Search Results (181)

This paper developed a probabilistic framework for system level reliability and risk assessment that coupled hydraulic loading with structural response and explicitly modelled cascading interactions and statistical dependence between components. The contribution is a system-level reliability and risk modelling methodology that integrates dynamic cascading interactions, non-stationary design-life reliability accumulation, and system-level optimisation within a unified Monte Carlo architecture. Dynamic Monte Carlo simulation was used to evaluate individual, joint, conditional, and system-scale probabilities of failure across varying flood magnitudes and design lives. Model verification confirmed that discretisation and sampling errors were small relative to parameter-driven variability. Results showed that long-term system reliability arose from the combined influence of flood frequency, exposure duration, and the strength of interaction between interdependent structures. Frequent loading accelerates the accumulation of failure probability through repeated events, whereas rare events contribute more slowly but dominate extreme outcomes, indicating that cumulative reliability cannot be inferred by the linear extrapolation of annual probabilities. In an examined diversion–levee–basin configuration, strong structural coupling amplified vulnerability by contracting joint stability margins and increasing conditional failure probabilities. The system-level optimisation of structural parameters over the examined design life reduced cumulative system failure probability from 0.305 to 0.153, whereas single-component optimisation redistributed risk within the system without reducing total system risk. The framework advances beyond static risk analysis by integrating time-dependent reliability, cascading dependencies, and design-life optimisation for system-scale mitigation. Full article

Global supply chains face increasing vulnerability to disruptions from geopolitical tensions, natural disasters, and demand shocks. The global trade network for railway vans, critical for transcontinental freight transport, remains understudied despite its foundational role in global logistics. This study addresses the gap in understanding how the railway vans trade network structure evolves and responds to different types of shocks, moving beyond static analyses to capture dynamic vulnerabilities. Using UN Comtrade data (2013–2024), multi-level network analysis examined structural evolution at macroscopic, mesoscopic, and microscopic scales. Three risk propagation models simulated supply disruption, demand shock, and cooperation disruption scenarios to assess systemic vulnerabilities. The network transformed from a polycentric to core-periphery structure, with China dominating exports (67 partners in 2024) and Germany leading European integration. Supply disruptions from Romania and Czechia affected up to 114 countries under low risk absorption capacity (α = 0.1), while demand shocks from the USA impacted 53 countries. The disruption of strategic trade links, such as China–Australia, triggered severe systemic risks. The systemic criticality of risk sources varies by shock type, requiring context-specific resilience strategies. The findings guide policymakers in identifying critical vulnerabilities and designing targeted interventions for enhancing supply chain resilience in infrastructure sectors. Full article

The aging of the population and the increasing prevalence of multimorbidity contribute to the widespread use of polypharmacotherapy, which in turn elevates the risk of adverse drug reactions and clinically significant drug–drug interactions. One of the key yet frequently underestimated issues in clinical practice is the prescribing cascade, which occurs when an adverse drug reaction is misinterpreted as a new medical condition, leading to the initiation of an additional medication. This phenomenon is particularly relevant in the older population, in whom altered pharmacokinetics and pharmacodynamics, together with reduced organ reserve, increase susceptibility to adverse drug events, including nephrotoxicity (renal impairment is used throughout the review as a clinically relevant example of organ-specific harm resulting from prescribing cascades, rather than as the sole focus of the analysis). This article discusses the mechanisms and clinical consequences of the prescribing cascade—with particular emphasis on renal function deterioration—as well as strategies for its prevention in the geriatric population. Analysis of the literature indicates that prescribing cascades remain insufficiently recognized in clinical practice, despite the availability of pharmacotherapy assessment tools such as The American Geriatrics Society (AGS) Beers Criteria and the STOPP/START criteria. Documented prescribing cascades have been shown to contribute to deterioration in health status and quality of life, an increased frequency of hospitalizations, and a greater burden on healthcare systems. Particularly concerning are cascades involving cardiovascular, neurological, and analgesic medications, which may induce or exacerbate renal injury, ultimately leading to chronic kidney disease and organ failure. Prescribing cascades represent a significant yet frequently underestimated threat to the efficacy and safety of pharmacotherapy in older adults. Their consequences may extend beyond reduced quality of life and increased treatment costs to include serious complications such as the development of renal failure. Enhancing clinicians’ awareness, conducting systematic medication reviews, and employing validated assessment tools are essential for the identification and prevention of prescribing cascades, thereby reducing the risk of renal injury and improving clinical outcomes. Full article

