Search Results (1,220)

Reliable short-term position forecasting may provide anticipatory state information for collision-risk assessment, communication management, and prediction-assisted control in Internet of Things (IoT)-enabled unmanned aerial vehicles (UAVs); these downstream functions are not evaluated directly here. This study reformulates UAV position prediction as a flight-wise, multi-horizon, three-dimensional forecasting problem and tests whether position, velocity, gravity-resolved acceleration, and quaternion-orientation histories improve predictive accuracy while measuring model-level edge-inference cost rather than end-to-end system latency. The dataset contains 3100 AirSim flights with high-rate kinematic, inertial, attitude, pressure, and magnetic-field measurements under variable horizontal wind. The reported generalization is flight-disjoint within one AirSim domain; route/scenario disjointness and transfer to physical UAVs are not established. Signals are converted to a common navigation frame, gravity-resolved, low-pass filtered, resampled to 50 Hz, and partitioned by flight identifier before normalization and window construction. Each learned model receives 2 s of history and predicts the complete next 1 s trajectory, with errors evaluated at 0.1, 0.5, and 1.0 s. The sensor-enriched LSTM (LSTM-PVAQ) is compared under matched conditions with persistence, constant-velocity, constant-acceleration, extended Kalman filter, reduced-feature LSTM, GRU, temporal convolutional network (TCN), and compact Transformer baselines. LSTM-PVAQ achieved 3D RMSE values of 0.043, 0.168, and 0.371 m at 0.1, 0.5, and 1.0 s, respectively. At 1 s, its RMSE was 21.7% lower than LSTM-PV, 13.1% lower than GRU-PVAQ, 9.3% lower than TCN-PVAQ, and 16.8% lower than Transformer-PVAQ. Its one-second ADE and FDE were 0.216 and 0.339 m. On a Raspberry Pi 5 CPU using one FP32 thread and batch size one, median neural forward-pass latency was 0.88 ms, well below the 20 ms model-update interval. The results show that gravity-resolved inertial and orientation histories improve multi-horizon prediction, while TCN-PVAQ remains an attractive lower-latency alternative. Full article

Unplanned pregnancy can disrupt higher education, yet less is known about how college women of color navigate educational pathways following pregnancy-related disruption. This qualitative phenomenological study examined the academic consequences of unplanned pregnancy and the conditions shaping persistence, interruption, return, and completion. A focused reanalysis was conducted using interviews from a broader study of sexual and reproductive health. Eleven college women of color with experiences of unplanned pregnancy participated in semi-structured interviews. Data were analyzed using reflexive thematic analysis. Four themes characterized participants’ educational experiences: pregnancy as academic disruption; relational and academic support for persistence; nonlinear educational pathways involving delay, return, redirection, and completion; and educational decision-making under pregnancy pressure. Pregnancy affected education through withdrawal, reduced academic engagement, health and financial pressures, and competing responsibilities. Family members, partners, childcare networks, and faculty flexibility supported continuation and return. Importantly, temporary interruption or changes in educational direction did not necessarily indicate educational disengagement. Educational persistence following unplanned pregnancy may instead involve adaptation, interruption, reentry, and altered pathways toward completion. The findings suggest that higher education institutions may better support pregnant and parenting students through coordinated academic, financial, childcare, and reentry assistance that reduces reliance on individual circumstances and informal support networks. Full article

Herein, the distributed variational Bayesian-assisted unscented Kalman filter (VB-UKF) is used to enhance the accuracy of pedestrian positioning. For this method, a distributed filter is employed. First, the UKF under colored measurement noise (CMN) is derived. The VB-assisted method is then derived. Subsequently, the Mahalanobis distance is used to determine whether the current noise estimate conformed to the navigation environment; if the Mahalanobis distance is higher than the preset threshold, the VB-assisted method is employed to update the noise, which can improve the UKF accuracy under CMN. The effectiveness of the proposed method was subsequently validated through two practical tests. Experimental results demonstrate that this method plays a notable role in reducing localization errors in the experiments. This observation emphasizes the efficacy of the proposed method. Full article

