Search Results (4,784)

Dynamic positioning (DP) systems are essential to maritime operations, as they ensure precise station keeping. Yet human error remains a major contributor to DP incidents, often interacting with technical failures and environmental conditions. This study proposes an adaptive probabilistic framework to characterise human-error contributions to DP risk and support targeted mitigation. We compare integrated Bayesian network (BN)/fuzzy analytic hierarchy process (AHP) and Bayesian network (BN)/Dempster–Shafer (D-S) theory to model causal relationships, aggregate uncertain expert judgements, and prioritise risk factors. Historical incident narratives, accident reports, and expert elicitation inform the model to analyse failure propagation and quantify factor contributions. In a representative DP case application, insufficient training, operator fatigue, and reduced situational awareness—together with software anomalies and adverse environmental loads—emerge as dominant contributors; BN backward analysis corroborates their diagnostic relevance. The approach yields actionable insights for risk reduction, including tailored training programmes, strengthened safety protocols, and integration of real-time monitoring. It provides an auditable, updateable basis for scenario-based training, software/maintenance assurance, and environment-aware operating envelopes, and is readily extendable as new evidence becomes available. Overall, the framework offers practical value for improving safety, operational continuity, and system resilience in DP operations. Full article

Introduction: Acute ischemic stroke demands interventions that restore perfusion and protect neurons within a narrow therapeutic window. We propose a unified theranostic platform that couples adaptive imaging, topology-aware decision-making, and immediate neuroprotective and micro-dosimetric intervention. Methods: The platform integrates three components. First, a topology-preserving MR–PET engine employs adaptive Poisson-disc sampling, partial Fourier constraints, and structured Hankel low-rank priors in a closed loop. Persistent-homology metrics quantify vascular graph uncertainty and guide subsequent k-space and PET projections, reducing acquisition time while preserving collateral topology. Second, immediate post-reperfusion delivery of glycolic acid attenuates glutamate-driven calcium influx and stabilizes mitochondrial function. Third, trace doses of sol–gel-derived, neutron-activated ⁹⁰Y₂O₃ microspheres provide sharply confined beta irradiation for micro-scale metabolic modulation. Results: In a porcine stroke model replicating the human recanalization workflow, the imaging engine maintained vascular Betti-number invariants within three percent of fully sampled reference scans while reducing acquisition time by nearly half. Glycolic acid reduced glutamate-induced intracellular calcium rise by approximately sixty percent in vitro and decreased infarct volume by thirty-eight percent in vivo. Micro-dosimetry confirmed a mean perivascular beta dose of twenty-eight grays, and histology demonstrated a forty-two percent increase in NeuN-positive neuronal survival compared with standard recanalization. Conclusions: These results demonstrate that intelligent compressed-sensing MR–PET, targeted micro-radioembolization, and glycolic acid neuroprotection can act synergistically to bridge diagnostic imaging and immediate intervention. By coupling imaging, decision-making, and therapy in a closed-loop manner and elevating topological fidelity from a reconstruction byproduct to a control variable, the proposed platform reframes MR–PET from passive diagnostics into an active, decision-driven theranostic system and establishes a foundation for future human trials. Full article

Lake Chapala is the main freshwater reservoir in Mexico and faces notable environmental pressure associated with urban, industrial, and agricultural activities, with documented evidence of heavy metal contamination. Thus, the artisanal fishers from Lake Chapala occupy a strategic position for understanding the socio-environmental dynamics of this lacustrine system. The objective of this study was to analyze their perceptions of pollution in Lake Chapala and the health risks associated with heavy metal contamination, with particular emphasis on the consumption of charal (Chirostoma spp.). Based on the results, 70% of fishers agreed that Lake Chapala is polluted, and 50% identified solid waste as the main source of contamination. Regarding water quality, 41% considered that it had not changed in recent years, while 37% perceived that it had deteriorated. With respect to heavy metals, 50% reported being aware of their presence in the lake; however, slightly more than 50% expressed concern about the possibility that charal could be contaminated. Fishers acknowledged the environmental pressure faced by Lake Chapala but prioritized risks based on visibility and their everyday experiences. Incorporating the perceptions of key stakeholders is essential for addressing socio-environmental problems, strengthening environmental communication and public health strategies, and effectively managing this lacustrine system. Full article