The increasing complexity and variability of operating conditions, along with the occurrence of low-probability cascading failures, imposes more stringent requirements on data-driven intelligent methods for power system stability analysis. This paper proposes an intelligent enhancement approach for transient voltage stability assessment in modern power systems, considering improved conditional generative adversarial network (CGAN)-based sample balancing. Firstly, an improved CGAN incorporating an enhanced feature-distance metric is developed to accurately capture the distribution characteristics of real samples, effectively alleviating training issues such as gradient vanishing and mode collapse during adversarial learning. Secondly, an intelligent sample enhancement method for transient voltage stability is established based on the improved CGAN, which effectively complements the initial dataset and ensures the predictive performance of intelligent models under extreme operating conditions. Finally, a transient voltage stability assessment framework integrating a convolutional neural network and a transformer is proposed to enable efficient extraction of low-dimensional features and achieve accurate evaluation of transient voltage stability states. Full article

As nuclear energy expands, nuclear emergency response systems increasingly exhibit strong human–machine–environment (H–M–E) coupling, long-duration operations, and multi-department coordination, in which minor disturbances can be amplified by feedback loops into cascading failures and loss of situational control. To address the inability of conventional static and linear methods to represent dynamic risk evolution and chaotic uncertainty, this study proposes an integrated “risk network–chaotic evolution–synchronization control” framework. Based on 12-year-old on-site comprehensive drill reports from a Chinese nuclear power base, we construct a directed H–M–E risk network in a semi-quantitative, qualitative–quantitative manner and identify critical nodes using a composite betweenness–PageRank risk metric. We further abstract the system into a three-dimensional nonlinear coupled dynamical model; phase portraits, Lyapunov exponents, and bifurcation analysis confirm threshold effects, period-doubling routes, and chaotic attractors, revealing nonlinear amplification under strong coupling. Finally, an adaptive chaotic synchronization controller driven by network coupling strength is designed. Simulations show all strategies suppress chaos and achieve synchronization, while the machine-dominated strategy offers the best speed–energy trade-off for emergency resource allocation. Full article

Power system blackouts remain a major concern for modern electricity networks, as they often result from cascading failures that lead to substantial load shedding and widespread service disruptions. This paper presents a dynamic resilience assessment of hybrid AC/DC power systems and investigates the effectiveness of voltage-source-converter-based high-voltage direct current (VSC-HVDC) technology in enhancing system resilience under outage contingencies. The study contributes by integrating protection devices and their settings into the analysis and by providing a quantitative evaluation of the system response to N-2 and N-3 contingencies using PSS^®E simulations. The demand not served index is used as a measure of resilience, and its cumulative distribution functions are computed to compare the performance of AC and DC interconnections. The results underscore the importance of VSC-HVDC links in mitigating cascading failures, highlighting their potential as a resilience-enhancing component in modern power grids. Full article

The generative mechanisms underlying multifractal scaling in complex systems remain a fundamental unsolved problem, limiting our ability to distinguish healthy from pathological dynamics, predict system failures, or understand how scale-invariant organization emerges across vastly different physical domains. We resolve this challenge by introducing threshold sensitivity analysis—an extension of Chhabra–Jensen’s direct method—as a framework that classifies cascade types by examining how scaling reliability varies across moment orders q. Different q values systematically probe weak fluctuations (negative q) versus strong fluctuations (positive q), and the coefficient of determination ($r^2$) of partition function regressions quantifies scaling reliability at each q. Analyzing $r^2\left(q\right)$ patterns in 280 cardiac recordings (healthy controls through fatal heart failure), 200 financial time series (global equity markets and currencies, 2000–2025), and 80 climate stations (tropical to continental zones, 2000–2025), we discover a universal diagnostic signature: symmetric expansion of valid scaling behavior under relaxed $r^2$ thresholds, spanning both weak and strong fluctuations. This threshold sensitivity fingerprint—predicted by synthetic cascade simulations but never before validated empirically—uniquely identifies additomultiplicative cascades, hybrid processes that randomly alternate between additive stabilization and multiplicative amplification. Critically, this symmetric signature persists universally across domains: cardiac dynamics maintain consistent patterns across health and disease states, financial markets show varying robustness across asset classes (currencies more variable than US equities) while preserving a hybrid structure, and climate systems exhibit geographical variations (subtropical/continental stronger than tropical) without altering fundamental cascade type. These findings suggest that additomultiplicative organization is a unifying feature of complex adaptive systems, offering a resolution to decades of debate between additive and multiplicative models. The $r^2\left(q\right)$ profiling provides a mechanistic diagnostic capable of detecting early dysfunction, assessing system resilience, and revealing how environmental constraints shape—but do not determine—the fundamental principles governing multifractal complexity. Full article

