The operational convergence between urban metabolism (UM) and urban landscape infrastructure planning (ULIP) can contribute to the informed visualization, spatialization, implementation, evaluation and maintenance of nature-based solutions, circular and climate-sensitive designs, and virtuous urban food-water-carbon nexuses. However, such integration remains underdeveloped and unstructured due to dissonances regarding spatial scales of analysis and intervention, unclear operational entry points of each party along the planning process, and divergent standpoints concerning the role of quali-quantitative landscape metabolism data. To bridge these gaps, we present a comprehensive yet open-ended workflow aiming to overcome critical shortcomings of UM and ULIP: lack of common data visualization cultures; arbitrariness implementing UM data into resource-aware planning decisions; and deficient sociometabolic scenario building capabilities. It does so by identifying shared interpretations of space, spatiality and spatialization; suitable spatial scales of interdisciplinary collaboration; and UM concepts, frameworks, models and instruments applicable to each stage of said process, emphasizing [geo]spatial and visually explicit, geographic information science-reliant approaches. This paper uses 52 records retrieved through a bias-aware, iterative bibliometric analysis using the Web of Science Core Collection (years 2015 to 2025), in accordance with the PRISMA Statement 2020.
The rapid growth of the low-altitude economy has resulted in a significant increase in the number of low, slow, and small (LSS) unmanned aerial vehicles (UAVs), raising critical challenges for secure airspace management and reliable trajectory planning. To address this, this paper proposes a cooperative radio-frequency (RF) detection and localization framework that leverages existing cellular base stations (BSs). The proposed approach features a robust scheme for LSS target identification, integrating a cell averaging-constant false alarm rate (CA-CFAR) detector with a micro- Doppler signature (MDS) based recognition method. Multi-station measurements are fused through a grid-based probabilistic algorithm combined with clustering techniques, effectively mitigating ghost targets and improving localization accuracy in multi-UAV scenarios. Furthermore, the Cramer-Rao lower bound (CRLB) is derived as a performance benchmark and reinforcement learning (RL)-based optimization is employed to balance localization accuracy against involved BS number. Simulation results demonstrate that increasing from one to multiple BSs can reduce the positioning error to near the CRLB, while practical experiments further verify the effectiveness of our framework. Furthermore, the proposed RL-based optimization can maintain high accuracy while minimizing resource usage, highlighting its potential as a scalable solution for ensuring airspace safety in the emerging low-altitude economy.
Laser-based additive manufacturing (LBAM) has transformed the production of complex metallic components through precise, layer-by-layer deposition. However, porosity defects can compromise the mechanical integrity of printed parts, necessitating effective real-time monitoring and defect detection methods. This study presents a novel, physics-informed framework for in situ porosity classification using shallow learning (SL) models and captured thermal data from a dual-wavelength pyrometer sensor. Unlike deep learning models that require high-resolution large datasets and extensive computational resources, our approach leverages engineered features from multi-orientation (0°, 90°, + 45°, and − 45°) thermal profiles – captured along the laser scan, transverse, and diagonal directions – to characterize melt pool behaviour. We introduce two physics-informed features, melt pool distance (MPD) and aspect ratio of maximum temperature to MPD (ARTM), alongside interpretable statistical feature set. To address the severe class imbalance in defect categories (no-, micro-, and macro- porosity), we apply Synthetic Minority Oversampling (SMOTE) and evaluate model performance using traditional metrics and a novel Classification Deviation Error (CDE) metric proposed to capture minority class misclassification. Our results demonstrate that SL models such as logistic regression achieve high classification accuracy (up to 95
The advancement of wireless networks has spurred an increasing demand for high-quality maritime communication services. This study presents an innovative unicast-multicast access and backhaul maritime communication network (UMABMCN), in which a high-altitude platform (HAP) provides HAP-to-vessel (H2V) unicast services to vessels and backhaul support to unmanned aerial vehicles (UAVs) through HAP-to-UAV (H2U) links. Additionally, multiple UAVs are deployed to deliver UAV-to-vessel (U2V) multicast transmission services to vessels. Specifically, we formulate a HAP-UAV-assisted unicast-multicast cooperation multi-objective optimization problem (UMCMOP) aimed at maximizing the sum achievable rate of base stations (BS)-to-vessel (B2V), maximizing the sum backhaul rate of H2U, and minimizing the energy consumption of UAVs via jointly optimizing communication connection between BSs and vessels, power allocations of UAVs, along with the placement of UAVs. The formulated UMCMOP is a mixed integer non-linear programming (MINLP) problem. To address this, we propose an enhanced multi-objective multi-verse optimization (EMOMVO-CGD) algorithm, which integrates a chaos probability operator, gray wolf exploitation operator, and discrete update operator. To further validate the performance of EMOMVO-CGD, a joint communication connection, power allocation and placement optimization (JCCPAPO) method is proposed. Simulation results demonstrate that the two proposed algorithms outperform benchmark strategies in optimizing the aforementioned objectives.
The increasing complexity and scale of modern telecommunications networks demand intelligent automation to enhance efficiency, adaptability, and resilience. Agentic AI has emerged as a key paradigm for intelligent communications and networking, enabling AI-driven agents to perceive, reason, decide, and act within dynamic networking environments. However, effective decision-making in telecom applications, such as network planning, management, and resource allocation, requires integrating retrieval mechanisms that support multi-hop reasoning, historical cross-referencing, and compliance with evolving 3GPP standards. This article presents a forward-looking perspective on generative information retrieval-inspired intelligent communications and networking, emphasizing the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems. We first provide a comprehensive review of generative information retrieval strategies, including traditional retrieval, hybrid retrieval, semantic retrieval, knowledge-based retrieval, and agentic contextual retrieval. We then analyze their advantages, limitations, and suitability for various networking scenarios. Next, we present a survey about their applications in communications and networking. Additionally, we introduce an agentic contextual retrieval framework to enhance telecom-specific planning by integrating multi-source retrieval, structured reasoning, and self-reflective validation. Experimental results demonstrate that our framework significantly improves answer accuracy, explanation consistency, and retrieval efficiency compared to traditional and semantic retrieval methods. Finally, we outline future research directions.