This study examines whether perceived interactivity is associated with tourists’ continuance intention toward generative AI travel assistants through two parallel post-use evaluations—perceived usefulness and flow experience—while accounting for individual technology-related dispositions. Drawing on the Stimulus–Organism–Response (S–O–R) framework, the model conceptualizes perceived usefulness as a cognitive–instrumental evaluation and flow experience as an experiential–absorptive evaluation, while examining personal innovativeness in information technology and technology anxiety as focal moderators, selected on theoretical grounds, of the interactivity–flow and interactivity–usefulness associations, respectively. Data were obtained through a purposive online survey of 853 tourists with prior experience using generative AI for travel-related purposes, and the proposed relationships were tested using partial least squares structural equation modeling (PLS-SEM). The findings show that perceived interactivity is positively associated with continuance intention, perceived usefulness, and flow experience. Both perceived usefulness and flow experience are positively associated with continuance intention and exhibit significant indirect associations between perceived interactivity and continuance intention when estimated simultaneously. The indirect association through perceived usefulness is numerically larger than that through flow experience, while the remaining direct association is consistent with the two evaluations providing a complementary but non-exhaustive account of continuance. Personal innovativeness is positively associated with flow experience, and the positive interactivity–flow association is estimated to be stronger at higher levels of personal innovativeness. Technology anxiety is negatively associated with perceived usefulness, and the positive interactivity–usefulness association is estimated to be weaker at higher levels of technology anxiety. Although statistically significant, both interaction magnitudes are small and therefore provide limited rather than strong evidence of association-specific heterogeneity across tourists. The model demonstrates moderate explanatory performance and positive but limited benchmark-relative out-of-sample predictive relevance. The study contributes to research on generative AI-enabled tourism by showing that perceived interactivity is concurrently associated with distinct and nonredundant instrumental and experiential forms of post-use value, each of which retains a separate association with continuance intention. This dual pattern is particularly relevant to travel decision-making, in which tourists frequently navigate complex and interdependent choices under experiential uncertainty while remaining engaged in an evolving planning process. Within the S–O–R framework, the study specifies a context-bound dual-evaluation account of generative AI post-adoption rather than claiming that the inclusion of additional mediators or moderators constitutes a general extension of the framework. The findings offer general practical considerations for designing generative AI travel assistants that combine responsive interaction with functional value and engaging user experiences. However, disposition-specific design and segmentation implications should be treated as tentative given the small interaction magnitudes. Full article

Background and Objectives: The rehabilitation of the atrophic posterior maxilla with pterygoid implant presents a significant challenge due to complex regional anatomy and limited visual access. This study aimed to compare the accuracy of a dynamic computer-assisted implant surgery system (dCAIS) for pterygoid implant placement in fully and partially edentulous maxillae using radiographic marker registration (RMR) and markerless tracing registration (MTR), respectively. Materials and Methods: Forty pterygoid implants were retrospectively evaluated in 40 patients, divided into two groups: fully edentulous (n = 20) and partially edentulous (n = 20). Implant placement was performed using a dynamic navigation system (dCAIS), utilizing either bone-anchored mini-screws or tooth-surface tracing as reference points. Accuracy was assessed by superimposing preoperative plans with postoperative cone beam computer tomography (CBCT) scans. Coronal, apical, and angular deviations were measured and analyzed using the Data Science Workbench (version 14). The level of statistical significance was set at α = 0.05 (two-tailed). Results: The mean deviations for fully and partially edentulous maxillae measured 1.24 ± 0.27 mm and 1.40 ± 0.27 mm at the coronal level (p = 0.065), 1.30 ± 0.49 mm and 1.30 ± 0.54 at the apical level (p = 0.995), and 0.72 ± 0.38° and 0.82 ± 0.43° in angular deviation (p = 0.560), respectively. No statistically significant differences were found between the two groups. Notably, mean angular deviation was remarkably low (<1°) in both groups. Conclusions: No statistically significant between-group differences in accuracy were detected in coronal, apical, and angular deviations between the markerless tracing registration method in partially edentulous patients and the bone-anchored radiographic marker registration method in fully edentulous patients. Full article

High-precision urban 3D localization is a critical foundation for vehicular Internet of Things (IoT) applications, yet conventional Global Navigation Satellite System (GNSS)-based localization is vulnerable to signal obstruction, multipath effects, and non-line-of-sight propagation in complex environments. To improve localization reliability, this paper proposes a consistency-guided quantitative–qualitative fusion (CG–QQF) framework with vision-based terrain constraints. The framework integrates heterogeneous quantitative sensors, including an absolute positioning source, inertial measurement unit (IMU), wheel encoders, and a steering angle sensor, for continuous metric state estimation, while a monocular camera provides qualitative terrain-slope information. Rather than treating visual perception as a direct metric observation, the proposed method introduces it as a conditional structural constraint that is activated only when it is consistent with the quantitative estimate, thereby regularizing the localization solution and suppressing vertical drift. Although ultra-wideband (UWB) positioning is adopted as the absolute positioning source in the experimental platform, it serves as a generic positioning module and can be replaced by GNSS-based techniques such as real-time kinematic (RTK) and precise point positioning (PPP). In an indoor scaled proof-of-concept experiment over a controlled four-lap dataset, CG–QQF achieves a 3D RMSE of 0.0575 m and a vertical MAE of 0.0042 m. Its 3D RMSE is approximately 4.0% lower than quantitative sensor fusion (QSF), 37.9% lower than absolute-positioning/inertial fusion (ABS–INS), and 49.9% lower than vision-assisted quantitative fusion (VA–QF). These results demonstrate that consistency-triggered qualitative constraints can complement metric sensor fusion without directly introducing uncertain visual measurements, providing a practical mechanism for improving the robustness and vertical stability of heterogeneous localization systems. Full article

