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This paper introduces an advanced Internet of Things (IoT)-driven smart furniture system designed to dynamically adapt to individual users by integrating deep reinforcement learning with federated meta-learning. Personalization is formulated as a Markov decision process, enabling the system to make optimized, sequential adjustments tailored to each user's behavior. To estimate hidden ergonomic preferences in real time, an adaptive Kalman filter is applied, while a sparse autoencoder reduces raw sensor signals by 82 %, preserving key temporal features essential for accurate modeling. In a comprehensive user study involving 48 participants and more than 160,000 time-series sensor samples, the framework significantly reduced cumulative user dissatisfaction by 43 % and cut energy consumption by 21 %, compared with conventional rule-based control systems. Real-time adaptations occur with an average latency of 280 ms, and constraints for ergonomics are upheld in 95 % of use cases, confirming the system operates swiftly and safely. Federated learning (FL) enables privacy-preserving collaboration across distributed furniture units. Training converges to 87 % of global performance within 30 global iterations, without any raw data exchange, reinforcing both scalability and data privacy. These empirical results strongly support the framework's suitability for deployment in health-aware workspaces, smart homes, and eldercare environments, delivering a robust, responsive, and interpretable solution for enriching human-furniture interaction.

The rapid shift to remote work following the COVID-19 pandemic transformed private residential spaces into permanent workplaces, often without professional ergonomic oversight. While occupational lighting is strictly regulated by standards such as EN 12,464-1, these norms are legally unenforceable in private homes due to privacy regulations. This study addresses the "Privacy-Safety Paradox" by conducting a comparative legal analysis of employer responsibilities versus privacy rights in the EU (including France, Germany, Italy, Portugal, and Poland) and the USA. It presents a case series analysis of five distinct home-office scenarios, evaluating illuminance levels (Eavg, Emin) and uniformity (U0) against occupational safety requirements. Results reveal a 100% non-compliance rate, with critical deficiencies identified in makeshift workspaces. The most severe case exhibited an average illuminance of only ~ 188 lx and a minimum of ~ 22 lx, which is drastically below the biologically required thresholds for visual health and circadian regulation. The study identifies a systemic "compliance gap" where legal protections of the home, such as the German Selbstauskunft model or the Italian "right to disconnect", limit the employer's ability to perform physical audits. To mitigate this risk, the authors propose a simplified self-assessment checklist designed to allow remote workers to identify and correct visual hazards without specialized photometric equipment.

Network meta-analysis (NMA) plays an important role in comparative effectiveness research, particularly in fields such as traditional and complementary medicine, where multiple interventions often need to be assessed within a single evidence framework. However, conducting NMA remains labor-intensive, methodologically demanding, and difficult to complete efficiently using conventional workflows. Although recent advances in large language models have created new opportunities for supporting evidence synthesis, their routine use in NMA is still constrained by limited transparency, prompt dependency, and difficulty integrating with structured analytical procedures. In this study, we introduce SmartEBM, a web-based human-AI collaborative platform designed to support the full workflow of NMA. SmartEBM provides an integrated environment for title and abstract screening, full-text screening, data extraction, risk of bias assessment, statistical analysis, and certainty of evidence assessment. The platform is organized around a human-in-the-loop model, in which AI-assisted functions support repeated and labor-intensive tasks while researchers retain oversight of methodological judgement, verification, and final decision-making. Through its six functional modules, SmartEBM offers low-code interfaces, structured outputs, and verification-oriented workspaces that help connect major steps of evidence synthesis within one platform. Rather than functioning as a stand-alone automation tool, SmartEBM is intended as a practical platform for end-to-end NMA support. This platform-oriented approach may help make evidence synthesis more manageable, traceable, and accessible in routine research practice, especially in complex review settings.

