Search Results (38)

With the advancement in electronic attack technologies, intelligent jamming poses a significant challenge to the reliable transmission of wireless communications. Traditional anti-jamming methods often fail to adapt to dynamic nonlinear jamming environments. This paper addresses nonlinear swept-frequency jamming by modeling anti-jamming communication as a sequential decision-making problem and proposes an intelligent anti-jamming method based on proximal policy optimization (PPO) to optimize dynamic channel selection. Firstly, the channel selection problem is formalized as a Markov decision process (MDP), where a state space integrating jamming patterns and communication status is designed, the channel set is defined as the action space, and a multi-objective reward function trades off jamming avoidance against switching overhead. A dual-network architecture comprising a policy network and a value network is constructed, and the PPO algorithm is employed for policy updates, where a clipping mechanism is used to enhance training stability. The system optimizes the anti-jamming strategy online through a closed-loop process of “sensing–decision–learning–communication”. Simulation results demonstrate that compared to conventional methods, the proposed method significantly improves key performance indicators such as packet success rate and throughput. It can rapidly track changes in jamming, exhibiting excellent real-time performance and environmental robustness, and thus provides an effective solution for reliable communication in dynamic jamming environments. Full article

Multi-agent systems are widely used in modern power systems, but they face challenges such as low data utilization, stringent triggering conditions, and poor environmental adaptability. This study proposes a multi-agent event-triggered control method based on the Proximal Policy Optimization (PPO) policy gradient algorithm. By maximizing the cumulative reward, the agents are driven to learn adaptive triggering strategies, which reduces communication frequency while ensuring system stability. A multi-agent reinforcement learning model is constructed, and the training results show that both the single-episode reward and the average reward significantly increase with the number of training episodes, thus verifying the effectiveness of the algorithm. Based on Lyapunov stability and LaSalle’s invariance principle, an event-triggering threshold is designed using an exponential decay function. Moreover, the sequential decision-making process under uncertain environments is described using the Markov decision process. In the case study with six agents, the triggering conditions effectively constrain the error growth and ensure system stability. The method is further extended to a 33-node power system, where each node is regarded as an agent to simulate voltage fluctuations under load variations. Compared with periodic sampling control, the event-triggered control exhibits faster convergence speed, higher steady-state accuracy, and stronger anti-interference capability, thus confirming its superiority in complex power systems. Full article

Alcohol use disorder (AUD) remains a crucial public health challenge worldwide. The currently available medications for AUD remain limited in the number and efficacy, meaning that the development of new treatments is of critical importance. Agonists of glucagon–like peptide–1 receptor (GLP–1RAs) have recently received attention as a potential anti–addiction treatment, particularly in AUD. This review presents data from preclinical studies in rodents and non–human primates, registered clinical trials, observational studies, and social media posts, investigating the effects of GLP–1RAs on alcohol–related behaviors and consumption. Several GLP1–RAs and tirzepatide (a dual agonist of GLP–1R and glucose–dependent insulinotropic polypeptide receptor; GIP–R) reduced alcohol consumption and alcohol–seeking behaviors, alcohol–induced locomotor stimulation and memory of alcohol reward, and suppressed relapse drinking in rodents. In addition, they prevent acute alcohol from activating the mesolimbic dopamine system. There are limited human data on the role of the GLP–1 system in AUD. In registered clinical trials, exenatide, semaglutide, and dulaglutide reduced alcohol consumption. Pharmacoepidemiologic studies documented a decreased risk of alcohol–related events in AUD patients using various GLP–1RAs and tirzepatide. Together, existing preclinical and clinical data suggest that GLP–1 is involved in the AUD process and imply the role of GLP1–RAs as a tentative treatment for AUD. Full article

The article examines the religio-ecological framework articulated in Ming–Qing morality books 勸善書, focusing on how animals, Ledgers of merit 功過格, and karmic 業報 are integrated into a system of moral causality. Within this framework, actions such as killing or saving animals are directly linked to karmic reward and punishment, generating a dual mechanism that combines moral technology with an ultimate logic of justice to cultivate ecological consciousness and enforce social discipline. A central contribution of the study is the articulation of a triadic analytical framework—merit–demerit ledgers, karmic narrative, and animal ethics—showing how these elements form a coherent system of measurable and actionable ethical practice. In doing so, the framework challenges a strictly human-centered worldview by foregrounding an interconnected ecological order in which humans and animals are bound together through shared moral obligations and karmic entanglements. The article further situates this religio-ecological mechanism within contemporary debates in environmental ethics and animal rights. Through comparison with modern approaches—such as anti-speciesism, animal welfare and rights discourse, and proposals for cross-species political communities—it identifies both points of convergence and structural divergence. It concludes by exploring how this historical model might be critically translated and revised for present-day conditions, proposing a “revised morality book” framework that is more publicly defensible and more amenable to institutional implementation. Full article

Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle–Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for episode-level opponent trajectory generation and planning within UAV-FPG, serving as an operationally more challenging simulator adversary for stress-testing anti-jamming policies under our evaluation protocol. Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model: relative to fixed-path baselines, iterative feedback-conditioned LLM planning tends to generate more adaptive trajectories and achieve higher opponent rewards in UAV-FPG. These findings are confined to the proposed simulation environment and are not intended as general claims about real-world jamming capability or onboard planning performance. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems. Full article

Traditional signature-based anti-malware tools often fail to detect zero-day ransomware attacks due to their reliance on known patterns. This paper presents a real-time ransomware detection framework that models system behavior as a Reinforcement Learning (RL) environment. Behavioral features—including file entropy, CPU usage, and registry changes—are extracted from dynamic analysis logs generated by Cuckoo Sandbox. A (DQN) agent is trained to proactively block malicious actions by maximizing long-term rewards based on observed behavior. Experimental evaluation across multiple ransomware families such as WannaCry, Locky, Cerber, and Ryuk demonstrates that the proposed RL agent achieves a superior detection accuracy, precision, and F1-score compared to existing static and supervised learning methods. Furthermore, ablation tests and latency analysis confirm the model’s robustness and suitability for real-time deployment. This work introduces a behavior-driven, generalizable approach to ransomware defense that adapts to unseen threats through continual learning. Full article

Sixth-generation (6G) wireless systems aim to integrate terrestrial, aerial, and satellite networks to support large-scale remote sensing and service delivery. In such non-terrestrial networks (NTNs), channels change quickly and the multi-tier architecture is heterogeneous, which makes real-time channel state acquisition and cooperative resource scheduling difficult. This paper proposes an FMA-MADDPG framework that combines a channel prediction module with a constraint-based multi-agent deep deterministic policy gradient scheme. The Fusion of Mamba and Attention (FMA) predictor uses a Mamba state-space backbone and a multi-head self-attention block to learn both long-term channel evolution and short-term fluctuations, and forecasts future CSI. The predicted channel information is added to the agents’ observations so that scheduling decisions can take expected channel variations into account. A constraint-based reward is also designed, with explicit performance thresholds and anti-idle penalties, to encourage fairness, avoid free-riding, and promote cooperation among heterogeneous agents. In a representative NTN uplink scenario, the proposed method achieves higher total reward, efficiency, load balance, and cooperation than several DRL baselines, with relative gains around 10–20% on key metrics. These results indicate that prediction-aware cooperative reinforcement learning is a useful approach for resource optimization in future 6G NTN systems. Full article

Traditional pedestrian evacuation models struggle to balance global exit guidance with local, individual decision making under hazards. We address this by decomposing long-term objectives into Particle Swarm Optimization (PSO)-based micro-goals and proposing a hybrid Cellular Automaton (CA) and PSO model. The hybrid design reduces the decoupling between spatial discretization and individual choices and more tightly couples hazard and density fields with movement decisions. Two contributions are central. First, we develop an autonomous following pathfinding mechanism (AFPM) that linearly blends the direction toward a PSO micro-goal with a herd following direction and adds a small reward for directional consistency. This mitigates path chaos from purely autonomous moves and congestion aggregation from purely herding moves. Second, we build a multi-dimensional interpretability and robustness framework that combines the empirical Cumulative Distribution Function (CDF) and a kernel-smoothed Probability Density Function (PDF) of key evacuation times (T_clear, T_95%_alive) together with vulnerability curves, to analyze the data and assess robustness. It combines Shapley Sobol analysis to quantify parameter effects on clearance time T_clear and the 95% survival evacuation time T_95%_alive, with CDF/PDF summaries and vulnerability curves to assess anti-interference performance. Experiments use a simulated underground shopping mall. In a 60 pedestrian case, a geometry-only baseline yields T_clear $\approx 33 \mathrm{s}$; hazard- and density-aware strategies produce slightly longer T_clear but reduce peak bottleneck congestion by 20–30%. When one exit is closed, the exceedance probability at $\tau =70 \mathrm{s}$ drops from 0.44 to 0.36, reducing long tail risk. Compared with geometry-based Dijkstra, the proposed model slightly increases clearance time while lowering peak congestion by 20–30%, achieving a balance between efficiency and safety. The model and evaluation protocol provide technical support for evacuation policy, facility layout, and emergency system design in large complex buildings. Full article

