Search Results (1,776)

Modern cyber–physical infrastructures rely heavily on alarm and notification systems to direct human attention when abnormal conditions occur. These mechanisms support timely and safe responses by informing operators and occupants about potential hazards. At the same time, research in human factors has shown that repeated or excessive alerts can weaken vigilance, slow reactions, and reduce confidence in warning systems. This behavioral pattern is commonly described as alarm fatigue. This paper examines how that vulnerability can be exploited intentionally. We refer to this adversarial strategy as alarm poisoning: the deliberate injection of false or misleading alerts in order to increase alarm pressure, erode trust in the monitoring infrastructure, and degrade organizational responsiveness over time. To study this process, we develop a stochastic Cybersecurity Dynamics model representing the interaction among attackers, defenders, alarm infrastructure, and a population of employees. Employee behavior is modeled through evolving trust and fatigue levels, while the overall system is formulated as a continuous–time Markov chain and simulated using the Gillespie Stochastic Simulation Algorithm. A Monte–Carlo campaign is used to analyze the resulting socio–technical dynamics under alternative attacker strategies. The study evaluates time-dependent trust, fatigue, and alarm-pressure trajectories, the distribution of times to behavioral collapse, and defender timing through Trust–Resilience–Agility–Mitigation (TRAM) metrics. The revised analysis also includes replication-sufficiency diagnostics, one-at-a-time sensitivity analysis, and threshold-robustness checks for the collapse criterion. The results show that false alarms with high perceived severity drive alarm pressure upward and degrade trust faster than nuisance-dominated campaigns, even when the total fake-alarm intensity is held constant across strategies. Collapse timing remains highly variable across stochastic realizations, and a non-negligible fraction of runs do not reach the collapse threshold within the simulation horizon. Sensitivity analysis indicates that the main qualitative ranking of attacker strategies is robust across most tested perturbations, with fatigue recovery and defender escalation emerging as particularly influential mechanisms. Overall, the findings support the view that alarm poisoning is a credible socio–technical attack vector and highlight the importance of rapid mitigation, robust alarm management, and human-centered defensive design in cyber–physical security systems. Full article

The increasing adoption of electric vehicles in residential buildings creates challenges for charging infrastructure management, particularly in pricing services to balance revenue, user satisfaction, and grid stability. Traditional pricing methods, such as fixed rates and time-of-use tariffs, cannot adapt to the dynamic nature of charging demand. We propose a reinforcement learning framework for dynamic pricing of residential EV charging stations. The framework formulates the pricing problem as a Markov decision process and employs proximal policy optimization to learn a pricing policy based on real-time conditions. The state representation includes ten features covering temporal indicators, charging loads, grid status, traffic, and weather. A multi-objective reward function balances revenue, station utilization, grid stability, and user satisfaction. The system is trained on 6878 charging sessions from a residential complex in Trondheim, Norway. Compared with fixed pricing and time-of-use pricing, the proposed method achieves an overall score of 0.569, representing improvements of 32.9% and 48.9%, respectively. Sensitivity analysis confirms that the model remains robust across different demand response assumptions. The main contributions include a custom reinforcement learning environment for residential EV charging and empirical evidence that learned policies outperform traditional pricing approaches. Full article

Human–AI systems often involve repeated interaction among users, organizations, and AI components rather than isolated model outputs. In such settings, cooperation can be pursued either by changing agent incentives or by adding an explicit accountability layer. We formalize the Institutional Monitoring and Ledger (IML) framework, which augments a Markov game with monitoring, evidence logging, delayed settlement, and review while leaving the base dynamics unchanged. We derive conservative incentive checks that clarify how detection quality, review accuracy, settlement delay, and sanction size jointly shape deterrence and wrongful-penalty risk. We then provide pilot evidence in two canonical sequential social dilemmas, Harvest and Cleanup, using five agents, PPO training, five training seeds per condition, and comparisons against PPO, inequity aversion, social influence, and IML ablations. In these settings, IML avoided some of the optimization instability observed in the representative internalization baselines tested here, made monitoring error directly visible through ledger records, and showed how false positives can accumulate into a persistent welfare cost. Agent-level analyses in these symmetric environments found nearly uniform measured enforcement burden, while temporal analyses showed that late-stage enforcement is increasingly dominated by residual false positives. These results do not establish legitimacy in human-facing settings or deployment readiness. They instead position IML as a framework with pilot evidence for studying accountability mechanisms in cooperative human–AI systems and highlight measurement error, review design, and due process as central design constraints. Full article

