ABSTRACT Academic research increasingly depends on the accessibility of digital material hosted online. We investigate hyperlink accessibility across the top five economics journals over a ten-year period and find that 52% of external hyperlinks are no longer accessible, a phenomenon known as link rot. An additional 25% of active hyperlinks no longer lead to the originally cited material. This decay undermines the transparency of research and poses challenges to maintaining the integrity of the academic record.

To examine gender-based disparities across each link of the American Heart Association's Chain of Survival for women experiencing out-of-hospital cardiac arrest, highlighting systemic, cultural and educational barriers that compromise equitable outcomes. A discursive review synthesizing epidemiological studies, public health data and qualitative research on cardiac arrest and gender disparities. A comprehensive search of databases including PubMed and CINAHL identified studies on gender differences in out-of-hospital cardiac arrest recognition, bystander intervention, emergency response and post-arrest care. Literature was critically analyzed using constant comparative analysis and organized according to the five links of the American Heart Association's Chain of Survival to identify recurring themes and evidence of disparity. Significant disparities were identified at every link in the Chain of Survival. Women are less likely to have cardiac symptoms recognized, receive bystander cardiopulmonary resuscitation or defibrillation and experience timely or guideline-concordant advanced life support and post-resuscitation care. Contributing factors include implicit bias, underrepresentation of women in resuscitation training materials and social norms that hinder rapid intervention. Gender disparities in cardiac arrest survival are systemic and multifactorial, resulting in 'broken links' across emergency response systems, public perceptions and healthcare education. Addressing these inequities requires reforms in public education, resuscitation training and clinical protocols that prioritize gender-sensitive and inclusive care. Nurses, as educators, advocates and caregivers, are uniquely positioned to drive transformational change in emergency and cardiac care. By championing women-centered and gender-sensitive resuscitation education, implementing inclusive practices and addressing intersectional barriers, nurses can help ensure equitable, responsive and just care. Advancing these priorities is essential for improving survival and neurological outcomes for women and advancing health equity in global healthcare. We adhered to the principles of the EQUATOR guidelines. This discursive paper did not meet the criteria for a specific standardized checklist. No patient or public involvement. This study did not include patient or public involvement in its design, conduct or reporting.

Have you ever lost hours navigating supplementary materials-clicking between the main text and dozens of auxiliary files only to encounter broken links, illegible figures, and undefined variables and acronyms? If so, you're not alone. What should support scientific communication has instead become an obstacle: supplementary information (SI) increasingly suffers from inconsistent formatting, poor accessibility, and fragmented organization that impedes rather than advances understanding. This is disheartening since the SI, if used effectively, has the power to enhance transparency, credibility, and reproducibility of research. Therefore, we propose 10 simple rules to help authors design SI that genuinely increase the impact of their research. The rules emphasize treating SI with the same care as the main text, using it strategically to support the scientific narrative while preserving clarity and focus. Key recommendations include creating a single, well-structured, self-contained SI master document; ensuring explicit cross-referencing between the main text and SI; making SI machine-readable; and avoiding the misuse of SI as a substitute for proper data repositories. We also highlight the importance of creativity in choosing appropriate formats and strict adherence to journal-specific guidelines. Finally, when available, we advocate the use of standardized templates to improve consistency, readability, and reuse across studies. By following these rules, authors can substantially increase the scientific impact of their work while at the same time contributing to more sustainable research practices.

To address the problems of knowledge circular dependency and learning path ambiguity in quadruped robot programming learning, a dynamic teaching path generation method that integrates structural and semantic information is proposed. A domain knowledge graph covering core knowledge and prerequisite relationships is constructed. R-GCN (Relational Graph Convolutional Network) is used to generate structured node embeddings through link prediction tasks. SBERT (Sentence-Bidirectional Encoder Representations from Transformers) is also used to generate semantic vector representations of knowledge points and user tasks. For a given programming task, seed nodes are retrieved based on semantic similarity, and a priori knowledge subgraph is constructed by back-tracing. The number of seed nodes is fixed to the top 5 knowledge points ranked by semantic similarity. The similarity threshold is implicitly determined by this ranking strategy rather than a fixed value. The weighting coefficients used in edge scoring and node prioritization are set to α = 0.6 and β = 0.7 based on validation experiments described in Sect. 2.4 and 2.5. All models are implemented using PyTorch and trained on a single NVIDIA RTX 3090 GPU. The batch size is set to 512, and early stopping is applied based on validation loss with a patience of 10 epochs. Relationship strength is quantified by calculating the dot product of the embedding vectors, automatically identifying and pruning the weakest links in circular dependencies to achieve intelligent link breaking. During topological sorting, nodes with high semantic match to the task are prioritized for learning path generation. Experiments show that the proposed method achieves superior performance in the quadruped robot programming task, with a Top-3 score of 0.88, higher than CompGCN's 0.73, a mean reciprocal rank of 0.68, and path integrity and structural rationality reaching 0.85 and 0.88, respectively. This method performs well in knowledge association modeling and path recommendation, effectively supports structured, task-driven programming teaching, and provides cognitively coherent learning support for beginners.

