The Internet of Things (IoT) is rapidly evolving into a vast ecosystem of interconnected devices that serve diverse domains, including smart homes, healthcare, and gaming. However, the increasing complexity of device behavior, growing cybersecurity threats, and the need for real-time personalized services pose significant challenges in design, performance, and user trust. Traditional AI approaches, while powerful at pattern recognition, often lack interpretability and symbolic reasoning capabilities crucial for sensitive, adaptive consumer environments. In this work, we address these challenges by proposing a hybrid neuro-symbolic AI framework for cyber threat analysis in consumer electronic platforms. Leveraging the NF-BoT-IoT-V2 dataset, we implemented and evaluated both 1D Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) to classify cyberattack behaviors with high fidelity. Our models achieved over 98% accuracy, demonstrating the effectiveness of neural architectures in real-world attack detection. To enhance trust and interpretability, we integrated Explainable AI techniques, including SHAP and LIME, to provide feature-level insights into model predictions. Furthermore, we outline the integration of symbolic reasoning and real-world use-case mappings in smart home automation, IoT networks, and gaming environments. This study presents a scalable and interpretable neuro-symbolic approach that advances the intelligence, security, and personalization capabilities of modern consumer electronics.

This study presents a new comparative analysis of the cognitive control methods of HVAC systems that assess reinforcement learning (RL) and traditional proportional-integral-derivative (PID) control. Through extensive simulations in various building environments, we have shown that while the PID controller provides stability under predictable conditions, the RL-based control can improve energy efficiency and thermal comfort in dynamic environments by constantly adapting to environmental changes. Our framework integrates real-time sensor data with a scalable RL architecture, allowing autonomous optimization without the need for a precise system model. Key findings show that RL largely outperforms PID during disturbances such as occupancy increases and weather fluctuations, and that the preferably optimal solution balances energy savings and comfort. The study provides practical insight into the implementation of adaptive HVAC control and outlines the potential of RL to transform building energy management despite its higher computational requirements.

Resolvability parameters of graphs are widely applicable in fields like computer science, chemistry, and geography. Many of these parameters, such as the metric dimension, are computationally hard to determine. This paper focuses on Möbius-type geometric graphs, including hexagonal Möbius graphs, barycentric subdivisions of Möbius ladders, and octagonal Möbius chains. It addresses several open problems related to their resolvability parameters, such as the exchange property and fault-tolerant metric dimension. Additionally, the study extends to 30 lower benzenoid hydrocarbons, computing their metric, edge metric, and mixed metric dimensions. These results support structure-property modeling, particularly for predicting the total π-electronic energy of such hydrocarbons. The paper concludes with new questions prompted by the findings.

This paper explores the intersection of game theory and mathematical transformations, specifically wavelet and Fourier transforms. We examine the foundational principles of game theory and introduce the mathematical bases of wavelet and Fourier transforms. By comparing these transformations, we uncover their unique strengths in enhancing game theoretical models. Wavelet transforms excel in dynamic, multi-scale scenarios due to their localization properties, while Fourier transforms are effective in periodic, stationary environments. Our analysis highlights the complementary nature of these transforms, proposing hybrid approaches for optimizing strategies and predicting outcomes in complex games. This study underscores the potential for interdisciplinary research to advance strategic decision-making.

Thermal transport in converging/diverging channels finds applications in various engineering fields, including heat exchangers, microfluidics, and biomedical devices, due to their ability to enhance heat transfer and control fluid flow. The use of nanoparticles cannot be circumvented because of their promising characteristics. These materials widely used in applied thermal engineering, to enhance or control thermal transport in machinery, transformer, chemical engineering. The flow in tank designed for oblique walls is common in practical situations and the dynamics of fluids are essential to maintain it for desired purposes. Therefore, the concept of traditional nanoliquid models is extended for advanced ternary fluids. To acquire the beneficial model results, the momentum slip, viscous dissipative phenomena and elastic walls conditions along with new innovative ternary nanoliquid characteristics are adopted for the model formulation. Then, the results with special emphasis on thermal, entropy optimization, shear drag and heat transport rate are obtained via RK scheme and a comprehensive physical description is provided. The ternary nanoliquid possesses remarkable thermal transport and dominant behaviour is inspected for entropy, shear drag and heat transfer rate at the elastic walls under \(Ec, Re, \alpha\) and \(\gamma\). These fluids are recommended for different application especially in applied thermal engineering.

As urban traffic density and congestion increase, effective urban traffic management becomes increasingly challenging, negatively impacting travel times and the overall efficiency of transportation systems. In this paper, a hierarchical Stackelberg model is presented to address both priority for emergency vehicles (EVs) and fairness for other vehicles. This model involves the Traffic Management Center (TMC) as the top-level authority, with emergency vehicles as the first-level leaders and regular vehicles (RVs) as the second-level followers. The multilevel decision-making structure enables real-time adjustments to prioritize critical traffic and ensure equitable treatment for regular traffic. Simulations were conducted under various traffic scenarios, including normal conditions, emergency vehicle priority, and peak traffic congestion. According to the results, the hierarchical Stackelberg model outperforms traditional models in terms of reducing average travel time, waiting time, and congestion. The model also incorporates fairness metrics such as Gini coefficients and skewness to ensure that regular vehicles are not disproportionately affected by emergency vehicle priority. According to these findings, the hierarchical Stackelberg model improves both traffic efficiency and fairness in complex urban environments, positioning it as a promising solution.