Search Results (642)

Accurate modelling of near surface wind speeds is essential for robust resource assessment, turbine design, and grid integration. This study presents a unified framework comparing four candidate marginal distributions—Weibull, Gamma, Lognormal, and Generalised Extreme Value (GEV)—across 21 years of daily observations from 11 sites in New South Wales and southern Queensland, Australia. Parameters are estimated by maximum likelihood, with L-moments used when numerical fitting fails. Univariate goodness-of-fit is evaluated via information criteria (Akaike Information Criterion, AIC; Bayesian Information Criterion, BIC) and distributional tests (Anderson–Darling, Cramér–von Mises, Kolmogorov–Smirnov). To capture spatial dependence, we fit an 11-dimensional regular vine (“R-vine”) copula to the probability-integral-transformed data, selecting pair-copula families by AIC and estimating parameters by sequential likelihood. A composite score (70% univariate, 30% copula) ranks distributions per location. Results demonstrate that Lognormal best matches central behaviour at most sites, Weibull remains competitive for bulk modelling, Gamma often excels in moderate tails, and GEV best represents extremes. All turbine yield results presented are illustrative, showing how statistical choices impact energy estimates; they should not be interpreted as operational forecasts. In a case study, 5000 joint simulations from the top-two models drive IEC V90 and E82 power curves, revealing up to 10% variability in annual energy yield due solely to marginal choice. This workflow provides a replicable template for comprehensive wind resource and load hazard analysis in complex terrains. Full article

Background: Demand for elective oocyte cryopreservation (OC) among healthy women delaying childbearing is rising worldwide. Yet, clinicians and patients often rely on limited or indirect evidence to predict age-specific mature oocyte yield. Robust, real-world benchmarks are needed to guide expectations, estimate live birth potential, and optimize treatment planning. Methods: We retrospectively analyzed 400 healthy women aged 30–41 undergoing their first elective OC cycle between 2019 and 2023 at a large, university-affiliated fertility center. Exclusion criteria included infertility, polycystic ovary syndrome, prior ovarian surgery, and other medical indications for OC. All cycles used a standardized GnRH antagonist protocol with an initial gonadotropin dose of 300 IU/day. Only mature (metaphase II) oocytes were cryopreserved. Age-specific percentiles for total and mature oocyte yield were modeled using the General Additive Model for Location, Scale, and Shape (GAMLSS), and nomograms were developed. Results: Mean age was 35.7 years (SD 2.3). Median total and mature oocytes retrieved were 13 (IQR 9–19) and 10 (IQR 7–15), respectively. At the 50th percentile, women aged 30, 35, and 40 yielded 20, 14, and 9 total oocytes, with 15, 11, and 6 mature oocytes cryopreserved. Nomograms across percentiles illustrated a consistent, progressive decline in yield with advancing age. Conclusions: Age-based nomograms derived from real-world data can offer a clinically relevant tool to estimate the likely oocyte yield per cycle. They can help set realistic expectations, guide the number of cycles needed to meet fertility goals, and support evidence-based, shared decision-making in elective OC. Full article

Remote Sighted Assistance (RSA) systems provide visually impaired people (VIPs) with real-time guidance by connecting them with remote sighted agents to facilitate daily travel. However, unfamiliar environments often complicate decision-making for agents and can induce anxiety in VIPs, thereby reducing the effectiveness of the assistance provided. To address this challenge, this paper proposes a video-based map assistance method. By pre-recording pedestrian path videos and aligning them with geographic locations, the system enables route preview and enhances navigation guidance. This study introduces a factor graph optimization (FGO) algorithm that integrates Global Navigation Satellite System (GNSS) and pedestrian dead reckoning (PDR) data for pedestrian positioning. It incorporates road-anchor constraints, a turning-point-based anchor-matching method, and a coarse-to-fine optimization strategy to improve the positioning accuracy. GNSS provides global reference positions, PDR offers precise relative motion constraints through accurate heading estimation, and anchor factors further enhance localization accuracy by leveraging known geometric features. We collected data using a smartphone equipped with a four-camera module and conducted tests in representative urban environments. Experimental results demonstrate that the proposed anchor-aided FGO-GNSS/PDR algorithm achieves robust and accurate positioning, effectively supporting video-based map construction in complex urban settings. With anchor constraints, the mean horizontal positioning error was reduced by 42% to 65% and the maximum error by 38% to 76% across all datasets. In this study, the mean horizontal positioning error was 1.36 m. Full article