Very High Frequency (VHF) radio communication systems face significant challenges in modern electromagnetic environments, including spectrum congestion, dynamic interference, and varying channel conditions. Existing adaptive approaches rely on static rule-based switching or single-cycle optimization, which cannot accumulate operational experience across decision cycles. This paper proposes a digital twin-enabled online learning framework (DT-MAB) for adaptive waveform selection in tactical VHF communication. The framework employs a contextual multi-armed bandit algorithm (Lin-UCB) that continuously learns the mapping from channel conditions to optimal configurations, with the digital twin serving as a virtual exploration sandbox that screens candidate configurations before physical deployment—preventing link disruptions during exploratory actions. An expanded configuration space of 63 candidates (7 waveforms × 3 MAC protocols × 3 power levels) is constructed, and a hierarchical performance evaluation model combining voice quality, bit error rate, communication delay, and transmission range is developed using the Analytic Hierarchy Process (AHP) as the reward function for online learning. Experimental results across 10 random seeds demonstrate that DT-MAB achieves the lowest mean cumulative regret, reducing regret by 29% relative to MAB without a digital twin and by 16.5% relative to PSO-based optimization on average. Ablation experiments confirm that removing virtual exploration increases performance drop events by 49% (from 250 ± 79 to 373 ± 6), demonstrating that the digital twin is a functionally indispensable component of the online learning architecture.

Broken Links, Broken Symmetry? Reflections on Technical Formalism and Evaluative Reciprocity

Low Earth Orbit (LEO) satellite communication technology has become a core solution to achieve global seamless networking by virtue of its global coverage capability, and it is also a key technology to make up for the coverage shortcomings of terrestrial communications and support the construction of space-air-ground integrated networks, which is widely used in civil services, emergency communications, ocean operations and other scenarios. However, LEO satellites feature high-speed motion characteristics, which forces user equipment to conduct frequent handover operations to maintain stable communication links. In addition, the complex and variable topology of satellite networks further aggravates the difficulty of user mobility management, and traditional handover algorithms are prone to load imbalance and link interruption. This paper optimizes the satellite handover process based on Deep Reinforcement Learning (DRL), and proposes a multi-objective optimization strategy to simultaneously achieve the optimization goals of controlling handover frequency, balancing satellite load and maximizing system throughput. Aiming at the demand of improving user service stability, this paper designs a brand-new reward function architecture and introduces a lightweight multi-layer neural network model to effectively reduce the computational complexity of the algorithm and adapt to the limited computing resources on satellites. Simulation results show that compared with existing mainstream technical schemes, the proposed method has better performance in improving the overall system throughput and reducing the average number of handovers, and can also effectively reduce the handover failure probability to ensure continuous service transmission.

U.S. immigration laws and policies are subject to rapid and constant changes, rendering the landscape for immigration legal research complicated, messy, and sometimes unreliable. This article explores an immigration researcher’s challenge of accessing, evaluating, and relying on immigration resources when laws and policies change almost daily through executive orders, regulatory changes, or the simple disappearance of information from government websites. Using a survey of research platforms and resources, including comprehensive legal research databases, specialized online libraries, and publicly accessible websites and newsletters, this analysis identifies the strengths and weaknesses of accessing immigration law and policy information. In an era defined by uncertainty, and when researchers are confronted with paywalls, subscription fees, broken links, and outdated information, this article can serve as a practical guide for navigating the complex and evolving maze of U.S. immigration law resources.

Equity-aware routing optimization for hazardous chemicals considering time-varying conditions and link disruption risks

Robust adaptive H∞ fault-tolerant predictive control for air-ground integrated highway emergency self-organizing network systems based on RBF-DNN approximation.