Although natural biopolymers represent promising sustainable packaging alternatives, their weak mechanical and barrier properties limit industrial use. While previous reviews focus on descriptive aspects of enzymatic modification, this review fills a critical literature gap by systematically bridging molecular-level laccase-driven reactions with quantitative techno-economic and safety and regulatory frameworks. We evaluate the kinetic and topological differences between direct tyrosyl-coupled protein homopolymerisation and mediator-assisted ‘graft-then-link’ polysaccharide strategies. Crucially, we analyse how entrapment versus surface-immobilised architectures dictate mass-transfer regimes, establishing their specific functional fitness for active oxygen scavenging or intelligent time-temperature monitoring. Beyond physical performance, we critically assess the translational bottlenecks currently hindering industrial scaling. For the first time, we integrate a quantitative techno-economic analysis using the Technology Readiness Level (TRL) framework, demonstrating that active film fabrication costs (EUR 0.01–0.10/m²) are heavily offset by high-protein food waste savings (>EUR 2.00/kg). Finally, we navigate European and US regulatory landscapes for enzymatically active materials and evaluate safety risks via the Threshold of Toxicological Concern (TTC) model and deterministic migration modelling. This comprehensive analysis establishes a ‘Safe-by-Design’ paradigm, guiding the scalable development of intrinsically safe, high-performance biocatalytic packaging. Full article

Health systems are complex adaptive systems whose structural uncertainty limits prediction-based planning, and generative AI is increasingly proposed to enrich the foresight used to navigate them. Whether cheaper, more fluent scenario production actually improves collective strategic judgement, however, remains largely untested in real organisations. This article reports a research-through-design study of an AI-assisted participatory foresight process run for the planned Girona Health Campus (Catalonia): ten domain workshops with about 250 professionals, 170 AI-assisted scenario drafts, and an organisation-wide SmartDelphi validation. Reconstructing the process abductively from its documentation, three behaviours recur. Generation increased the number of scenarios but not the range of futures they covered, pulling repeatedly toward technological resolution: 80% of the analysed scenarios referenced technology and 61% were legible as techno-optimistic accounts. The decisive work therefore migrated downstream to synthesis, where situated meaning was preserved, diluted, or lost. The characteristic failure mode was not poor output but over-trust in fluent output, which participants countered only when the design required them to contest and restate it. Validation itself functioned as organisational diagnostics, exposing a consistent desirability–feasibility gap that varied with the kind of change each future demanded. We consolidate these findings into five transferable design principles and argue that, once the capacity to imagine futures becomes abundant, methodological attention must shift from producing more scenarios to managing that abundance while safeguarding quality. Full article

The ubiquity of surveillance networks in public infrastructure presents a significant, yet underutilized, opportunity to assist vulnerable populations, particularly individuals who are blind or have low vision (BLV). However, transforming these passive video feeds into active guidance systems requires accurate camera calibration, a process that is traditionally labor-intensive and unscalable in large facilities. This paper introduces a novel, automated framework that leverages a mobile quadruped robot (Boston Dynamics Spot) as a dynamic calibration agent. We propose a single-pass approach with two planar calibration algorithms, which mines unstructured robotic trajectories to construct distinct geometric features on the ground plane. By synthesizing “virtual rectangles” from the robot’s odometry, our method first recovers camera focal length through vanishing point estimation by constructing virtual rectangles, and then solves for extrinsic 6-DoF pose using two algorithms: our proposed Virtual Rectangle (ViR) algorithm and a standard planar Perspective-n-Point (PnP) algorithm. Experimental validation using real-world data demonstrates the system’s robustness to sensor noise, maintaining focal length errors around $5\%$, rotational errors around $5^{°}$ and relative translation errors of approximately $10\%$, while synthetic simulations indicate focal length errors generally under 10%, rotational errors under ${1.5}^{°}$, and translation errors also around 10% despite heavy image and object disturbances. This work eliminates the need for manual calibration targets for dynamic camera calibration, effectively converting static security infrastructure into a metric sensing network capable of supporting high-fidelity social robotics applications. Full article

