Search Results (5,178)

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from $2.609\mathrm{m}$ to $1.417\mathrm{m}$ and improved selected-view PSNR from $13.80\mathrm{dB}$ to $16.46\mathrm{dB}$. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map. Full article

Smartphone GNSS metadata remains an underexplored source for evaluating navigation-signal integrity in real-world conditions. We investigated GPS behavior recorded by a Samsung Galaxy A72 smartphone across two European flights (EXP03: Rome–Bucharest, n = 377; EXP10: Bucharest–Lisbon, n = 78) and 275 terrestrial reference photographs (Cabo da Roca, Portugal). A total of 730 photographs were analyzed using a seven-indicator taxonomy, conceptually inspired by Receiver Autonomous Integrity Monitoring (RAIM) principles, covering anti-spoofing, anti-sniffing, and anti-tampering checks. GPS capture rates reached 100% (Timestamp Camera) and 83.3% (native camera) up to 11,439 m WGS84, among the highest EXIF altitude profiles reported to date. Velocity spikes (1560–1875 km/h), one at cruise altitude and one during landing, were indistinguishable from GPS spoofing at the EXIF level, and their physical origin is undetermined. Phantom geolocation was absent in flight (0/442) versus 37.3% on the ground, consistent with GPS constellation visibility as the primary factor. In total, 88% (n = 920) of JPEG files lacked the standard EOI marker, generating false positives in integrity validators. Findings are device-specific, derived from non-independent observations, and require replication before generalizing to other GNSS receivers or latitudes. The framework offers a methodological basis for EXIF integrity analysis relevant to EU AI Act Article 10(3) data quality and UAV precision-agriculture georeferencing. Full article

To address the challenges of limited storage space and mobile launch-and-landing operations for vehicle-mounted multirotor UAV swarms, this paper proposes a stackable Cone-Sleeve UAV swarm system. The UAV airframe incorporates a through-body central sleeve integrated with conical guidance structures, which cooperate with a vertical guide rod mounted at the center of the mobile platform to achieve geometric passive pose correction during landing. In addition, a UWB-based onboard local positioning and navigation system is developed, in which a tightly coupled UWB/IMU estimator is employed to achieve high-precision relative state estimation for the UAV swarm. A five-stage finite state machine (FSM) schedules the landing sequence. During terminal landing, a motion feedforward control strategy is introduced for dynamic motion compensation and to ensure seamless state transitions. Simulation and vehicle-mounted experimental results demonstrate that, strictly under low-speed (≤0.5 m/s), constant-velocity straight-line motion conditions, the proposed system enables autonomous vertical takeoff and landing as well as rapid stacked launch-and-landing operations for multiple UAVs. The cooperative guidance strategy integrating active control and the Geometric Passive Guidance (GPG) mechanism improves the precision and speed of swarm landing operations. The proposed system provides a feasible system-level solution for the storage, transportation, and autonomous rapid launch-and-landing of high-density UAV swarms. Full article

Time governs human experience, yet human beings continually seek to govern time. Within Jewish mysticism, time functions as a foundational and elusive dimension of human experience, serving as a dynamic arena for theosophical speculation, theurgical action, and spiritual transformation. In this context, the influential nineteenth-century Hasidic work Bnei Yissachar by Rabbi Tzvi Elimelech Shapira of Dinov (1783–1841) occupies a unique position, offering a systematic and nuanced theology of sacred time. While previous scholarship has recognized the importance of sacred time in his thought, this study uncovers a complex reciprocal temporal mechanism that balances two dialectical poles: cosmic subordination and human mastery. Through a close reading of Shapira’s homilies, this article demonstrates how he navigates the tension between fixed, pre-ordained divine temporal matrices, typified by the linguistic and numerical hidden structure of the Sabbath, and active human agency capable of reshaping the qualitative influx of time, illustrated by the sanctification of the New Moon and the Passover Seder night. By integrating Lurianic kabbalistic frameworks, gematria, and linguistic ontology with embodied ritual practices, Shapira reframes the Jewish calendar as an interactive, experience-oriented relationship between the human and the divine. Full article

