Search Results (4,187)

Bacterial biofilms remain a major challenge in clinical infections due to their dense extracellular polymeric substance (EPS) matrix and strong resistance to conventional antibiotics. This study reports manganese dioxide (MnO₂) nanoparticles capable of autonomous navigation toward bacterial clusters, mechanical penetration of biofilm structures, redox-driven membrane disruption, and synergistic oxidative stress. The nanoparticles exhibit directional movement attributed to a combination of negatively charged surface potential, asymmetric topology, and catalytic reactivity toward bacterial metabolites. MnO₂ demonstrates potent antibiofilm activity against MRSA and MDR E. coli (>98% eradication) and partial activity against Pseudomonas aeruginosa. Time-lapse microscopy, EPR spectroscopy, XPS analysis, and SEM imaging reveal that MnO₂ disrupts both EPS and cell membranes while maintaining structural integrity throughout treatment. Cytotoxicity assays confirm ≥85% viability in human fibroblasts and keratinocytes at therapeutic concentrations. MnO₂ shows controlled biodegradation into Mn²⁺ ions, which participate in physiological pathways and undergo renal clearance. These findings support MnO₂ nanoparticles as promising biofilm-targeting agents for topical formulations, wound care, and implant coatings. Full article

Global Navigation Satellite Systems are vulnerable to jamming and spoofing threats, compromising several critical applications. Existing detection methods based on hardware solutions (antenna array, spectrogram) are low-latency and accurate but require expensive hardware, while machine learning solutions are the most effective but require extensive training and lack adaptability. This work proposes an edge-based, statistical threat detector using crowdsourced GNSS data and fuzzy logic to integrate multiple anomaly indicators. A key feature is a C-/N₀-based crowdsourcing metric. Experiments show detection precision up to 88% for jamming and 97% for spoofing, with false positive rates around 1–2% and an average detection time of 10 s. Full article

Robot-assisted implant surgery (RAIS) represents the most advanced form of digitally guided implant placement, integrating virtual planning with mechanically constrained execution and real-time control. Although multiple systematic reviews suggest superior accuracy with robotic systems, the magnitude and clinical relevance of these gains remain uncertain at the highest level of evidence. This umbrella review, conducted according to PRISMA 2020 and Joanna Briggs Institute guidelines, aimed to synthesize and critically appraise systematic reviews and meta-analyses evaluating the accuracy of Robot-assisted implant surgery (RAIS) in dental implantology. Search across five major databases identified seven eligible reviews published between 2023 and 2025, including clinical, cadaveric, and in vitro evidence. Across reviews, RAIS consistently demonstrated the highest placement accuracy, with pooled mean coronal deviations of 0.60–0.73 mm, apical deviations of 0.63–0.70 mm, and angular deviations typically between 1.4° and 1.7°. Comparative meta-analyses reported significant reductions in linear (−0.15 to −0.21 mm) and angular deviations (−1.2° to −1.4°) compared with dynamic navigation. Despite these technical advantages, evidence linking improved accuracy to enhanced implant survival, reduced complications, or superior patient-reported outcomes was limited. Robotic workflows were associated with longer setup times, while safety profiles were comparable to other guided techniques. Overall, RAIS provides the highest placement accuracy currently reported; however, further high-quality clinical trials are needed to clarify its impact on long-term clinical outcomes and cost-effectiveness. Full article

The rapid development of low earth orbit (LEO) satellite constellations for navigation augmentation represents significant challenges in optimizing coverage performance while minimizing system complexity. A hybrid optimization algorithm based on pelican optimization algorithm and quantum particle swarm optimization (POA-QPSO) is proposed in this paper for multi-objective optimization design of dual-layer Walker constellations. The algorithm integrates the global search capability of the POA and the local exploitation ability of QPSO, effectively balancing exploration and exploitation through a probability-driven dual-phase search mechanism, a three-tier adaptive parameter adjustment strategy, and a pareto frontier maintenance mechanism. Probability factor and quantum tunneling facilitate low-cost deep search in complex non-convex environments. Experiments demonstrate that the algorithm outperforms MOPOA and MOPSO on ZDT test functions, with an 18.5% improvement in IGD metrics. In LEO constellation optimization, the designed dual-layer configuration (800 km/144 satellites in the first layer and 1426 km/56 satellites in the second layer) achieves a 92.7% global coverage, with an average PDOP of 1.78 and 5.8 visible satellites in polar regions. Furthermore, comparative benchmark tests show that the proposed solution outperforms most mainstream algorithms and performs better than traditional medium Earth orbit satellite systems in mid-to-high latitude regions. This research provides an efficient solution for LEO navigation augmentation system design. Full article