This study addresses the issue of communication link interruptions in train platoon control under the complex operating environment of high-speed railways. An adaptive trajectory tracking control approach based on a finite-time sliding mode is proposed under jointly connected switching topologies. The proposed framework ensures platoon consensus under jointly connected switching topologies, where the leader’s information need not reach all followers in each topology but only collectively over a finite interval, thus relaxing connectivity requirements and enhancing practicality. Within this framework, the sliding surface is designed to incorporate relative error signals determined by the communication topology, and an adaptive mechanism is employed to effectively handle unknown external disturbances without requiring prior knowledge of their bounds or derivatives. Simulation results demonstrate that, compared with MPC and non-adaptive approaches, the proposed strategy reduces position errors by approximately 80% and velocity errors by around 40%, significantly improving platoon tracking accuracy. Furthermore, simulations under both periodic and random topology switching indicate that periodic switching achieves performance closer to that of a fully connected topology, further enhancing trajectory tracking effectiveness.

As typical natural disasters in coastal areas, node failure and link interruption caused by typhoons directly threaten the operation stability of the freight multimodal transportation network (FMTN) in urban agglomerations. Such disruptions, in turn, restrict the sustainable development of the regional transportation and logistics system. In order to scientifically assess the FMTN resilience level in coastal area urban agglomerations under typhoon disturbances, this study constructs a resilience assessment method that integrates structural performance and functional performance. The Spatial Local Failure model and the Monte Carlo method, combined with fragility curves, are used to dynamically simulate the damage process of FMTN nodes and links by different typhoons intensities. By constructing FMTN resilience performance function, the resilience ratio is used to quantitatively assess the damage resistance and resilience maintenance level of FMTN under disturbances. This study also analyzes the resilience difference between FMTN and its sub-networks. The Typhoon Bebinca case is applied to validate the application of FMTN assessment method. The results show that FMTN exhibits stronger invulnerability and robustness under typhoon disturbances, and its resilience is significantly better than that of sub-networks. Specifically, when a strong typhoon hits, the FMTN resilience ratio only decreases by 0.13, while the resilience ratio of each sub-network decreases significantly by 0.21, 0.42, 0.46 and 0.57, respectively. FMTN resilience under typhoon disturbances is further assessed through an example analysis. And it verifies not only the comprehensive advantage of FMTN under typhoon disturbances but also the rationality and practicability of the assessment method. The findings can provide an important theoretical basis and technical support for resilience assessment, disaster prevention, mitigation planning, and the sustainable development of FMTN in coastal area urban agglomerations. It is of great practical significance to promote the efficient operation of China’s FMTN.

BackgroundReproducing published findings from clinical trials is a critical component of scientific transparency, yet it remains a challenging and under-practiced task. Despite increasing emphasis on reproducibility and data reuse in research policies, only few real-world examples exist where several teams have reproduced complex analyses using clinical trial data. In this case study, the aim was to reproduce the key findings of a high-impact clinical trial on rectal cancer treatment using shared trial data.MethodWe organized a multi-team datathon, where each team was provided with the same dataset and supporting material, and was tasked to reproduce the results of the CAO/ARO/AIO-04 trial, with optional additional analyses. We contacted the original investigators for access and reuse of the data, as well as information on the clinical and scientific aspects of the study.ResultsFive teams used R or Python to reproduce the statistical results, and the corresponding scripts can be found on Gitlab. The key findings on disease-free survival (DFS) were consistently reproduced by most teams, reinforcing confidence in the main trial conclusions. Result robustness was investigated using different analytical software or statistical models. Some challenges were encountered because supplementary material of the original study was not easily found. Minor reporting issues were also identified in the reproduced paper.ConclusionsReproduction of a major oncology clinical trial confirmed the reliability of its main conclusions. Divergences highlighted reporting gaps—such as incomplete protocols and broken links—that future trials should address. This case study demonstrates the value of systematic reproducibility checks for the transparency of clinical research and the challenges in data sharing for reproducibility.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13063-026-09646-y.