This study proposes a novel hybrid interactive navigation framework for mobile robots, designed to enable robots to operate under dual constraints imposed by both the environment and the speech of accompanying humans in challenging future collaborative working scenarios. By leveraging human perceptual capabilities, the proposed framework significantly enhances the obstacle-avoidance capabilities and flexibility of robots. Specifically, an innovative composition algorithm is introduced to integrate traditional costmap-based navigation with a newly proposed LLM-assisted voice-based interaction method, thereby achieving real-time human–robot collaborative navigation with complementary advantages. Within this framework, robots can not only rely on spatial sensors to avoid obstacles but also follow verbal instructions from humans to bypass hazards that are difficult to detect. Moreover, the volume of speech is innovatively incorporated as a fusion weight, allowing the accompanying human to naturally guide the robot through voice volume modulation. To validate the feasibility and performance of the proposed framework and algorithm, we conducted both simulation and real-world experiments. A series of ablation and comparative studies was conducted to evaluate the merits and limitations of various configurations, ultimately providing optimal configurations based on the results. This work expands the scope of real-time human–robot interaction in navigation, offering new perspectives for future research. Full article

Accurate velocity estimation in GPS-denied environments remains a core challenge for autonomous unmanned aerial vehicle (UAV) navigation. Our previous work demonstrated that fusing optical flow (OF) with dead reckoning (DR) substantially reduces position drift compared to inertial-only solutions. However, velocity estimates derived from dense optical flow are degraded by two systematic effects: (1) incorrect metric scaling when the camera footprint covers heterogeneous terrain types—particularly at forest–field transitions where the visible surface elevation differs significantly from bare-ground elevation; and (2) flow magnitude bias introduced by scene texture and structural properties. This paper presents three targeted improvements to a software pipeline for optical flow-assisted UAV navigation. First, single-point above-ground-level (AGL) estimation is replaced by camera footprint area mean sampling over co-registered Digital Terrain Model (DTM) and Digital Surface Model (DSM) rasters, with the surface model adopted consistently for OF metric scaling. Second, a one-dimensional Kalman filter with an innovation gate suppresses velocity spikes caused by abrupt terrain transitions. Third, a compact data-driven correction module uses selected flow, texture, and terrain descriptors to estimate a multiplicative velocity correction factor aligned with GPS-derived reference speed available during calibration and offline evaluation but not required during GPS-denied operation. Experiments on three real PX4-logged flight missions (Log 258 for calibration and Logs 259–260 for independent evaluation) totalling 7.3 min show terrain-dependent behaviour. On the mixed forest–field validation flight (Log 260), the improved pipeline reduces velocity mean absolute error (MAE) by 71% (1.04 m/s → 0.30 m/s) and dead-reckoning position MAE by 88% (59.8 m → 7.1 m), compared to the baseline from our previous work. On a flat open-terrain validation flight (Log 259), the terrain-aware modifications leave the terrain-insensitive baseline essentially unchanged, providing a control case for the proposed DSM-based scaling mechanism. Full article

The advancement of precision agriculture and smart tillage relies on high-precision, real-time perception of unmanned ground vehicle (UGV) positions. In large-scale farmland operations, conventional Global Navigation Satellite System (GNSS)-based positioning may suffer from short-term signal loss, degrading accuracy. Multi-unmanned aerial vehicle (UAV)-assisted vehicle localization based on direction-of-arrival (DOA) estimation can provide critical positioning compensation for UGV, where compressed sensing (CS)-based DOA-assisted localization algorithms are commonly employed. However, existing schemes neither account for the bias induced by the local positional oscillation of UAVs, nor address the limited accuracy and real-time performance of CS-based DOA estimation, restricting their agricultural deployment. To this end, this paper first develops an assisted-localization architecture that explicitly incorporates the local positional offsets of multiple UAVs, together with a corresponding array signal reception model. To overcome the accuracy–efficiency trade-off of conventional CS-DOA methods, an adaptive local overcomplete dictionary (LOD) is then constructed to robustly refine the angular resolution around the region of interest. With the number of sources K assumed to be known and fixed, a particle swarm optimization (PSO)-based local refinement algorithm is further introduced to adaptively optimize the DOA estimates within the constructed local dictionary, thereby improving estimation robustness under low-SNR and coherent-source conditions. Consequently, the proposed method improves robustness while maintaining favorable localization accuracy and computational efficiency in the simulated scenarios. Simulation results show that it substantially reduces localization error compared with state-of-the-art algorithms, suggesting its potential as a localization-assistance approach for UGV navigation in sustainable tillage. Full article