To address the challenges of GNSS signal blockages and severe multipath effects in complex urban environments, this paper proposes a tightly coupled precise point positioning (PPP)/inertial navigation system (INS) integration method based on an adaptive robust Kalman filter (ARKF) for smartphones. The proposed method integrates a robust estimation module based on the IGG-III weight function and an adaptive factor derived from vehicle dynamic intensity and geometric precision indicators, to mitigate observation outliers and dynamic model errors. To evaluate the positioning performance of this algorithm, two typical vehicle experiments based on the GNSS and inertial measurement unit (IMU) chipsets of the Xiaomi Mi 8, as well as an external H30 IMU, were conducted. Experimental results show that in the urban expressway environment, the horizontal root mean square (RMS) error of the loosely coupled PPP/INS solution was reduced by 74.17% compared with the conventional PPP solution, while the maximum horizontal positioning error of the tightly coupled PPP/INS solution was reduced by 44.82% compared with the loosely coupled PPP/INS solution. In the complex urban road and tunnel environments, the proposed ARKF-based tightly coupled PPP/INS method achieved a 36.79% reduction in horizontal RMS error compared with the tightly coupled PPP/INS solution based on the standard extended Kalman filter (EKF) and demonstrated more robust positioning performance in the tunnel. Full article

This paper proposes a method for reducing the initial azimuth estimation time of a north-finding system employing low-cost sensors and a compact 3-axis turntable. The system is capable of operating in non-horizontal environments, magnetically disturbed environments, and environments where Global Navigation Satellite System (GNSS) signals are unavailable. Detection of due north without prior azimuth information was evaluated through indoor experiments under the aforementioned conditions. During each rotation, the compact 3-axis turntable was kept horizontal and the acceleration and angular velocity were measured in 16 directions at 22.5$°$ intervals. By including the final position coinciding with the initial one, a total of 17 measurement points were obtained per lap. This process was repeated for 77 laps. For statistical evaluation, 5000 bootstrap replications were generated. Detection of due north was then performed using these datasets and the relationship between the number of laps and the estimation error was statistically analyzed. Consequently, it was confirmed that the root mean square (RMS) error becomes less than 1$°$ after ten laps, corresponding to a data acquisition time of approximately 1.3 h. Compared to our previous study, the required estimation time is reduced by approximately 2 h. Full article

Global Positioning System (GPS) positioning accuracy is strongly affected by inaccurate noise modeling and non-Gaussian pseudorange measurement errors, including heavy-tailed disturbances and abnormal outliers caused by multipath propagation and signal degradation. Conventional extended Kalman filters (EKFs) generally assume Gaussian measurement noise with fixed covariance matrices, which limits their robustness under degraded measurement conditions. This study proposes a Student-t robust quantum-behaved particle swarm optimization-based extended Kalman filter (ST-QPSO-EKF) for adaptive GPS state estimation. The proposed framework combines quantum-behaved particle swarm optimization (QPSO) with a Student-t-based robust measurement update, where the process-noise scaling factor, measurement-noise scaling factor, and Student-t degrees-of-freedom parameter are jointly optimized. The optimized parameters are obtained through an offline calibration stage and subsequently applied in the recursive GPS filtering process. A nonlinear GPS navigation simulation was conducted using Gaussian, Student-t heavy-tailed, and outlier-contaminated pseudorange measurement scenarios. The proposed method was compared with conventional EKF, QPSO-EKF, and Student-t EKF using 20 independent Monte Carlo realizations. The results demonstrate that QPSO-EKF provides improved accuracy under nominal Gaussian conditions, whereas ST-QPSO-EKF achieves superior performance under non-Gaussian measurement environments. Under Student-t heavy-tailed noise, ST-QPSO-EKF reduced the position RMSE to 3.814 m, while under outlier-contaminated noise it achieved a position RMSE of 3.952 m, outperforming the other compared methods. In addition, the proposed method maintained comparable online computational cost because the QPSO optimization was performed offline. The results indicate that jointly optimizing covariance parameters and Student-t robustness provides an effective strategy for improving GPS positioning reliability under complex pseudorange measurement conditions. Full article