Underground mining environments pose significant challenges for automated hazard detection due to low illumination, restricted visibility, and the absence of Global Navigation Satellite System (GNSS) coverage. These factors limit situational awareness and delay inspection efforts, particularly after disruptive events when rapid assessment is essential for safety. This study addresses this problem by developing a dual-pipeline framework for 2D–3D detection that uses 360° imaging and LiDAR-based machine learning to identify people, vehicles, and positional changes in underground settings without requiring personnel to re-enter hazardous areas. The objective was to create a system capable of recognizing objects and monitoring spatial changes under real underground mine conditions. The 2D component used a Ricoh Theta Z1 camera to collect panoramic images, and a YOLO (You Only Look Once) v8n model was fine-tuned using datasets representing low light, shadowed underground scenes. The 3D component employed an Ouster OS1-070-64 LiDAR sensor, and point clouds were processed through denoising, ICP alignment, surface reconstruction, manual annotation, and 2D projection. A YOLO-based model was then trained to detect objects and measure displacement between LiDAR scans. Results demonstrated strong performance for both components. The fine-tuned YOLOv8n model reliably detected personnel and vehicles despite challenging lighting and visual clutter, while the 3D pipeline localized objects in the registered LiDAR frame and quantified vehicle displacement between consecutive scans by comparing 3D bounding-box centroids after ICP alignment (displacement vector and magnitude). These findings indicate that the combined 2D–3D system can effectively support automated hazard recognition and environmental monitoring in GNSS-denied underground spaces. Full article

Monocular camera localization in known environments is a critical task for applications like autonomous navigation, augmented reality, and robotic positioning, requiring precise spatial awareness. Unlike localization in unknown environments, which builds maps in real time, this leverages pre-existing data for higher accuracy. This review comprehensively analyzes monocular camera localization methods in known environments, categorizing them into 2D-2D feature matching, 2D-3D feature matching, and regression-based approaches. It consolidates foundational techniques and recent advancements, providing inter-class and intra-class performance comparisons on mainstream datasets. Key findings show that 2D-3D methods generally offer the highest accuracy, especially in structured outdoor environments, due to robust use of 3D spatial information. However, recent scene coordinate regression methods, such as ACE and ACE++, achieve comparable or superior performance in indoor scenes with more efficient pipelines. This review highlights challenges and proposes future directions: (1) synthetic data generation to meet deep learning demands, while addressing domain gaps; (2) improving generalization to unseen scenes and reducing retraining; (3) multi-sensor fusion for enhanced robustness; (4) exploring transformer-based and graph neural network architectures; (5) developing lightweight models for real-time performance on resource-constrained devices. This review aims to guide researchers and practitioners in method selection and identify key research directions. Full article

Background/Objectives: Japan has experienced a marked decline in human papillomavirus (HPV) vaccination coverage, reaching less than 1%, after the government suspended its proactive recommendation in 2013, following media reports of symptoms alleged to be adverse events caused by the vaccine. Although the recommendation was reinstated in 2022 after comprehensive safety reviews, vaccination rates have remained modest. We aimed to assess HPV vaccine acceptability and identify factors associated with acceptance among staff at a university hospital. Methods: We administered a web-based questionnaire in February 2024 to 2761 hospital employees, assessing demographic and professional characteristics, HPV-related knowledge, awareness about vaccine effectiveness, adverse events, and catch-up programs, as well as acceptability across four hypothetical scenarios reflecting publicly funded and self-funded vaccination programs. Logistic regression analyses were conducted to identify factors associated with acceptability. Results: Among 1132 respondents (response rate 41.0%), acceptability exceeded 75% in the publicly funded scenarios but was approximately 45% in the self-funded scenarios. In multivariable analyses of the publicly funded scenarios, younger age, being a medical professional, greater HPV vaccine knowledge levels, and awareness about HPV vaccine effectiveness or catch-up vaccination were positively associated with acceptability; awareness about adverse events showed negative associations. In the self-funded scenarios, women were less likely to accept vaccination, but greater knowledge levels and awareness of catch-up vaccination remained positively associated with acceptability. Conclusions: These findings suggest that strategies tailored to specific population characteristics are important for improving HPV vaccine acceptability. Full article