Accurate perception of tugboat operational status is essential for optimising port scheduling efficiency and ensuring operational safety. However, existing AIS-based methods often struggle to capture the fine-grained and asymmetric manoeuvring characteristics of tugboats, particularly in distinguishing assisted berthing from unberthing operations. To address these limitations, this study proposes a hybrid recognition framework integrating multidimensional feature engineering with spatiotemporal dynamics. First, a speed-threshold-based sliding window algorithm segments trajectories into sailing and berthing states. Second, a 15-dimensional feature vector—comprising statistical and descriptive features from speed, heading, and trajectory morphology—is constructed to characterise tugboat behaviour. Notably, morpho-logical descriptors such as the ‘Overlap Ratio’ serve as implicit spatial proxies, capturing geographical constraints without reliance on Electronic Navigational Charts. A three-layer fully connected neural network (FCNN) is then developed to classify segments into “Cruising” and “Assisting in Berthing/Unberthing.” Finally, a speed-dynamics rule further distinguishes berthing from unberthing based on opposing temporal evolution patterns. Experiments on real AIS data from Ningbo–Zhoushan Port demonstrate that the model achieves an F1-score of 0.90 and a recall of 0.93 for assistance-related operations. Permutation importance analysis confirms that integrating kinematic and morphological features enables interpretable and precise intent inference. This study offers a high-precision, low-dependency solution for tugboat operation identification, supporting intelligent port surveillance and sustainable maritime management. Full article

High-resolution marine gravity fields are critical for interpreting seafloor structure, investigating marine geodynamics, and enabling gravity-aided navigation. However, sparse shipborne observations, heterogeneous multi-source geodetic datasets, and the inability of conventional methods to handle nonlinear inversion limit accurate gravity recovery. To overcome these limitations, we propose a spectral physics-informed constraint deep-learning framework based on a multi-channel Swin Transformer to reconstruct high-resolution marine gravity anomaly fields. The model ingests multi-source geodetic inputs organized as 64 × 64 grid patches centered near each computation point and fuses them to predict the target gravity anomaly. We adopt a remove–compute–restore (RCR) strategy that isolates residual gravity signals, which improves numerical stability and accelerates training. Inputs include satellite-altimetry-derived vertical gravity gradients, vertical deflections, mean sea surface height, and topography; the model is trained on over 430,000 shipborne gravity samples from the South China Sea (0–30° N, 105–125° E). To enforce physical consistency, we embed a spectral-domain physics constraint derived from potential-field theory into the loss function; this constraint helps recover short-wavelength gravity signals. We also introduce an adaptive multi-domain multi-scale feature fusion module (AMAMFF) to improve the integration of heterogeneous inputs, and we demonstrate its benefits in experiments across complex terrain. Validation against independent shipborne gravity checkpoints yields an RMS error of 3.09 mGal, indicating a substantial performance advantage over existing deep-learning approaches and conventional gravity-field models. Full article

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future. Full article

LGBTQ+ individuals continue to experience substantial barriers to accessing affirming healthcare, including discrimination, structural inequities, and provider-level limitations. This study aimed to develop an emergent grounded theory model of constructing wholeness in healthcare. Methods: This study employed a secondary constructivist grounded theory analysis of qualitative data from The Rainbow Connections Study, a community-based system dynamics project. Data were collected through eight group model-building sessions conducted via Zoom with 28 LGBTQ+ participants, including older adults, youth, transgender and gender-diverse individuals, and staff from the LGBTQ+ community center who also held service and practitioner roles; analytic claims are framed to reflect this mixed-role sample. Sessions were audio- and video-recorded, transcribed verbatim, and analyzed using open, axial, and selective coding procedures. Constant comparative methods, reflexive memoing, and member checking were used to support analytic rigor and trustworthiness. Results: Analysis revealed a dynamic process in which LGBTQ+ individuals encounter external forces within healthcare systems that alternately support or fragment their sense of self. In response, participants engaged in four interconnected internal processes—interconnecting selves, intra-community support, self-determined care, and meaning-finding—that facilitated movement toward wholeness. These processes were non-linear, iterative, and present across diverse identities and life stages. Conclusions: The emergent theory of Constructing Wholeness in Connecting to Healthcare highlights that LGBTQ+ healthcare experiences extend beyond access and utilization to include identity integration, community reliance, and meaning making. Supporting LGBTQ+ health requires healthcare approaches that affirm wholeness, reduce structural harm, and recognize the central role of community in navigating care. Full article

