Open Radio Access Network (O-RAN) is transforming the telecommunications landscape by enabling flexible, intelligent, and multi-vendor networks. Central to its architecture are xApps hosted on the Near-Real-Time RAN Intelligent Controller (Near-RT RIC), which optimize network functions in real time. However, the concurrent operation of multiple xApps with conflicting objectives can lead to suboptimal performance. This paper introduces a generalized Conflict Management scheme for Multi-Channel Power Control in O-RAN xApps (COMIX), designed to detect and resolve conflicts between xApps. To demonstrate COMIX, we focus on two Deep Reinforcement Learning (DRL)-based xApps for power control: one maximizes the data rare across UEs, and the other optimizes system-level energy efficiency. COMIX employs a standardized Conflict Mitigation Framework (CMF) for conflict detection and resolution and leverages the Network Digital Twin (NDT) to evaluate the impact of conflicting actions before applying them to the live network. We validate the framework using a realistic multi-channel power control scenario under various conflict resolution policies, demonstrating its effectiveness in balancing antagonistic objectives. Evaluation results show that COMIX achieves up to 60% energy savings across different Service-Level Agreement (SLA) policies compared to a baseline conflict-unaware system, with negligible impact (around 3%) on system throughput. While this study considers power control xApps, the COMIX framework is generalizable and can be applied to any xApp conflict scenario involving resource contention or KPI interdependence.

Predictive maintenance for marine engines requires forecasts that are both accurate and technically interpretable. This work introduces BEACON, a hybrid architecture that combines a bidirectional long short-term memory encoder with attention pooling, a Kolmogorov Arnold network and a lightweight multilayer perceptron for cylinder-level exhaust gas temperature forecasting, evaluated in both centralized and federated learning settings. On operational data from a bulk carrier, BEACON outperformed strong state-of-the-art baselines, achieving an RMSE of $0.5905$, MAE of $0.4713$ and $R^2$ of approximately 0.95, while producing interpretable response curves and stable SHAP rankings across engine load regimes. A second contribution is the explicit evaluation of explanation stability in a federated learning setting, where BEACON maintained competitive accuracy and attained mean Spearman correlations above $0.8$ between client-specific SHAP rankings, whereas baseline models exhibited substantially lower agreement. These results indicate that the proposed hybrid design provides an accurate and explanation-stable foundation for privacy-aware predictive maintenance of marine engines.

The rapid growth of global energy consumption in buildings and the increasing adoption of renewable energy sources have created an urgent need for intelligent Home Energy Management Systems (HEMS). This work addresses the challenge of optimizing both individual home energy usage and community-wide energy distribution through peer-to-peer (P2P) trading. In the proposed framework, each smart home jointly optimizes the comfort maintenance, the energy cost and the community energy efficiency based on renewable generation, temperature dynamics, and grid prices. We propose a marketbased energy exchange mechanism with dynamic price setting and two complementary methods (threshold-based and rewardbased) to mitigate market manipulation in P2P energy trading. To enable decentralized coordination inside the energy community, the multi-agent Deep Reinforcement Learning (DRL) with actorcritic architecture is proposed to ensure the continuous and adaptive control of indoor temperature systems, battery storage, and price setting using real-world market prices and energy consumption data. The quantitative validation across multiple homes demonstrates a 39.3% reduction in grid dependency of the reward-based mechanism compared to unregulated trading. The framework maintains indoor temperatures consistently within comfort bounds while enabling intelligent battery management that adapts to price fluctuations, with state-of-charge levels peaking at 85% during low-price periods and decreasing to 40% during high-demand intervals. Overall, the approach establishes a self-sustaining energy ecosystem within residential communities, balancing market integrity and operational efficiency.