This study examines how gay-Mizrahi men in Israel navigate the communicative processes through which workplace subjectivities are constructed, negotiated, and legitimized. Mizrahiness refers to a Jewish ethno-cultural position associated with Middle Eastern and North African ancestry, historically shaped by unequal relations with Ashkenazi hegemony and intersecting class, geographic, cultural, and gendered hierarchies. Based on semi-structured interviews with self-identified gay-Mizrahi men across diverse occupational sectors, this study examines how sexuality, ethnicity and professionalism are read together in everyday organizational interactions. The findings reveal three strategic mechanisms: identity calibration, hyphenated subjectivity, and compensatory professionalism. Identity calibration refers to the ongoing tuning of identity visibility, timing, intensity, and communication. Hyphenated subjectivity captures the relational process through which gayness and Mizrahiness are not simply combined but continuously negotiated in relation to social expectations and organizational norms. Compensatory professionalism describes intensified investment in excellence, credentials and self-control to counter anticipated devaluation. These mechanisms show that organizational belonging is not a static outcome of diversity or inclusion but an advanced communicative achievement. Thus, this study contributes to workplace communication and Diversity, Equity and Inclusion (DEI) scholarship by illustrating how marked workers achieve professional legibility. We argue that DEI practice must move beyond visible categories of diversity towards affective and communicative dimensions through which inequality is reproduced. Full article

As China’s dominant urban housing form, gated residential communities deploy layered spatial access controls governing who may enter and move through neighbourhood space. While neighbourhood social sustainability has attracted substantial scholarly attention, how micro-spatial governance arrangements affect the inclusiveness of residential built environments toward essential service workers remains underexplored. Drawing on Lefebvre’s theory of the production of space, this study uses food delivery riders—who navigate gated community access controls dozens of times daily—as an analytical lens to evaluate how neighbourhood spatial governance shapes social inclusiveness. Vignette-based survey data from 445 riders across 157 cities in 27 Chinese provinces were analysed using structural equation modeling. Results show that cumulative anxiety from procedural delays, detours, and elevator waiting is the dominant pathway through which spatial governance undermines riders’ perceived spatial inclusion—operationalised through occupational dignity indicators—with elevator-based spatial stratification identified as a concrete, low-cost intervention target within the spatial-channeling mechanism. Technology-mediated access partially mitigates face-to-face exclusion but leaves underlying spatial inequalities intact. Demographic invariance across all tested variables confirms that diminished inclusion arises from the governance regime itself. The findings position neighbourhood spatial governance as a measurable dimension of urban social sustainability—one concretely testable through the experience of essential service workers—and identify low-cost built-environment interventions for inclusive residential design. While the study focuses on riders as a single user group, the analytical framework is transferable to evaluating how residential built environments accommodate diverse non-resident populations. Full article

In an era of information explosion and overload, individuals face a constant influx of mixed-quality, contradictory data. Navigating this complex landscape requires critical thinking, which enables individuals to dynamically adjust their reasoning and refine judgments. Consequently, accurately measuring this dynamic ability is essential. However, existing assessments predominantly focus on single-round argumentation and fail to capture the iterative process, potentially yielding unrepresentative evaluations of real-world performance. To address this gap, this study develops the Iterative Argumentation Task (IAT), a novel assessment tool that presents participants with a sequence of interrelated passages simulating real-world information flow. By capturing the process of evaluation, position changes, and argument revisions, the IAT evaluates three ability dimensions: single-round argument analysis, inter-argument relationship analysis, and argument integration. We recruited 355 undergraduates to validate the IAT. Confirmatory factor analysis provided evidence for the proposed measurement structure. Furthermore, correlations with established critical thinking and reasoning tests demonstrated convergent and discriminant validity, respectively. Moreover, performance differences between the undergraduates and 33 debaters established known-groups validity. Finally, latent profile analysis identified four distinct profiles across the IAT dimensions. Together, these findings establish the IAT as a theoretically sound and practically effective tool for assessing critical thinking. Full article

