Search Results (1,453)

With the continuous increase in the penetration rate of renewable energy sources, the uncertainty of new energy output has brought significant risks and challenges to the planning strategy of integrated energy systems. Meanwhile, power grid operators and heat network operators, belonging to different stakeholder entities, exhibit complex cooperative-competitive game relationships, making it difficult to balance the interests of all parties. To address this issue, this paper proposes a game theory and optimal planning strategy for electricity-heat multiple heterogeneous energy systems based on a deep temporal clustering method from the perspective of different stakeholders. Firstly, typical scenarios of renewable energy output are generated through the deep temporal clustering method. Simultaneously, the charging and discharging behaviors of energy storage devices are utilized to assist the distribution system in new energy consumption. This paper incorporates battery life degradation costs into the objective function on the power grid side to achieve accurate accounting of energy storage device dispatch expenses. Additionally, an optimal dispatch model is established on the heat network side, upon which a game framework for multiple heterogeneous energy systems is constructed. The construction capacity and installation location of each flexible device can be determined through planning decisions in typical multi-scenario situations. Considering the non-convex and nonlinear characteristics of the model, this paper employs an improved firefly algorithm to achieve optimal solution search and rapid convergence. Finally, the effectiveness and feasibility of the proposed method are demonstrated through a case study of an electricity-heat energy system. Full article

The Internet of things (IoT) is rapidly pervading daily life and linking everything. Although higher connectivity offers many benefits, including higher productivity, robotic processes, and decision-making guided by data, it also poses a number of security dangers. Modern risks to data authenticity and confidence are getting harder to handle through typical central safety solutions. In this paper, we present a detailed investigation of the latest innovations and approaches for assessing reputation and confidence in the blockchain-empowered Internet of Things (BIoT) area. A comprehensive literature search was conducted across major electronic databases, including IEEE, Springer, Elsevier, Wiley, MDPI, and top indexed conference proceedings. The publication year was restricted to the period from 2018 to 2025. The methodological quality of a total of 122 studies met the inclusion criteria assessed using predefined quality measures. We figure out existing flaws at each layer of IoT architecture, illustrating how autonomous, transparent, and impenetrable blockchain ledgers address these flaws. Plus, we analytically compare public, private, consortium, and hybrid blockchain networking architectures to emphasize the underlying compromises among security, reliability, and decentralization. We also assess how reputation evaluation techniques evolved over time, moving from classical fuzzy logic and weighted average models to modern mature game theory and machine learning (ML) models, addressing their limitations in terms of computational overhead, scalability, adaptability, and deployment feasibility in IoT systems. Additionally, we outline future directions for BIoT system trust assessment and identify research limitations and potential solutions. Our research indicates that although ML-driven models offer more accurate predictions for identifying illicit node activities, they are still constrained by limited unbalanced data and high processing overhead. Full article

Coupling interference in densely integrated high-frequency communication architectures leads to significant degradation in transmission efficiency, particularly in modern 5G and GHz-range platforms. From a mathematical perspective, mitigating such interference can be formulated as a multi-criteria optimization problem involving competing design objectives and interacting control mechanisms. In this paper, we develop an equilibrium-based optimization framework by modeling coupling suppression as a finite non-cooperative game. Isolation mechanisms are represented as strategic players whose actions are defined over constrained design spaces, while utility functions incorporate coupling minimization, insertion-loss penalties, and fabrication complexity. Under this formulation, stable mitigation strategies are characterized through Nash equilibrium conditions. To address the inherent trade-offs among performance metrics, the equilibrium computation is integrated with a Pareto multi-objective optimization scheme, yielding Nash–Pareto optimal configurations that balance electromagnetic isolation performance with implementation feasibility. Numerical full-wave simulations in the 2–12 GHz frequency band demonstrate that the proposed equilibrium solutions achieve substantial interference suppression, with reductions exceeding 30 dB compared with conventional baseline designs. The proposed framework provides a mathematically structured approach for interference mitigation and offers a generalizable methodology for multi-objective optimization in high-frequency communication systems. Full article

