Internet-of-Things (IoT) devices in smart homes are vulnerable to passive traffic fingerprinting, where an adversary captures encrypted IEEE 802.11 frames and identifies devices using MAC-layer metadata such as packet sizes and inter-arrival times. Existing defenses based on padding, traffic shaping, or synthetic cover traffic can remain vulnerable because artificial timing signatures are detectable by machine learning classifiers. This paper proposes a game-theoretic framework for evaluating Wi-Fi MAC-layer cover-traffic injection defenses. We introduce donor-based mimicry injection, in which the access point injects a replica of a paired device's authentic traffic into each device's stream. We compare donor mimicry with fixed-rate, exponential, and uniform synthetic baselines across 198 scenario instances (156 unique defender configurations) and eight classifiers using 10-fold cross-validation. Donor mimicry at 100% bandwidth overhead reduces the best attacker's balanced accuracy to 33.5%, whereas synthetic methods at equal overhead reach 93.9%, showing that behavioral realism, rather than injected volume alone, drives effectiveness. Modeling the interaction as a finite two-player zero-sum game yields a mixed-strategy Nash equilibrium with game value 0.247 within the evaluated strategy space; a deployable deterministic defense holds the best pairing-unaware attacker to 25.9% balanced accuracy, near the four-class random baseline of 25%. A pairing-aware robustness analysis shows that an attacker who can orient the donor-induced identity swap recovers near-baseline accuracy, so the four-class protection presumes pairing secrecy and the durable effect is pair-level anonymity. The defense operates at the access point and requires no IoT device modifications.

A novel mode-free reinforcement learning (RL) algorithm is proposed for the optimal control of unknown nonlinear system presented by the interval type-2 fuzzy (IT2F) model. The optimal control is converted into a zero-sum game, where the control input and the external disturbance are the opposing competitors. Based on the RL method, a model-based policy iteration (PI) algorithm is constructed to solve the fuzzy stochastic coupled algebraic Riccati equations of nonlinear system. Considering that the dynamic parameter is difficult to obtain completely in real systems, a model-free fuzzy control algorithm is designed under the framework of the RL algorithm. An optimal control of IT2F system is realized without parameter information, only requiring the state and input information. Furthermore, the asymptotic stability with $H_{\infty } $ performance index is ensured by the Lyapunov function. Finally, the effectiveness is tested by the semi-car active suspension model (SCASM).

Motor imagery based brain-computer interface (MI-BCI) has been extensively researched for neurorehabilitation and motor assistance, while the performance of previous MI-BCI systems for online decoding motor intentions is still not satisfactory. Therefore, a game theory-based adaptive human-machine joint (AHMJ) learning method integrating subject learning and decoder updating was proposed for MI-BCI decoding, by which multi-class MI for unilateral upper limb can be successfully decoded online. On the one hand, a novel MI training method was proposed to facilitate subjects' learning to generate separable electroencephalogram (EEG) data, where the MI training process was modeled as a two-player zero-sum minimax game and the task difficulty was adaptively regulated according to each subject's performance by solving the minimax problem. On the other hand, a new online adaptive algorithm was designed to ensure stable updating of the decoding model, integrating knowledge distillation and prototype-guided domain adaptation for different MI training intervals. Online MI-BCI experiment on a total of fourteen healthy subjects and online simulation experiment on a public dataset from twenty-five healthy subjects were conducted. Compared with the traditional method that relies solely on subject learning and the previous human-machine joint learning method, the average decoding accuracy was significantly improved by 9.7% and 5.6% (paired t-test, both $p < 0.01$), respectively. The online adaptive algorithm also outperformed previous updating approaches in both accuracy and stability. The proposed AHMJ learning method can be applied to improve the online MI-BCI decoding accuracy for neurorehabilitation and motor assistance.

This article investigates the stochastic watermarking protection strategy for cyber-physical systems (CPSs) in the presence of linear stealthy attacks. Unlike deterministic watermarking, stochastic watermarking strategies are developed to counteract scenarios where watermarking signals are deciphered. More specifically, attackers aim to inject attack signals into the vulnerabilities of stochastic watermarking strategies, while defenders intend to intercept attack signals as much as possible. Using zero-sum game theory, we investigate the interactive decision-making process between both sides, proving that the equilibrium strategy depends on maximum attack/defense probabilities and time-varying watermarking parameters. Furthermore, we also present criteria for selecting watermarking parameters to reduce the attacker's payoff. In particular, if the attack probability is not an unknown variable, the proposed defense strategy remains effective. Finally, the validity of the theoretical results is demonstrated through simulation examples.