The E-Research Institutional Cloud Architecture (ERICA) is a code-driven orchestration framework that automates the configuration and management of Amazon Web Services (AWS) resources to provide trusted research environments (TREs) for sensitive data. Independent ERICA TREs are now operational in universities and government agencies. The framework was developed by the University of New South Wales, Australia, with support from the Australian Research Data Commons. ERICA was designed to overcome the limitations of traditional on-premise TREs by providing secure, scalable, and flexible cloud environments that protect privacy while enabling advanced, data-intensive research. Using an infrastructure-as-code model, ERICA delivers consistent, reliable, and error-free setup of Project Spaces. It integrates robust security features, including encryption-at-rest and in transit, and multi-factor authentication. Hosted in AWS onshore data centres, ERICA ensures data sovereignty while supporting diverse operating systems and high-performance computing configurations. The architecture also allows rapid deployment of new AWS services, including generative AI tools, within research workspaces. ERICA implements the Five Safes framework-covering safe projects, people, data, settings, and outputs-to ensure compliance and secure research. Its modular architecture enables multiple independent TREs, each governed by host-institution policies and capable of supporting hundreds of Project Spaces. This flexibility allows replication across any jurisdiction with AWS public cloud infrastructure. However, reliance on AWS introduces challenges, including charges in US dollars and delayed rollout of new services in smaller regions. ERICA represents a step change in providing privacy-by-design cloud infrastructure for sensitive, data-intensive research. By combining strong governance with the scalability of AWS, it enables researchers to work securely with large, complex datasets while rapidly adopting cutting-edge analytical tools. ERICA TREs offer a replicable, future-proof model for supporting secure research at scale.

Shared immersive environment sports venues, virtual classrooms, and collaborative workspaces require multiple users to stream 360° videos simultaneously over the same edge network, yet every existing adaptive bitrate system optimises each viewer in isolation. This self-interested behaviour triggers a bandwidth auction that chronically starves the most uncertain viewers: Jain's Fairness Index for ten independently optimised agents routinely falls below 0.85. We present FairEdge360, a hierarchical multi-agent reinforcement learning framework that reformulates multi-user 360° streaming as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP) and proves, formally, that fairness and quality are complementary rather than competing objectives. Three tightly coupled innovations make this possible. First, a Lightweight Uncertainty Estimator (LUE) a compact 8385-parameter four-layer MLP evaluates per-device viewport prediction confidence cti=σ(w4⊤h3) in under approximately 2.1 ms on commodity smartphones (95th percentile, iPhone 12 A14 Bionic), enabling selective edge offloading that reduces device energy consumption by 38.9%. Second, a variational Graph Neural Network compresses each agent's 256-dimensional GRU state into a 32-byte INT8 latent, transmitted over a dynamic RTT-gated neighbourhood graph at 96 bytes per agent per 500 ms 75% less overhead than competing approaches. Third, the edge coordinator maximises the Nash social welfare objective NSW=(∏i=1NQi)1/N, whose gradient ∂NSW/∂Qi∝1/Qi automatically prioritises the most disadvantaged viewer; a formal proof guarantees that every Pareto-optimal policy satisfies Qi/∑jQj≥1/N. Counterfactual advantage estimation correctly attributes each agent's marginal contribution to the global reward, eliminating the credit-assignment ambiguity inherent in standard multi-agent baselines. Evaluated on 284 users, 52 omnidirectional videos, and 10,000 real network traces spanning 4G LTE, 5G mmWave, HSDPA, and campus WiFi, FairEdge360 raises Jain's Fairness Index from 0.934 to 0.976 (+4.5%), improves worst-case user quality-of-experience from MOS 2.54 to MOS 3.21 (+26.4%), and halves rebuffering rate from 2.1% to 1.1%, all within a 20 ms motion-to-photon budget on a commodity smartphone.