Antivirus systems have evolved from static pattern matchers into complex algorithmic ecosystems that encapsulate the broader logic of modern cybersecurity. This review deconstructs their internal architecture, tracing the transition from deterministic string-matching automata to probabilistic, behavioral, and cloud-assisted paradigms. Foundational modules such as scanners, heuristic analyzers, behavioral monitors, and sandbox environments operate as interconnected computational strata, forming adaptive feedback loops that mirror principles of distributed intelligence. Signature-based methods, such as Aho-Corasick, Boyer-Moore, and Wu-Manber, remain core to real-time filtering, while probabilistic reasoning through Bayesian inference, Markov modeling, and Hidden Markov Models extends detection to polymorphic and metamorphic threats. Behavioral analysis, empowered by Support Vector Machines, deep neural architectures, and temporal models, enables semantic inference over system-call graphs and runtime telemetry. Moreover, cloud-assisted frameworks integrate federated learning and global reputation graphs, which transform detection into a collective intelligence process. Full article

This study investigates the impact of standby activation strategies on system reliability. The results show that a delayed activation strategy effectively improves system reliability. Additionally, to tackle the difficulty of deriving analytical solutions for reliability metrics under the Weibull distribution, a non-homogeneous Markov model based on the delayed activation strategy is introduced. The system’s residual life is modeled computationally using the state transition method. The numerical results suggest that the proposed method aligns closely with Monte Carlo simulations. It significantly improves computational efficiency while maintaining high accuracy, thus confirming its effectiveness. Full article

Fully leveraging the four-wheel independent drive characteristics of distributed-drive electric vehicles has become essential for enhancing their driving range. However, conventional regenerative braking strategies applied to such vehicles often fail to consider individual wheel slip ratios, which can easily lead to wheel lock and low energy recovery efficiency. To address these issues, this paper proposes a novel energy management method that integrates hybrid braking control with intelligent connected speed planning. A hierarchical control strategy for the hybrid braking system is first developed, explicitly accounting for the slip ratio of each wheel. The upper-level controller calculates the slip ratio for each wheel based on vehicle speed and wheel speed information and subsequently determines the braking torque distribution between the front and rear axles. The lower-level controller then allocates the motor braking torque and hydraulic braking torque to each wheel, subject to system constraints such as battery status and motor torque limits. Building on this framework, vehicle state and road information are incorporated as inputs to formulate a Markov decision process, which optimizes traffic efficiency, energy economy, and ride comfort as multiple objectives. The deep deterministic policy gradient (DDPG) algorithm is employed to achieve collaborative optimization of speed planning and energy management. Simulation results demonstrate that the proposed DDPG-based control strategy outperforms both rule-based control methods and classical dynamic programming algorithms in terms of comprehensive performance across traffic efficiency, energy consumption, and ride comfort. These findings validate its superiority in complex traffic conditions. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

This paper presents on-policy and off-policy algorithms for the $H_{\infty }$ control of continuous-time mean-field stochastic Markov jump systems. Using online state and input data, these algorithms can learn control and disturbance strategies without the need for prior knowledge of system matrices. Under the standard assumptions that the system is mean-square stabilizable and detectable, we rigorously prove the monotonicity, boundedness, and convergence of the proposed iterative algorithms to obtain the unique stabilizing solution of cross-coupled generalized algebraic Riccati equations. Moreover, the off-policy algorithm features high data efficiency because the collected data can be utilized again after each iteration. Numerical simulations demonstrate the effectiveness of two algorithms and explicitly show that the off-policy algorithm achieves a faster convergence rate compared to its on-policy counterpart. Full article

