Historically, power systems have been developed predominantly based on thermal or hydro resources, depending on the local resources availability and system needs. The distinct characteristics of these technologies shaped how adequacy requirements were defined, allowing systems to be classified as either capacity-constrained or energy-constrained. However, as variable renewable sources (VRS) such as wind and solar become increasingly dominant in system expansions, these one-dimensional approaches are no longer sufficient to ensure reliability or investment efficiency, due to the inherent uncertainty in resource availability and lack of dispatchability. Although some electricity markets already consider products related to energy and capacity dimensions independently, most adequacy mechanisms and market designs continue to focus on a single requirement — typically capacity — or are tailored to promote specific technologies. This paper aims to the ongoing debate on electricity market design by characterizing evolving adequacy requirements and proposing a redefinition of adequacy products that can induce efficient expansion. It emphasizes that adequacy products must be jointly designed and monitored across both the energy and capacity dimensions. The Brazilian power system, with its high share of hydropower and rapidly growing VRS participation, offers a relevant case study for these challenges and provides lessons that are broadly applicable to other contexts. The analysis shows that by aligning market products with multidimensional adequacy requirements, policymakers and regulators can avoid inefficient technological mandates, promote competitive neutrality, and support a more robust and cost-effective expansion of power systems in the decarbonization era. • In the decarbonization era, power systems requirements need to be recharacterized. • Adequacy mechanisms must consider jointly energy and power capacity dimensions. • Redefinition of adequacy products can induce efficient power system expansion. • Multidimensional adequacy products can avoid inefficient technological choices.

CA-YOLO for abnormal behavior detection in power systems

The increasing exposure of modern power systems to climate-induced hazards, cyber threats, and operational uncertainty has intensified the energy trilemma of sustainability, security, and affordability. Rapid decentralisation and digitalisation driven by renewable integration, microgrids, and active demand participation have rendered conventional planning and operational approaches inadequate for ensuring resilient and sustainable electricity networks. This challenge requires adaptive, data-driven frameworks that provide real-time situational awareness, predictive intelligence, and coordinated control under uncertainty. This paper presents a PRISMA-guided systematic review of Digital Twin (DT) technologies, with a particular focus on Climate-Aware Digital Twins (CADTs) and AI-driven analytics for enhancing sustainability and resilience in modern power systems. The review synthesises recent advances in predictive forecasting, decentralised energy management, grid resilience enhancement, and hazard-informed system restoration. Key application domains include microgrid resilience, demand response optimisation, renewable-dominated networks, and satellite-assisted recovery following extreme events. Enabling technologies such as machine learning, edge-cloud computing, blockchain, and the Internet of Things (IoT) are examined as foundational components supporting real-time synchronisation, secure data exchange, and autonomous control. Across the reviewed literature, four persistent challenges are identified: data latency and availability constraints, high computational and modelling complexity, limited interoperability with legacy infrastructure, and unresolved cybersecurity risks. Building on these findings, the paper proposes a strategic roadmap for integrating DTs with climate-aware forecasting and adaptive control architectures, highlighting pathways towards intelligent, self-healing, and hazard-resilient power systems. • Digital twins enhance energy resilience and predictive grid management. • AI-driven forecasting mitigates climate risks in decentralised energy systems. • Satellite-assisted monitoring enables faster post-disaster grid restoration. • Resilient grids ensure autonomous power supply during extreme events. • Blockchain and AI enhance security, optimise energy flow, and support grid resilience.

This paper offers a comprehensive optimisation tool for the design and assessment of hybrid maritime power systems that combine internal combustion engines, fuel cells, and battery energy storage systems. Using a surrogate-assisted NSGA-II algorithm, the framework concurrently reduces operational expenditure, CO₂ emissions, and life cycle cost assessment. Under constant technical criteria, including system weight and volume, with and without waste heat recovery, four fuel pathways—diesel, LNG, methanol, and ammonia—are evaluated. The results reveal considerable economic and environmental differences compared to the diesel baseline: LNG increases LCCA by 0.5 % (€1.2M) and global warming potential (GWP) by 2 % (1752 kg), while acidification potential (AP) and aerosol formation potential (AFP) decrease by 91 % (914 kg and 1118 kg, respectively). Methanol reduces LCCA by 14.3 % (€35.3M), GWP by 36 % (35,540 kg), and AP/AFP by 81 %, offering a cost-effective and environmentally balanced solution. Ammonia eliminates GWP, AP, and AFP, though with a 10.7 % (€60M) increase in LCCA, demonstrating its potential for long-term decarbonisation. The findings show clear Pareto fronts for every fuel, suggesting that the possible design area is significantly influenced by fuel type. The framework offers practical guidance for designing energy-efficient, low-emission vessels, aiding in sustainable marine energy transitions.

PSTG-Net: A physics-informed spatiotemporal multimodal dynamic graph network for wind power forecasting

Neuronal ensembles, defined as groups of coactive neurons, are physiological modules of the cerebral cortex. Calcium imaging and optogenetics have enabled mapping and manipulating ensembles with single-cell resolution in mouse visual cortex, providing evidence of their importance. Ensembles dominate cortical activity and are generated endogenously or by sensory stimulation. Ensembles are imprinted by activating neurons synchronously and can be reactivated by "pattern completion" trigger cells. Intrinsic excitability mediates ensemble coactivation and reactivation, while UP states shield ongoing ensembles from external inputs. Neurons can belong to different ensembles, forming a combinatorial system that encodes visual stimuli accurately and stably. Ensembles contain pyramidal neurons and interneurons and inhibited "offsemble" cells. Cross inhibition makes ensembles orthogonal to one another, while astrocytic activation increases ensemble occurrence. Ensembles can last for weeks, providing a substrate for long-term information storage, and they also capture the recent history of stimulus presentation, implementing short-term memory. Optogenetic manipulation of ensembles demonstrates that they are necessary and sufficient for visual discrimination and perceptual states. Ensembles are altered in mouse models of epilepsy, schizophrenia, Alzheimer's disease, autism spectrum disorders, and medically induced loss of consciousness. An ensemble model of the cortex is proposed in which ensembles are functional units that activate each other via trigger cells and silence nondesired ensembles by cross-inhibition. This generates a map of orthogonal attractor states, forming a computationally powerful memory and processing system. Ensembles are likely involved in many brain diseases, so manipulating them could offer avenues for new therapeutics.