Daily equity return forecasting is challenging due to low signal-to-noise ratios, heavy-tailed innovations, and persistent distribution drift. We study one-step-ahead log-return prediction using daily market variables and return-based transformations. We propose a CNN–LightGBM hybrid that transfers a last-step CNN embedding to a gradient-boosted tree regressor through explicit embedding standardization, which stabilizes the representation interface for tree learning. To reduce train-to-evaluation mismatch under drift, we adopt split-wise, training-only standardization with a recency-aware fit-latest-W rule. Return-related predictors are anchored on a one-sided wavelet-denoised close series, while other market channels are retained in their original form to preserve episodic extremes. Experiments on NIFTY50 with walk-forward model selection show statistically reliable accuracy gains over Naive0 and competitive performance against representative deep sequence baselines, and the supplementary evaluations on HDFC and INDA provide additional out-of-sample evidence on these two assets under the same strictly chronological protocol. A long-or-cash decision rule based on the return forecasts yields positive risk-adjusted performance under realistic transaction-cost assumptions, supporting the practical relevance of the predictive signal. Full article

Critical congenital heart disease (CCHD) is a major cause of neonatal morbidity and mortality. Pulse oximetry screening enables early detection, potentially reducing complications and improving outcomes. This study evaluated pediatric residents’ knowledge, attitudes, and practices (KAP) related to CCHD screening in Saudi Arabia. A cross-sectional survey was distributed to pediatric residents across Saudi Arabia. The questionnaire assessed knowledge, attitude, and practice regarding CCHD screening. A total of 123 pediatric residents in training were included in the study. Of these, 57 (46.3%) were male, and 66 (53.7%) were female. A progressive increase in mean scores was observed with advancing training years (p = 0.010). A significant difference was observed in knowledge scores based on completion of a cardiology rotation (p = 0.006). A progressive increase in attitude scores was observed with each successive year of training. Current year in training showed a statistically significant association with attitude scores (p < 0.001). Completion of a newborn nursery or NICU rotation was also significantly associated with higher attitude scores (p = 0.027). Similarly, attitude scores were significantly higher among those who had completed a cardiology rotation (mean = 12.99, SD = 1.52) compared to those who had not (mean = 11.60, SD = 1.84; p < 0.001). While practice scores were not statistically different across most groups, senior residents demonstrated better adherence to screening. Residents exhibit increasing awareness and positive attitudes with experience; however, practical implementation remains inconsistent. Targeted education and standardized protocols are necessary to improve outcomes. A positive correlation was observed between knowledge and attitude scores (r = 0.346, p < 0.001). Full article

To address the limited detection accuracy of casting defects—including pores, inclusions, and looseness—in digital radiography (DR) images, which stems from their small scale, high morphological variability, and interference from complex background textures, we propose MTS-YOLOv11: an edge–discrepancy collaborative defect detection framework tailored for casting DR imagery. Built upon YOLOv11, MTS-YOLOv11 incorporates three key innovations: (1) a Multi-Scale Edge Information Enhancement System (MSEES), integrated into the C3K2 module of the backbone network, to strengthen discriminative feature extraction for minute defects; (2) a TripletAttention mechanism embedded in high-level backbone stages to jointly calibrate channel–spatial dependencies and suppress texture-induced spurious responses under complex backgrounds; (3) a Scale-Discrepancy-Aware Gated Fusion (SDAGFusion) module positioned immediately before the detection head, enabling scale-discrepancy-aware gated fusion of multi-scale features, emphasizing defect regions while suppressing background interference. Experimental results show that on the casting DR dataset, MTS-YOLOv11 achieves mAP@0.5 = 96.5% and mAP@0.5:0.95 = 68.5%—improvements of 1.3 and 1.2 percentage points over the baseline YOLOv11—across all three defect categories. Moreover, on the same platform, MTS-YOLOv11 achieves an inference speed of 359.07 FPS, compared with 346.86 FPS for the baseline. Meanwhile, the model has 2.72M parameters and 7.8G FLOPs. These results indicate a consistent improvement in detection accuracy while maintaining a practical balance between precision and computational efficiency. Moreover, cross-dataset generalization tests on newly acquired industrial DR data show that MTS-YOLOv11 consistently outperforms the baseline across evaluation metrics, suggesting improved robustness to unseen imaging conditions and supporting its potential for real-world foundry inspection. Full article