This study proposes a methodology to identify deteriorated pipes in water distribution networks using prior system information and routine chlorine residual data. While bulk chlorine decay k_bulk can be measured in laboratories, wall decay k_wall depends on pipe material, diameter, and ageing, particularly in unlined metallic pipes. Empirical data were used to estimate k_wall, which was integrated into a Bayesian inference framework solved with Markov Chain Monte Carlo. Applied to an Italian network with synthetic chlorine data, this method demonstrated effectiveness across three test scenarios, exploiting the contrast between k_wall and k_bulk to detect deteriorated pipes within a computationally efficient environment. Full article

Generative AI systems trained on synthetic data exhibit progressive degradation known as model collapse. This paper provides a theoretical explanation of this phenomenon using Shannon’s Data Processing Inequality (DPI), modeling iterative synthetic-data training as a Markov chain of lossy transformations. We show that mutual information with respect to the original data distribution must decrease monotonically, yielding qualitative predictions for exponential decay tendencies and indicating that information loss arises from general finite-precision and capacity constraints rather than from any specific architectural mechanism. Building on this analysis, we introduce the AI conceptual theorem, a generalized stability limit for computable systems. The theorem states that any purely computational system that generates outputs iteratively under finite precision, bounded capacity, and without external low-entropy input must experience cumulative information degradation after a finite number of steps. DPI-based collapse emerges as a special case of this broader principle. The framework is intended as a conceptual information-theoretic perspective rather than a fully formalized theory, with several assumptions intentionally simplified to highlight the underlying entropic mechanism. The results should therefore be interpreted as principled limits that motivate further empirical and mathematical investigation rather than as definitive closed-form predictions. Together, DPI and the AI Theorem provide a unified information-theoretic framework for understanding degradation in synthetic training, long-horizon inference, and other iterative computational processes. The resulting predictions are quantitatively falsifiable and offer guidance for designing more stable and information-preserving AI systems. Full article

This study proposes a scalable Explainable Artificial Intelligence (XAI)–driven Bayesian–AI decision–control framework for multi-objective optimisation in uncertain and dynamic systems. The framework integrates Bayesian networks, stochastic control, and expected utility theory within a unified probabilistic architecture. Unlike traditional black-box models, the proposed framework provides intrinsic interpretability through probabilistic reasoning and dependency-aware modelling. This allows users to understand how decisions are formed and how variables influence outcomes. To further strengthen explainability, the framework incorporates post hoc XAI techniques, including SHAP-based feature attribution and sensitivity-based local explanations. These methods quantify the contribution of each variable and provide clear explanations at both global and local levels. The system is formulated as a stochastic state-space model and implemented as a closed-loop adaptive architecture. It updates decisions continuously as new data becomes available. Scalable inference is achieved using variational inference, Markov Chain Monte Carlo, and Sequential Monte Carlo methods. This ensures efficient performance in complex and high-dimensional environments. A simulation study based on 370 observations shows that the proposed framework improves decision quality, robustness under uncertainty, and transparency compared to conventional methods. Explainability is evaluated using Fidelity, Stability, and Transparency metrics. The results confirm that the model produces consistent and reliable explanations. The framework supports human-centred decision-making by providing visual analytics and clear probabilistic explanations. This makes it suitable for high-stakes applications such as cyber–physical systems, intelligent platforms, and real-time AI systems. The main contribution of this study is the integration of intrinsic probabilistic interpretability with post hoc XAI techniques into a single, scalable framework. This approach bridges a key gap in XAI research and offers a practical and transparent solution for decision-making under uncertainty. Full article

