Arrhythmia Burden and Clinical Responses Under Continuous Monitoring in Heart Failure: Observations From the ALLEVIATE-HF Trial.

Enabling community management of patients with heart failure: the impact of a specialist nurse-facilitated shared-care model

High-stakes decision environments continue to face cognitive demands, accountability pressures, and uncertainty, even as artificial intelligence is increasingly embedded in decision support across critical infrastructure, safety-sensitive domains, healthcare, and aviation. This scoping review examines how the design of the human–AI interface shapes cognitive workload, safe reliance, and trust calibration in such settings. A PCC-framed question and a PRISMA-ScR-guided process were used to identify studies published between 2015 and 2025 in Scopus, PubMed/MEDLINE, Web of Science, ScienceDirect, IEEE Xplore, and the ACM Digital Library. These were screened and charted using an extraction template. Seventeen studies were chosen, covering clinical decision support, sepsis management, medical imaging, power-grid congestion management, telehealth diagnosis, air traffic control, medication verification, and maintenance. In most situations, interfaces that combine interactive verification, actionable uncertainty communication, selective transparency, and support for intermediate reasoning were more effective than static explanation designs; however, deployment remains constrained by methodological heterogeneity, limited real-world integration, small samples, limited real-world integration and inconsistent measures. This review proposes a thematic structure linking deliberative support, oversight-preserving design, and calibrated transparency, and offers a roadmap for embedding trustworthy human–AI interfaces in safety-critical decision support systems.

The widespread expansion of Electric Vehicles (EVs) and the integration of charging stations into Active Distribution Networks (ADNs) present unprecedented challenges and opportunities for grid management. The paper presents a novel coordinated congestion management framework, which effectively integrates Long Short-Term Memory (LSTM) networks with a modified Deep Deterministic Policy Gradient (DDPG) algorithm enhanced by the Gurobi solver, termed LGDDPG to achieve adaptive, event-aware EV charging. A modified IEEE 13 bus radial distribution system was examined, with Electric Vehicle Supply Equipment (EVSEs) connected to the buses at Grid Connection Points (GCPs). Each EVSE acts as a decentralised reinforcement learning (RL) agent and functions as a cluster of electric vehicle loads that autonomously adjusts its charging rate by utilising PMU-based index data with inter-agent communication. The proposed test environment is exposed to diverse event scenarios, enabling the RL agent to effectively explore the state space, thereby testing the adaptability of the system. To evaluate the economic viability of the proposed framework, a dynamic congestion tariff was introduced, and its performance was systematically benchmarked against conventional pricing structures and alternative RL approaches, thereby demonstrating both its effectiveness and robustness. This concordance-distributed intelligence and fairness mechanism guarantees the equal allocation of available grid capacity among EVSEs, as measured by Jain index at the aggregated system levels. Significant findings from exploratory data analysis demonstrate a 97.5% success rate and narrow variability in cost analysis for the proposed framework when evaluated with other baseline methods, demonstrating substantial improvements in the effectiveness, fairness, and economic advantage of adaptive charging. • Adaptive ADN congestion management is done via dynamic adjustment of EVSE power. • LGDDPG framework enhances fair allocation of power and satisfies constraint limits. • SGS Index enables grid observability and awareness via synchrophasor feedback. • Dynamic congestion tariff is effective in comparison to other pricing models.

Optimizing traffic efficiency and emergency response through enhanced dynamic charging lanes, autonomous platooning systems, and 6G connectivity

Congestion in deregulated power markets poses a significant challenge to power system reliability, economic dispatch, and market efficiency. This paper proposes a novel multi-stage congestion management framework that integrates generator rescheduling using Particle Swarm Optimisation (PSO) with a severity-based, hierarchical application of demand-side strategies, specifically Real-Time Pricing (RTP) and Adaptive Load Shedding. The methodology is designed to progressively relieve congestion by activating increasingly stringent measures only when preceding steps prove insufficient. Simulations conducted on the IEEE 30-bus and IEEE 118-bus test systems under artificially induced congestion conditions demonstrate the effectiveness and scalability of the proposed framework. In the IEEE 30-bus case, the total generation cost decreases from 2280.90 USD/hr (post-congestion) to 1633.09 USD/hr, with all line flows restored within thermal limits. Application to the larger IEEE 118-bus system further validates the approach, reducing the generation cost from 335,965 USD/hr (congested) to 193,681 USD/hr after the complete three-stage process, i.e., a 42.35 % reduction, while total demand falls moderately from 6055 MW to 5579 MW, i.e., a 7.86 % as congestion is fully eliminated. Results show that although PSO-based generator rescheduling significantly reduces overloads, it is insufficient for full congestion clearance, therefore necessitating the successive deployment of RTP and adaptive load shedding. Compared to conventional single-method solutions, the proposed strategy achieves enhanced technical efficiency, demand-side flexibility, and operational robustness. This work contributes a scalable and adaptive solution for congestion management in emerging electricity markets, particularly in environments transitioning to market-based operation.

