Search Results (63)

Background: Patient navigation has been recognized as a promising strategy to address fragmented cancer care; however, evidence from low- and middle-income countries (LMICs) remains limited, particularly regarding how navigation-related needs are associated with patient-reported outcomes. Objective: This study aimed to examine the association between multidimensional patient navigation needs and quality of life (QoL) among women with gynecological cancer in Indonesia. Methods: A cross-sectional study was conducted among 128 women diagnosed with gynecological cancer at a referral hospital in Indonesia. Patient navigation needs were assessed using a 37-item multidimensional instrument developed based on international frameworks, while QoL was measured using the EORTC QLQ-C30. Data were analyzed using Pearson correlation and multiple linear regression to evaluate the relationships and relative contributions of navigation need domains to QoL. Results: The mean global health status score indicated relatively low QoL (Mean = 41.7, SD = 31.0). Most domains of patient navigation needs were significantly and negatively associated with QoL (p < 0.001), with the strongest correlation observed for total navigation needs (r = −0.657). Multivariable analysis showed that administrative and financial needs showed the strongest association with poorer QoL (β = −0.373, p < 0.001), followed by psychosocial, cultural, and family support needs (β = −0.356, p < 0.001). In contrast, late-stage clinical needs were positively associated with QoL (β = 0.206, p = 0.005). The model explained 59.5% of the variance in QoL. Conclusions: Patient navigation needs are strongly associated with QoL among women with gynecological cancer, highlighting the critical role of system-level and psychosocial factors in shaping patient outcomes. Addressing administrative complexity, financial burden, and psychosocial support gaps is essential for improving QoL in LMIC settings. These findings provide novel evidence for developing context-specific, integrated patient navigation models to enhance cancer care delivery. Full article

Traditional optimization methods for interior permanent magnet synchronous motors (IPMSMs) often treat the stator and rotor as independent design domains, which limits the potential for suppressing torque fluctuations due to the neglected electromagnetic coupling between these components. This paper proposes a synergistic optimization strategy for a 120 kW IPMSM, aiming to overcome the inherent limitations of conventional unilateral optimization in design space exploration and achieve global performance enhancement through cross-domain collaboration. By establishing a unified surrogate model incorporating both stator slot geometries and rotor pole topologies, the collaborative effect of seven high-sensitivity design variables is systematically analyzed. The NSGA-II algorithm, coupled with a Kriging surrogate model, is employed to navigate the complex trade-offs among average torque, torque ripple, and cogging torque. Results demonstrate that the synergistic approach achieves a 28.1% reduction in torque ripple while maintaining high average torque, demonstrating superior improvement over conventional stator-only or rotor-only optimization schemes. Analysis based on Maxwell stress tensors and air-gap permeance functions reveals that the proposed method achieves simultaneous suppression of cogging torque and torque ripple by effectively harmonizing the 24th and 48th spatial harmonics. This study provides an efficient synergistic design methodology for the comprehensive performance enhancement of traction motors, offering practical reference value for the engineering development of high-performance electric vehicles. Full article

Reliable monocular depth estimation (MDE) is essential for agricultural robots and unmanned platforms, where low-cost visual perception is required for safe navigation and scene understanding in complex field environments. However, general-purpose depth foundation models remain limited by substantial domain gaps in agriculture, while full fine-tuning of large backbones is computationally expensive and less suitable for deployment on resource-constrained platforms. In this paper, an efficient agricultural MDE framework, termed AgriLoRA-DA, is proposed based on Depth-Anything-V2. Specifically, the pretrained DINOv2 encoder is kept frozen and adapted using LoRA in selected attention projections, while the original Dense Prediction Transformer (DPT) decoder is replaced with a lightweight Lite-FPNHead to reduce decoding overhead and improve deployment efficiency. Experiments conducted on the WE3DS dataset indicate that, although Depth-Anything-V3 provides the strongest zero-shot generalization among the evaluated baselines, target-domain adaptation is still necessary for WE3DS agricultural scenes. After adaptation, AgriLoRA-DA achieves the best overall performance with AbsRel = 0.0133, SqRel = 3.518, RMSE = 132.264, log10 = 0.0057, and delta1 = 0.9990, while requiring only 0.19 M (0.87%) trainable parameters. These results suggest that parameter-efficient adaptation and lightweight decoding provide a practical direction for deployable depth estimation in crop-row scenes similar to WE3DS, while broader cross-dataset validation remains an important direction for future work. Full article