Family medicine and primary care face ongoing pressures for access, with demand for care overwhelming the capacity to deliver it. Common strategies to maximize access often operate to the exclusion or detriment of continuity of care. During the August 2025 Family Medicine Leadership Consortium meeting, rather than presuming a tradeoff between the two as inevitable, the proposition that family medicine is committed to improving access to continuity of care was put forth.This article summarizes concepts and models for improving access that simultaneously preserve or elevate continuity of care. A full understanding of access requires nuanced data around demand and capacity at the day-to-day level. This data can be used to prioritize both access and continuity by incorporating both into appointment templates, right-sizing panels, and directing capacity to the times of greatest demand. Investing in innovative team models such as those incorporating upskilled medical assistants, or panels shared among small teams of physicians and advanced practice practitioners, allow expanded capacity to deliver access while maintaining continuity. Advanced access scheduling models build on foundations of managing demand and capacity to most efficiently match day-to-day visit capacity to demand and maximize access to the continuity clinician.Improving access and continuity of care do not have to be a zero-sum game-by valuing and centering continuity among access interventions, healthcare systems can shift the paradigm between access and continuity from "either/or" to a symbiotic relationship, with access meaning access to continuity of care.

In this article, a novel homotopic reinforcement learning (RL) framework is proposed to address the distributed cluster consensus problem in continuous-time multiagent systems (MASs). For the first time, the investigated issue is formulated as a zero-sum differential game using a proposed minmax game policy, in which a local regulation error is introduced to characterize the deviation of each agent from its assigned leader. Based on this formulation, a set of group game algebraic Riccati equations is derived to obtain the optimal control law. To overcome reliance on known system models, these equations are solved using a data-driven homotopic policy-iteration scheme that leverages online state and input information. In contrast to conventional learning schemes, the proposed approach embeds a homotopic process that relaxes the requirement for an admissible initial policy. Rigorous stability and convergence analyses are provided, and the effectiveness of the proposed method is further demonstrated through theoretical analysis and numerical simulations.

Safety and stability are two critical concerns in pursuit-evasion (PE) problems in an obstacle-rich environment. Most existing works combine control barrier functions (CBFs) and reinforcement learning (RL) to provide an efficient and safe solution. However, they do not consider the presence of disturbances, such as wind gust and actuator fault, which exist in many practical applications. This article integrates CBFs and a sliding mode control (SMC) term into RL to simultaneously address safety, stability, and robustness. However, this integration is significantly challenging due to the strong coupling between the CBF and SMC terms. Inspired by the Stackelberg game, we handle the coupling issue by proposing a hierarchical design scheme where SMC and safe control terms interact with each other in a leader-follower manner. Specifically, the CBF controller, acting as the leader, enforces safety independently of the SMC design; while the SMC term, as the follower, is designed based on the CBF controller. We then formulate the PE problem as a zero-sum game and propose a safe, robust RL framework to learn the min-max strategy online. A sufficient condition is provided under which the proposed algorithm remains effective even when constraints are conflicting. Simulation results demonstrate the effectiveness of the proposed safe, robust RL framework.

Local-hybrid (LH) density functionals admix exact exchange locally in real space and thereby can be powerful tools to ameliorate the usual zero-sum game between reducing self-interaction errors and modeling static correlation. But as with other hybrid functionals the practical use of LHs for large systems is limited by the cost of evaluating exact-exchange quantities self-consistently. Here we introduce an energy-corrected local-hybrid framework, EC(LH)@(m)GGA, in which a computationally expedient semi-local reference density is used for a single post-SCF evaluation with an advanced LH. Using the recent neural-network-based LH25nP LH as a prototype, we show that the EC route based on GGA or meta-GGA orbitals preserves the characteristic accuracy profile of the parent local hybrid and reaches state-of-the-art rung 4 performance on the GMTKN55 test suite (WTMAD-2 around 2.4-2.7 kcal/mol, depending on grid). The top performance of LH25nP for spin-restricted bond dissociation as a strong-correlation measure is retained in this framework. The dominant post-SCF overhead in timing is governed by the EC grid. For the practical gridsize 3, the total EC(LH)@(m)GGA cost is only about ~2-3× that of a GGA single point, typically about an order of magnitude less than a full LH SCF. Overall, EC(LH)@(m)GGA provides a simple post-SCF route to state-of-the-art rung-4 energetics at a cost close to semi-local DFT, applicable to large systems.

