Search Results (17,772)

The simulator for hydrological unstructured domains (SHUD) is a cutting-edge, distributed hydrological model based on the finite volume method, representing the next generation of coupled surface–subsurface hydrological simulations. Its applicability in high-altitude, cold regions covered by snow and permafrost, such as the Yellow River source region, necessitates rigorous validation. This study employed the China Meteorological Forcing Dataset (CMFD) to simulate streamflow in the Yellow River source region from 2006 to 2018, comprehensively assessing the suitability of the SHUD model in this area. The SHUD model excels in simulating monthly streamflow in the Yellow River source region, while its performance at the daily scale is comparable to existing models. It demonstrated significantly better performance in the warm season compared to the cold season, particularly in the middle and lower reaches of the region. Distinct seasonal and regional differences were observed in simulation performance across sub-basins. However, the model encounters limitations when simulating the extensively distributed permafrost areas in the upstream region, primarily due to oversimplification of the permafrost thawing and freezing processes, which points the direction for future model improvements. Additionally, the model’s shortcomings in accurately simulating peak streamflow are closely related to uncertainties in calibration strategies and meteorological data inputs. Despite these limitations, the calibrated SHUD model meets the hydrological simulation needs of the Yellow River Source Region across various temporal scales, providing significant scientific reference for hydrological simulation and streamflow prediction in cold regions with snow and permafrost. Full article

Purpose: Is machine learning (ML) superior to the traditionally used logistic regression (LR) in prediction of neurodevelopmental outcomes in preterm infants? Objectives: To develop and internally validate a ML model to predict neurodevelopmental impairment (NDI) in very preterm infants (<31 weeks) at 36 months corrected age, using clinical predictors. Methods: A retrospective cohort of very preterm infants (23^0–30⁶ weeks) born between January 2004 and December 2016 in Nova Scotia, Canada. Survivors with neurodevelopmental assessment at 36 months corrected age were included. The study sample was randomly split (80:20) into a development and testing datasets. We compared four methods: LR, elastic net (EN), random forest ensemble (RF) and gradient boosting (XGB), in relation to discrimination (AUC), calibration, and diagnostic properties. Results: Of 811 eligible infants, 663 were included (mean gestational age 28 weeks, mean birth weight 1137 g and 52% male). Of those, 195 (29%) developed NDI and 468 (71%) did not. On internal validation using the testing dataset, all four models provided good discrimination of NDI with comparable AUC. RF was superior to the other three methods with a higher AUC (0.79 vs. 0.74, 0.74, and 0.73 for XGB, EN and LR, respectively), but all models have overlapped CIs. Conclusions: In this population-based cohort of very preterm infants, RF was superior to conventional LR in prediction of NDI at 3 years corrected age. Accurate prediction of preterm infants at risk of NDI enables early referrals for intervention programs and resources allocation toward those who are most likely to benefit. Full article

Trust is a crucial human factor in automated supervisory control tasks. To attain appropriate reliance, the operator’s trust should be calibrated to reflect the system’s capabilities. This study utilized eye-tracking technology to explore novel approaches, given the intrusive, subjective, and sporadic characteristics of existing trust measurement methods. A real-world scenario of alarm state discrimination was simulated and used to collect eye-tracking data, real-time interaction data, system log data, and subjective trust scale values. In the data processing phase, a dynamic prediction model was hypothesized and verified to deduce and complete the absent scale data in the time series. Ultimately, through eye tracking, a discriminative regression model for trust calibration was developed using a two-layer Random Forest approach, showing effective performance. The findings indicate that this method may evaluate the trust calibration state of operators in human–agent collaborative teams within real-world settings, offering a novel approach to measuring trust calibration. Eye-tracking features, including saccade duration, fixation duration, and the saccade–fixation ratio, significantly impact the assessment of trust calibration status. Full article

Water management has evolved significantly, but sustainability remains a critical challenge. Ancient Roman aqueducts, despite their engineering marvel, operated with constant flow, leading to substantial water waste. Later, rooftop reservoir systems continued this inefficiency, as excess water would overflow. Only recently have demand-driven networks shown potential for reducing waste, though substantial water leaks continue to undermine these efforts. Achieving true sustainability in water distribution requires minimizing leaks through the use of models that adopt accurate water demand scenarios and identifying an optimal peak factor (PF). In fact, water distribution networks (WDNs) are commonly designed, analyzed, and calibrated using deterministic demand scenarios based on average annual consumption and scaled by a chosen PF. However, for efficient design and management, it is essential to associate a probabilistic value with the consumption data used in the analyses. This study introduces a novel methodology for estimating PFs with a specific return period at the District Meter Area (DMA) scale, utilizing extreme value statistical analysis. The generalized Pareto distribution (GPD) models were applied to provide more reliable PF estimates. The proposed methodology was validated using hourly residential consumption data from a DMA located in Southern Italy, demonstrating its effectiveness in improving the accuracy of WDN design. Full article