Trans-border data circulation across multi-jurisdictional boundaries faces an operational conflict between ownership provenance prerequisites and data minimisation mandates, compounded by the tight coupling of large data payloads with synchronous state consensus ledgers, which forces replication of feature matrices across all consensus nodes and leads to network saturation. Existing frameworks remain unequipped to resolve this, as coupling in-band payload routing with synchronous state ledgers generates communication overheads scaling with data volume. The proposed Trusted Data Space with Registration (TDSR) implements a four-layer protocol stack. A dual-plane topology establishes a decoupled storage–ledger mechanism, partitioning asynchronous payload datastores and synchronous consensus ledgers to sustain throughput independent of data dimensionality. Navigating this infrastructure, the Unified Data Resource Identifier (UDRI) executes out-of-band cross-domain routing without exposing verifier intents. Driven by the Oblivious Data Asset Registration (ODAR) mechanism, a two-phase, four-algorithm lifecycle dictates end-to-end ownership provenance. This execution shifts hypothesis testing to isolated sandboxes via an algorithm-agnostic mathematical contract, capping external data transit at a constant leakage bound. A deployed testbed across the Guangdong-Hong Kong-Macao Greater Bay Area validates the proposed architecture, supporting data circulation across divergent legal jurisdictions. Full article

Autonomous flight of unmanned aerial vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments critically depends on accurate and robust visual localization. To tackle the challenges of cross-view domain discrepancies and real-time high-precision matching, we propose GRiM-Net, a two-stage joint optimization visual localization network. First, a global retrieval module aggregates features and selects the most similar satellite map candidate patches from a pre-built index, efficiently narrowing the search from the global map to a local region. Next, a fine matching module performs pixel-level keypoint detection and description on the query image and candidate patches. Bidirectional matching and weighted homography estimation are then used to map the UAV image center to satellite coordinates, yielding precise geographic positions. Both modules share a backbone with domain-adaptive batch normalization, and joint optimization of global retrieval triplet loss with fine matching keypoint, descriptor, and homography reprojection losses enables synergistic enhancement of feature representations. Ablation and comparison experiments conducted on public urban cross-view benchmarks demonstrate that GRiM-Net can achieve efficient and robust geographic coordinate regression for UAVs, providing a practical localization component for broader navigation systems. Full article

Autonomous tracked amphibious robotic systems operating across water and land environments are essential for coastal inspection, disaster response, environmental monitoring, and complex terrain exploration. However, discontinuous water–land dynamics, unstable medium switching, and safety-critical control under environmental uncertainty pose significant challenges to existing amphibious navigation and path planning methods, where global reachability and adaptive decision-making are difficult to unify. Motivated by these challenges, this paper proposes CD-HSSRL, a Cross-Domain Hierarchical Safe-Switching Reinforcement Learning framework for autonomous tracked amphibious navigation. Specifically, a Cross-Domain Global Reachability Planner is developed to construct unified cost representations across heterogeneous water–land environments, a Hierarchical Safe Switching Policy enables stable medium-transition decision-making through option-based policy decomposition with switching regularization, and a Safety-Constrained Continuous Controller integrates action safety projection and risk-sensitive reward shaping to ensure collision-free control during complex shoreline interactions. These components are jointly optimized to achieve robust cross-domain navigation. The experimental results in the Gazebo + UUV simulation environment show that the proposed method demonstrates competitive performance compared with baseline approaches, achieving higher success rates and lower collision rates across water, land, and transition environments. In particular, in cross-domain scenarios, the proposed method improves success rates by approximately 20% compared to conventional RL methods while maintaining stable performance under environmental disturbances. Robustness and ablation studies further verify the effectiveness of hierarchical switching and safety-constrained control mechanisms. Overall, this work establishes an integrated framework for safe and robust cross-domain navigation of tracked amphibious robotic systems, providing new insights into hierarchical safe-switching architectures for multi-medium autonomous robots. Full article