To make a decision or determine the order of things, people commonly use binary choices such as tossing a coin, with an assumption that the empirical probability equals to the theoretical probability. However, recent understanding on empirical coin-toss probability is contrasting, i.e., even or uneven by quantic or deterministic mechanism, wherein such discrepancies yet remain to be resolved. Here, we investigated whether the probability of stone selection in the game of Go is equal for both parties across Go games to establish the stone selection as a binary choice model for studying the discrepancy between theoretical and empirical probability. The stone selection process is considered as a sequential strategic interaction similar to other binary choice games such as matching pennies. The large-scale data analyses of professional Go matches (n = 21,212 among 311 Go masters) showed that the black stone selection was significantly higher for higher-ranked players compared with lower-ranked players. Moreover, in situ stone selection games between amateur Go players (the number of participants: n = 293, 2 games each) demonstrated that the amateur players with upper master levels had significantly higher chances for selecting black stones than players with lower master levels. The results support that actual chance for human binary choice may not be equal to theoretical odds, unlike usual expectations. The present study may contribute to providing input or insights to expand its discussion regarding the discrepancy between theoretical and empirical probability in binary choices, including strategy in game theory and elucidating the underlying mechanisms.

Diabetic neuropathy (DN) is the most prevalent and debilitating complication of diabetes, with a notable absence of effective disease-modifying therapies in clinical practice. This article proposes a shift in the pathological progression of DN from focusing on "metabolic toxicity" to an integrated dysfunction within the "immune-metabolic network." We analyze the core mechanisms underlying this network and highlight how the diabetic microenvironment may drive immune cells to shift abnormally from the "Warburg effect" to "metabolic inflexibility" and "metabolic paralysis," ultimately failing to resolve inflammation and causing persistent tissue damage. Furthermore, we identify a "zero-sum game" between Schwann cells (SCs) in their roles in immune response and metabolic support. Pro-inflammatory signals trigger the collapse of the "lactate shuttle" mechanism, exposing neurons to the dual insults of "hunger" and "toxicity." At the molecular level, we highlight ZBP1 as a critical switch, sensing mitochondrial damage and is proposed to trigger the assembly of the PANoptosome complex, which forms the terminal execution pathway for neurodegenerative lesions. Given the current gap between animal models and clinical realities, we propose employing spatial transcriptomics to examine subpopulation differences between the nerve sheath and endoneurium, alongside the development of novel precision therapies targeting NLRP3 or utilizing metabolic reprogramming to restore immune repair functions. In conclusion, framing DN within the immune-metabolic network provides a new approach to overcoming the therapeutic impasse and developing truly effective interventions.

In this article, an advanced critic learning technique is established to handle the continuous-time (CT) multiplayer zero-sum game (MZSG) problem with asymmetric constraints. To begin with, a novel asymmetric constraint algorithm is presented, which relaxes the restrictions on the control matrices compared to prior related studies. Ulteriorly, the Hamilton-Jacobi-Isaacs equation, the optimal controls, and the worst disturbances are deduced for asymmetric constrained MZSGs (ACMZSGs). Since the acquired Hamilton-Jacobi-Isaacs equation is intractable to solve, an advanced critic learning scheme is built to attain the approximations of the optimal controls and the worst disturbances. It is noteworthy that this article develops a new weight tuning rule to lower the need for the initial admissible controls. Immediately after that, the stability analysis of the control system is given. In the end, the load frequency control problem for a two-area power system with linear dynamics is considered, and the simulation for a nonlinear system is performed to test the feasibility of the suggested control scheme. In particular, comparative experiments are established to further demonstrate the efficacy of the proposed weight tuning rule and the applicability of the proposed asymmetric constraint algorithm.