ABSTRACT The rapid integration of mobile ad hoc networks (MANETs), the Internet of Things (IoT), and 6G technologies is creating highly dynamic, decentralized communication systems that demand secure, intelligent, and adaptive routing. Traditional routing methods struggle to cope with frequent topology changes, diverse device behavior, and increasing security threats. To address these challenges, this paper proposes a novel routing framework called NC‐RADTGNN (non‐convolutional return‐aligned decision transformer graph neural network). Unlike conventional deep learning approaches, the proposed model captures complex network structures without relying on convolution operations and aligns routing decisions with long‐term performance goals rather than short‐term actions. A lightweight cleaner fish optimization algorithm is used to fine‐tune learning parameters, improving convergence and efficiency. Additionally, a multi‐criteria decision‐making (MCDM) mechanism dynamically selects blockchain consensus protocols to enhance trust and security among distributed nodes. Experimental evaluation on 50,000 routing samples demonstrates that NC‐RADTGNN achieves 99.9% routing accuracy, 0.1% model loss, and the lowest latency (22.5 ms) among recent state‐of‐the‐art approaches. It also reduces routing overhead, control packet load, and link breakage while improving throughput and delivery ratio. Overall, the proposed framework provides a robust, secure, and scalable routing solution for future MANET‐IoT‐6G environments, combining intelligence, adaptability, and trust management within a unified architecture.

This paper proposes a method for interpolating missing images in large-scale datasets used for training Vision-Language Models (VLMs). Recent large-scale datasets are often distributed not by hosting the image files directly on servers, but by providing CSV files that contain download links and the corresponding text for each image. As a result, many images become unavailable due to broken links, making it difficult to reproduce the VLM performance reported in previous studies. To address this issue, we propose an interpolation method that generates images reflecting the characteristics of the missing ones by optimizing the latent variables of a Latent Diffusion Model based on the associated text information. We applied this method to generate substitute images for pretraining a VLM, specifically CLIP, and confirmed that the resulting zero-shot performance was comparable to or even better than that obtained using the original dataset before image loss. These results demonstrate that the proposed method can serve as a practical approach for supplementing datasets with missing images.

Free-space light propagation is inevitably influenced by atmospheric turbulence, which leads to scintillation, arrival-of-angle (AOA) fluctuation, or even optical link interruption. Conducting outfield experiments directly often involves haze pollution, uncontrollable weather, and greater labor and material costs. Therefore, it is of great significance to carry out the laboratory investigation of laser propagation based on a turbulence simulator with good performance. The turbulence simulator proposed in this work has the advantages of having a wide inertial region, good controllability, and high experimental repeatability. An AOA fluctuation measurement system is established, assisted by the home-built turbulence simulator. Simultaneously, the variance, power spectra, and probability distribution of AOA fluctuation were collected and analyzed with two laser beam diameters using a comparative approach. The experimental results show the suppression effect of the broad laser beam on AOA fluctuation. Additionally, the variation of the atmospheric refractive index structure constant is inverted by the AOA fluctuation variance.

Vehicular Ad Hoc Networks (VANETs), a subclass of Mobile Ad Hoc Networks (MANETs), rely on efficient routing protocols to maintain reliable communication in highly dynamic and decentralized environments. While numerous routing strategies have been proposed, their performance varies significantly with the choice of underlying mobility models that simulate real vehicular movement. This study presents a two-phase evaluation of five widely used routing protocols—Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Destination-Sequenced Distance Vector (DSDV), Optimized Link State Routing (OLSR), and Geographic Routing Protocol (GRP)—across fourteen realistic mobility models, including the Intelligent Driver Model (IDM), Car-Following Model (CFM), and Lighthill–Whitham–Richards (LWR) model. Simulations are executed using Simulation of Urban Mobility (SUMO), Network Simulator 3 (NS-3), and the Veins framework. The performance is assessed using eight key metrics: Packet Delivery Ratio (PDR), End-to-End Delay (E2ED), Throughput, Jitter, Energy Consumption, Packet Loss Ratio (PLR), Normalized Routing Load (NRL), and Link Breakage Rate (LBR). Among the evaluated protocol–mobility combinations, AODV paired with the CFM model demonstrated comparatively favorable performance under the considered simulation configuration: 93% PDR, 79.4 ms E2ED, 332 kbps throughput, 3.4 ms jitter, 3.48 J energy consumption, 7% PLR, 0.3 NRL, and 5 link breakages per simulation. This study provides a systematic and unified comparative evaluation of five widely used routing protocols across fourteen simulation-based vehicular mobility models. The findings provide scenario-specific comparative insights for simulation-based VANET performance evaluation and offer practical, simulation-based guidance for selecting suitable protocol–mobility model combinations under the evaluated experimental conditions.