The strong electromagnetic interference environment on the battlefield has brought new challenges to the networking collaboration of jammers and the estimation of jamming effects. Traditional successful jamming indicators are difficult to meet the needs of continuous, low-power, and flexible jamming, causing difficulties in emergency scheduling of jamming resources. Aiming at the overall degradation of the communication party’s signal reception quality, this paper proposes the restrictive conditions of “overall limited jamming” and the analysis and evaluation index of “multistage jamming-to-signal ratio (J/S)”, which meets the scheduling requirements of distributed jamming resources in harsh environments. Based on the jammer layout that can achieve overall high-intensity jamming, the electromagnetic environment estimation, power scheduling, and collaboration strategies of jammers are designed, a communication countermeasure game algorithm under blocked networking collaboration is established, and the independent dynamic scheduling of jamming resources is realized. The experimental results show that the Concentric Circle Broadcasting Algorithm (CCBA) not only maintains effective communication jamming (the proportion of high-intensity jamming is no less than 50%, and the proportion of normal signal reception of communication nodes is no more than 6%), but also extends the system operation duration by 66.8–269.6% compared with the comparative algorithms for the 600 MHz fixed-frequency and 1 MHz bandwidth communication system. This work is limited to the line-of-sight (LOS) scenario, and future research will extend it to non-line-of-sight (NLOS) scenarios. Full article

As product aesthetics increasingly drive consumer preference, quantitative evaluation remains hindered by subjective evaluation biases and the black-box nature of modern artificial intelligence. This study proposes an advanced machine learning framework incorporating sensitivity-aware morphological features for the aesthetic evaluation of industrial products, with automotive design as a representative case. An aesthetic index system and its quantitative formulations are first developed to capture the morphological characteristics of product form. Subjective weights are determined via grey relational analysis (GRA), while objective weights are calculated using the coefficient of variation method (CVM) integrated with the technique for order preference by similarity to an ideal solution (TOPSIS). A game-theoretic weighting approach is then employed to fuse subjective and objective weights, thereby establishing a multi-scale aesthetic evaluation system. Sensitivity analysis is applied to identify six key indicators, forming a high-quality dataset. To enhance prediction performance, a novel model—improved lung performance-based optimization with backpropagation neural network (ILPOBP)—is proposed, where the optimization process leverages a maximin latin hypercube design (MLHD) to enhance exploration efficiency. The ILPOBP model effectively predicts aesthetic ratings based on limited morphological input data. Experimental results demonstrate that the ILPOBP model outperforms baseline models in terms of accuracy and robustness when handling complex aesthetic information, achieving a significantly lower test set mean absolute relative error (MARE = 4.106%). To further enhance model interpretability, Shapley additive explanations (SHAP) are employed to elucidate the internal decision-making mechanisms, offering reverse design insights for product optimization. The proposed framework offers a novel and effective approach for integrating machine learning into the aesthetic assessment of industrial product design. Full article

This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framework, named eXCube2, uses a three-dimensional SNN architecture NeuCube that is spatially structured according to a human brain template. The BIAI models developed in eXCube2 are trainable on spatio- and spectro-temporal data using brain-inspired learning rules. Such models are explainable in terms of revealing patterns in data and are adaptable to new data. The eXCube2 models are implemented as software systems and tested on speech and video data of subjects expressing emotional states. The use of a brain template for the SNN structure enables brain-inspired tonotopic and stereo mapping of audio inputs, topographic mapping of visual data, and the combined use of both modalities. This novel approach brings AI-based emotional state recognition closer to human perception, provides a better explainability and adaptability than existing AI systems. It also results in a higher or competitive accuracy, even though this was not the main goal here. This is demonstrated through experiments on benchmark datasets, achieving classification accuracy above 80% on single-modality data and 88.9% when multimodal audio–visual data are used, and a “don’t know” output is introduced. The paper further discusses possible applications of the proposed eXCube2 framework to other audio, visual, and audio–visual data for solving challenging problems, such as recognizing emotional states of people from different origins; brain state diagnosis (e.g., Parkinson’s disease, Alzheimer’s disease, ADHD, dementia); measuring response to treatment over time; evaluating satisfaction responses from online clients; cognitive robotics; human–robot interaction; chatbots; and interactive computer games. The SNN-based implementation of BIAI also enables the use of neuromorphic chips and platforms, leading to reduced power consumption, smaller device size, higher performance accuracy, and improved adaptability and explainability. This research shows a step toward building brain-inspired AI systems. Full article