This paper is based on the fish lateral line sensing mechanism and aims to determine the coupling relationship between the flow field sensing signal and target source position information. Firstly, according to the flow field distribution characteristics of the target source, the equivalent multipole model of the flow field disturbance during the underwater motion of the SUBOFF model is constructed, and then the target localization function based on the least squares method is established according to the theory of potential flow, and the residual function of the target localization is solved optimally using the quasi-Newton method (QN) to obtain the estimated position of the target source. On this basis, a curved bionic lateral line sensing array is constructed on the surface of a robotic fish, and the estimated location of the target source is obtained. The curvilinear bionic lateral line sensing array is constructed on the surface of the robotic fish, and the effectiveness and robustness of the above localization methods are analysed to validate whether the fish lateral line uses the pressure change to sense the underwater target. Full article

In this study, a lightweight deep neural network framework WeedLoc3D based on multi-task learning is proposed to meet the demand of accurate three-dimensional positioning of weed targets in automatic laser weeding. Based on a single RGB image, it both locates the 2D keypoints (growth points) of weeds and estimates the depth with high accuracy. This is a breakthrough from the traditional thinking. To improve the model performance, we introduce several innovative structural modules, including Gated Feature Fusion (GFF) for adaptive feature integration, Hybrid Domain Block (HDB) for dealing with high-frequency details, and Cross-Branch Attention (CBA) for promoting synergy among tasks. Experimental validation on field data sets confirms the effectiveness of our method. It significantly reduces the positioning error of 3D keypoints and achieves stable performance in diverse detection and estimation tasks. The demonstrated high accuracy and robustness highlight its potential for practical application. Full article

Accurate detection of reinforcing bars (rebars) in concrete structures using ground penetrating radar (GPR) is crucial for effective structural evaluation but remains challenging, particularly when asphalt overlays compromise signal clarity. This study evaluates the performance of deep learning-based rebar detection using the You Only Look Once version 8 (YOLOv8) object detection model across three GPR datasets categorized as clear, interfering, and blurry. Models trained on each category were applied across varying conditions to assess generalization and robustness. A filtering algorithm was introduced to eliminate redundant and overlapping detections, thereby significantly improving the accuracy of YOLOv8-based predictions. The YOLOv8 approach outperforms traditional analytical techniques, especially under noisy or complex scenarios. In blurry GPR images where analytical methods fail, the filtered YOLOv8 model accurately detects rebar with a count that closely matches the ground truth. Across different datasets, the YOLOv8 approach demonstrates improved consistency in both location and quantity estimation, with filtered predictions correcting substantial over-detection seen in raw outputs. The study presents a practical framework for applying deep learning to GPR data, enhancing the reliability of rebar detection under diverse field testing and evaluation conditions. The findings highlight the importance of developing tailored training datasets and post-processing strategies when deploying AI tools for in-service bridge inspections. Full article

This study presents an optimized methodology for the placement of water film thickness sensors, integrating information theory with experimental validation. Initially, the two-dimensional shallow-water equations are employed to simulate the spatiotemporal evolution of water film thickness across the entire runway, providing a comprehensive foundational dataset. By applying information entropy theory, the total information content at each runway grid point is quantified. Analysis indicates that grid points with higher total information content generally correspond to regions of greater water film thickness. The optimal placement for a single sensor is determined by identifying the location that maximizes total information content, and its effectiveness is validated through controlled rain–fog experiments. The results demonstrate that positioning a single sensor at a site with higher water film thickness reduces the overall mean estimation error by 57%, thereby enhancing prediction accuracy. By extending the single-sensor placement framework, the total information content across all runway points is recalculated, and additional rain–fog experiments are conducted to verify the optimal locations. By incorporating a correlation coefficient–distance (C–D) model to define each sensor’s influence radius, a collaborative multi-sensor placement strategy is developed and implemented at Seletar Airport, Singapore. The findings show that sensor locations with higher water film thickness correspond to increased total information content, and that expanding the number of deployed sensors further improves estimation accuracy. Compared with conventional placement approaches, which rely on subjective judgment and long-term operational experience, the proposed method enhances estimation accuracy by over 23% when deploying two sensors. These results provide a robust basis for the strategic placement of runway water film thickness sensors and contribute to more precise assessments of pavement surface conditions. Full article

Device-free localization and tracking (DFLT) has emerged as a promising technique for location-aware Internet-of-Things (IoT) applications. However, most existing DFLT systems based on narrowband sensing networks suffer from reduced accuracy in indoor environments due to the susceptibility of received signal strength (RSS) measurements to multipath interference. In this paper, we propose a real-time DFLT system leveraging ultra-wideband (UWB) sensors. The system estimates target-induced shadowing using two UWB RSS measurements, which are shown to be more resilient to multipath effects compared to their narrowband counterparts. To enable real-time tracking, we further design an efficient measurement protocol tailored for UWB networks. Field experiments conducted in both indoor and outdoor environments demonstrate that our UWB-based system significantly outperforms its traditional narrowband DFLT solutions in terms of accuracy and robustness. Full article