Alcohol use disorder (AUD) is a widespread, multifaceted disorder involving overproduction of pro-inflammatory cytokines, oxidative liver injury, and dysfunction of the brain’s dopaminergic reward circuits. Korean red ginseng (KRG), an herbal supplement derived from Panax ginseng, has demonstrated qualities potentially useful to the treatment of AUD, including antioxidative, anti-inflammatory, neuroprotective, and anxiolytic effects. This review examines active constituents of KRG, their pharmacological actions, and evidence supporting KRG’s therapeutic potential in the context of AUD, while also assessing its safety profile, adverse effects, and potential drug interactions. KRG’s main bioactive constituents, ginsenosides, appear to have roles in modulating alcohol-metabolizing enzymes, ethanol-activated inflammatory cytokine cascades, and neurological systems disrupted by AUD, including GABAergic and dopaminergic pathways. Evidence from animal models and limited small-scale human trials suggests KRG may alleviate symptoms of alcohol withdrawal, enhance cognitive performance, and attenuate anxiety through these pathways. While generally safe for consumption, several case reports and animal studies have indicated KRG’s potential to pose a variety of risks in vulnerable populations at high, prolonged doses, including hepatotoxicity, cardiovascular changes, mood disturbances, and hormonal effects. Furthermore, KRG’s neuromodulating role and influence on cytochrome P450 enzymes make it liable to interact with several medications, including warfarin, midazolam, selegiline, and serotonergic agents. Overall, KRG shows promise as a complementary supplement in managing aspects of AUD, though current evidence is limited by low sample sizes, inconsistent reports regarding nuances of ginsenosides’ mechanisms, and a low number of human trials. Further human-focused research is needed to elucidate its safety, efficacy, and mechanism. Full article

In existing phased-array radar systems, anti-jamming strategies are mainly generated through manual judgment. However, manually designing or selecting anti-jamming decisions is often difficult and unreliable in complex jamming environments. Therefore, reinforcement learning is applied to anti-jamming decision-making to solve the above problems. However, the existing anti-jamming decision-making models based on reinforcement learning often suffer from problems such as low convergence speeds and low decision-making accuracy. In this paper, a multi-aspect improved deep Q-network (MAI-DQN) is proposed to improve the exploration policy, the network structure, and the training methods of the deep Q-network. In order to solve the problem of the $ϵ$-greedy strategy being highly dependent on hyperparameter settings, and the Q-value being overly influenced by the action in other deep Q-networks, this paper proposes a structure that combines a noisy network, a dueling network, and a double deep Q-network, which incorporates an adaptive exploration policy into the neural network and increases the influence of the state itself on the Q-value. These enhancements enable a highly adaptive exploration strategy and a high-performance network architecture, thereby improving the decision-making accuracy of the model. In order to calculate the target value more accurately during the training process and improve the stability of the parameter update, this paper proposes a training method that combines n-step learning, target soft update, variable learning rate, and gradient clipping. Moreover, a novel variable double-depth priority experience replay (VDDPER) method that more accurately simulates the storage and update mechanism of human memory is used in the MAI-DQN. The VDDPER improves the decision-making accuracy by dynamically adjusting the sample size based on different values of experience during training, enhancing exploration during the early stages of training, and placing greater emphasis on high-value experiences in the later stages. Enhancements to the training method improve the model’s convergence speed. Moreover, a reward function combining signal-level and data-level benefits is proposed to adapt to complex jamming environments, which ensures a high reward convergence speed with fewer computational resources. The findings of a simulation experiment show that the proposed phased-array radar anti-jamming decision-making method based on MAI-DQN can achieve a high convergence speed and high decision-making accuracy in environments where deceptive jamming and suppressive jamming coexist. Full article

Background: A rat robot can be constructed by electrically stimulating specific brain regions to control rat locomotion and behavior. The rat robot makes full use of the rat’s motor function and energy supply and has significant advantages in motor flexibility, environmental adaptability, and covertness. It can be widely used in disaster search and rescue, terrain survey, anti-terrorism, and explosion-proof tasks. However, the motor control of existing rat robots mainly relies on the virtual whisker touch produced by the electrical stimulation of the barrel area of the somatosensory cortex and the virtual reward generated by the electrical stimulation of the medial forebrain bundle. The methods requires substantial experimental training to encourage the animals to match the virtual sensation with the motor behavior. However, the conditioned reflexes acquired by the animals will gradually disappear after a period of time at the end of the experiments, which will lead to a decrease in the stability of the motor control system. Methods: In this study, we developed a new method to gain control of inclined movement in rats by the electrical stimulation of the ventral tegmental area (VTA) of the midbrain and motor control of steering in rats by the electrical stimulation of nigrostriatal (NS) pathway. Results: The results showed that the electrical stimulation of the rat VTA could induce stable inclined movement in rats and that the neuromodulatory effect significantly correlated with the electrical stimulation parameters. In addition, the electrical stimulation of the NS pathway was able to directly and stably induce the steering movements of the head and trunk to the contralateral side of the stimulated side of the rat. Conclusions: These findings are of great importance for the motor control of rat robots, especially in the field environment with many slopes. In addition, the rat robot constructed based on this method does not need pre-training while ensuring reliability, which greatly improves the preparation efficiency and has certain practical application value. Full article