Cash dividends, as a tangible form of monetary distribution, serve as a fundamental mechanism for remunerating investors for their capital commitments. Beyond manifesting a firm’s commitment to fulfilling its social responsibilities toward shareholders, such distributions potentially shape corporate deliberations regarding accountability toward a broader spectrum of stakeholders. Drawing on behavioral explanations of corporate decision-making, this study examines the association between cash dividend payouts and environmental protection tax burdens among Chinese A-share listed companies from 2018 to 2023. The empirical results indicate a significant and robust negative association between corporate cash dividend payouts and environmental protection tax burdens. Mechanism analysis suggests that this cross-domain behavioral consistency is primarily channeled through the proactive fulfillment of corporate environmental responsibilities. Further inquiry reveals that both government environmental subsidies and media coverage exert positive moderating effects on this relationship. Notably, this observed negative association is particularly pronounced in firms characterized by lower executive environmental awareness, those operating in regions with lenient environmental regulations, companies navigating economic downturns, and those situated within low-pollution industries. This research provides novel evidence for the “governance complementarity” hypothesis, suggesting that financial accountability and environmental stewardship are mutually reinforcing rather than mutually exclusive. Furthermore, it offers a pioneering micro-behavioral perspective on how firms in emerging economies can harmonize shareholder wealth distribution with green transition objectives. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

Generative AI is becoming integral to daily workflows, fostering a novel form of functional cognitive AI dependency distinct from pathological addiction. While emerging research acknowledges this phenomenon, the specific psychological mechanisms underpinning its development remain underexplored. Incorporating self-efficacy erosion into the reinforcement-based framework, this study investigates whether instant gratification and perceived AI efficacy as key drivers of AI dependency. We examine the model using Structural Equation Modeling (SEM) with cross-sectional data collected from 576 users who have engaged with AI. The results show that both instant gratification and efficient rewards are positively associated with individuals’ AI dependency. Furthermore, users’ self-efficacy erosion significantly mediates the positive relation, supporting the hypothesis that greater reliance on AI is related to lower self-belief and stronger AI dependency. Moderation analyses further indicate that task-domain self-efficacy and social norms strengthen these positive associations. These findings provide empirical support for a mechanism associated with functional AI dependency and offer insights for navigating human–AI interaction while promoting balanced AI adoption. Full article

Lightweight local feature extractors are essential for real-time SLAM. However, they frequently struggle with perceptual aliasing and low localization accuracy in texture-sparse or repetitive environments. This paper introduces the Geometric-Contextual Alignment Network (GCA-Net), a framework designed to address these instabilities through a Geometric-Contextual Alignment (GCA) module. The proposed GCA module integrates global contextual priors into the feature stream. By employing Context-based Feature-wise Linear Modulation (C-FiLM), the network mitigates perceptual aliasing by prioritizing structurally reliable regions. To enhance spatial precision, we incorporate a Depthwise Separable Atrous Spatial Pyramid Pooling (DS-ASPP) stage to expand the Effective Receptive Field (ERF). This design provides robust multi-scale anchoring, which significantly reduces localization jitter under large viewpoint shifts. Extensive evaluations on MegaDepth, ScanNet and HPatches demonstrate that GCA-Net achieves high sub-pixel precision and robust cross-domain generalization. On the MegaDepth benchmark, GCA-Net outperforms the vanilla XFeat by 8.0% in Area Under the Curve (AUC) at a 5° threshold (AUC@5°). Furthermore, it yields a 23.6% relative improvement over the SuperPoint baseline while using compact 64-floating-point (64-f) descriptors. These results indicate that the GCA mechanism helps capture complex spatial structures that typically require much heavier architectures. By balancing matching accuracy with computational efficiency, GCA-Net provides an effective framework for autonomous navigation on edge computing platforms. Full article

