Search Results (2,509)

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Forecasting stock market crashes is difficult because such events are rare, highly nonlinear, and shaped by latent structural and behavioral forces. This study introduces a calibrated and interpretable Random Forest framework for detecting pre-crash conditions through structural feature engineering, early-warning calibration, and model explainability. Using daily data on global equity indices and major large-cap stocks from the U.S., Europe, and Asia, we construct a feature set that captures volatility expansion, moving-average deterioration, Bollinger Band width, and short-horizon return dynamics. Probability-threshold optimization significantly improves sensitivity to rare events and yields an operating point at a crash-probability threshold of 0.33. Compared with econometric and machine learning benchmarks, the calibrated model attains higher precision while maintaining competitive F1 and MCC scores, and it delivers meaningful early-warning signals with an average lead-time of around 60 days. SHAP analysis indicates that predictions are anchored in theoretically consistent indicators, particularly volatility clustering and weakening trends, while robustness checks show resilience to noise, structural perturbations, and simulated flash crashes. Taken together, these results provide a transparent and reproducible blueprint for building operational early-warning systems in financial markets. Full article

Nanotechnology is becoming a key tool in plant biotechnology, enabling nanoparticles (NPs) to deliver biomolecules with high precision and to enhance plant and tissue resilience under stress. However, the literature remains fragmented across genetic delivery, in vitro regeneration, stress mitigation, and germplasm cryopreservation, and it still lacks standardized, comparable protocols and robust long-term safety assessments—particularly for NP use in cryogenic workflows. This review critically integrates recent advances in NP-enabled (i) genetic engineering and transformation, (ii) tissue culture and regeneration, (iii) nanofertilization and abiotic stress mitigation, and (iv) cryopreservation of plant germplasm. Across these areas, the most consistent findings indicate that NPs can facilitate targeted transport of DNA, RNA, proteins, and regulatory complexes; modulate oxidative and osmotic stress responses; and improve regeneration performance in recalcitrant species. In cryopreservation, selected nanomaterials act as multifunctional cryoprotective adjuvants by suppressing oxidative injury, stabilizing cellular membranes, and improving post-thaw viability and regrowth of sensitive tissues. At the same time, NP outcomes are highly context-dependent, with efficacy governed by dose, size, and surface chemistry; formulation; plant genotype; and interactions with culture media or vitrification solutions. Evidence of potential phytotoxicity, persistence, and biosafety risks highlights the need for harmonized reporting, mechanistic studies on NP–cell interfaces, and evaluation of environmental fate. Expected outcomes of this review include a consolidated framework linking NP properties to biological endpoints, identification of design principles for application-specific NP selection, and a set of research priorities to accelerate the safe and reproducible translation of nanotechnology into sustainable plant biotechnology and long-term germplasm preservation. Full article

Given the central importance of maritime logistics to global trade, accurate and efficient vessel motion forecasting is essential for strengthening supply chain resilience and improving operational efficiency. However, traditional physical and statistical models often fail to effectively capture the multivariate, noisy, and strongly coupled nature of maritime dynamics. In this manuscript, we adapt the DSformer architecture for ship motion forecasting, leveraging its dual sampling and dual-attention design to address the multi-scale and cross-variable dependencies inherent in maritime data. Across three real-world datasets, the adapted DSformer reduces prediction error by 23% and training time by 70% compared with 13 state-of-the-art (SOTA) baselines. Moreover, we identify a consistent relationship between sampling strategies and sea states, where dense sampling performs best under stable conditions, whereas moderately sparse sampling with multi-head attention improves robustness under turbulent environments. These results apply the algorithm’s new capabilities to the daily management of maritime logistics. By adapting the architecture to real-world operational settings and optimizing its key parameters, the approach enables efficient, real-time vessel forecasting and decision support across global supply chains. Full article

Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide. Full article

Soil salinization remains a critical constraint on global land sustainability, severely limiting agricultural output and ecosystem resilience. To address this issue, a field trial was implemented to investigate the interactive benefits of vermicompost (VC) and a novel soil conditioner derived from coal gasification slag-based soil conditioner (CGSS) in mitigating saline–alkali stress. The perennial forage grass Leymus chinensis, valued for its ecological robustness and economic potential under adverse soil conditions, served as the test species. Five treatments were established: CK (unamended), T1 (CGSS alone), T2 (VC alone), T3 (CGSS:VC = 1:1), T4 (CGSS:VC = 1:2), and T5 (CGSS:VC = 2:1). Study results indicate that the combined application of CGSS and VC outperformed individual amendments, with the T4 treatment demonstrating the most effective results. Compared to CK, T4 reduced soil electrical conductivity (EC) by 12.00% and pH by 5.17% (p < 0.05), while markedly enhancing key fertility indicators—including soil organic matter and the availability of nitrogen, phosphorus, and potassium. Thus, these improvements translated into superior growth of L. chinensis, reflected in significantly greater dry biomass, expanded leaf area, and increased plant height. Additionally, the T4 treatment increased soil microbial richness (Chao1 index) by 21.5% and elevated the relative abundance of the Acidobacteria functional group by 16.9% (p < 0.05). Hence, T4 treatment (CGSS: 15,000 kg·ha⁻¹; VC: 30,000 kg·ha⁻¹) was identified as the optimal remediation strategy through a fuzzy comprehensive evaluation that integrated multiple soil and plant indicators. From an economic perspective, the T4 treatment (corresponding to a VC-CGSS application ratio of 2: 1) exhibits a lower cost compared to other similar soil conditioners and organic fertilizer combinations for saline–alkali soil remediation. This study not only offers a practical and economically viable approach for reclaiming degraded saline–alkali soils but also advances the circular utilization of coal-based solid waste. Furthermore, it deepens our understanding of how integrated soil amendments modulate the soil–microbe–plant nexus under abiotic stress. Full article

Sustainable port development in coastal regions necessitates robust frameworks for quantifying hydrodynamic risks under climate change. To bridge the gap between generic guidelines and site-specific resilience planning, this study proposes and applies a numerical modeling-based risk assessment framework. Within the context of the Port Master Plan, the framework is applied to the critical case of Takoradi Port in West Africa, employing a two-dimensional hydrodynamic model to simulate current fields under three current regimes, “Normal”, “Stronger”, and “Estimated Extreme” scenarios, for the first time. The model quantifies key hydrologic parameters such as current velocity and direction in critical zones (the approach channel, port basin, and berths), providing actionable data for the Port Master Plan. Key new findings include the following: (1) Northeastward surface currents, driven by the southwest monsoon, dominate the study area; breakwater sheltering creates a prominent circulation zone north of the port entrance. (2) Under extreme conditions, the approach channel exhibits amplified currents (0.3–0.7 m/s), while inner port areas maintain stable conditions (<0.1 m/s). (3) A stark spatial differentiation in designed current velocities for 2–100 years return periods, where the 100-year extreme current velocity in the external approach channel (0.87 m/s at P1) exceeds the range in the internal zones (0.01–0.15 m/s) by approximately 5 to 86 times. The study validates the framework’s utility in assessing hydrodynamic risks. By integrating numerical simulation with risk assessment, this work provides a scalable methodological contribution that can be adapted to other port environments, directly supporting the global pursuit of sustainable and resilient ports. Full article

Rural self-constructed homes in China’s cold-temperate regions often exhibit poor energy performance due to limited budgets and substandard construction, leading to a high reliance on active systems and low climate resilience. This study assesses four passive cooling strategies, nighttime natural ventilation (NNV), envelope retrofitting (ER), window shading (WS), and window-to-wall ratio adjustment (WWR), under 2040–2080 representative future climate conditions using energy simulation, multi-objective optimization, sensitivity analysis, and life-cycle cost assessment. Combined measures (COM) cut annual cooling demand by ~43% and representative peak cooling loads by ~50%. NNV alone delivers ~37% cooling reduction with rapid payback, while ER primarily mitigates heating demand. WS provides moderate cooling but slightly increases winter energy use, and WWR has minimal impact. Economic and sensitivity analyses indicate that COM and NNV are robust and cost-effective, making them the most suitable strategies for low-energy, climate-resilient retrofits in cold-climate rural residences. Since statistically extreme heat events are not explicitly modeled, the findings reflect relative performance under representative climatic conditions rather than guaranteed resilience under extreme heatwaves. Full article

