for machine rotational speed and tool feed rate.The research provides valuable insights into the impact of various operational conditions on CO 2 production in material removal manufacturing processes.Main and interaction effects contribute to the knowledge in sustainability manufacturing.Results of undergraduate independent research as another education approach showed how it valuable is to get students to explore the impact of re-adjusting engineering situations to solve problem and improvement objectives along with challenges and how experimental results are facts to change theoretical perspectives and assumptions.These findings contribute to advancing knowledge of sustainability manufacturing in student independent research, supporting the current groundwork for further exploration of optimal education approaches for effective teaching and mentoring strategies for sustainable manufacturing engineering and technology learning.The students' thoughts and learning developments about the topic were stunning, from learning the fundamentals and exploring the real-world constraints to practically challenging the theoretical aspects.This work shares an engineering student's impression of practicing sustainability in a research work project.

Assessment of infrastructure conditions is currently a topic of great importance, but also a challenging one. Many significant pieces of infrastructure were built decades ago and have, since then been exposed to load cycles, weather, special events, and all other design scenarios. During this time period, a structure will experience structural degradation and reduction in remaining service life. In addition, over such a long time span, service conditions are likely to change and other phenomena can occur with unknown effects on the structures, which may not have been taken into consideration during original design. This spontaneously raises questions about the structure’s condition and real time performance. Such questions are typically driven by a limited confidence in performance, directly stemming from limited information available. A monitoring system provides comprehensive information about the structures status and provides vital support for Engineers and Asset Managers involved in decision-making processes.