Abstract
Integrated Access and Backhaul (IAB) technology in cellular networks operating in the 3.x GHz band combines access and backhaul functionalities within a wireless framework, reducing dependence on fiber-based solutions and enabling cost-efficient, flexible network expansion. Deploying a mobile IAB (MIAB) in obstruction-prone environments, such as seaports, offers on-demand capacity and resilience but poses unique challenges due to severe shadowing from dense physical obstacles. This paper presents a three-dimensional, obstacle-aware model for optimal MIAB placement and scheduler selection in networks comprising user equipments (UEs) and fixed IABs (FIABs). We evaluate user and backhaul association patterns under different scheduling strategies, including Round-Robin (RR) and Weighted Round-Robin (WRR), ensuring that both MIABs and FIABs meet UE application-layer capacity demands without exceeding backhaul limits. A genetic algorithm (GA)-based optimizer is employed to explore deployment configurations under varying FIAB densities, number of UEs, and obstacles. Results show that MIAB assistance yields the greatest benefits in sparse FIAB networks and low-UE scenarios, with capacity gains reaching up to 350%. MIAB delivers the greatest added value in the presence of obstacles. In contrast, dense FIAB deployments exhibit diminishing returns from MIAB integration. Across most of the evaluated conditions, WRR outperforms RR by enabling fairer and more adaptive resource blocks (RBs) allocation. These findings provide practical guidance for targeted MIAB deployment strategies that balance infrastructure investment, environmental constraints, and scheduling policies.

Abstract
Virtual production (VP) is becoming central to film and television education, with universities offering degree programs, minors, tracks, electives, and short-term credentials. This review of 115 English-language sources, including 55 curricula from 49 higher education institutions (HEI), shows VP as a socially uneven, tool-weighted formation clustered in well-resourced Anglophone systems. Curricula overwhelmingly foreground real-time workflows, engine-driven pipelines, and stage operations over story development, audio design, and game-adjacent or interactive practices. The core tools include the Unreal Engine, motion-capture systems, and LED volumes, framed as prestige infrastructure rather than collective capacity. Programs emphasize employability, production-style blocks, and 'learning by doing real jobs', supporting industry transition but compressing experimentation, critique, and cross-cultural perspectives. Competency stacks map robust technical cores but reveal structural gaps in leadership, narrative, sound, and AI/ML literacy. The findings argue that evaluating VP education requires analyzing how programmes distribute technological and symbolic capital, organize human-machine networks, and produce learning spaces. Future research should model VP curricula as sociotechnical networks, measure AI integration maturity, test transferability, track longitudinal outcomes, map non-English ecosystems, and formalize stage pedagogy frameworks.

Abstract
Swarm robotics enables groups of autonomous agents to coordinate and perform tasks beyond the capabilities of individual robots. This state-of-the-art review focuses on the defining features, principles, and algorithms of swarm robotic systems, with an emphasis on recent advances. It examined classical and modern bio-inspired coordination strategies, decentralized control algorithms and hybrid approaches, highlighting their strengths, limitations and applicability to real-world deployments. Key challenges, such as scalability, robustness, adaptability and the gap between simulation and hardware implementation are analyzed.

Abstract
Software architecture is reflected across multiple artifacts, making it difficult to communicate without proper documentation, which often becomes outdated or unreliable. We propose an approach to support Living Documentation by generating architectural diagrams from Docker Compose files. We implement our approach as a prototype tool that we name Infragenie and conduct an empirical study to show the viability of the approach. The study involved sending questionnaires to maintainers of 378 GitHub repositories. We received 36 responses. Infragenie-generated diagrams were rated as better or much better for most of the 12 projects with previous diagrams. Over 70% of the respondents agreed that our approach improved documentation completeness, consistency, and accessibility, and more than 90% recognized its effectiveness in capturing key architectural elements. We conclude that by using Docker Compose files we were able to provide useful architectural diagrams.

Abstract
Federated Graph Neural Networks (FedGNNs) have emerged as a promising paradigm for decentralized graph learning across distributed data silos. However, the influence of underlying communication topologies on model accuracy and efficiency remains underexplored. This study presents a topology-aware benchmarking framework for federated GNNs, systematically evaluating the impact of network structure and aggregation strategy on performance and communication overhead. The proposed framework functions as a synthetic, communication-level digital twin that emulates Federated Learning interactions and topology-dependent dynamics under controlled conditions. Four learning schemes-Centralized, Local, FedAvg, and FedAvg-Fedadam-were assessed across three representative topologies: Barab & aacute;si-Albert (BA), Watts-Strogatz (WS), and Erd & odblac;s-R & eacute;nyi (ER). Results demonstrate that centralized training achieved the highest mean ROC-AUC (0.63), while FedAvg-Fedadam attained the best F1-score (0.038), balancing local adaptation and global convergence. Among topologies, BA and WS yielded higher average AUC values (approximately 0.57 and 0.56, respectively) than ER (approximately 0.39). Communication analysis revealed FedAvg as the most efficient strategy, requiring only approximately 3.8 x 105 bytes cumulatively. These findings highlight key trade-offs between accuracy, robustness, and communication efficiency in federated graph learning and provide empirical guidance for topology-aware optimization of distributed GNNs. While the experiments rely on representative synthetic topologies, the insights offer indicative relevance and potential applicability to Internet-of-Things (IoT), vehicular, and cyber-physical networks, where communication structure and bandwidth constraints critically influence collaborative intelligence. By modeling canonical connectivity patterns and releasing our code and data, the proposed benchmarking framework offers a reproducible basis for comparing emerging federated graph architectures under constrained communication conditions.

Abstract
Accurate retrieval of post-surgical images plays a critical role in surgical planning for breast cancer patients. However, current content-based image retrieval methods face challenges related to limited interpretability, poor robustness to image noise, and reduced generalization across clinical settings. To address these limitations, we propose a multistage retrieval pipeline integrating saliency-based explainability, noise-reducing image pre-processing, and ensemble learning. Evaluated on a dataset of post-operative breast cancer patient images, our approach achieves contrastive accuracy of 77.67% for Excellent/Good and 84.98% for Fair/Poor outcomes, surpassing prior studies by 8.37% and 11.80%, respectively. Explainability analysis provided essential insight by showing that feature extractors often attend to irrelevant regions, thereby motivating targeted input refinement. Ablations show that expanded bounding box inputs improve performance over original images, with gains of 0.78% and 0.65% contrastive accuracy for Excellent/Good and Fair/Poor, respectively. In contrast, the use of segmented images leads to a performance drop (1.33% and 1.65%) due to the loss of contextual cues. Furthermore, ensemble learning yielded additional gains of 0.89% and 3.60% over the best-performing single-model baselines. These findings underscore the importance of targeted input refinement and ensemble integration for robust and generalizable image retrieval systems.