With the evolution of smart grids, power communication networks are increasingly required to support high-bandwidth and diversified services such as high-definition video, real-time control, and positioning—services that impose dual challenges of communication capacity and spectrum constraints—under severe resource limitations. Conventional orthogonal modulation schemes exhibit significant limitations in spectral efficiency and concurrent access capabilities, particularly in supporting high-density user environments. To address this, we propose a communication system based on non-orthogonal overlapped chirp modulation, in which the intrinsic symmetry properties of chirp waveforms are utilized to enhance system design and performance. We first construct the system architecture with a multi-symbol concurrent transmission scheme and introduce continuous orthogonal phase modulation to improve symbol distinguishability and mitigate inter-symbol interference—an approach that effectively harnesses signal symmetry for interference suppression. At the receiver, a low-complexity demodulation algorithm based on correlation matrix computation is developed, further improved through oversampling techniques that exploit temporal and spectral symmetry in signal design. Monte Carlo simulations confirm that the proposed system outperforms traditional orthogonal chirp and orthogonal frequency division multiplexing systems in bit error rate performance and spectral efficiency across varying signal-to-noise ratios and modulation schemes. The proposed NOOC system achieves spectral efficiency scaling linearly with concurrency level K, reaching up to 16 bits/s/Hz for K = 16 with BPSK, compared to 1 bit/s/Hz in orthogonal systems. The study provides both a theoretical foundation and practical insights for developing symmetry-aware, efficient, and reliable air interface technologies suitable for future power-private networks. Full article

Screw-drive in-pipe robots are widely used for inspection and maintenance of pipeline infrastructure because their tilted-wheel locomotion enables continuous traversal of horizontal, vertical, and curved pipes. However, most existing designs rely on passive spring mechanisms to generate wall-contact forces, making traction performance highly sensitive to pipe-diameter variations, friction changes, and manufacturing tolerances. This paper presents an adaptive screw-drive in-pipe robot that integrates adjustable radial geometry, embedded Hall-effect force sensing, and closed-loop gripping-force control. A unified mechanical–geometric model is developed to describe the coupling between actuator displacement, spring compression, wheel-tilt geometry, and pipe-diameter variation. Based on this model, a minimum safe gripping-force condition is derived and used to define a reference force for real-time control. A proportional–derivative controller regulates the gripping force of the front traction module, while a rear stabilizing module ensures axial alignment and suppresses body rotation. Simulation results under realistic diameter transitions and external disturbances demonstrate stable force regulation, preservation of a positive traction margin, and reduced unnecessary actuator effort. The proposed approach enables robust and energy-aware screw-drive locomotion in variable-diameter pipelines. A physical prototype of the robot has been fabricated to support the forthcoming experimental campaign; however, the validation presented in this study is limited to modeling and simulation, with experimental evaluation planned for future work. Full article

Accurate indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GPS-denied environments, especially for small-object detection and under low-light conditions. We propose Robust Wavelet-Aware YOLO (RWA-YOLO), a vision-based detection framework that integrates a wavelet-aware attention fusion module with a dual multi-path aggregation mechanism to enhance small-object detection and multi-scale feature representation. UAV-mounted LEDs are utilized to ensure robust visual perception in low-light indoor scenarios. The UAV’s three-dimensional position is estimated through multi-view geometric triangulation without relying on external beacons or artificial markers. Beyond static localization, the system is validated under dynamic flight conditions, demonstrating smooth and temporally coherent trajectory reconstruction suitable for real-time control loops (update rate $\approx 25\mathrm{FPS}$). Extensive experiments in real indoor environments achieve centimeter-level localization accuracy (root mean square error: 9.9 mm, 95th percentile error: 13.5 mm), outperforming state-of-the-art vision-based methods and achieving accuracy comparable to or better than representative hybrid ultra-wideband–vision systems reported in the literature. These results confirm the effectiveness, robustness, and real-time capability of RWA-YOLO for indoor UAV navigation in constrained environments. Full article