Large Language Models are increasingly used for high-level robotic reasoning, yet their latency and stochasticity complicate their direct use in low-level control. Moreover, extracting actionable navigation cues from multimodal context incurs inference costs that are challenging for embedded platforms. We present a plug-and-play framework that augments a finite-state machine with asynchronous velocity suggestions generated by a Large Language Model, using an off-the-shelf DistilGPT-2 model running on-device on a Jetson AGX Orin. The system extracts task-relevant cues from the current context and integrates them only if they satisfy deadline, schema, and kinematic validation, thereby preserving a deterministic 50 Hz control loop with a <5 ms fallback path. We compare multiple Large Language Models for embedded robot control and quantify trade-offs among model size, inference time, and output validity. To assess whether the Large Language Models add value beyond signal processing, we include an ablation against a standard smoothing baseline; the results indicate that the Large Language Models contribute anticipatory, context-dependent adjustments that are not captured by filtering alone. Experiments in Gazebo and on a real TurtleBot3 reduce the final position error from 0.246 m to 0.159 m and improve trajectory efficiency from 0.821 to 0.901 without increasing control-loop latency. Approximately 80% of the Large Language Models’ outputs pass validation and are applied. Overall, the framework reduces developer effort by enabling behavioral changes at the prompt level while maintaining interpretable, robust edge-based navigation. Full article

Background: The use of augmented reality (AR) in orthopaedics is growing rapidly but is mainly limited to pre-operative planning and teaching. This study is one of the first to describe the intraoperative application within revision arthroplasty for the positioning of customised partial pelvic replacements. Methods: In a proof-of-concept study an AR environment was used during surgery in 11 cases to enhance implant positioning. Postoperatively, a voxel-based CT deviation analysis was carried out to determine the COR deviation and the cup plane deviation angle. Additionally, digital implant superimposition was conducted. Results: Implantation was possible in all cases with a mean COR deviation vector of 4.2 (SD 2.5; 1.2–9.3) mm and a cup plane deviation angle of 4.4 (SD 2.5; 0.7–8.1)°. The implant analysis showed a superimposition of 0.69 (SD 0.15; 0.38–0.88) (Dice-Score calculation). Conclusions: This study is able to report promising results for AR in orthopaedic surgery, showing improved intraoperative feedback in complex operations, resulting in increased accuracy. However, the integration of AR poses a new challenge to the surgical team, especially because the AR users are facing a significantly increased level of intraoperative stress. Further development of this auspicious tool, as well as a conceivable combination with navigation, is necessary to facilitate broader usage. Full article

Adaptability and flexibility in teaching with digital technologies are essential for instructors to navigate dynamic and ever-evolving educational contexts. However, little has been done to investigate the underlying competencies required of instructors to fluently integrate technologies into their instructional practices. This study employed the Delphi method to address this gap and identify the competencies of a digitally fluent educator in higher education. Through three rounds of data analysis, 36 experts across multiple higher education institutions reached consensus on 14 competencies, including 8 knowledge, 3 skills, and 3 dispositions as indicators of an educator fluent in applying digital tools. The final list of competencies highlights the importance of metacognitive skills, conditional knowledge, and a disposition to be adaptable when defining fluency in digital instruction. The findings indicate a differentiation between digital fluency and previous paradigms such as digital literacy and digital competency. Implications for competency-based professional development opportunities and future research are discussed. Full article