This paper presents the design and simulation-based validation of an acoustic navigation system intended for operation within a swarm of underwater vehicles. The system is deployed on a mobile leader unit, while the remaining vehicles, referred to as followers, navigate relative to the leader. The proposed solution utilises two or three acoustic transmitters mounted at the front and rear, and, in the option with three transmitters, also in the middle of the leader platform. These transmitters periodically emit acoustic signals that are received by the follower vehicles. By measuring the time-of-flight of the received signals, followers estimate their distances to the transmitters. This dual(triple)-range information, combined with Kalman filter dead-reckoning, enables relative position estimation with respect to the leader, supporting coordinated swarm movement without reliance on external positioning infrastructure such as GPS, which is unavailable underwater. The system was evaluated in a simulation environment across multiple scenarios with varying levels of distance-measurement error. Rather than modelling detailed acoustic signal propagation, the study focuses on assessing the robustness of the positioning method to measurement inaccuracies. The results demonstrate that the proposed configuration provides useful relative positioning accuracy under a range of error conditions and identifies the operating conditions in which its performance deteriorates, supporting the feasibility of the proposed approach for leader–follower coordination in underwater swarms. Full article

Abnormal ship behavior detection is important for maritime traffic surveillance, navigation safety, and risk prevention. However, existing methods often depend on handcrafted features or a single reconstruction or prediction signal, which limits their ability to detect both sustained trajectory abnormalities and abrupt vessel movement changes. This paper proposes a Dual-Error Fusion LSTM–Transformer framework, referred to as DEFLT, for AIS-based abnormal ship behavior detection. A motion-aware vessel representation was first constructed by combining the geographical position, speed over ground, course over ground, and their temporal variations. An LSTM autoencoder reconstructs historical trajectory windows, while a Transformer prediction module estimates subsequent vessel states. The standardized reconstruction and prediction errors are fused into a unified anomaly score to capture complementary evidence from historical trajectory inconsistency and unexpected future motion. Experiments were conducted using real-world AIS data collected during September 2019 from four representative Danish waters. The study considers four abnormal behaviors: speed anomalies, course anomalies, loitering, and route deviations. Compared with KNN, LOF, Isolation Forest, Random Forest, the LSTM-AE, and the Transformer, DEFLT achieves F1-scores of 0.96, 0.97, 0.88, and 0.93 across the four study areas. For type-specific detection, the Macro-F1 values range from 0.61 to 0.86, while Macro-Recall remains between 0.88 and 0.96. Friedman and post hoc Wilcoxon signed-rank tests further demonstrate that DEFLT provides a significant and consistent improvement over all baseline methods. These results verify the effectiveness of dual-error fusion for detecting heterogeneous abnormal ship behaviors from AIS trajectories. In operational settings, DEFLT can serve as an alert-prioritization tool for vessel traffic services and port authorities by directing attention to atypical trajectories that require timely review, thereby supporting safer and more resource-efficient maritime traffic coordination. Full article

Background: Rural and resource-constrained settings face major barriers to timely cancer screening, diagnosis, and treatment due to limited specialist availability and centralised service-delivery models. In the Eastern Cape, a largely rural province with a constrained oncology workforce, a decentralised–centralised hybrid model was introduced to improve access to cancer care. Nelson Mandela Academic Hospital serves as the central referral hub within this model. This study evaluates the implementation process and impact of this decentralised cancer care model on service utilisation, access, and continuity of care. Methods: A quantitative quasi-experimental pre–post implementation and quality improvement evaluation was conducted using retrospectively collected routine service utilisation and programme data from April 2023 to February 2025. The study assessed the impact of a decentralised oncology care model on access, service integration, and utilisation outcomes. Data from facility registers and district health information systems were managed using Microsoft Excel and analysed using Stata and IBM SPSS Statistics. Descriptive statistics, correlation analysis, and linear regression were used to compare pre- and post-implementation changes in patient volumes, screening coverage, referral completion, workforce capacity, gender distribution, and service uptake. The intervention decentralised screening, diagnosis, follow-up, and patient navigation services to district and satellite facilities while centralising specialised oncology care at referral centres to improve accessibility, efficiency, and continuity of care. Results: Cancer patient attendance increased substantially over the study period, from 355 patients in April 2023 to a peak of 1039 in April 2024, with a sustained upward trend (B = 18.03, p = 0.005), reflecting an average monthly increase of 18 patients. Female patients accounted for most visits, while male attendance showed a significant increasing trend (B = 8.03, p < 0.001). Service integration improved, with strong positive correlations between new patient registrations, follow-up care, palliative services, and inpatient admissions, indicating an expanding continuum of care. Breast and cervical cancers contributed the highest service burden, while cervical and lung cancers showed significant upward trends. Seasonal variation in attendance was observed, particularly during festive periods. From an implementation perspective, screening coverage for priority cancers increased by 18%, while 732,349 individuals were reached through community awareness initiatives. Access improved substantially, evidenced by a reduction of 56,400 km in cumulative patient travel distance over one year. Workforce capacity was strengthened through the training of 517 healthcare workers, and 1943 patients received structured navigation support. Referral efficiency and continuity of care improved, although persistent bottlenecks were observed in diagnostic and referral pathways. Conclusions: The decentralised–centralised oncology care model demonstrated improved cancer service utilisation, access, and continuity of care in a rural, resource-limited setting. However, increasing patient volumes and interconnected service demands place additional pressure on health system capacity. Sustained investment in workforce development, screening—particularly for cervical cancer—and system efficiency is required. This model provides a scalable and context-appropriate framework for strengthening oncology services in similar low-resource settings. Full article