The integration of renewable energy sources (RES) in power systems introduces significant uncertainty. As this uncertainty increases, transmission system operators maintain higher operational margins to ensure reliable operation. This limits the available transmission capacity and contributes to grid congestion. As a result, the system operator relies more on costly remedial actions such as generation redispatch and RES curtailment. This paper presents a chance-constrained congestion management model to minimize expected RES curtailment and generation cost in hybrid AC/DC grids. The model is based on a tractable Stochastic Optimal Transmission Switching (SOTS) formulation that optimizes the switching state of AC and DC transmission lines, RES curtailment, HVDC converter set-points, and generator dispatch. It captures continuous non-Gaussian uncertainty in RES forecast errors using Polynomial Chaos Expansion. The tractability of the model is further improved using relaxation of binary variables. Three case studies are conducted. First, the proposed model is compared against a stochastic optimal power flow and a MINLP SOTS model using a 5- bus AC/DC test system. Second, the impact of binary relaxation parameter and solver tolerance on the output probability distributions is analyzed using a 67- bus AC/DC test system. Third, scalability is demonstrated on a 588- bus AC/DC test system under different RES penetration levels and switching configurations. The results show that the model substantially reduces RES curtailment and operational costs. Moreover, it is at least an order of magnitude faster than the MINLP formulation while maintaining accuracy in the output distributions, and it remains tractable for large systems.

Day-to-day route choice and congestion management under information heterogeneity

Congestion management is a key therapeutic target in heart failure, closely linked to both prognosis and quality of life. Loop diuretics play an important role in the management of decongestive therapy, but their efficacy is often limited by diuretic resistance and empiric dosing strategies. In this context, the concept of sequential nephron blockade - combining agents acting on different tubular segments, such as thiazide diuretics - has gained relevance. More recently, sodium-glucose co-transporter 2 inhibitors have emerged as effective decongestive agents, offering modest but sustained osmotic-diuretic effects, synergistic with loop diuretics, and a favorable safety profile with low impact on electrolytes, blood pressure, or renal function. Optimal decongestion, however, requires a tailored and dynamic approach, guided by clinical and laboratory markers (e.g. early spot urinary sodium), aimed at enhancing diuretic response while minimizing risks such as worsening renal function, electrolyte disturbances, or metabolic alkalosis. Patient education and home-based monitoring - potentially supported by point-of-care technologies - are critical to improve adherence and reduce inappropriate diuretic use in the chronic management of heart failure.

Multi-agent real-time pricing mechanism for airport congestion management with online distributed reinforcement learning

Emergency supply distribution networks face significant resilience challenges during large-scale disasters because of hub congestion and demand–supply mismatches. This study addresses this issue by proposing and comparing two congestion management strategies for hybrid hub-and-spoke rail–road intermodal networks: a waiting versus path redistribution strategy using backup hub mechanisms. A multi-objective optimization model was constructed to maximize network resilience, minimize transportation time, and reduce costs. Resilience was measured by the demand gap weighted by demand urgency. A rolling horizon optimization framework is established to address the temporal dynamics of disaster relief operations. A Q-learning-enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is developed to solve the optimization problem, constructing a 16-dimensional state space by integrating objective function values and population diversity metrics for intelligent local search. Using severely affected areas from the Wenchuan earthquake as a case study, the experimental results demonstrate that the improved algorithm reduces average objective function values by 17.75%, 49.82%, and 19.92%, respectively, compared with the standard NSGA-II. Incorporating demand urgency factors reduces the material shortage index by 53.41%, better reflecting humanitarian priorities. By comparing the average function values across Periods 1–6, the first four periods are suitable for the path reallocation strategy, while the subsequent two periods should adopt the “continue waiting” strategy. The study provides actionable insights for emergency managers in optimal strategy selection in disaster relief operations.