Background: Hypospadias is a common congenital anomaly in boys, marked by ectopic urethral meatus and a wide range of anatomical variants such as chordee and atypical glans morphology. Despite advancements in surgical techniques, complication rates remain high and unpredictable due to heterogeneity in anatomy and a lack of standardized preoperative assessments. Retrospective studies suggest associations between specific anatomical features and postoperative complications; however, high-quality prospective, multicenter evidence is currently lacking. Methods: The PREDICT-H (Prospective Research on Essential Determinants Influencing Complication Trends in Hypospadias) study is a multicenter, prospective cohort study aiming to enroll approximately 1450 boys aged 1–12 years undergoing primary hypospadias repair at ten or more tertiary pediatric urology centers. A standardized preoperative assessment protocol will document detailed anatomical parameters, including urethral plate width and length, glans size, meatal location, chordee severity, and GMS score. Intraoperative variables and surgical techniques will be recorded. Postoperative outcomes, including urethrocutaneous fistula, meatal stenosis, and recurrent chordee, will be assessed at ≥6 months follow-up. Statistical analyses will include multivariate logistic regression and advanced modeling to identify independent predictors and develop a validated risk prediction nomogram. Interobserver reliability of anatomical assessments will also be evaluated. Results: As this is a study protocol, results are not yet available. Data collection is ongoing and will be analyzed upon completion of the planned follow-up period. The primary outcome will be the incidence of postoperative complications and the development of a predictive nomogram for individualized risk estimation. Conclusions: The PREDICT-H study is designed to provide robust, prospective evidence on the anatomical determinants of postoperative complications in hypospadias surgery. The development of a validated, clinically applicable risk prediction tool could standardize preoperative assessment and enhance individualized surgical planning. Findings from this study are expected to support evidence-based practice and inform future clinical guidelines. Full article

Comprehensive, standardised, multi-temporal inventories of rainfall-induced landslides linked to soil moisture remain scarce, especially in tropical regions. Addressing this gap, we present a multi-source urban inventory for Brazil’s Baixada Santista region (1988–2024). A key advance is the introduction of geographical and temporal confidence classifications, which indicates precisely how each landslide’s location and occurrence date are known, thereby addressing a previously overlooked criterion in Brazil’s landslide data treatment. The inventory comprises 2534 records categorised by spatial (G1–G3) and temporal (T1–T3) confidence. Notable findings include the following: (i) confidence classifications enhance inventory reliability for research and early warning, though precise temporal data remains challenging; (ii) multi-source integration with UAV validation is key to robust inventories in urban tropical regions; (iii) soil moisture complements rainfall-based warnings, but requires local calibration for satellite-derived estimates; (iv) data gaps and biases underscore the need for standardised landslide documentation; and (v) the framework is transferable, providing a scalable model for Brazil and worldwide. Despite limitations, the inventory provides a foundation for (i) susceptibility and hazard modelling; (ii) empirical thresholds for early warning; and (iii) climate-related trend analyses. Overall, the framework offers a sustainable, practical, transferable method for worldwide and contributes to strengthening disaster information systems and early warning capacities. Full article

Tree diameter at breast height (DBH) is a fundamental metric in forest inventory and management. This paper presents a novel method for DBH estimation using the built-in light detection and ranging (LiDAR) and red, green and blue (RGB) sensors of an iPhone 13 Pro, aiming to improve measurement accuracy and field usability. A single snapshot of a tree, capturing both depth and RGB images, is used to reconstruct a 3D point cloud. The trunk orientation is estimated based on the point cloud to locate the breast height, enabling robust DBH estimation independent of the capture angle. The DBH is initially estimated by the geometrical relationship between trunk size on the image and the depth of the trunk. Finally, a pre-computed lookup table (LUT) is employed to improve the initial DBH estimates into accurate values. Experimental evaluation on 294 trees within a capture range of 0.25 m to 5 m demonstrates a mean absolute error of 0.53 cm and a root mean square error of 0.63 cm. Full article