Unmanned aerial vehicle (UAV) communication networks are highly vulnerable to eavesdropping due to their open and dynamic air–ground channels, making physical layer security (PLS) a critical design requirement. Existing security mechanisms often struggle to adapt to large-scale UAV swarms operating under power and coordination constraints. To address this challenge, this work presents a dual-game framework that enables a group of legitimate UAVs to form optimal coalition structures through an internal coalition game, while countering coordinated eavesdropping attacks from adversarial UAVs. The framework is specifically designed for demanding real-world conditions, considering maximum power restrictions of individual UAVs and the need for secure and efficient communication with ground nodes. By jointly minimizing communication cost and maximizing swarm utility, the proposed approach enhances both security and resource efficiency. Extensive simulation results demonstrate that the proposed approach achieves up to $10\%$ improvement in secrecy rate compared with conventional frameworks, validating its effectiveness for securing large-scale UAV networks. Full article

The study develops a game-theoretic evaluation framework for cyber deception that quantifies deception benefit relative to an otherwise matched non-deceptive baseline and links strategic outcomes to information disclosure. A defender–attacker interaction is modeled through a paired design consisting of a baseline game without deception and a corresponding decoy-enabled deception game, enabling direct measurement of deception impact through two operational metrics: the value of deception, defined as the baseline-referenced change in defender equilibrium utility attributable to deception, and the price of transparency, defined as the marginal loss induced by increased observability of the true system state. The analysis characterizes defender-optimal deception strategies, derives interpretable bounds and break-even conditions under which deception becomes ineffective due to cost or detectability, and establishes approximation properties that support scalable allocation rules. To complement equilibrium-based evaluation, the study introduces an information-theoretic uncertainty construct that captures the extent to which deception preserves attacker uncertainty after observation, providing a mechanism-level interpretation of when and why value of deception degrades as transparency increases. Computational experiments across heterogeneous scenarios demonstrate consistent cross-setting comparability, reveal tradeoffs among decoy realism, budget, and attacker rationality, and identify regimes in which simplified allocation heuristics approach optimal performance. Full article

The digital divide between large enterprises and SMEs (Small and Medium-sized Enterprises) within industrial clusters poses a significant challenge to achieving collective digital transformation, exacerbated by the quasi-public goods, attributes of digital inclusion ecosystems, and the prevalence of free-riding behavior. This paper investigates whether platform enterprises, as core actors occupying structural holes in cluster networks, can foster the co-construction of a digitally inclusive ecosystem. We developed a complex network public goods game model, incorporating performance feedback into a modified Fermi learning to capture firms’ adaptive decision-making based on historical and social aspirations. The model simulates strategic interactions on both small-world and scale-free networks, characteristic of industrial clusters. Numerical simulations reveal that: (1) The core driver of co-construction is the investment return coefficient; (2) Performance feedback amplifies individual rationality, accelerating the formation or collapse of cooperation depending on the investment return coefficient; (3) Platform empowerment—specifically, selectively connecting and incentivizing cooperative firms—effectively promotes ecosystem co-construction, with this strategy proving most impactful when investment returns are moderate. Furthermore, while this selective empowerment strategy benefits the cluster overall, its effect on the platform’s own revenue is network-dependent, showing a more pronounced decline in small-world structures. This study provides a novel analytical framework for understanding strategic interactions in digital inclusion and offers practical insights for policymakers and platform leaders in orchestrating collaborative digital transformation. Full article