Simulating protective cordon pricing to balance congestion management and affordability in San Francisco

Kampala City, Uganda’s capital, faces severe and escalating traffic congestion due to rapid urbanisation, population growth, and rising vehicle ownership, leading to substantial economic losses, increased emissions, and degraded quality of life. Traditional reactive traffic management systems are inadequate for addressing dynamic urban mobility patterns in resource constrained environments. This study develops an intelligent traffic congestion prediction framework using machine learning classification. A dataset of 500 observations from 15 major road segments is processed through rigorous preprocessing, domain-informed feature engineering (including capacity utilisation and flow efficiency), and ensemble classifiers to categorise congestion into four actionable severity levels: Low, Medium, High, and severe. Experimental evaluation on a stratified test set shows that XGBoost outperforms Random Forest, achieving 84.0% overall accuracy and the highest precision (0.808%), recall (0.840%), and F1-score (0.820%). Feature importance analysis highlights capacity utilisation, vehicle density, and flow efficiency as the dominant predictors, consistent with fundamental traffic flow theory. The proposed system establishes a scalable, interpretable foundation for proactive congestion management in developing cities. With future enhancements in real-world data integration and temporal-spatial modelling, it holds strong potential to support adaptive traffic control, incident response, and data-driven urban planning in Kampala and similar contexts.

Distribution systems, particularly in densely loaded urban areas, frequently experience transformer overloading during peak consumption hours. Rather than resorting to costly infrastructure reinforcement or complex Flexible AC Transmission Systems (FACTS), this paper proposes a cost-effective optimization and control approach utilizing a Continuously Variable Series Reactor (CVSR). Unlike FACTS devices that rely on expensive power electronics, the CVSR capitalizes on magnetic core saturation to regulate reactance, offering a robust solution at a fraction of the cost (~$10/kVAr). The CVSR regulates the reactance on the primary side of the transformer via magnetic core saturation to control power distribution among transformers. In this study, the steady-state framework of the CVSR is derived and integrated into power flow formulations. The proposed model is validated using a two-parallel transformer system and tested on the heavily meshed IEEE 342-Node Low Voltage Networked Test System (LVNTS). Simulation results demonstrate the CVSR effectively relieves transformer overloading by redistributing power flow, thereby enhancing system flexibility and allowing utilities to avoid or defer extensive grid upgrades.

Traffic congestion is one of the major challenges in modern transportation systems. Mathematical modeling plays a crucial role in understanding and managing highway traffic flow. Differential equation models are widely used to represent the dynamic behavior of traffic density, speed, and flow over space and time. This paper discusses the role of differential equation models in highway congestion management. Key traffic flow models such as the Lighthill–Whitham–Richards (LWR) model and Greenshields fundamental relationship are explained. A numerical example is also provided to illustrate how differential equations help analyze congestion conditions. The results demonstrate that these models help traffic engineers predict congestion formation and implement control strategies such as speed regulation and traffic flow management

Swarm robotics has emerged as a transformative paradigm for accomplishing complex tasks through the coordinated operation of large numbers of simple robots. As swarm sizes scale from tens to hundreds of agents, effective control strategies become increasingly critical to prevent congestion, maintain system throughput, and ensure safe coordination. This review surveys the state of the art in swarm robotics control, with particular emphasis on coordination mechanisms and congestion management. Four major control paradigms are examined: centralized trajectory planning, reactive collision avoidance, spatial partitioning, and learning-based adaptive methods. Beyond these established approaches, the paper explores emerging hybrid frameworks, including Centralized Training with Decentralized Execution (CTDE) architectures, Graph Neural Network (GNN)-based coordination, and hybrid rule-learning integration. For each paradigm, a formal mathematical analysis is provided, covering computational complexity, convergence properties, and stability guarantees. To support rigorous and reproducible evaluation, this paper proposes a standardized assessment framework comprising mandatory performance metrics, scalability benchmarks, and systematic ablation protocols. Finally, open challenges are identified and future research directions outlined, with the aim of advancing the development of robust and deployable swarm robotic systems.