Selecting a sustainable healthcare waste treatment method is a complex multi-criteria problem influenced by environmental, economic, social and technological factors. This study addresses key gaps in the literature by proposing an intuitionistic fuzzy AHP–TOPSIS framework that explicitly models cognitive uncertainty and expert hesitation, while demonstrating its application through a real-world case study in Adana, Türkiye. In contrast to prior studies utilizing fewer criteria, our framework evaluates four treatment alternatives—incineration, steam sterilization, microwave, and landfill—across 17 comprehensive criteria that directly integrate circular economy principles such as resource recovery and energy efficiency. The results indicate that steam sterilization is the most sustainable option, demonstrating superior performance across environmental, economic, social, and technological dimensions. A 15-scenario sensitivity analysis ensures ranking resilience across varying decision contexts. Furthermore, a systematic comparative analysis highlights the methodological advantages of the proposed framework in terms of analytical granularity and robustness compared to existing models. The study also offers step-by-step operational guidance, creating a transparent and policy-responsive decision-support tool for healthcare waste management authorities to advance sustainable practices. Full article

Wetlands in dryland regions are rapidly degrading under the combined effects of climate change and human regulation, yet long-term, seasonally resolved assessments of surface water extent (SWE) and its dynamics remain scarce. Here, we map and analyze seasonal surface water extent (SWE) over the period 2000–2024 in the Ile River Delta (IRD), south-eastern Kazakhstan, using Landsat TM/ETM+/OLI data within the Google Earth Engine (GEE) framework. We integrate multiple indices using the modified Normalized Difference Water Index (mNDWI), Automated Water Extraction Index (AWEI) variants, Water Index 2015 (WI2015), and Multi-Band Water Index (MBWI) with dynamic Otsu thresholding. The resulting index-wise binary water maps are merged via ensemble agreement (intersection, majority, union) to delineate three SWE regimes: stable (persists most of the time), periodic (appears regularly but not in every season), and ephemeral (appears only occasionally). Validation against Sentinel-2 imagery showed high accuracy F1-Score/Overall accuracy (F1/OA ≈ 0.85/85%), confirming our workflow to be robust. Hydroclimatic drivers were evaluated through modified Mann–Kendall (MMK) and Spearman’s (r) correlations between SWE, discharge (D), water level (WL), precipitation (P), and air temperature (AT), while a hybrid ensemble–occurrence framework was applied to identify degradation and transition patterns. Trend analysis revealed significant long–term declines, most pronounced during summer and fall. Discharge is predominantly controlled by stable spring SWE, while discharge and temperature jointly influence periodic SWE in summer–fall, with warming reducing the delta surface water. Ephemeral SWE responds episodically to flow pulses, whereas precipitation played a limited role in this semi–arid region. Spatially, area(s) of interest (AOI)-II/III (the main distributary system) support the most extensive yet dynamic wetlands. In contrast, AOI-I and AOI-IV host smaller, more constrained wetland mosaics. AOI-I shows persistence under steady low flows, while AOI-IV reflects a stressed system with sporadic high-water levels. Overall, the results highlight the dominant influence of flow regulation and distributary allocation on IRD hydrology and the need for ecologically timed releases, targeted restoration, and transboundary cooperation to sustain delta resilience. Full article

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets. Full article

The growing incorporation of distributed energy resources (DER) in power distribution grids, although pivotal to the energy transition, increases operational variability and amplifies the exposure to disturbances that can compromise resilience and the continuity of service during contingencies. Addressing these challenges requires both a shift toward flexible distribution architectures and the adoption of advanced power electronics interfacing systems. In this setting, this paper proposes a resilience-oriented strategy for medium-voltage (MV) distribution systems and clustered hybrid AC/DC microgrids interfaced through solid-state transformers (SSTs). When a fault occurs along an MV feeder segment, the affected microgrids naturally transition to islanded operation. However, once their local generation and storage become insufficient to sustain autonomous operation, the proposed framework reconfigures the power routing within the cluster by activating an emergency low-voltage DC (LVDC) power path that bypasses the faulted MV section. This mechanism enables controlled power sharing between microgrids during prolonged MV outages, ensuring the supply of priority loads without oversizing SSTs or reinforcing existing infrastructure. Experimental validation on a reduced-scale SST prototype demonstrates stable grid-forming and grid-following operation. The reliability of the proposed scheme is supported by both steady-state and transient experimental results, confirming accurate voltage regulation, balanced sinusoidal waveforms, and low current tracking errors. All tests were conducted at a switching frequency of 50 kHz, highlighting the robustness of the proposed architecture under dynamic operation. Full article