Cross-view geo-localization (CVGL) is a critical technology used in unmanned aerial vehicles (UAVs) and widely applied in navigation and target localization tasks. However, owing to the extreme perspective disparity between UAV oblique views and satellite vertical views, CVGL still involves significant challenges, including geometric distortion caused by viewpoint differences, drastic appearance inconsistencies, and the difficulty in bridging semantic gaps between heterogeneous data. To address these issues, we propose a novel CVGL method named dynamic-feature collaborative optimization and semantic-alignment network (DFSA), designed to extract robust feature representations and achieve fine-grained alignment. Specifically, the DFSA employs a residual-based vision transformer as the backbone to capture global context while alleviating the training instability and feature collapse often associated with standard transformers. To bridge the semantic gap between global and local features, we design a feature optimization module comprising a local feature enhancer and a global feature aggregator. This module establishes a closed-loop collaborative system that facilitates top-down semantic guidance and bottom-up detail feedback. Furthermore, we introduce a semantic segmentation and alignment module that adaptively partitions images into semantic regions based on feature response distributions, shifting the matching granularity from the global level to the semantic region level to effectively overcome feature mismatches caused by positional offsets and scale variations. Extensive experiments conducted on the University-1652 and SUES-200 datasets demonstrate the superior image retrieval performance of the proposed DFSA. Specifically, DFSA achieves a Recall@1 of 94.87% and an Average Precision (AP) of 95.32% on the University-1652 dataset and maintains highly competitive Recall@1 performances between 96.83% and 99.25% across various altitudes on the SUES-200 dataset. These results validate the model’s effectiveness in handling extreme viewpoint changes for UAV-based cross-view image retrieval tasks. Full article

As emerging economies navigate the global “twin transition”, decoupling industrial growth from environmental degradation has become an urgent imperative. This study investigates the fundamental role of digital infrastructure in driving Industrial Green Innovation Efficiency (IGIE). Utilizing panel data from 30 Chinese provinces spanning 2012 to 2022, we first measure the environment-adjusted IGIE employing a three-stage global SBM-DEA model to mitigate external environmental interferences and statistical noise. Subsequently, a two-way fixed-effects model, fortified by a Bartik-type instrumental variable (IV-2SLS) approach, is constructed to identify causal impacts. The descriptive results reveal that China’s overall IGIE exhibits a fluctuating upward trajectory, although absolute efficiency levels remain low. Furthermore, significant regional variations characterize the national landscape, manifesting as Eastern leadership, Central catch-up, Western improvement, and Northeastern volatility. Crucially, empirical baseline estimations confirm that digital infrastructure exerts a robust positive effect on IGIE. Mechanism analyses further demonstrate that this efficiency enhancement is primarily driven by the promotion of digital inclusive finance and localized technological diffusion. These findings theoretically enrich the understanding of digital empowerment and provide a practical blueprint for policymakers globally to formulate targeted infrastructure investments and differentiated regional transformation strategies. Full article