We model an epidemic spread process involving nodes that (a) experience non-trivial asymptomatic infectious periods and (b) adapt by avoiding contacts with symptomatic infectious nodes. These modeling choices reflect ample evidence that infectious individuals are often mistakenly perceived as safe contacts due to lack of symptoms and that individuals adapt to an epidemic by avoiding contacts deemed to be of risk. We capture these choices in the [Formula: see text] (Susceptible-Asymptomatic Infected-Symptomatic Infected-Susceptible) model, where we explicitly distinguish between asymptomatic and symptomatic infectious individuals. We adopt an individual-based mean-field epidemic modeling approach and formulate the system of differential equations via continuous-time Markov chain analysis. We first consider non-adaptive homogeneous and heterogeneous mixing scenarios over arbitrary static contact networks. We derive the expression for the basic reproduction number, [Formula: see text], and establish that under otherwise similar conditions the individual infection probabilities at the metastable state of [Formula: see text] dominate those in the conventional SIS model. Then, we focus on a contact-adaptive setting, where nodes avoid interactions with known infectious neighbors or reconnect with neighbors who have recovered, to study how the time-varying contact network, asymptomatic infections and their combinations affect the epidemic spread dynamics. Overall, asymptomatic infections restrict nodes' capacity to adapt (link breaking), resulting in higher link density and consequently higher epidemic prevalence. Besides, in their presence, the retarding effect of the link-breaking mechanism on epidemic prevalence is considerably mitigated. We numerically analyze how the effective link-breaking rate and the size of the asymptomatically infected population affect the link density and, ultimately, the epidemic prevalence. The epidemic threshold appears to scale inversely with the population of asymptomatic nodes, namely the epidemic starts to spread at lower infection rates when the number of asymptomatic infections is higher.

Software-Defined Networking (SDN) represents an advanced architectural paradigm that separates control logic from data forwarding, enabling centralized and adaptive network management. This study investigates SDN resilience under link failure conditions and evaluates the effectiveness of failover routing in restoring network performance. Simulation-based experiments are conducted on a redundant logical topology implemented using Python and the NetworkX library, covering three operational phases: normal operation, link failure, and failover recovery. The results show that link failures cause a noticeable degradation in network performance, with normalized link utilization increasing due to traffic concentration on limited paths. After failover routing is activated, network performance improves significantly, achieving up to approximately 15–20% higher normalized link utilization compared to the failure state, indicating successful traffic rerouting and service restoration. These findings demonstrate that failover routing enables rapid recovery and maintains service continuity despite link disruptions. This study contributes to SDN resilience research by providing a reproducible logical-topology simulation framework and quantitative evidence that proactive, controller-based failover routing effectively enhances network robustness under link-failure scenarios, offering practical insights for resilient SDN design in data center and enterprise environments.

Purpose Web citations are essential for scholarly integrity, but their reliability is threatened by link rot and content drift. Fields like Library and Information Science (LIS), which depend heavily on web references, face significant challenges in preserving digital citations. This longitudinal study (2005–2025) aims to investigate the decay and recovery of web citations in LIS journals, offering actionable solutions to preserve digital scholarship. Design/methodology/approach This 20-year longitudinal study employed a quantitative approach to examine web citation decay in LIS literature. We analyzed 2,886 citations from 608 articles published in four leading journals. Findings Three key findings emerged from the analysis. First, web citations are now decaying exponentially, with accessibility dropping from 87% for citations 0–5 years old to 38% for those over 10 years old. Furthermore, permanent link rot has tripled from 5% in 2012 to 15% in 2025. Second, preservation outcomes vary dramatically by domain (e.g. .edu domains show 93% accessibility versus 42% for .com domains) and content format (e.g. PDFs maintain 92% accessibility compared to 41% for database-driven content). Third, although recovery tools have improved (the Wayback Machine’s success rate increased by 171%), their benefits are offset by new challenges such as failures caused by dynamic content, which now account for 19% of all failures. The study advocates for mandatory archiving protocols and persistent identifiers to safeguard scholarly records, highlighting the urgent need for systemic reforms. Originality/value This study offers three key innovations: (1) It provides the first longitudinal evidence that link rot is accelerating despite improvements in archiving tools, thereby revealing a paradox in preservation efforts; (2) it identifies dynamic content as a significant new error category, accounting for 19% of failures; (3) it demonstrates a phenomenon of “preservation resistance” in 15% of citations that are unrecoverable by any method. These findings redefine the challenges of modern link rot.