Autonomous systems are expanding rapidly, driving a demand for robust perception technologies capable of navigating challenging, unstructured environments. While urban autonomy has made significant progress, off-road environments pose unique challenges, including dynamic terrain and limited communication infrastructure. This research addresses these challenges by introducing a novel three-part solution for off-road autonomous vehicles. First, we present a large-scale off-road dataset curated to capture the visual complexity and variability of unstructured environments, providing a realistic training ground that supports improved model generalization. Second, we propose a Confusion-Aware Loss (CAL) that dynamically penalizes systematic misclassifications based on class-level confusion statistics. When combined with cross-entropy, CAL improves segmentation mean Intersection over Union (mIoU) on the off-road test set from 68.66% to 70.06% and achieves cross-domain gains of up to ~0.49% mIoU on the Cityscapes dataset. Third, leveraging semantic segmentation as an intermediate representation, we introduce a spatial overlay video encoding scheme that preserves high-fidelity RGB information in semantically critical regions while compressing non-essential background regions. Experimental results demonstrate Peak Signal-to-Noise Ratio (PSNR) improvements of up to +5 dB and Video Multi-Method Assessment Fusion (VMAF) gains of up to +40 points under lossy compression, enabling efficient and reliable off-road autonomous operation. This integrated approach provides a robust framework for real-time remote operation in bandwidth-constrained environments. Full article

For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax $\tau$ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5–9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic–physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available. Full article

Geographical exclaves face distinctive development challenges as spatial separation creates cross-border dependencies and institutional vulnerabilities. Musandam Governorate, Oman’s exclave separated from the mainland by United Arab Emirates (UAE) territory, exemplifies how exclave status shapes development trajectories, cross-border interactions, and population resilience. This study examines Musandam’s socio-economic dynamics, development patterns, and cross-border relationships, addressing gaps in understanding how exclave residents navigate spatial discontinuity while maintaining mainland and cross-border connections. Mixed methods combined quantitative assessment using the adapted Vera Carstairs Index (VCI) across seven domains (education, skills, employment, housing, living environment, household facilities, health) with qualitative fieldwork spanning four campaigns (2019–2023). Semi-structured interviews with 47 residents across all four wilayaat (provinces), complemented by citizen science approaches engaging twelve community participants, documented mobility patterns and cross-border transactions. Secondary data from the 2010 Population Census and national statistics provided contextual depth. Findings reveal two of four Musandam wilayaat (Daba and Khasab) ranking in the lower half nationally, with low health scores (ranks 1 and 9) and education institution deficits reflecting structural integration into transnational economic and services systems. COVID-19 border closures amplified pre-existing dependencies, converting eight-month isolation into a humanitarian crisis with food shortages, medicine unavailability, and social fragmentation. Residents maintain stronger functional connections with UAE cities than with mainland Oman despite preserving national identity. Policy implications emphasize six strategic priorities: higher education institutions, transportation infrastructure, marine fisheries development, tourism enhancement, small-medium enterprise facilitation, and residential land provision. Full article

Named entity recognition (NER), a core task in natural language processing (NLP), remains constrained by heavy reliance on annotated data, limited cross domain generalization, and difficulty in recognizing name entities out of vocabulary entities. In specialized domains such as analysis of Global Navigation Satellite System (GNSS) countermeasures, including anti-jamming and anti-spoofing, where datasets are small and domain knowledge is scarce, existing models exhibit marked performance degradation. To address these challenges, we propose HybridNER, a framework that integrates locally trained span-based models with large language models (LLMs). The approach employs a span prediction metasystem that first fuses outputs from multiple base learners by computing span to label compatibility scores and assigns an uncertainty estimate to each candidate entity. Entities with uncertainty above a preset threshold are then routed to an LLM for a second stage classification, and the final decision integrates both sources to realize complementary strengths. Experiments on multiple general purpose and domain specific datasets show that HybridNER achieves higher precision, recall, and F1 than traditional ensemble methods such as majority voting and weighted voting, with especially pronounced gains in specialized domains, thereby improving the robustness and generalization of NER. 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