The Tumor Microenvironment (TME) resists conventional treatments by sending out signals that weaken the immune system. Tumor cells vary significantly, and treatments can lead to resistance. This paper focuses on constructing an effective module for reprogramming the tumor ecosystem using Computational Intelligence-Guided Nanoplatforms. It hypothesizes that computationally optimized fuzzy deep learning, integrated with nanoplatform-mediated drug delivery, can dynamically reprogram tumor-infiltrating T-cells to overcome immunosuppression in the TME, thereby enhancing cytotoxicity and therapeutic response. A novel nanotechnology-integrated deep fuzzy learning framework—Seagull Optimized Sugeno Fuzzy Deep Learning (SgOSF-DL)—is proposed to reprogram T-cell behavior in real-time. Multi-omic data from tumor-infiltrating T-cells are encoded and analyzed using fuzzy logic to determine their immune state (suppressed, exhausted, or active), guided by key biomarkers such as Granzyme B and PD-1. The optimized model governs the release of IL-21 and checkpoint inhibitors via nanoplatforms composed of PLGA, gold nanoshells, and iron oxide particles. Fuzzy rules are formulated using optimized parameters to evaluate the TME. Simulation results confirm that the proposed SgOSF-DL model accurately distinguishes between cancer and healthy cells. It alters cancer behavior by reducing tumor burden, lowering PD-1, and boosting Granzyme B expression. The model achieves 96.5% accuracy in classifying T-cell states, reduces tumor count by 69.2%, and decreases PD-1 expression by 61% for active immune function. It also offers faster therapeutic classification (0.017 seconds) with an activation consistency of 92.8%. Fuzzy logic enables transparent decision-making, aiding clinicians in understanding the treatment rationale.

This paper introduces a novel three-dimensional financial risk system that exhibits complex dynamical behaviors, including chaos, multistability, and a butterfly attractor. The proposed system is an extension of the Zhang financial risk model (ZFRM), with modifications that enhance its applicability to real-world economic stability assessments. Through numerical simulations, we confirm the system’s chaotic nature using Lyapunov exponents (LE), with values calculated as $L_1=3.5547,$ $L_2=0,$ $L_3=-22.5642$, indicating a positive Maximal Lyapunov Exponent (MLE) that confirms chaos. The Kaplan–Yorke Dimension (KYD) is determined as D_k = 2.1575, reflecting the system’s fractal characteristics. Bifurcation analysis (BA) reveals parameter ranges where transitions between periodic, chaotic, and multistable states occur. Additionally, the system demonstrates coexisting attractors, where different initial conditions lead to distinct long-term behaviors, emphasizing its sensitivity to market fluctuations. Offset Boosting Control (OBC) is implemented to manipulate the chaotic attractor, shifting its amplitude without altering the underlying system dynamics. These findings provide deeper insights into financial risk modeling and economic stability, with potential applications in financial forecasting, risk assessment, and secure economic data transmission.

This paper introduces an enhanced five-dimensional Chaotic Supply Chain Model (5DCSCM) by incorporating a transport lag variable into a previously established four-dimensional model. The newly added differential equation in the transit dynamics of the supply chain model captures the inherent lag between customer demand and the physical response in transportation, modeled as a first-order transport lag system. Through comprehensive numerical simulations, the influence of various system parameters—including customer demand rate, delivery efficiency, information distortion, contingency reserve, safety stock, and transportation lag—are examined. The study utilizes bifurcation diagrams and a Lyapunov Exponent (LE) to investigate tran-sitions between periodic and chaotic behavior. Additionally, the model is extended with offset boosting control, allowing for controlled amplitude adjustment without altering the underlying chaotic dynamics. Offset boosting control (OBC) is useful in chaotic supply chain systems because it stabilizes inventory and order fluctuations by counter-acting the amplification of small disturbances, reducing the bullwhip effect, and im-proving overall system reliability and responsiveness. As an application, integral sliding mode control (ISMC) technique has been applied to achieve complete synchronization between a pair of the 5DCSCM. Synchronization based on ISMC is useful in chaotic supply chain systems because it ensures robust coordination between different tiers, suppresses chaos-induced fluctuations, and maintains stable inventory and order patterns even under disturbances and uncertainties.

In this work, we present a new four-dimensional chaotic hyperjerk system with a half-line of equilibrium points. In the chaos literature, it is well-known that chaotic systems with an infinite number of equilibrium points exhibit hidden attractors. Thus, we deduce in this research work that the new chaotic hyperjerk system has hidden attractors. We next study the new chaotic hyperjerk system for a dynamic analysis using bifurcation plots and Lyapunov Exponents (LE) diagrams.We exhibit that the new hyperjerk system has a special property of multistability with coexisting attractors. Using Multisim version 14.2, we carry out an electronic circuit simulation for the proposed 4-D chaotic hyperjerk system with a half-line of equilibrium points. Finally, as an application in control engineering, we apply backstepping control for achieving antisynchronization of a pair of new chaotic hyperjerk systems taken as master-slave systems, which has important applications in communication systems.

This study explores the chaotic behavior, rotational symmetry, multistability, and dynamic properties of a newly proposed 4-D finance system (N4DFS), modified from the Lu financial model by incorporating an absolute function nonlinearity. This modification increases the system’s complexity, leading to a more chaotic attractor, evidenced by a higher maximal Lyapunov exponent (MLE) compared to the original Lu system. The system exhibits rotational symmetry about the y-axis and shows the saddle points. Bifurcation Analysis (BA) shows that the system transitions between chaotic, periodic, and stable states, depending on parameters (a, b, c, p, q). Multistability is also demonstrated through the coexistence of different attractors under varying initial conditions. Furthermore, Offset Boosting Control (OBC) enables flexible amplitude adjustments while maintaining the system’s overall dynamic behavior. This research contributes to the understanding of chaotic financial models and offers insights for future studies in chaotic systems and financial dynamics.