Dengue remains a significant health burden in Thailand, with over 160,000 cases reported in 2023. Although two dengue vaccines are approved, uptake remains limited. This study assessed Knowledge, Attitudes, and Practices (KAP) toward dengue and behavioural drivers of vaccine willingness using the Capability, Opportunity, Motivation–Behaviour (COM-B) framework, which posits that health behaviours arise from capability (knowledge/skills), opportunity (environmental/social enablers), and motivation (beliefs/drivers). A cross-sectional online survey was conducted in September 2024 among 600 Thai adults aged 20–60 years. The questionnaire, adapted from the GEMKAP study, generated composite KAP and COM-B scores (0–100%). Willingness to vaccinate was measured on a 0–10 Juster scale, with multivariable regression identifying behavioural predictors. Of 600 respondents, 40% were male, with a median age of 40 years, and 23% were in high-dengue-burden areas. Knowledge scores were moderate (51%), and dengue prevention practices were low (40%). The proportion of respondents with high willingness to vaccinate (score 8–10) was 68%, which was positively associated with Reflective Motivation and Physical Opportunity. Hesitancy centred on vaccine side effects (29%) and cost concerns (13%). These findings suggest that despite generally favourable attitudes, vaccine uptake is hindered by safety, cost, and awareness gaps. Physician communication and the integration of vaccines into schools, workplaces, and primary care, along with education and vector control, are key for sustainable national coverage. Full article

Unmanned aerial vehicle (UAV) swarms can form sparse virtual antenna arrays (VAAs) for airborne sensing and communications, but their beamforming performance is highly vulnerable to quasi-static formation drift and the limited number of snapshots available within each coherent processing interval. This paper proposes a beacon-aided self-calibration and robust beamforming framework for narrowband UAV-swarm uplinks in strong-interference, low-snapshot regimes. We consider one signal of interest (SOI) and multiple co-channel interferers characterized by their coarse direction-of-arrival (DOA) information. The key idea is to exploit a single dominant non-SOI emitter as a strong calibration source (beacon) to learn the quasi-static geometry drift from data. First, the beacon spatial signature is extracted from the sample covariance matrix via eigenvector–steering-vector alignment, and a correlation-based gate is used to decide whether geometry calibration is reliable. When the gate is passed, the inter-UAV position drift is estimated from element-wise steering ratios to build a calibrated array manifold. Second, using the calibrated steering vectors and coarse DOA information, the interference-plus-noise covariance matrix (INCM) is reconstructed through a low-dimensional non-negative power fitting with mild diagonal loading. Finally, a geometry-aware minimum-variance distortionless response (MVDR) beamformer is designed based on the reconstructed INCM. Simulations on coprime-inspired UAV formations with a single dominant interferer show that the proposed scheme recovers most of the SINR loss caused by geometry mismatch and consistently outperforms baseline MVDR, worst-case MVDR, a recent covariance-reconstruction baseline, and URGLQ in the low-snapshot regime. For example, in a representative setting with $N_{\mathrm{uav}}=7$, ${\sigma }_p=0.10$, ${\mathrm{INR}}_c=30$ dB, and $L=10$, the proposed method achieves approximately 14 dB output SINR at ${\mathrm{SNR}}_{\mathrm{in}}=10$ dB, outperforming nominal SCM-MVDR by about 13 dB and approaching a genie-aided MVDR bound within a few dB, while retaining a computational complexity comparable to standard MVDR. Full article

Whether firms translate climate risk perception into energy-related operational productivity remains unclear. Panel data on non-financial Chinese firms (2012–2023) are used to examine the association between climate risk perception (CRP) and energy productivity (EE). Firm-level CRP is constructed from management discussion and analysis (MD&A) sections using a term frequency–inverse document frequency (TF–IDF)-weighted, Word2Vec-expanded climate-risk lexicon. Energy productivity (EE) is measured as the natural logarithm of operating revenue per total energy consumption unit converted into tons of coal equivalent, capturing the economic value generated per energy input unit. Two-way fixed-effects models with firm-level clustered standard errors show a positive CRP–EE association. Digital transformation, proxied by an annual report text-based index across five digital technology domains, partially mediates this association, which is stronger when analyst coverage is higher and weaker when financing constraints are more severe. The results are robust to an alternative CRP proxy based on raw keyword frequency, dynamic specifications, and an instrumental-variable approach exploiting province-year extreme-weather exposure (share of days meeting extreme temperature or precipitation thresholds), using leave-one-province-out aggregation as the instrument and systematic heterogeneity across state ownership, pollution intensity, and high-tech status. This study extends CRP research from disclosure-oriented to energy-productivity outcomes, and highlights how digital capabilities, information scrutiny, and financial friction shape climate-aware energy productivity improvements. Full article