Operations scheduling of mass customized products is vital in the modern make-to-order (MTO) supply chains. In these systems, order acceptance decisions should be coordinated with available capacity in different sections of the supply chain while considering their potential correlations and interactions. One of the fundamental challenges in optimization of these systems is the computation time of solving models with multiple coupling constraints between supply chain units. This paper addresses this issue by proposing a game-based framework that decomposes the related mixed integer programming mathematical model and it is coordinated and solved using integrated game-based Alternating Direction Method of Multipliers (ADMM). The proposed Stackelberg Leader-Follower game optimizes order acceptance decisions while considering the requirements in supply, production planning, maintenance, inventory, and distribution units. To validate the efficiency of the proposed framework, the model is tested with a simulated four-layer supply chain. The results of experiments proved that decompositions of the model to smaller subsections and solving it in a distributed manner not only optimizes supply chain participating units but also coordinate their movements to achieve the global optimal solution. The proposed framework offers managers a practical decision layer that preserve local autonomy of the supply chain units and reduce their data sharing and computation burdens and concerns. Full article

With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical patterns and noise; WOA optimizes Informer’s hyperparameters for high-precision prediction. An index system covering voltage out-of-limit, regulation capacity, and new energy consumption risks is established, with weights determined by fusing AHP and PCA via game theory. Case studies on the improved IEEE 33-bus system show the method effectively characterizes generalized load characteristics and accurately evaluates risks under different scenarios, supporting safe operation. Full article

The governance of the rural water environment is essential for improving the quality of life of rural residents and advancing the construction of ecological civilization. However, the current governance system faces issues such as fragmented governance entities and low collaborative efficiency. Therefore, in this study, we focus on the intergovernmental collaborative governance mechanism for rural water environments. Drawing on complex network theory and evolutionary game theory, we employ complex network analysis and construct a complex network evolutionary game model among government departments, and we further conduct numerical simulations to examine the evolutionary dynamics of intergovernmental collaboration in rural water environmental governance. The findings show the following: (1) The reward and punishment mechanism, collaborative gain coefficient, and loss intensification trend coefficient all positively influence the participation rates of local governments. When these parameters exceed certain thresholds, they can rapidly and stably increase the proportion of participating nodes. (2) Nodes with stronger environmental preferences respond more directly to the collaborative gain coefficient, while the loss intensification trend coefficient promotes cooperation by amplifying the cost of non-cooperation. (3) The heterogeneity in economic preferences of local governments affects the stability of cooperation. Governments with stronger environmental priorities are more inclined to form the core of cooperation, whereas those driven by stronger economic priorities are more vulnerable to parameter fluctuations, leading to instability in overall participation levels. Reducing or eliminating this heterogeneity can improve both participation rates and the stability of cooperation. These findings offer theoretical support for designing intergovernmental collaborative governance mechanisms for rural water environments and provide practical guidance for calibrating reward–punishment schemes, identifying key coordinating departments, and stabilizing cross-departmental participation, thereby facilitating an efficient transition in rural water environmental governance models. Full article

