Power systems are being reshaped by decarbonization, digitalization, and high shares of renewables. At the same time, increasingly severe extreme conditions expose the limits of traditional reliability frameworks, calling for risk-aware, resilience-oriented approaches to address high-impact, low-probability (HILP) events. In this context, this paper presents a comprehensive overview of the foundations of power system resilience. It revisits the transition from reliability to resilience, formalizes key concepts and metrics, and introduces advanced approaches for resilience assessment, including fragility-based modeling, cascading failure analysis, and tail-risk indicators. The paper further examines resilience-oriented investment planning, operational strategies across all event phases, and the role of distributed energy resources, microgrids, and cybersecurity. The analysis highlights that resilience extends reliability by focusing on extreme conditions, fundamentally reshaping decision-making and requiring coordinated strategies across infrastructure, operation, and governance.
Power systems face increasing uncertainties that create nonlinear regime-dependent dynamics. Critical Clearing Time (CCT) remains a key transient stability metric, yet analytical relations with operating conditions are rarely tractable. Advanced machine learning techniques offer accurate CCT prediction and partial interpretability, but fail to uncover the governing functional dependencies. This paper introduces a Piecewise Symbolic Regression (Pc-SR) framework that automatically discovers regime-conditioned equations linking system variables to CCT. Pc-SR combines cost-complexity-pruned decision trees for task-aware partitioning with symbolic models in each region. Validation on synthetic data confirms recovery of correct partitions and equations under noise, while tests on single-machine infinite-bus variants rediscover analytical CCT equations. Applied to a modified 39-bus system with inverter-based resources, Pc-SR produces interpretable, regime-specific CCT surrogates matching black-box accuracy while exposing nonlinearities and interactions. This framework advances beyond descriptive explainability, providing transparent models to accelerate stability screening and support operator insight into complex dynamic behaviors.
Battery energy storage systems (BESS) are key units to deal with fluctuating renewable generation by offering frequency responses (FRs). To facilitate the deployment of grid-scale BESS for FR, this paper proposes energy management and planning methods for BESS in the context of end-state FR service markets in the UK. Driven by the specific market mechanisms, the energy management method dynamically adjusts half-hour operational baseline profiles to mitigate deviations of state of energy forecasts from target energy footroom/headroom levels. Then, the lifecycle BESS operation and economics are simulated, based on which the equivalent annual annuity of the BESS project is maximised by optimising BESS capacities and target energy levels as well as FR bidding capacities. The proposed methods are applied to a stand-alone BESS providing either or both Dynamic Containment and Dynamic Moderation services, and discussed around FR delivery performance and sensitivity of planning results to clearing prices.
MXenes, a rapidly expanding family of two-dimensional transition metal carbides and nitrides, have emerged as promising materials for hospital-on-chip (HoC) diagnostics because of their high electrical conductivity, chemically tunable surfaces, and versatile biofunctionalization. Despite substantial advances in device performance, the molecular origins of signal generation, selectivity, and long-term stability remain poorly defined. This review establishes coordination chemistry as a mechanistic framework for interpreting MXene-based biosensing, highlighting how metal–ligand interactions at the biointerface can govern the analytical performance alongside intrinsic electronic conductivity. Ligand-field effects, hard–soft acid–base (HSAB) principles, redox-active coordination environments, and coordination-mediated charge transfer were examined in relation to biomolecular recognition, interfacial electron transfer, and signal transduction. Particular emphasis is placed on the influence of surface terminations (–O, –OH, –F, and –Cl), defect-associated metal sites, and dynamic ligand exchange on sensitivity, selectivity, antifouling behavior, signal fidelity, and operational stability. Coordination-engineered architectures, including MXene–metal nanoparticle hybrids, metalloporphyrin- and phthalocyanine-functionalized MXenes, MXene–metal–organic framework heterostructures, and assemblies incorporating metalloenzymes, aptamers, and antibodies, have been evaluated across electrochemical, optical, field-effect transistor, photoelectrochemical, and piezoelectric sensing platforms. Coordination-controlled nanozyme catalysis, oxidative degradation, biofouling, and interfacial electron transfer pathways are further considered in the context of device reliability and clinical translation. Oxidative instability, heterogeneous surface chemistry, limited clinical validation, and the absence of standardized manufacturing processes remain major barriers to implementation. Emerging strategies for next-generation MXene-enabled HoC diagnostics include ligand-programmable interfaces, single-atom coordination sites, metalloprotein-inspired biointerfaces, computational ligand field engineering, and artificial intelligence-assisted materials discovery.
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in quick, heuristic decisions, System 2 relies on logical reasoning for more accurate judgments and reduced biases. Foundational Large Language Models (LLMs) excel at fast decision-making but lack the depth for complex reasoning, as they have not yet fully embraced the step-by-step analysis characteristic of true System 2 thinking. Recently, reasoning LLMs like OpenAI's o1/o3 and DeepSeek's R1 have demonstrated expert-level performance in fields such as mathematics and coding, closely mimicking the deliberate reasoning of System 2 and showcasing human- like cognitive abilities. This survey begins with a brief overview of the progress in foundational LLMs and the early development of System 2 technologies, exploring how their combination has paved the way for reasoning LLMs. Next, we discuss how to construct reasoning LLMs, trace the evolution of various reasoning models, and examine the core methods that enable advanced reasoning behind them. Additionally, we provide an overview of reasoning benchmarks, offering an in-depth comparison of the performance of representative reasoning LLMs. Finally, we explore promising directions for advancing reasoning LLMs and maintain a real-time GitHub Repository to track the latest developments. We hope this survey will serve as a valuable resource to inspire innovation and drive progress in this rapidly evolving field.