Cell shape acquisition is a fundamental biological process that allows cells to establish and maintain morphologies adapted to their specialised functions while preserving tissue integrity. In plants, this process is strongly influenced by the presence of the cell wall, a dynamic extracellular network of polysaccharides and proteins that surrounds the plasma membrane and physically connects neighbouring cells. By constraining and directing cellular expansion, the cell wall plays a central role in controlling cell shape. In Arabidopsis leaves, epidermal pavement cells adopt a characteristic jigsaw-puzzle-like morphology through the formation of interdigitating lobes and necks, providing a powerful model system for dissecting the mechanisms underlying complex plant cell shape acquisition. Here, we demonstrate the involvement of the glycoside hydrolase BETA-GALACTOSIDASE 10 (BGAL10) in pavement cell morphogenesis. Using high-resolution time-series imaging, we analysed cell growth dynamics alongside the spatial expression and subcellular localisation of BGAL10, revealing a prominent role for BGAL10 in mature cells, particularly at curved regions of the cell wall along pavement cell lobes. Furthermore, Brillouin microscopy revealed altered mechanical properties in the bgal10-1 mutant, most notably at lobe-indentation interfaces and cell junctions. Together, our results indicate that BGAL10 fine-tunes lobe outgrowth, likely through modification of the hemicellulose matrix, thereby regulating cell wall extensibility and mechanical stress distribution during pavement cell shape acquisition.

An optimization framework for integrating electric vehicles and carbon capture: Bridging cost gaps via electric vehicles deployment

Towards the future low-inertia power systems: A review on grid-forming inverter control - advances, challenges, and opportunities

Combined VDCM-MPC control strategy for resonance suppression in shipboard DC power system

Toxic effects of penconazole on the polychaete Nereis (Hediste) diversicolor: Oxidative stress, fatty acid alterations, and histopathology.

An interpretable power system transient stability assessment method with expert guiding neural regression tree

A comprehensive review of lead acid battery end-of-life management: Future perspectives and guidelines

The rise in renewable energy resource (RES) consumption has prompted academics to maximize RES performance. Many studies address the optimal placement of photovoltaic (PV) units and DSTATCOM units, as well as reconfiguration hurdles, by specifying the number of PV units used in the simulation. Optimization alone cannot improve power system operational efficiency without considering the microgrid's PV unit count. Hence, the objective of this work is to address multi-objective problems in the context of modifying radial distribution systems to operate as a microgrid (MG), with the aim of ensuring the microgrid's security and stability while achieving optimal performance. In this paper, the Firefly algorithm (FA), an optimization technique, is used to determine the size, location, and DSTATCOM within specified ranges to determine the number of PV units to install and the open network lines. The Twain 84-bus system was employed to assess the suggested method's accuracy and efficacy. The outcomes amply illustrated the approach's superiority and efficacy in enhancing MG performance. The proposed method addresses PV unit installation and DSTATCOM installation and reconfiguration in the MG. Also, it enhances the voltage profile and reduces losses of both reactive and active power.

Optimizing the location of distributed generation units in electrical distribution networks is a well-established approach to improving grid performance, particularly for enhancing reliability and reducing operational losses. Recent large-scale blackouts in Spain and Portugal have underscored the vulnerability of power systems and the urgent need for more resilient, cost-effective, and efficient grid configurations. However, integrating distributed generation involves significant investment, operational, and maintenance costs, making optimal placement a critical challenge. Integrating optimally located distributed generation units into distribution networks using a modified artificial bee colony algorithm will substantially reduce power losses, voltage deviations, and total electricity costs compared to the conventional artificial bee colony and other state-of-the-art optimization methods. The main contributions of this work are: (1) the development of an enhanced artificial bee colony algorithm incorporating inverse population initialization and a bounded-round search strategy to improve diversity and avoid premature convergence; (2) the introduction of a chaotic cosine-based neighborhood search around the global best to accelerate convergence and enhance solution quality; and (3) the application and validation of the proposed method on IEEE 33-bus and 69-bus distribution systems, demonstrating its effectiveness in reducing power losses and improving voltage profiles. The proposed algorithm achieved reductions in total power loss of up to 82.33% for the IEEE 33-bus system and 86.6% for the IEEE 69-bus system, outperforming the basic artificial bee colony and other recent optimization techniques. The findings confirm that the modified artificial bee colony algorithm is a robust and efficient tool for distributed generation allocation, providing significant improvements in grid resilience, reliability, and operational efficiency in the face of increasing energy demands and instability risks.

Design and implementation of a nonlinear proportional-integral-derivative controller for enhanced frequency regulation in hybrid renewable power systems with energy storage integration

BESS degradation effects over operational and investment decisions in power system expansion planning

Sustained ERK signaling couples the injury response to organizer formation during Hydra head regeneration.

Wildfire risk assessment of nuclear power plant off-site power systems using human-activity–informed localized inputs: A case study of the Kori nuclear power plant

Deep reinforcement learning-based energy management strategy integrating physics information and expert system: efficient regenerative braking energy recovery in urban rail transit traction power system