Shared resources enhance productivity, yet at the same time open pathways for biological and digital contamination, turning physical or digital hygiene into a cooperation dilemma prone to free-riding. Here, we introduce a game of sequential sharing of common resources, an empirically parameterized evolutionary model of population dynamics in sequential-use settings such as gyms or shared workspaces. The success of the strategies implemented in the model, which involve equipment cleaning before or after use, is based on the trade-offs between cleaning costs, contamination risks and social incentives to mitigate disease transmission. We find that cooperative hygiene can be achieved by lowering the effective costs of cleaning, strengthening pro-social incentives and monitoring population-level noncompliance. Remarkably, the stability of fully altruistic populations is primarily affected by the cleaning costs. In contrast, increasing effective infection costs, for example, through punishment, appears less important in this case. The model's evolutionary dynamics exhibit multi-stability, hysteresis and abrupt shifts in strategy composition, broadly consistent with empirical observations from shared-use facilities. Our framework offers testable predictions and is amenable to quantitative calibration with behavioural and environmental data. Our predictions can be used to inform the design of cost-effective public health and digital security policies. A bird that flies away leaves no trace (Japanese proverb).

The determination of hazardous gases in confined environments, such as underground mines, is essential for ensuring suitable occupational health conditions. Although gas chromatography coupled with mass spectrometry (GC-MS) is the gold-standard analytical technique for gas analysis, its application is not feasible in this context due to the bulkiness of the instrumentation and the inability to provide real-time responses. Alternative low-cost sensing techniques, such as metal oxide semiconductors (MOXs), are limited by low molecular selectivity, susceptibility to humidity interference, and long-term instability. Herein, we propose an integrated and compact analytical platform that combines multiple quantum-cascade lasers (QCLs) with substrate-integrated hollow waveguides (iHWGs) acting as miniaturized gas cells for the real-time quantification of hazardous gases, including SO2, H2S, CO2, CH4, NO, and NO2, in confined workspaces such as underground mines. Each QCL was selected and tuned to match the absorbance profile of a particular target analyte, with the exception of H2S, which was converted to SO2 via UV irradiation prior to quantification. An Arduino-based system facilitates signal acquisition and processing, enabling rapid data interpretation and wireless communication with external devices. At optimized conditions, the system enables the quantification of all target gases near their permissible exposure limits (PELs) in underground mines, with limits of detection of 0.3, 7, 100, 100, 5, and 3 ppmv for SO2, H2S, CO2, CH4, NO, and NO2, respectively. The entire system has a footprint of 470 × 320 × 100 mm, which is compatible with real-world deployment scenarios in underground mines. This advanced sensing platform provides real-time monitoring of toxic gases in confined environments with high molecular selectivity and suitable sensitivity. Designed for harsh conditions, it can operate reliably at high humidity, dust, vibrations, and electromagnetic interferences.

Articulated endoscopic instruments can enhance dexterity within constrained workspaces; however, their complexity and cost limit accessibility for training, prototyping, and early-stage evaluation. Additive manufacturing offers a practical approach for developing mechanically functional, low-cost articulated devices for engineering exploration and simulation-oriented applications. This study describes the mechanical design, bench-top characterization, and preliminary proof-of-concept assessment of two additively manufactured, cable-driven endoscopic prototypes: a flexible endoscope with an integrated working channel and bidirectional distal articulation, and a 4-mm steerable surgical forceps with independent jaw actuation. Both devices were fabricated using fused deposition modeling. Bench-top experiments quantified distal deflection, motion repeatability, force-related behavior, simulated task execution, and user perception. The flexible endoscope achieved effective distal deflections of 178° ± 2° vertically and 171° ± 3° horizontally while maintaining compatibility with standard 2.8-mm endoscopic instruments. During cyclic testing, it demonstrated sub-centimeter positional consistency after 1000 articulation cycles, with 95th percentile errors below 8 mm in both directions. The steerable forceps exhibited a geometric articulation range of 180°, an effective distal deflection of 85° ± 2°, and repeatable tip motion over 500 cycles, with a mean positional error of 0.26 mm. Simulated tasks were completed with high user perception scores for both prototypes. Fabrication costs were USD 166.49 for the endoscope and USD 27.75 for the forceps. These findings suggest that cable-driven architectures combined with additive manufacturing can reproduce key kinematic principles of articulated endoscopic instruments using low-cost fabrication methods. The prototypes should be interpreted as early-stage engineering demonstrators rather than clinically deployable devices.