As one of the fastest growing economies in the world, it is critical for India to build climate-resilient infrastructure. Its rapidly increasing population growth and the consequent migration to urban areas, coupled with climate risks, have made infrastructure development a key priority area in India. Thus, ensuring that existing infrastructure and future constructions integrate strategies to address climate-related risks is vital. Despite the growing recognition of the importance of legal and policy landscapes on climate-resilient infrastructure, there is a very limited number of studies on this topic, especially in India. Thus, this study sought to bridge this gap by evaluating the influence of the Indian legal and policy framework on climate-resilient infrastructure. To this end, an analytical, comparative, and evaluative approach was adopted, employing benchmarks from international legal provisions and best practices from Japan’s legal and policy systems. In addition to doctrinal legal analysis using primary and secondary sources, case studies on the Mumbai Coastal Road Project and the Chandigarh–Manali Highway were performed to assess how the extant laws operate in the field. The findings indicate that while India’s legal system is gradually incorporating climate-risk considerations into its infrastructure sector, the effects of these considerations remain constrained due to institutional co-ordination challenges and limited enforceable obligations. Therefore, streamlining climate-resilient infrastructure governance requires more robust climate change legislation and improved implementation mechanisms. The findings of this study provide useful insights for legislators and policymakers in strengthening climate resilience integration within India’s infrastructure governance framework. Full article

As contemporary global supply chains have become interconnected and exposed to diverse escalating cyber threats, Cyber Supply Chain Risk Management (C-SCRM) has rapidly evolved as a managerial imperative to safeguard security, robustness, and resilience, and hence ensure organizational sustainability and growth. While the concept of C-SCRM has recently received much attention among scholars, practitioners, and policymakers as an emerging field of study, its conceptual utility and theoretical foundation remain undeveloped. To address this gap, this paper provides a systematic literature review of C-SCRM using a hybrid approach that integrates bibliometric and concept evaluation analysis to ensure the goodness of the concept. A total of 175 relevant peer-reviewed scholarly articles from the Web of Science (WOS) Core Collection were collected and analyzed. The review reveals that the concept has many strengths, in terms of its interdisciplinary conceptual foundation and growing managerial relevance, but it also suffers from conceptual diffusion, overlapping terminology, and limited construct operationalization that inhibits theory development, hinders empirical accumulation, and limits practitioners’ ability to operationalize C-SCRM as a strategic resource. This review contributes to the C-SCRM literature by providing (1) a historical overview and intellectual structure of C-SCRM; (2) a synthesis and comparative analysis of the existing definitions; (3) an evaluation of the conceptual adequacy and theoretical relevance that underpin C-SCRM research based on established criteria and (4) conceptual and empirical research directions as well as an integrative framework. Based on the insights, our review might facilitate the improvement of multidimensional construct clarity and validation in future empirical studies and could be a useful tool for managers to benchmark C-SCRM maturity in practice. Full article

Small- and medium-sized enterprises (SMEs) are central to sustainable development in transition economies, yet their financial fragility often limits resilience and the capacity to invest in innovation and responsible practices. Despite growing interest in SME, financial health and its role in sustainability, empirical evidence from small transition economies like Montenegro remains scarce, particularly on how liquidity and profitability dynamics underpin conditions for SDG-aligned growth. This study addresses this gap by analyzing how core financial indicators—cash position, capital structure, and working capital efficiency—affect liquidity and profitability among 345 Montenegrin SMEs across manufacturing, services, and trade. Using OLS and robust regression models, results reveal that a higher cash-to-revenue ratio and moderate leverage significantly enhance both short-term solvency and profitability, while working capital efficiency shows nuanced effects and sector-specific patterns emerge in capital-intensive industries. These findings highlight financial management as a foundation for SME resilience, creating financial preconditions for innovation and digital investments in aligned with SDGs (goals 8, 9, 12). Policy recommendations focus on improving finance access and financial literacy to foster innovation-driven, sustainable SME models aligned with the 2030 Agenda. Full article