Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.

Background: Women have consistently shown lower quit rates in tobacco cessation programs compared to men. This gender disparity persists despite comprehensive interventions and access to cessation resources. While prior studies suggest that factors such as social support, chronic disease burden, and socioeconomic status may contribute to these differences, there is limited empirical evidence to confirm these mechanisms. Aims: This study aimed to investigate potential mechanisms underlying gender differences in quit rates in a tobacco cessation program, testing whether demographic, socioeconomic, health, or behavioral factors explain the observed disparities. Methods: Participants were assigned to one of three smoking cessation interventions: an in-person program (CEASE), a self-help approach, or an online/hybrid program. The main outcome measured was smoking abstinence, evaluated three months after the intervention. Secondary analyses explored whether demographic, socioeconomic, health, or tobacco use-related factors mediated the association between gender and quit rates. Results: Women had significantly lower quit rates than men (p < 0.01). This association remained significant after adjusting for demographic, socioeconomic, health, and addiction-related factors. While women reported higher social support and a higher prevalence of chronic cardiometabolic conditions, these factors did not explain the gender disparity in quit rates. Conclusions: Gender differences in quit rates persist despite controlling for known factors that could influence cessation success. Although women had higher social support, they had lower quit rate. Future research should explore unmeasured variables, such as psychological, biological, and structural influences, to develop more effective cessation strategies tailored for women.

Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments.

Background: Menthol-flavored tobacco products are disproportionately used in low-income African American communities, a result of decades of targeted marketing and systemic inequities. Menthol use has been associated with lower quit rates, often compounded by factors such as lower trust in healthcare systems, reduced access to cessation programs, and other structural barriers. Despite this, few studies have systematically examined the explanatory mechanisms that might clarify why menthol-flavored tobacco is linked to poorer cessation outcomes among participants in tobacco cessation programs. Aims: This study aimed to investigate the potential mechanisms by which menthol tobacco use is associated with lower quit rates across three types of smoking cessation interventions. Methods: Participants were randomized into one of three smoking cessation interventions: in-person (CEASE), self-help, or online/hybrid programs. Smoking abstinence was assessed three months post-intervention as the primary outcome. Secondary analyses explored whether demographic, socioeconomic, or behavioral factors mediated the association between menthol use and quit rates across the intervention arms. Results: Menthol tobacco use was significantly associated with lower quit rates (p < 0.01). This association was not explained by demographic, socioeconomic, health, or addiction-related factors. While menthol use was associated with lower education and employment levels, demographic characteristics, physical or mental health, or addiction did not explain the effect of menthol on tobacco cessation. These findings suggest that the lower quit rates observed among menthol users cannot be attributed to any third factors assessed in this study. Conclusions: Menthol tobacco use independently predicts lower quit rates, and the mechanisms behind this disparity remain unclear. The consistent findings across different intervention types highlight the need for further research to uncover the underlying pathways and to design targeted strategies to improve cessation outcomes for menthol users.