The NIH Human BioMolecular Atlas Program (HuBMAP) Data Portal (https://portal.hubmapconsortium.org/) serves as a comprehensive repository for multimodal, multi-scale spatial and single-cell data from healthy human tissues. As of June 2026, the portal hosts 9,232 public datasets from 25 data types spanning 29 organ classes across 498 donors. Portal infrastructure and user interfaces support data search and discovery, visualization, and analysis directly in web browsers. These capabilities include metadata- and data-driven search, collaborative Workspaces with access to high-performance compute, and interactive Vitessce visualizations across non-spatial, 2D, and 3D spatial datasets. Data-type-specific uniform processing pipelines and rigorous quality control processes ensure comparability of results across laboratories, organs, and donors, while externally processed community-contributed datasets provide complementary perspectives. Here we describe portal functionality, infrastructure, and design, and highlight its role as a platform for large-scale spatial single-cell research across diverse data types, organs, and scales.

Assistive robotics are increasingly adopted in manufacturing to support human work. In automotive assembly, confined spaces and takt-time constraints mean technological support affects biomechanical, cognitive, psychosocial, and organisational dimensions of work. This paper presents a multi-dimensional evaluation of an upper-limb exoskeleton and a proximity-aware cobot through two studies: a focus group with automotive practitioners addressing safety, trust, acceptance, and implementation barriers, and an experimental study with workers performing assembly tasks. The technologies were assessed independently and in combination, combining objective posture and performance indicators with subjective measures of workload, usability, comfort, trust, and acceptance. While each technology reduced biomechanical load alone, their combined use yielded no additive benefits and introduced cognitive and coordination costs that offset physical gains. Findings emphasise the importance of multi-dimensional evaluation when integrating assistive technologies in automotive assembly. This study combines practitioner insights and experimental evaluation of exoskeleton and cobot use in automotive assembly. Each technology reduced biomechanical load alone, but combining them in confined workspaces added cognitive and coordination demands that offset physical benefits.

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. By combining analytical modeling, real time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7 DOF Kinova robotic arms (Kinova Inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves R2=0.940, RMSE =42.0 mm, MAE =28.7 mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova Inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems.

Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder-Elbow-Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot's joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines.

Rapid sequence intubation (RSI) in helicopter emergency medical services (HEMS) is conventionally performed at the scene before transport, potentially delaying time to definitive care. The feasibility of performing RSI during flight in civilian HEMS operations has not been established in the United Kingdom. We evaluate the feasibility, safety, and temporal efficiency of performing simulated in-flight RSI in an AW169 helicopter under operational flight conditions. A prospective proof-of-concept study using high-fidelity simulation was conducted across 2 phases (May 2023, January 2025) at the Air Ambulance Charity Kent Surrey Sussex. Eight simulations were completed by 4 distinct clinical teams comprising operationally experienced HEMS physicians and paramedics. Scenarios replicated a standardized traumatic brain injury scenario requiring RSI during the return transit phase. The primary outcome was time from RSI checklist initiation to confirmed intubation. Secondary outcomes included overall mission times, safety events, and crew-perceived feasibility assessed via post-scenario questionnaires. All simulations (8/8, 100%) achieved successful first-pass intubation. Median time from checklist initiation to confirmed intubation was 5 minutes (interquartile range [IQR]: 5-7). Median total mission time from base departure to RSI completion was 42 minutes (IQR 40-44). No safety events, procedural complications, or communication failures occurred. Crew questionnaires (93% response rate) confirmed unanimous perceived feasibility, with participants identifying adequate workspace, effective communication, and manageable equipment accessibility. High-fidelity simulation demonstrates that in-flight RSI is technically feasible in an AW169 helicopter, with consistent procedural times and no safety events. These findings may support further evaluation of in-flight RSI as a complementary strategy for time-critical patients where scene-based airway management may delay definitive care.