Compound geological disaster chains pose major challenges for disaster prevention in mountainous regions due to their complex mechanisms and cascading impacts. This study investigates a landslide–debris flow–flash flood hazard chain that occurred on 21 July 2024 in the Xujia River catchment, Mianning County, Sichuan Province, China. This event is used as a representative case to improve the understanding of the formation and amplification mechanisms of breach-type debris flows through dynamic inversion constrained by sedimentary records. The objective is to reconstruct the evolution of the event and assess its downstream hazard extent. Post-disaster sedimentary and geomorphological records, including deposit distribution, channel aggradation, and flow traces, were systematically analyzed based on remote sensing interpretation, unmanned aerial vehicle surveys, and detailed field investigations. These sedimentary data were used as key constraints to estimate debris flow magnitude and mobility under different rainfall scenarios. A rainfall flood scenario-based estimation method was applied to quantify debris flow magnitude, and numerical simulations were conducted using the Rapid Mass Movement Simulation model to reproduce debris flow propagation and deposition processes. The results indicate that prolonged antecedent rainfall triggered slope failure in a tributary, leading to the accumulation of landslide-derived material and the formation of a temporary channel blockage. The subsequent breach of this blockage significantly amplified debris flow discharge, velocity, and sediment outflow, resulting in downstream hazard expansion. Simulation results constrained by sedimentary evidence show that peak discharge and solid material output under breach conditions were approximately three times higher than those of rainfall-driven scenarios under comparable rainfall frequencies. These findings demonstrate that sedimentary records provide critical constraints for the inversion of landslide debris flow disaster chain dynamics and highlight the effectiveness of post-disaster evidence based numerical assessment for hazard analysis and risk mitigation in debris flow-prone mountainous catchments. Full article

Mineral resource extraction and urban expansion in resource-based cities have progressively degraded regional ecosystems, leading to increasingly fragmented ecological patterns. Ecological network resilience plays a critical role in maintaining regional ecological stability. In this study, we integrated landscape ecology and systems science to develop a network model and assess the resilience of ecological networks in the coal mining subsidence area with high groundwater levels. This study employed morphological spatial pattern analysis (MSPA) and circuit theory to construct the ecological network. A cascading failure model was further applied to simulate network dynamics under three attack strategies. Based on a comparative analysis of these strategies, we introduce the concept of “dangerous nodes” to identify priority conservation areas. The research results show that 101 ecological source areas and 255 ecological corridors were identified in the study area. Topologically, its ecological network is characterized by a small number of core nodes and a large number of secondary nodes. When the adjustable parameter is $\alpha <1.2$, targeting low-degree nodes may inflict more severe damage on the network. When $\alpha >1.2$, attacks against nodes with a high-degree or high betweenness centrality may have significant cascading failure implications. Our results show that the network’s critical threshold $T_c$ depends on the number of dangerous nodes in the attack set. The distribution of these nodes differs substantially between low-degree attacks and those targeting high-degree or high betweenness centrality nodes. These findings advance ecological network optimization and provide practical guidance for ecosystem conservation and restoration in resource-based cities. Full article

Reciprocating compressor air volume control systems have been extensively investigated, with a primary objective of reducing energy consumption and associated carbon footprints. As a multi-actuator system, failures in this energy-efficient configuration can trigger severe operational disruptions with cascading consequences. To address this, we initially constructed numerical models of the multi-actuator energy-efficient system to decode the variational patterns of compressor dynamic pressure pulsations and connecting-rod small-end bush tribological behaviors under partial actuator fault conditions, thereby establishing foundational data for fault degradation stratification. Building upon this, we propose a Prognostics and Health Management (PHM) algorithm using fault degradation analysis, thereby materializing self-recovery functionality in response to various fault conditions. Experimental validation demonstrates that the self-recovery algorithm successfully contained deterioration propagation through proactive intervention. The system achieved autonomous healing within 8 s (mild faults) and 13 s (moderate faults), constraining discharge fluctuations and vibration amplitude within allowable thresholds. This study establishes a solution framework for preserving multi-actuator energy-efficient systems’ health, accuracy, and economy. Full article

