Several sources of flexibility in transmission and, especially, distribution networks are being unlocked by advances in information and communication technologies, aggregators, and new flexibility markets. However, maximizing benefits for both transmission and distribution system operators in a coordinated way requires new algorithms, modeling tools, and modernization of regulatory frameworks. Such approaches must account for uncertainties, the physical and operational constraints of flexibility providers and the grid itself, constraints on information exchange, and scalability, including computational requirements and time constraints. Given the diverse contexts and jurisdictions around the world, there is no single recipe for achieving coordination, but important trends and shared challenges are emerging. This paper surveys the complexities of coordination from technical, market, and technological perspectives, and outlines current practices, proposed approaches, and future research directions to effectively manage, coordinate, model, and leverage flexibility across voltage levels.
Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more elaborated explanations of the results of food recommendation systems. By combining LLM with a hybrid extraction of key variables using SHAP, we obtain dynamic, convincing and more comprehensive explanations to lay user, compared to those in the literature. This approach enhances user trust and transparency by making complex recommendation outcomes easier to understand for a lay user.
We study the Job Shop Scheduling Problem with machine Availability Constraints (JSSP-AC) within a quantum-annealing framework. Using a dummy-job transformation, both fixed and variable machine unavailability periods are incorporated into a standard time-indexed quadratic unconstrained binary optimization (QUBO) model. We then introduce a constructive heuristic ℋ , its preemptive variant ℋ^* , and three annealing-based methods: ℳ_1 , based on a naive time horizon; ℳ_2 , using the tighter bound returned by ℋ ; and ℳ_3 , which additionally uses the heuristic solution as a warm start in a reverse-annealing setting. A proof-of-concept experiment on D-Wave hardware confirms that the proposed formulation can be embedded and solved on small instances. On a broader benchmark, ℋ outperforms repaired dispatching heuristics, while the tighter horizon reduces QUBO size by 29.9 ℳ_1 to 5.0 ℳ_2 and 3.3 ℳ_3 , highlighting the value of classical bounds and warm starts in quantum annealing for scheduling under machine unavailability.
Scientific progress relies on a complex and interconnected network of scholarly publications housed within digital libraries. Although citations serve as the primary mechanism for linking this knowledge, a reference alone does not capture the rich contextual information in which the cited work is discussed. This limitation poses challenges for digital libraries attempting to accurately analyze scholarly influence. To address this issue, we use Citation Context Extraction (CCE) that is a foundational task for transforming raw citation links into meaningful, semantically enriched representations that can support advanced bibliometric and knowledge graph analyses. In this paper, we propose a novel twofold methodology to enhance CCE. First, we introduce an improved evidence-based extraction framework that leverages a richer set of linguistic and statistical signals to more accurately identify citation context sentences, extending beyond similarity-driven state-of-the-art approaches. Second, we propose an LLM-based framework that employs structured prompt engineering to enable deeper, more nuanced, and more explainable semantic interpretation of extracted citation contexts. We evaluate our methods on two distinct corpora: ACL-ARC, a domain-specific dataset in computational linguistics, and SDP-ACT, a multidisciplinary dataset spanning multiple scientific fields. Comparative experiments against established baselines demonstrate consistent improvements in citation context quality. Our findings contribute toward the development of more intelligent, interpretable, and semantically grounded digital library systems capable of mapping scholarly discourse and intellectual lineage more effectively.
Sentinel-1 is a unique resource for global flood monitoring, providing systematic, weather-independent Synthetic Aperture Radar (SAR) imagery with unprecedented coverage. To overcome limitations of on-demand flood mapping services that depend on human operators to collect and interpret satellite images, a fundamentally new approach was adopted by the Global Flood Monitoring (GFM) service. This service, which was launched in 2021 as part of the Copernicus Emergency Management Service (CEMS), processes all Sentinel-1 land images acquired in VV polarisation fully automatically in near-real time. This article presents the first comprehensive analysis of GFM’s scientific achievements and challenges during its initial years of operation. To map floods reliably under diverse environmental conditions, GFM combines three complementary flood-mapping algorithms with reference water datasets to differentiate flooded areas from permanent and seasonal water bodies. The service also offers a novel flood-likelihood layer and contextual information to highlight areas where flood mapping is unreliable or not feasible. These data layers were derived from a global 20 m backscatter datacube containing approximately 379 billion land surface pixels. This datacube also made it possible to generate the first global Sentinel-1 flood archive (2015 to present). Our performance analysis shows that GFM typically delivers flood maps within five hours of image acquisition. However, a significant percentage of floods may go undetected due to coverage gaps. Initial evaluation results show that good accuracies are achieved for larger-scale floods and regions in the temperate and tropical zones, while accuracies are lower for smaller-scale floods and arid environments. The GFM service will continue to improve service quality by enhancing flood detection capabilities using improved algorithms and additional data, such as the VH channel from Sentinel-1 or L-band data from the upcoming ROSE-L mission.