Understanding whether prefrontal cortex is necessary for conscious experience remains central to ongoing debates between global workspace and recurrent processing theories of consciousness. To dissociate neural processes underlying perceptual awareness from those related to report and access, we combined report and no-report visual masking paradigms with magnetoencephalography (MEG) to characterize the spatiotemporal and network dynamics of visual awareness. Across both report and no-report conditions, early and sustained responses emerged in occipital and temporal cortices, beginning at ∼60-70 ms post-stimulus. Posterior sensory regions consistently encoded stimulus presence and category information irrespective of reporting demands, and exhibited enhanced α-band recurrent coupling for visible stimuli, suggesting stable perceptual representations supported by local recurrent interactions. In contrast, prefrontal activity emerged only when explicit report was required, beginning at ∼100 ms and persisting thereafter. Time-resolved decoding revealed that anterior regions encoded stimulus information exclusively under report conditions and failed to represent categorical content when reporting was absent. Moreover, long-range fronto-posterior connectivity selectively increased during report, indicating task-dependent integration beyond posterior sensory processing. These findings demonstrate that posterior cortical dynamics are sufficient to support perceptual awareness, whereas prefrontal cortex contributes to report-related access and global integration. By disentangling awareness from reporting, this work provides evidence favoring posterior-centered accounts of phenomenal consciousness and clarifies the functional role of anterior cortex in conscious processing.

In small intestine examinations, wireless capsule endoscopes rely on natural intestinal peristalsis for movement. However, this dependence on physiological processes often results in uncontrolled motion, potentially leading to inadequate assessments and an increased risk of missed diagnoses. To enhance the efficacy and comprehensiveness of these inspections, it is crucial to achieve more stable observations through effective anchoring of the capsule endoscopes. This study presents a novel anchoring capsule robot characterized by its simple structure and compact design. The structure incorporates a geometrically enclosed cam-slider mechanism that enables safe, continuous, and reversible deployment of anchoring legs under an external magnetic field, allowing the capsule to generate high anchoring forces. Additionally, an open-architecture planar electromagnetic coil array is proposed to precisely control the extension of the legs and provide a practical bedside workspace. In vitro tests and in vivo porcine experiments demonstrate that the capsule effectively counteracts intestinal peristaltic forces to maintain positional stability, while ensuring reliable release under dynamic physiological conditions.

Computed tomography is a widely used imaging method for non-destructive testing. However, standard CT systems face fundamental limitations when scanning large objects such as car bodies, which must fit between the X-ray source and detector, both of which need freedom of movement around the specimen. Twin-robotic CT systems with high degrees of freedom address these limitations by enabling free positioning of the X-ray source and detector in space, making non-destructive CT testing of large objects feasible. However, achieving collision-free positioning of the robots is a challenging problem that is often neglected in theoretical representations of twin-robot CT configurations. This paper presents a systematic methodology for performing region-of-interest scans on large objects. The approach exploits the test object's geometry to determine robot reachability, which serves as the foundation for trajectory planning by incorporating accessible regions. By leveraging both rotational and translational degrees of freedom, including variable source-detector distances and detector rotations, the methodology expands the range of collision-free poses, thereby increasing reachability and enabling more flexible trajectory design. The methodology is modular and adapts to arbitrary system configurations and test samples via computer-aided design (CAD)-based geometry definition, where the test object determines the collision-free workspace. It is demonstrated on a BMW 4-series body-in-white through comprehensive batch simulation across 273 Region of Interest (ROI) positions, evaluating reachability improvements achieved through the introduced degrees of freedom using a data completeness trajectory optimization criterion.