Introduction: Cardiac amyloidosis, primarily comprising transthyretin amyloid cardiomyopathy (ATTR-CM) and light-chain amyloidosis with cardiac involvement (AL-cardiac), is an increasingly recognized contributor to the global heart failure burden. Management has shifted from supportive care to disease-modifying agents targeting specific stages of the amyloid cascade. This registry-based review qualitatively characterizes the current pharmacological clinical trial landscape through a registry-based analysis. Methods: A structured qualitative analysis of ClinicalTrials.gov was conducted for interventional trials registered between January 2015 and November 2025. Following PRISMA principles, studies were screened to include pharmacological interventions with explicit cardiac targeting while excluding neuropathy-dominant amyloidosis. Trial-level data regarding therapeutic classes, study phases, enrollment, and primary outcome domains were extracted and synthesized. Results: A total of 18 trials met the inclusion criteria (14 ATTR-CM; 4 AL-cardiac), representing a total enrollment of 4924 participants across 11 unique agents. Five therapeutic classes were identified: amyloid-clearing monoclonal antibodies (44.4% of trials), TTR silencers, TTR stabilizers, fibril-modifying agents, and cardiac phenotype-directed therapies. Monoclonal antibodies represented the largest class by both trial count and enrollment (3075 participants). Clinical events (n = 7) and safety/tolerability (n = 5) were the most frequent primary outcome domains. ATTR-CM trials dominated the landscape, accounting for 77.7% of the total study population, while parallel-group placebo-controlled designs were the most common study architecture (n = 10). Conclusions: The therapeutic landscape for cardiac amyloidosis is transitioning toward stage-specific, mechanism-based interventions. While ATTR-CM currently dominates research efforts, the expansion of silencers and monoclonal antibodies reflects an increasing capacity to intercept the amyloid cascade at distinct molecular checkpoints. However, significant heterogeneity in outcome measures and the shift toward diagnosing milder disease pose challenges for demonstrating clinical efficacy. Future priorities include standardized progression markers and addressing barriers to global access for these high-cost therapies. Full article

Network slicing is a core enabler of multi-tenant 5th Generation (5G) architectures, allowing heterogeneous services to coexist over shared infrastructure. However, ensuring effective isolation between slices remains a critical security challenge, as failures may enable cross-slice interference, data leakage, or cascading service disruption. This article analyses security vulnerabilities affecting 5G network slicing from a risk-oriented perspective, with particular emphasis on isolation weaknesses across orchestration, virtualization, network, and interface layers. Due to the technical immaturity and instability of current open-source slicing platforms, experimental validation of security mechanisms proved infeasible. These limitations are therefore treated as empirical evidence informing a structured vulnerability taxonomy and a qualitative risk assessment grounded in confidentiality, integrity, and availability. Building on this analysis, the article proposes a conceptual security framework that integrates defence-in-depth, zero-trust principles, continuous monitoring, and adaptive response mechanisms to enforce isolation dynamically. Aligned with established standards and regulatory references, the framework provides a coherent theoretical foundation for future experimental validation and the secure design of resilient 5G network slicing architectures. Full article

The dynamic behavior of the DS306 detacher, a critical component in industrial fiber processing lines, plays a decisive role in maintenance performance and overall operational reliability. This study introduces a strengthened preventive maintenance strategy that leverages vibration analysis and dynamic modeling with a strong emphasis on early fault anticipation. A detailed numerical finite element model of the detacher was developed to determine its natural frequencies, critical modes, and dynamic response under real operating conditions. Experimental vibration measurements were conducted to validate the numerical model and identify characteristic frequencies associated with imbalance and wear. The results show that the proposed predictive framework not only reproduces the machine’s dynamic behavior with high accuracy but also anticipates mechanical degradation trends well before the occurrence of critical failures. This early-warning capability allows maintenance teams to plan interventions proactively, significantly reducing unexpected downtime, avoiding cascading damage, and improving long-term equipment availability. Overall, the study provides a robust and practical methodology for dynamic diagnosis, fault prediction, and optimized preventive maintenance in industrial rotating machinery. Full article