Background/Objectives: Although lung cancer is the leading cause of cancer-related deaths in the United States, rates of screening have remained persistently low nationwide. This study sought to identify barriers, facilitators, and support strategies necessary for implementing a novel community health navigator workflow to improve lung cancer screening uptake in both rural and urban settings. Methods: Semi-structured interviews were conducted with primary care providers (n = 5), community scientists (n = 7), community health navigators (n = 4), and radiology staff (n = 2). Interview transcripts were analyzed using a rapid qualitative analysis approach. Three authors coded based on the Consolidated Framework for Implementation Research (CFIR) and the Expert Recommendations for Implementing Change (ERIC) frameworks using a hybrid deductive–inductive approach. Results: Participants highlighted several primary barriers: access to knowledge and information (e.g., knowledge of eligibility, knowledge of insurance coverage), IT infrastructure (e.g., quality of pack-year data), relative priority (e.g., need to discuss other conditions), and patient needs and resources (e.g., time off work, transportation, difficulty scheduling). Key facilitators for screening were again IT infrastructure (e.g., automated electronic health record alerts) as well as relational connections (e.g., trust between patients and providers). To address provider-level barriers, participants recommended educational meetings, using clinical champions, and providing feedback on current lung cancer screening rates. To address patient-level barriers, participants recommended health education tools, providing transportation vouchers, hosting weekend lung cancer screening clinics, and assisting with scheduling. Conclusions: A community navigator approach to lung cancer screening should address key barriers to implementation on both the patient and provider level, including knowledge, prioritization, and patient access. Full article

Immersive visualization can support interpretation of architectural heritage in historical paintings, yet translating 2D pictorial evidence into navigable 3D scenes remains challenging. Conventional workflows rely on physical survey data, while direct generative AI (GenAI) may produce structural hallucinations and lack historical constraints. This study proposes a human-in-the-loop GenAI-assisted framework for producing immersive 3D visualization prototypes rather than historically verified reconstructions. It integrates multi-view image generation, knowledge-informed review, single-image-to-3D generation, topology inspection, and perceptual calibration. Four fragments from the Northern Song Dynasty painting Along the River During the Qingming Festival were examined as a single-case proof of concept. Across three tested model pairs, raw AI assets were generated in approximately 3–4 min and were suitable for distant-background use; close-up visualization required 1–2 h of refinement, while basic structural editability required 4–5 h of post-processing, reducing the initial time advantage. A mixed-methods study with nine domain experts and 30 non-expert participants used the UES-SF, an adapted VisAWI, and semi-structured interviews analyzed through inductive thematic analysis. All eight subscale scores exceeded their neutral midpoints after Bonferroni correction (all adjusted $p<0.001$), indicating favorable perceptions of the guided experience. Interviews suggested potential for spatial exploration, museum interpretation, and education. However, geometric discontinuities, detail loss, color deviation, and historical-semantic errors remained, requiring expert review and manual correction. Transferability beyond this artwork and architectural tradition remains untested. Full article

This study addresses target-to-ROV relative localization in an unmanned surface platform-assisted UAV–ROV search-and-recovery system. Because the submerged ROV is not assumed to be visible from the air, the UAV observes the floating target and a GNSS-equipped ROV-associated surface buoy in the same image. The buoy position and target-to-buoy image displacement are combined to construct a world-frame target-position measurement, whose covariance accounts for buoy GNSS uncertainty and correlated image-projection errors. An upward-looking ROV imaging sonar provides range–bearing measurements. A delay-aware extended Kalman filter fuses the asynchronous observations using sea-state- and confidence-dependent covariance adaptation and normalized-innovation gating. ROV acoustic/inertial navigation uncertainty is propagated into the sonar measurement covariance and the reported relative-state covariance, avoiding duplication of the same navigation error in the aerial channel. The method is evaluated using a JONSWAP-based temporal disturbance model, Monte Carlo simulations, and single-factor and joint sea-state–occlusion–delay sensitivity tests. Under the nominal sea-state-5 condition, the proposed method achieves a mean ROV-frame relative RMSE of 0.992 m, compared with 1.083 m for ROV-only localization and 1.054 m for fixed-covariance fusion, with no run exceeding the 5 m divergence threshold. The results demonstrate improved relative-localization robustness within the simulated environment. Full article