This study examines a graph-based learning framework for modelling associations between work-from-home (WFH) furniture layouts and users' affective responses in immersive virtual reality (VR). Forty-three participants evaluated two layout conditions-self-arranged and researcher-arranged-in a controlled 4.2 × 4.2 m room environment, yielding 85 valid layout cases. Layouts were reconstructed in Unity and rated on six outcomes: perceived visual complexity, satisfaction, concentration, sense of control, inclination to use the workspace, and perceived performance. Each layout was represented as a spatial graph, with furniture and architectural elements encoded as nodes and proximity-based relationships encoded as edges. A GraphSAGE model with attention pooling jointly predicted the six outcomes under weighted MSE, achieving a mean absolute error of 0.684 and variance-weighted R² of approximately 0.659 in five-fold cross-validation. Compared with non-graph baseline models, the graph-based model showed lower prediction error in this dataset while also providing interpretable node- and edge-level importance patterns. GNNExplainer highlighted desk-related relationships, including desk-window, desk-door, and desk-storage configurations, as influential subgraphs. Rather than establishing a general theory of space-emotion interaction, the study demonstrates the methodological value of graph-based representation for evaluating how object-level spatial relationships in WFH layouts are associated with user experience.

The design of active pneumatic upper limb exoskeletons is complicated by the challenge of reliably determining a kinematically safe workspace. Existing analytical kinematic methods are not sufficient to predict geometric collisions between elements of closed kinematic chains, which poses risks of mechanical damage and threats to user safety during exoskeleton operation. This paper proposes a hybrid algorithm for verifying the workspace of a pneumatic exoskeleton, combining analytical modelling in MATLAB R2020b based on the Product of Exponentials (PoE) method with high-performance static simulation in the Unity environment. At the initial stage, a discrete set comprising 758 million positions of the upper exoskeleton manipulator was generated. Subsequently, a multithreaded two-stage filtering process was implemented: analytical verification of rod stroke limits and angular constraints, followed by the detection of physical intersections of solid-state meshes using the PhysX engine. The results indicate that while the analytical model filters out 99.6% of invalid configurations. Yet, among the remaining positions-formally correct from a mathematical standpoint-up to 50% lead to critical geometric collisions or breaks in the kinematic chain. The computational efficiency of the proposed architecture enabled full static workspace verification in under 20 min. A reachable zone topology was established, revealing pronounced asymmetry and the presence of a "manoeuvrability core" in the user's anterior hemisphere. The developed algorithm generates a verified set of kinematically safe exoskeleton states, providing a foundation for the kinematic safety layer of a hierarchical control system. These findings demonstrate the necessity of complementing analytical kinematics with physical collision detection when designing hybrid kinematic mechanisms, and the approach can be applied to verify collision-free movement trajectories in various robotic systems. The approach can be applied to verify collision-free movement trajectories in simulation, with physical validation deferred to future work.

To describe the advantages of an echocardiographer-led workflow combining transjugular intracardiac echocardiography (ICE) and transesophageal echocardiography (TEE) guidance for tricuspid transcatheter edge-to-edge repair. Ultrasound-guided right internal jugular access was obtained for sterile ICE catheter insertion. Intraprocedural imaging was directed entirely by the echocardiographer from the head of the bed, with side-by-side transjugular ICE and TEE probe control within a single ergonomic workspace. This configuration optimizes ergonomics and streamlines workflow while allowing the interventionalist to focus solely on device implantation. Careful attention is required to minimize transjugular access-site hematoma and infection. Safe transjugular ICE manipulation requires continuous fluoroscopic and TEE guidance. Effective workflow integration requires advanced intraprocedural imaging expertise. Transjugular ICE complements TEE to enhance imaging synergy, improve ergonomics, and streamline workflow during tricuspid transcatheter edge-to-edge repair, with potential to improve efficiency and procedural success.