Cascade dams describe an arrangement of several dam structures built along a flow path. Failure of one upstream dam in the cascade system can trigger catastrophic consequences to the downstream dams, as evidenced recently in the Edenville Dam and Sanford Dam. Previous research has mainly focused on rainfall-induced dam failures, although recent failures have demonstrated a combination of floods and earthquakes. Moreover, limited studies have analyzed the sensitivity of dam breach parameters, such as dam breach height and width in dams arranged in a cascade system for seismic events. Most hydraulic simulations that model seismic-induced dam failures assume the complete collapse of dams to analyze the downstream consequences. Hence, this study presents a novel analysis in simulating earthquake-induced failures in a cascade dam system, considering the sensitivity of dam breach parameters. In addition, dam breach parameters have been derived from the structural analysis of dams employing Finite Element Models (FEMs) to a critical Peak Ground Acceleration (PGA) of 0.3 g. Two-dimensional hydrodynamic simulations, along with the full dynamic wave equations, are undertaken in the study to model the earthquake-induced cascade dam failures. The results further elaborate on the significance of modeling cascade dam failures in terms of the consecutive arrival of floods and total flow compared to individual dam failures. Sensitivity analysis of dam breach parameters shows that the breach height is more significant than the breach width and breach slope. However, its significance decreases as the dam breach flood flow path increases in distance. The study further confirms the novel utilization of structural analysis to derive dam breach parameters for seismic-induced dam failures of concrete arch dams and rockfill dams, which will guide the optimization of disaster mitigation strategies and the operational resilience of the dams. Full article

The blood–brain barrier (BBB) is a major obstacle to the development of brain-targeted drug delivery systems, restricting greater than 98% of small molecules (<500 Da) and virtually all large-molecule drugs from entering the brain tissues from the bloodstream, resulting in suboptimal drug doses and therapeutic failure in the treatment of Alzheimer’s disease (AD). However, the advent of nanotechnology has provided significant solutions to the BBB challenges, enabling particle size reduction, enhanced drug solubility, reduced premature drug degradation, extended and sustained drug release, enhanced drug transport across the BBB, increased drug target specificity and enhanced therapeutic efficacy. In corollary, a library of brain-targeted surface-functionalized nanotherapeutics has been widely reported in the current literature. These promising in vitro, in vivo and pre-clinical results from the existing literature provide quantitative evidence for the relative clinical utility of each of the techniques, indicating remarkable capacity for brain-targeted carrier systems; many of them are still being tested in human clinical trials. However, despite the recorded research successes in drug transport across the BBB, there are currently no clinically proven medications that can slow or reverse the progression of AD because most of the novel therapeutics have not been successful during the clinical trials. Therefore, the main option for the treatment of AD is symptomatic treatment using cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists. Although these therapies help to alleviate symptoms of AD and improve patients’ quality of life, they neither slow the progression of disease nor cure it. Thus, an effective disease-modifying therapy for the treatment of AD is an unmet clinical need. It is apparent that a deeper understanding of the structural complexity and controlling dynamic functions of the BBB in tandem with a comprehensive elucidation of AD pathogenesis are crucial to the development of novel nanocarriers for the effective treatment of AD. Therefore, this narrative review describes the contextual analysis of several promising strategies that enhance brain-targeted drug delivery across the BBB in AD treatment and recent research efforts on two major AD biomarkers that have revolutionized AD diagnosis, amyloid-beta plaques and phosphorylated tau protein tangle, as potential targets in AD drug development. This has led to the Food and Drug Administration (FDA)’s approval of two intravenous (IV) anti-amyloid monoclonal antibodies, Lecanemab (Leqembi^®) and Donanemab (Kisunla^®), which were developed based on the Aβ cascade hypothesis for the treatment of early AD. This review also discusses the recent shift in the Aβ cascade hypothesis to Aβ oligomer (conformer), a soluble intermediate of Aβ, which is the most toxic mediator of AD and could be the most potent drug target in the future for a more accurate and effective drug development model for the treatment of AD. Furthermore, various promising nanoparticle-based drug carriers (therapeutic nanoparticles) that were developed from intensive research are discussed, including their clinical utility, challenges and prospects in the treatment of AD. Overall, it suffices to state that the advent of nanotechnology provided several innovative techniques for overcoming the BBB and improving drug delivery to the brain; however, their long-term biosafety is a relevant concern. Full article