The cooperative patrol control of multiple unmanned surface vehicles (Multi-USVs) in dynamic aquatic environments presents significant challenges in global coverage efficiency and system robustness. The study proposes a cooperative patrol control algorithm for multiple unmanned surface vehicles (Multi-USVs) based on a hybrid embedded task state information model and reward reshaping techniques, addressing global coverage challenges in dynamic aquatic environments. By integrating patrol, collaboration, and obstacle information graphs, the algorithm generates kinematically feasible control actions in real time and optimizes the exploration-cooperation trade-off through a dense reward structure. Simulation results demonstrate that the algorithm achieves 99.75% coverage in a 1 km × 1 km task area, reducing completion time by 23% and 74% compared to anti-flocking and partition scanning algorithms, respectively, while maintaining collision rates between agents (CRBAA) and obstacles (CRBAO) below 0.15% and 0.5%. Compared to DDPG, SAC, and PPO frameworks, the proposed training framework (TFMUSV) achieves 28% higher rewards with 40% smaller fluctuations in later training stages. This study provides an efficient and reliable solution for autonomous monitoring and search-rescue missions in complex aquatic environments. Full article

We aimed to assess the efficacy, safety, and pharmacokinetics of an oral risperidone solution and two types of supplementations with PUFAs. We assigned 39 participants with mild ASD (mean age ± standard deviation = 14.6 ± 6.0 years) to three treatment groups (each n = 13): RIS-OS; equal doses of 240 mg of omega-3 PUFA docosahexaenoic acid and omega-6 PUFA arachidonic acid (1:1) (aravita); and omega-6 precursor linoleic acid (480 mg) and omega-3 precursor alpha-linolenic acid (120 mg) (4:1) (awake). The primary outcome was the Autism Diagnostic Interview—Revised score. The secondary outcomes were the Social Responsiveness Scale (SRS) and Aberrant Behavior Check scores. The results of the linear mixed-effects model revealed that the RIS-OS group exhibited significant improvement in the SRS subscale scores of social motivation at weeks 8, 12, and 16 compared with the aravita and awake groups, as well as in the SRS subscale score of social mannerisms at weeks 12 and 16 compared with the aravita group. Moreover, the RIS-OS group showed a trend towards significantly lower plasma ceruloplasmin (Cp) levels. Their plasma insulin-like growth factor (IGF) levels were significantly higher at week 8 than in the subsequent weeks. The high Cp and IGF levels may be attributed to reduced neuroinflammation. These findings demonstrate, firstly, that reduced inflammation through increased anti-inflammatory proteins such as Cp and IGF has clinical effects on the motivation–reward system and mannerisms in patients with ASD through the amelioration of dopamine D2, 5-HT2a, and 5-HT2b dysfunction. Full article

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users. Full article

Oleoylethanolamide (OEA) is a lipid with anti-inflammatory activity that modulates multiple reward-related behaviors. Previous studies have shown that OEA treatment reduces alcohol self-administration (SA) while inhibiting alcohol-induced inflammatory signaling. Nevertheless, the specific mechanisms that OEA targets to achieve these effects have not been widely explored. Here, we tested the effects of OEA treatment during alcohol SA, extinction or previous to cue-induced reinstatement of alcohol seeking. In addition, we measured gene expression changes in the striatum and hippocampus of relevant receptors for alcohol consumption (Drd1, Drd2, Cnr1, Oprm) as well as immune-related proteins (Il-6, Il-1β, Tlr4) and the brain-derived neurotrophic factor (Bdnf). Our results confirmed that when administered contingently, systemic OEA administration reduced alcohol SA and attenuated cue-induced reinstatement. Interestingly, we also observed that OEA treatment reduced the number of sessions needed for the extinction of alcohol seeking. Biochemical analyses showed that OEA induced gene expression changes in dopamine and cannabinoid receptors in the striatum and hippocampus. In addition, OEA treatment modulated the long-term immune response and increased Bdnf expression. These results suggest that boosting OEA levels may be an effective strategy for reducing alcohol SA and preventing relapse. Full article