High-precision 6D pose estimation for pick-and-place operations remains a critical problem for industrial robot arms in manufacturing. This study introduces an analytics-based solution for 6D pose estimation designed for a real-world industrial application: it enables the Staubli TX2-60L (manufactured by Stäubli International AG, Horgen, Switzerland) robot arm to pick up metal plates from various locations and place them into a precisely defined slot on a brake pad production line. The system uses a fixed eye-to-hand Intel RealSense D435 RGB-D camera (manufactured by Intel Corporation, Santa Clara, California, USA) to capture color and depth data. A robust software infrastructure developed in LabVIEW (ver.2019) integrated with the NI Vision (ver.2019) library processes the images through a series of steps, including particle filtering, equalization, and pattern matching, to determine the X-Y positions and Z-axis rotation of the object. The Z-position of the object is calculated from the camera’s intensity data, while the remaining X-Y rotation angles are determined using the angle-of-inclination analytics method. It is experimentally verified that the proposed analytical solution outperforms the hybrid-based method (YOLO-v8 combined with PnP/RANSAC algorithms). Experimental results across four distinct picking scenarios demonstrate the proposed solution’s superior accuracy, with position errors under 2 mm, orientation errors below 1°, and a perfect success rate in pick-and-place tasks. Full article

Accurate estimation of groundwater levels in river basins is essential for effective water resource planning. Innovations in deep learning and artificial intelligence (AI) have been introduced into this field to enhance the accuracy of long-term groundwater level estimation. This study employs the Transformer deep learning model to estimate groundwater levels, with a benchmark comparison against the long short-term memory (LSTM) model. These models were applied to estimate groundwater levels in the Yellow River Basin, where approximately 1100 monitoring wells are located. Monthly average groundwater level data from the period 2018–2023 were collected from these wells. The two models were used to estimate groundwater levels for the period 2003–2017 by incorporating remote sensing information. The Transformer model was enhanced to simultaneously capture features from both historical temporal data and surrounding spatial data, while automatically enhancing key features, effectively improving estimation accuracy and robustness. At the basin-averaged scale, the enhanced Transformer model outperformed the LSTM model: R² increased by approximately 17.5%, while RMSE and MAE decreased by approximately 12.4% and 10.9%, respectively. The proportion of poorly predicted samples decreased by an average of approximately 12.1%. The estimation model established in this study contributes to improving the quantitative analysis capability of long-term groundwater level variations in the Yellow River Basin. This could be helpful for water resource development planning in this densely populated region and likely has broad applicability in other river basins. Full article

Precise odor source localization in turbulent and sparse environments plays a vital role in enabling robotic systems for hazardous chemical monitoring and effective disaster response. To address this, we propose Cooperative Gravitational-Rényi Infotaxis (CGRInfotaxis), a distributed decision-optimization framework that combines multi-agent collaboration with hybrid cognitive strategy to improve search efficiency and robustness. The method integrates a gravitational potential field for rapid source convergence and Rényi divergence-based probabilistic exploration to handle sparse detections, dynamically balanced via a regulation factor. Particle filtering optimizes posterior probability estimation to autonomously refine search areas while preserving computational efficiency, alongside a distributed interactive-optimization mechanism for real-time decision updates through agent cooperation. The algorithm’s performance is evaluated in scenarios with fixed and randomized odor source locations, as well as with varying numbers of agents. Results demonstrate that CGRInfotaxis achieves a near-100% success rate with high consistency across diverse conditions, outperforming existing methods in stability and adaptability. Increasing the number of agents further enhances search efficiency without compromising reliability. These findings suggest that CGRInfotaxis significantly advances multi-agent odor source localization in turbulent, sparse environments, offering practical utility for real-world applications. Full article

Wet areas, which are locations in the landscape that consistently retain moisture, and channel networks are important landscape compartments, with key hydrological and ecological functions. Hence, defining their spatial boundaries is an important step towards sustainable watershed management. In catchments of developing countries, wet areas and small order channels of river networks are rarely mapped, although they represent a crucial component of local livelihoods and ecosystems. In this study, topographic attributes generated with a 30 m SRTM DEM were used to map wet areas and stream networks of two tropical catchments in Central Brazil. The topographic attributes for wet areas were the local slope and the slope curvature, and the Topographic Wetness Index (TWI) was used to delineate the stream networks. Threshold values of the selected topographic attributes were calibrated in the Santa Maria catchment, comparing the synthetically generated wet areas and drainage networks with corresponding reference (map) features, and validated in the nearby Santa Maria basin. Drainage network and wet area delineation accuracies were estimated using random basin transects and multi-criteria and confusion matrix methods. The drainage network accuracies were 67.2% and 70.7%, and wet area accuracies were 72.7% and 73.8%, for the Santa Maria and Gama catchments, respectively, being equivalent or higher than previous studies. The mapping errors resulted from model incompleteness, DEM vertical inaccuracy, and cartographic misrepresentation of the reference topographic maps. The study’s novelty is the use of readily available information to map, with simplicity and robustness, wet areas and channel initiation in data-scarce, tropical environments. Full article