Peer action coordination in middle childhood is thought to benefit from socio-cognitive abilities such as emotion understanding and inhibitory control, but empirical evidence for their role is limited. This study replicates and extends a previous study by examining whether emotion understanding and inhibitory control correlate with children’s peer action coordination in a cooperative sensorimotor problem-solving task. To test this hypothesis, 6- to 10-year-old children (N = 108, M = 8 years, 8 months, 46.3% girls, 53.7% boys) completed the Test of Emotion Comprehension and the Attention Network Task. To assess children’s performance in coordinating their actions with a peer, they were asked to complete the Labyrinth Ball Game—a sensorimotor task that they first performed individually and then together with a peer. Contrary to expectations, there was no direct association between emotion understanding or inhibitory control and children’s peer action coordination after controlling for age, gender, and individual sensorimotor skills. However, a significant interaction between age and gender revealed that older boys showed greater cooperative action coordination performance than younger boys, whereas girls’ performance remained stable across age. These findings challenge the view that individual socio-cognitive abilities straightforwardly support cooperative success, suggesting that peer action coordination in middle childhood may rely on more complex mechanisms, such as gender-specific communicative strategies or social play, rather than on emotion understanding and inhibitory control. Full article

This article evaluates the use of probing classifiers to modify the internal hidden state of a chess-playing transformer, which has been trained on sequences of chess moves and can generate new moves with prompted. Probing classifiers are a technique for understanding and modifying the operation of neural networks in which a smaller classifier is trained to use the model’s internal representation to learn a probing task. The aim of this research is to discover whether the learned model possesses an editable internal representation of the chess game, despite being trained without explicit information about the rules of chess. We contrast the performance of standard linear probes against Sparse Autoencoders (SAEs), a latent space interpretability technique designed to decompose polysemantic concepts into atomic features via an overcomplete basis. Our experiments demonstrate that linear probes trained directly on the residual stream significantly outperform probes based on SAE latents. When quantifying the success of interventions via the probability of legal moves, linear probe edits achieved an 88% success rate, whereas SAE-based edits yielded only 41%. These findings suggest that while SAEs are valuable for specific interpretability tasks, they do not enhance the controllability of hidden states compared to raw vectors. Finally, we show that the residual stream respects the Markovian property of chess, validating the feasibility of applying consistent edits across different time steps for the same board state. Full article

Background: Addictive disorders are characterized by the dysregulation of neural circuits involved in reward processing, salience attribution, emotional regulation, and cognitive control. Traditional neuroimaging paradigms based on static or two-dimensional stimuli show limited ecological validity and may fail to capture the contextual complexity of real-world addictive triggers. Immersive virtual reality (VR) offers a novel approach to simulate realistic, multisensory environments capable of eliciting craving and emotional responses. Although several reviews have examined VR in addictive disorders, most combined immersive and non-immersive tools and did not restrict inclusion to studies with brain-based outcomes. Methods: This systematic review with narrative synthesis was conducted in PubMed/MEDLINE and APA PsycINFO for studies published up to 30 December 2025. This systematic review followed PRISMA 2020 and was prospectively registered in PROSPERO; due to heterogeneity, findings were synthesized narratively. Eligible studies included human participants with substance-related or behavioral addictions and employed immersive VR paradigms (e.g., head-mounted display–based environments) combined with neuroimaging or neurophysiological measures (EEG, fMRI, fNIRS, PET, or DTI). Risk of bias was assessed using ROB-2 or ROBINS-I, and overall certainty of evidence was evaluated with the GRADE framework. Results: Ten studies met the inclusion criteria, encompassing over 1450 participants with alcohol, nicotine, methamphetamine, opioid use disorders, and internet gaming disorder. Immersive VR was associated with craving-related neural responses across modalities, involving prefrontal, insular, limbic, and striatal networks. EEG studies reported spectral power changes associated with craving and attentional salience, while fMRI, fNIRS, and PET studies demonstrated activation and modulation of executive control and reward-related circuits. Preliminary longitudinal and interventional studies indicate that repeated VR exposure may induce neurobiological changes consistent with therapeutic modulation. Conclusions: Immersive VR combined with neuroimaging supports the use of immersive VR as an ecologically grounded framework to probe addiction-related brain circuits; however, larger trials and standardized reporting are needed to strengthen clinical translation. Future studies should prioritize adequately powered randomized designs, harmonized VR cue-reactivity paradigms, and transparent neuroimaging reporting to enable reproducibility and cumulative inference. Full article