Autonomous ground vehicles (AGVs) operating in mountainous and off-road environments need to perform complex local navigation while simultaneously handling severe terrain undulation, intricate obstacle distributions, and task-driven observation requirements. Traditional navigation architectures often rely on decoupled planning and control modules, which makes it difficult to rigorously coordinate nominal guidance with terrain related safety and kinematic constraints. To address these challenges, this paper proposes a unified terrain-aware local navigation framework based on dynamical system modulation and control barrier functions (DSM-CBF), where traversal safety, operational agility, and observation quality are jointly considered. A DSM-based nominal guider is developed to generate continuous and smooth motion commands with natural obstacle avoidance behavior by reshaping the local vector field through anisotropic modulation. The resulting nominal control is then refined by a CBF-based safety filter formulated as a slack-variable-augmented hard quadratic program, so that forward invariance of the safety set can be guaranteed under obstacle clearance, terrain traversability, and speed constraints. Task-related observation metrics, including bistatic geometry as well as range and azimuth resolutions, are further incorporated into the optimization objective to preserve favorable sensing quality during bounded-speed terrain-following motion. Comprehensive simulations and real-vehicle experiments verify the effectiveness and practical executability of the proposed framework. The simulation results show that it satisfies the radial-resolution requirement with an achieved value of approximately 1.01 m relative to the 1.13 m design threshold and improves mission efficiency by about 27% under the observation-quality-prioritized setting; compared with the SOTA methods, the proposed method achieves zero terminal error, maintains an admissible obstacle-clearance margin with minΓobs=1.0439, reduces the turn-RMS by 86.1% and 83.6% relative to A* and D* Lite, respectively, and reduces the computational runtime by 97.9% compared with MPC-CBF, while real-vehicle tests further demonstrate safe and smooth traversal on a physical rugged-terrain AGV platform.
Established data governance frameworks provide valuable guidance for large enterprises, yet their assumptions about stable funding, dedicated governance teams, and mature infrastructure often do not reflect the realities of small and medium-sized enterprises (SMEs). Recent research has explored the use of semantic technologies and knowledge graphs to automate governance tasks, improve traceability, and support policy enforcement. Yet, whether these approaches effectively address SME-specific needs remains unclear. This paper presents a systematic literature review of knowledge-graph-based data governance approaches, focusing on their relevance to SMEs. Following the PRISMA method, we analyzed evidence from 43 peer-reviewed studies published between 2020 and 2025 through multiple lenses, including core data governance activities (Define, Implement, Monitor), components of the Data Governance Institute framework, and stages of the data lifecycle. Using thematic network analysis we synthesize recurring limitations and structural patterns across the literature. Our findings show that while semantic technologies demonstrate strong potential for automating policy enforcement, metadata integration, and interoperability, most approaches are designed for mature enterprises. They commonly assume formal governance hierarchies, specialized expertise, and sustained operational capacity, conditions that are often absent in SMEs. As a result, governance designs are skewed toward technical controls and early lifecycle stages, with limited attention to monitoring, organizational roles, decision rights, and end-to-end governance. Building on these findings, this study advances a socio-technical perspective on data governance in SME contexts by reframing governance as a lifecycle-embedded and practice-oriented capability rather than a purely technical implementation. It further conceptualizes knowledge graphs as boundary objects that mediate between governance intent, technical enforcement, and organizational practice. Our key contributions include a structured synthesis of knowledge-graph-based data governance research, identifies eight recurring gaps that hinder SME applicability, and derives nine design principles alongside a conceptual research framework to guide future research and the development of SME-suitable governance solutions.
Although cross-domain few-shot learning (CDFSL) for hyperspectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealistic data augmentation procedure in the form of external noise to enlarge the sample size, thus greatly simplifying the issue of data scarcity. They involve a large number of parameters for model updates, being prone to the overfitting problem. To the best of our knowledge, only one work has explored the strength of the foundation model, yet it still relies on a conventional foundation model for RGB images. This paper proposes the MIxup FOundation MOdel (MIFOMO) for CDFSL of HSI classifications. MIFOMO is built upon the concept of a remote sensing (RS) foundation model, pre-trained across a large scale of RS problems, thus featuring generalizable features. The notion of coalescent projection (CP) is introduced to quickly adapt the foundation model to downstream tasks while freezing the backbone network. The concept of mixup domain adaptation (MDM) is proposed to address the extreme domain discrepancy problem. Last but not least, the label propagation concept is implemented to cope with noisy pseudo-label problems. Our rigorous experiments demonstrate the advantage of MIFOMO, where it beats prior art by up to a 14% margin. The source code of MIFOMO is open-sourced in https://github.com/Naeem-Paeedeh/MIFOMO for reproducibility and further study.
Microgrids play a key role in distributed energy systems by facilitating the integration of renewable energy sources, maintaining system reliability, and creating economic value. However, optimising their operation remains challenging, as it requires balancing conflicting objectives under operational constraints. This paper presents a novel customer-centric adaptive microgrid energy management framework using a two-stage ε-constraint multi-objective optimisation approach to balance competing objectives: minimising operational costs and emissions, enhancing technical performance through load factor improvement, and maximising customer benefits via dynamic reward allocation. The proposed two-stage response mechanism, formulated as a non-convex MINLP, exploits the flexibility of both economic- and comfort-oriented consumers within a coordinated system optimisation framework. The first stage models consumer participation using a coefficient of participation (CoP) and an incentive-based structure, enabling significant load adjustments. In the second stage, the microgrid operator exploits the aggregated flexibility of comfort-oriented consumers to solve the multi-objective problem and generate feasible optimal solutions. To improve computational efficiency, this paper reformulates the model as an MIQP using the Big-M method and McCormick relaxation. The framework is validated against real-world data from an Australian grid-connected microgrid. Results indicate that, relative to a no-flexibility baseline, economic-oriented consumers reduce individual energy consumption by up to 33%, with load factor improvements of up to 10%. The analysis also reveals clear trade-offs among the objectives by generating solutions with an average computation time of approximately 3 seconds per feasible solution, highlighting the effectiveness of the proposed coordinated energy management in balancing technical, economic, and social objectives in microgrid operation.
Data centers (DCs) are becoming some of the most energy-intensive and grid-influential infrastructures in modern power systems, driven by the explosive growth of artificial intelligence (AI) workloads, cloud computing, and data-intensive digital services. Conventional facilities were designed primarily for reliable computing service delivery, but high-density AI workloads and fast load transients now demand more flexible and controllable energy-computing integration. Grid-interactive data centers (GDCs) provide a pathway for this transition by coordinating computational workloads, energy storage, renewable generation, cooling infrastructure, and grid-facing control functions. However, existing studies continue to address energy management, workload flexibility, demand response, power quality, dynamic load modeling, and ancillary-service participation as largely disjoint research streams. To address this fragmentation, this review provides a systems-level synthesis of next-generation GDCs for sustainable and resilient energy systems. It traces the evolution from conventional and energy-aware data centers to AI-optimized DCs and next-generation GDCs. The review identifies key enabling technologies, including grid-forming interfaces, flexible workload scheduling, distributed energy storage systems, AI-based control, digital twins, and cybersecurity, and introduces a six-layer GDC architecture that integrates physical infrastructure, energy systems, communication and control, intelligence and co-optimization, grid interfaces, and stakeholder-market interaction. The review further examines operational management strategies, grid integration challenges, sustainability metrics, and deployment barriers, and highlights digital-twin-based operation, coordinated smart inverters, and fast multi-timescale optimization frameworks as critical research directions for future GDC deployment.