With the development of intelligent transportation systems, vehicular applications demonstrate diverse characteristics, including computation-intensive processing and stringent latency requirements. Traditional computation offloading strategies struggle to cope with the highly dynamic, multi-node, and multi-task concurrent vehicular network environment and generally overlook the risk of cross-zone communication failures caused by high-speed mobility. To address this issue, this paper designs a computation offloading algorithm based on multi-agent reinforcement learning. This method comprehensively considers four heterogeneous features including queue load, communication links, task attributes, and computing resources, establishes a multi-layer collaborative computing architecture integrating task migration and result return mechanisms, and further constructs an optimization model aimed at minimizing the weighted sum of latency and energy consumption. This model is formalized as a multi-agent Markov decision process, and an improved Multi-Agent Proximal Policy Optimization(MAPPO)-based MATPPO-T algorithm is designed to solve it, achieving one-step joint optimization of task offloading, resource allocation, and task result migration. Experimental results demonstrate that the proposed method reduces the total system cost by approximately 22% on average compared to benchmark algorithms such as MAPPO and PPO, while consistently maintaining the lowest offloading overhead and fastest convergence speed, validating its robustness and scalability in dynamic vehicular edge networks. Full article

With increasingly stringent greenhouse gas emission regulations in the shipping industry, there is an urgent need for an efficient energy management strategy for new energy ship power systems. However, existing dispatch models often overlook the dynamic energy-saving potential of active drag reduction technologies and lack effective optimization algorithms capable of handling high-dimensional, multi-constrained problems. To address these problems, this paper proposes a novel integrated dispatch framework for hybrid energy ship power systems that incorporates air lubrication systems. First, a unified multi-energy dispatch model is established, coupling the dynamic operation of air lubrication systems with electrical, thermal, and propulsion energy flows. Second, an Improved Traffic Jam Optimizer algorithm is proposed, which enhances global exploration and local exploitation through a nonlinear parameter adaptation mechanism, differential mutation strategy, and dynamic hybrid search architecture. Convergence analysis based on Markov chain theory is provided to guarantee algorithmic reliability. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of convergence speed, solution accuracy, and stability. Furthermore, integrating air lubrication systems into the ship power system reduces total operating costs and greenhouse gas emissions by up to 20.569% and 6.310%, respectively. Full article

The critical role of green consumption in mitigating carbon emissions is widely acknowledged. As a prerequisite for green consumption, consumer green awareness (CGA) plays a pivotal role in advancing sustainable development. This study constructs a comprehensive indicator system for CGA from the three dimensions of “antecedent-behavior-outcome” and measures the CGA levels of 30 provinces in China from 2014 to 2022. Using the Theil index, kernel density estimation, Moran’s I, and Markov chain methods, we analyze its spatiotemporal evolution characteristics. Furthermore, spatial econometric models are applied to explore its driving factors. The results show that China’s CGA exhibits sustained growth during the study period, but regional disparities are widening, driven by inter-regional rather than intra-regional differences. Moreover, China’s CGA gradually demonstrates the long-tailed and multimodal distribution, accompanied by emerging spatial clustering effects. In terms of transition dynamics, CGA demonstrates a short-term “gradient lock”, which is substantially alleviated when spatial spillover effects are incorporated. Additionally, we find that economic development, the advancement of emerging industries, accelerated urbanization, emphasis on education, and policy guidance significantly promote CGA, while overconsumption inhibits CGA. Among these factors, economic development, informatization, e-commerce, education, and policy guidance show significant spillover effects. Full article

This study addresses the optimal scheduling of a proton exchange membrane fuel cell (PEMFC)-based building combined cooling, heating, and power (CCHP) system, aiming to improve operational efficiency and flexibility under coupled electricity–thermal–cooling demands and load uncertainty. A physics-informed scheduling environment was developed using component models and multi-energy balance constraints, including a PEMFC with waste-heat recovery, a lithium bromide absorption chiller, a reversible heat pump with condenser heat recovery to thermal storage, a battery energy storage system, and a hot-water thermal storage tank. The dispatch problem was formulated as a Markov decision process and solved using deep reinforcement learning with TD3; performance was evaluated on typical summer and winter days, and robustness was tested by generating 100 scenarios with 30% demand perturbations. The results show that TD3 learns coordinated multi-energy dispatch patterns consistent with seasonal operation and reduces hydrogen consumption relative to a rule-based strategy under uncertainty while requiring millisecond-level inference time. Dynamic programming achieved slightly lower hydrogen consumption but incurred orders-of-magnitude higher computation time. Overall, TD3 provides a practical trade-off between near-optimal performance, robustness, and real-time applicability for PEMFC-based building CCHP scheduling. Full article