To achieve autonomous and reliable all-weather cross-domain aerospace navigation, this study proposes an adaptive fault-tolerant federated Kalman filter (AFTFKF) for an INS/SRNS/CNS integrated navigation system to enhance system robustness against measurement outliers. First, a noise estimator based on maximum likelihood estimation (MLE) and aided by a sequential probability ratio test (SPRT) is introduced to handle slowly growing outliers. Second, a double residual-based Chi-square test (DCST) information factor is designed to mitigate the impact of inaccurate local state estimation in subsystems under abruptly changed outliers. Finally, the SPRT-MLE-based noise estimator and the DCST-based information factor are integrated into the federated Kalman filter framework to construct the complete AFTFKF. Simulation results demonstrate that the proposed method achieves superior accuracy and strong stability for SINS/SRNS/CNS integrated navigation in the presence of outliers. Full article

To address the insufficient reliability of manned submersible navigation systems in complex deep-sea environments, this paper proposes an adaptive fusion navigation method based on multi-dimensional confidence assessment. This study proposes a method establishing a four-dimensional evaluation framework for the USBL (Ultra-Short Baseline) positioning system. The framework encompasses signal quality, geometric precision, environmental attenuation, and data stability. It enables the quantitative, real-time assessment of system reliability. Consequently, it facilitates an adaptive weight adjustment mechanism. Experimental results demonstrate that under harsh conditions featuring jump point anomalies and data loss, the proposed algorithm achieves an average position error of 1.15 m. This represents a 53.1% improvement over conventional methods, with the enhancement reaching 58.9% in scenarios specifically affected by jump points. The proposed method study effectively enhances the navigation reliability of manned submersibles in complex underwater acoustic environments, thereby demonstrating significant engineering application value. Full article

This paper presents the design and experimental evaluation of a bio-inspired gecko robot, focusing on mechanical design, vision-based obstacle perception, and rhythmic locomotion control as enabling technologies for future obstacle avoidance in complex environments. The robot features a 17-degrees-of-freedom mechanical structure with a flexible spine and multi-jointed limbs, providing a physical basis for adaptive locomotion. For perception, a custom obstacle detection dataset was constructed from the robot’s onboard camera view and used to train a YOLOv5-based detection model. Experimental results show that the trained model achieves a mean average precision (mAP) of 0.979 and a maximum F1-score of 0.97 at an optimal confidence threshold, demonstrating reliable real-time obstacle perception under diverse indoor conditions. For motion control, a central pattern generator (CPG) based on Hopf oscillators is implemented to generate rhythmic locomotion. Experimental evaluations confirm stable diagonal gait generation, with coordinated joint trajectories oscillating at 1 Hz. The flexible spine exhibits periodic lateral deflection with peak amplitudes of ±15°, ±10°, and ±8° across spinal joints, enhancing locomotion continuity and turning capability. Physical robot experiments further demonstrate smooth straight-line crawling enabled by the coupled limb–spine motion. While visual perception and CPG-based locomotion are experimentally validated as independent subsystems, their real-time closed-loop integration is not implemented in this study. Instead, this work establishes a system-level framework and experimental baseline for future perception–motion coupling, providing a foundation for closed-loop obstacle avoidance and autonomous navigation in bio-inspired gecko robots. Full article

A high-definition (HD) map development framework providing real-time visualization of multimodal perception data for state estimation, motion planning, and decision-making in autonomous navigation is presented and experimentally validated. The proposed framework integrates synchronized visual and LiDAR data and generates consistent frame transformations to construct accurate and interpretable HD maps suitable for navigation in dynamic environments. In addition, the framework enables flexible customization of essential map elements, including road features and static landmarks, facilitating efficient map generation and visualization. Building upon the developed HD map visualizer, a semantic-aware visual odometry (VO)-based pose estimation module is designed and verified through extensive evaluations and under perceptually degraded conditions. To ensure the reliability of synchronized multimodal data used by downstream perception and pose estimation modules, a sensor health monitoring system is also developed and validated in urban canyon scenarios with intermittent or unavailable global navigation satellite system (GNSS) measurements. Experimental results demonstrate that the proposed HD map visualizer and associated perception modules are transferable for autonomous navigation and can be effectively employed as benchmarking tools for state estimation and motion planning algorithms in autonomous driving. Full article