Congestion signals heart failure progression and drives decompensation. Reliable management strategies remain poorly developed. We evaluated 12-months of congestion-guided clinical management following implantation of an inferior vena cava (IVC) sensor. Data were combined from two prospective studies (FUTURE-HF and FUTURE-HFII) (N=65, mean age 65.7±9.5 years; 75.4% NYHA III; 90.8% HFrEF). Patients recorded daily IVC parameters. Adjudicated safety outcomes, sensor-derived IVC area measurement versus CT imaging, medication adjustments, and clinical outcomes at 12-months were analysed.No adjudicated device or procedure-related serious adverse events occurred. Excellent correlation was observed between sensor-derived and CT-derived IVC area (n=44; R2=0.97; mean relative error <5%). Patient adherence was 93% and a sustained, significant reduction in IVC area was observed (8.1%, p<0.005), correlated with clinical improvements (p<0.001), despite no significant change in body weight. Improvements were observed in NYHA functional class (Class III: 74.5% improved to 40.0%; p<0.01) and NT-proBNP (median 1697 reduced to 998 ng/L; p<0.001). HF events (HFEs) were lower post-implant (0.31/year, 1.67/year pre-implant; 84.5% relative reduction; rate ratio: 0.18; 95% CI: 0.08-0.29). Medication adjustments (n=415) included diuretic titration (57%) and increased use of guideline directed medical therapy from baseline to 12-months (28%). Congestion management through ambulatory IVC monitoring demonstrated excellent safety, sustained accuracy, and high levels of patient adherence at 12-months after sensor implantation. This was associated with improved HF congestion status and a lower observed rate of HFEs, supporting investigation of congestion-guided management using IVC monitoring in a pivotal randomized clinical trial.

Power system control rooms are undergoing a profound transformation as renewable integration, distributed energy resources, sector coupling, and increasing operational uncertainty reshape the technical, organisational, and cognitive demands of grid operation. At the same time, Digital Twins and Agentic Artificial Intelligence offer new possibilities for monitoring, forecasting, reasoning, and decision support. However, existing control room architectures remain fragmented and insufficiently structured to support the coherent integration of digital models, intelligent reasoning systems, human operators, and regulatory accountability mechanisms in safety-critical power system environments. This article addresses that gap through a PRISMA ScR-informed scoping review combined with a structured architectural synthesis process. The study develops Infostructure as a reference architectural framework for situation awareness in future power system control rooms. The framework is derived from a synthesis of operational challenges, regulatory constraints, and human AI collaboration requirements identified across the scientific and regulatory literature. Infostructure formalises four interrelated architectural layers, Physical, Semantic, Orchestration, and Cognitive, constrained by cross cutting governance and compliance principles. The architectural coverage and internal coherence of the framework are illustrated through representative transmission and distribution system use cases, including wide area disturbance anticipation, distribution level congestion management, and cross organisational coordination during extreme events. A structured research and validation agenda is further outlined to support empirical evaluation and phased implementation. By transforming review-based synthesis into a coherent architectural formalisation, Infostructure contributes a rigorous foundation for the evolution of transparent, accountable, and resilient power system control rooms.

This study investigates the impact of monitoring infrastructure characteristics (specifically sensor penetration and measurement accuracy) on the effectiveness of voltage regulation and congestion management within distribution networks. As distribution system operators transition toward active management, the integration of Distributed renewable Generation (DG) and demand response introduces significant physical and cyber-physical uncertainties. To address these challenges, a smart grid service framework has been employed to optimize flexibility resources from aggregated users and DG inverters through a genetic algorithm. The framework was tested on the IEEE European Low Voltage Test Feeder across various scenarios defined by distributed monitoring systems' penetration and their measurement accuracy. Results show that sensor penetration has a dominant impact: increasing monitoring coverage from 0% to 100% raises the percentage of cases with fewer than one residual congestion from 46.2% to 91.9% (sensors with an accuracy class of 2%), reaching 97.9% with an accuracy class of 0.5%, while voltage violations are eliminated under full monitoring. These findings suggest that widespread sensor deployment, with a suitable measurement accuracy, is a fundamental prerequisite for reliable and efficient smart grid operation.

The increasing electrification of transport and heating sectors through electric vehicles and heat pumps presents significant challenges for low-voltage distribution grids, leading to potential congestion situations. We present a decentralized agent-based optimization approach for congestion management that leverages both legally mandated consumer flexibility and voluntary battery flexibility. This multi-agent system enables individual households to participate in grid congestion management through autonomous decision-making while aiming to preserve household data sovereignty and to achieve privacy-preserving coordination via minimal information exchange. Our approach incorporates the German Energy Industry Act (§ 14a EnWG) framework and utilises the Alternating Direction Method of Multipliers (ADMM) for decentralized optimization. Evaluation results using the IEEE-33 test system demonstrate significant peak load reduction, complete elimination of thermal constraint violations, and real-time feasibility. These findings demonstrate that decentralized coordination can effectively manage grid congestion while maintaining privacy and regulatory compliance, offering a scalable alternative to traditional centralized approaches for future smart grid implementations.