Reinforcement learning (RL), a key artificial intelligence technique, has been widely studied and applied over the past two decades to solve various optimization control problems. Generally speaking, there are two basic frameworks for RL-based control design, i.e., on-policy and off-policy RL (OffP-RL). The essential distinction between the two frameworks lies in whether the policy used to generate training data is the behavior policy or the target policy. In on-policy RL-based control methods, the data used for evaluating the target control policy at each iteration must be collected from the system under the target policy itself. In contrast, in OffP-RL methods, the system data is generated by other behavior control policies. It addresses the inadequate exploration problem in on-policy RL methods, making OffP-RL methods more practical and easier to implement. In this article, the recent advances in OffP-RL-based control methods are classified into three categories based on the number of controllers/players involved, i.e., single-/two-/multiplayer. For the single-player case, it is an optimal control problem, which aims to use OffP-RL to learn the optimal control policy, which minimizes the performance index. In the two-player case, most works focus on the $H_{\infty } $ control problem and the two-player zero-sum game, using learning to find the Nash equilibrium. In the multiplayer case, a multiplayer game involves a single system with multiple control inputs, while a multiagent system consists of multiple systems with independent control inputs. Finally, related applications of OffP-RL-based control and future work are analyzed.

Recent years have seen the psychological community focus on traditional masculinity as a problem that needs to be addressed. Concurrently, cognitive behavioral therapists have increasingly been women or men who identify with feminist theory. Concerns have been raised that good intentions to address women's issues have transmogrified into open anti-male bias even as men's functioning has declined, including their enrollment in the behavioral health professions. Portraying traditional masculinity as "toxic" is likely to cause harm, not only in stigmatizing many men and their families, but also by reducing trust in the profession. As such, many men who would have benefited from cognitive behavioral therapy (CBT) will decline to get it. Suggestions for a path forward that do not portray men's issues and women's issues as a zero-sum game are offered.

Modeling pedestrian-vehicle interactions (PVIs) is essential for automated driving systems in mixed traffic environments but challenging due to the stochasticity and complexity of human behaviors. Conventional PVI models rely on strong assumptions like perfect information and unbounded rationality. For more realistic modeling of PVIs at unsignalized crosswalks, we propose a two-agent non-zero-sum non-cooperative dynamic game incorporating Quantal Response Equilibrium. This Game Theoretic Quantal Response Equilibrium (GT-QRE) approach accommodates human agents' limited cognition and bounded rationality, while accounting for unobserved heterogeneity in road users' rationality and sequential decision-making during crossing activities. We then develop an integrated simulation platform that links pedestrians' and vehicles' decisions with their movements to simulate their interactions, where GT-QRE is used to capture the decision-making behaviors of both pedestrians and vehicles, Social Force Model (SFM) and Model Predictive Control (MPC) are used to simulate pedestrian and vehicle motions. The proposed method is calibrated and validated using empirical data at four selected crosswalks in three cities in China. The key findings include: (i) both pedestrian and vehicle exhibit limited rationality in PVI; (ii) compared to conventional Nash Equilibrium (NE), our method improves prediction of crossing decisions by 13.0% for pedestrians and 36.1% for vehicles, highlighting PVIs decision process are inherently dynamic rather than static;(iii) the GT-QRE approach effectively captures the dynamic decision-making behavior of pedestrians and vehicles during interactions; (iv) the simulation platform, which integrates GT-QRE decision-making with SFM/MPC motion models, realistically reproduces vehicle and pedestrian movements at crosswalks, yielding low longitudinal/lateral displacement and velocity errors.

The number of available response options is a central design feature in any experimental psychology paradigm, with binary response paradigms restricting the range of behavioural responses into staying or shifting. Participants (n = 72) played nine blocks of a novel zero-sum game, Dice Dual, while we parametrically manipulated the different number of response options (2, 4, 6) against an unexploitable computerized opponent. When the number of responses exceeded two, this allowed us to identify two types of shift behaviours: between-category shifts relevant to mental model exploitation and within-category shifts reflective of random exploration. Between-category shifts were sensitive to outcome valence, exhibiting an increased shift rate when more response options were available after win, while shift rates were independent of response availability after loss. In contrast, within-category shifts were only sensitive to response availability, with increased shift rates when more response options were available, irrespective of outcome valence. Identifying multiple, functionally different classes of shift behaviour using nonbinary response paradigms allows us to more easily separate attempts at exploitation and exploration. The data also offer more general models of performance regarding suboptimal decision making, where negative outcomes constrict the range of factors considered in future mental model updates. (PsycInfo Database Record (c) 2026 APA, all rights reserved).