Background/Objectives: Vision Transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performances in image classification, especially in the domain of medical imaging analysis. However, ViTs struggle to capture high-frequency components of images, which are critical in identifying fine-grained patterns, while CNNs have difficulties in capturing long-range dependencies due to their local receptive fields, which makes it difficult to fully capture the spatial relationship across lung regions. Methods: In this paper, we proposed a hybrid architecture that integrates ViTs and CNNs within a modular component block(s) to leverage both local feature extraction and global context capture. In each component block, the CNN is used to extract the local features, which are then passed through the ViT to capture the global dependencies. We implemented a gated attention mechanism that combines the channel-, spatial-, and element-wise attention to selectively emphasize the important features, thereby enhancing overall feature representation. Furthermore, we incorporated a multi-scale fusion module (MSFM) in the proposed framework to fuse the features at different scales for more comprehensive feature representation. Results: Our proposed model achieved an accuracy of 99.50% in the classification of four pulmonary conditions. Conclusions: Through extensive experiments and ablation studies, we demonstrated the effectiveness of our approach in improving the medical image classification performance, while achieving good calibration results. This hybrid approach offers a promising framework for reliable and accurate disease diagnosis in medical imaging. Full article

A novel approach to developing lateral flow assays (LFAs) for the detection of CYFRA 21-1 (cytokeratin 19 fragment, a molecular biomarker for epithelial-origin cancers) is proposed. Magnetic bioconjugates (MBCs) were employed in combination with advanced optical and magnetic tools to optimize assay conditions. The approach integrates such techniques as label-free spectral-phase interferometry, colorimetric detection, and ultrasensitive magnetometry using the magnetic particle quantification (MPQ) technique. For the first time in LFA applications, the MPQ-based and colorimetry-based detection methods were compared side by side, and superior analytical performance was demonstrated. The limit of detection (LOD) of 0.9 pg/mL was achieved using MPQ, and 2.9 pg/mL with optical detection. This study has demonstrated that MPQ provides elimination of signal saturation, higher sensitivity (slope of the calibration curve), and a 19-fold wider dynamic range of detected signals. Both optical and magnetic detection results are comparable to the best laboratory-based tests with the added benefits of a 20-min assay duration and the LFA format convenience. The assay effectiveness was validated in human serum and artificial saliva, and high recovery rates were observed. The proposed approach offers rapid and reliable detection of molecular biomarkers and holds significant potential for point-of-care diagnostics, particularly in resource-limited settings. Full article

Sustainable long-term performance of water quality swales, a common stormwater control measure (SCM), requires a futuristic view that considers the impact of socio-economic conditions. The impact of five socio-economic scenarios on a water quality swale in Knightdale, North Carolina, USA, was assessed using WinSLAMM, a stormwater quality model. Scenarios included changing annual average daily traffic (AADT) and maintenance regimes mimicking environmental protection and degradation. Statistical performance evaluation criteria (e.g., RMSE, R²) were used to assess model suitability and calibration for runoff volume and sediment. Results indicated that sediment delivery to the swale increased with AADT, and reduced maintenance negatively impacted swale performance. While the reduced AADT during the COVID-19 pandemic provided short-term water quality benefits, a lack of maintenance impacted treatment through the swale. SCM inspection and maintenance is critical for accommodating increased AADT and enhancing swale life-cycle. This exploratory impact assessment focused on the socio-economic axis of climate change scenario framework and underscored the importance of sound environmental policies for sustainable swale performance. Future studies are needed in other areas to influence local environmental policies. Full article

Catechins are pivotal determinants of tea quality, with soil environmental factors playing a crucial role in the synthesis and accumulation of these compounds. To investigate the impact of changes in tea garden soil environments on the catechin content in sun-dried tea, this study measured the catechin content in soil samples and corresponding tea leaves from Nanhua, Yunnan, China. By integrating the variations in catechin content with those of 17 soil factors and employing COX regression factor analysis, it was found that pH, organic matter (OM), fluoride, arsenic (As), and chromium (Cr) were significantly correlated with catechin content (p < 0.05). Further, using the LASSO regression for variable selection, a model named LCLN-CA was constructed with four variables including pH, OM, fluoride, and As. The LCLN-CA model demonstrated high fitting accuracy with AUC values of 0.674, 0.784, and 0.749 for catechin content intervals of CA ≤ 10%, 10% < CA ≤ 20%, and 20% < CA ≤ 30% in the training set, respectively. The validation set showed AUC values of 0.630, 0.756, and 0.723, respectively, indicating a well-calibrated curve. Based on the LCLN-CA model and the DynNom framework, a visual prediction system for catechin content in Yunnan sun-dried tea was developed. External validation with a test dataset achieved an $Accuracy$ of 0.870. This study explored the relationship between soil-related factors and variations in catechin content, paving a new way for the prediction of catechin content in tea and enhancing the practical application value of artificial intelligence technology in agricultural production. Full article

To enhance the accuracy and reliability of the discrete element simulation software EDEM 2023 for pavement asphalt mixture simulation, three representative coarse aggregate particles were modeled in 3D using the SolidWorks 2018 software and imported into the EDEM 2023 software for particle filling. The Hertz–Mindlin with bonding contact model was used to construct the EDEM simulation model of asphalt mixtures, and the quadratic regression model of asphalt mixtures’ splitting tensile strength and four bonding parameters, namely, normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress, was found by the response surface methodology. The results show that the relationship between the significance magnitude of the four bonding parameters on the splitting tensile strength of the asphalt mixture simulation model is as follows: critical normal stress > shear stiffness per unit area > normal stiffness per unit area > critical shear stress. The calibration results of the bonding parameters were used for simulation verification, and the relative error between the simulation and actual splitting tensile strength was only −2.48%. The feasibility of this bonding parameter calibration method is demonstrated, and it can lay a foundation for EDEM to simulate the performance of asphalt mixtures on pavements with high-precision simulation. Full article

Wave prediction is crucial for ensuring the safety and disaster mitigation of coastal areas, helping to support marine economic activities. Currently, many deep learning models, such as the temporal convolutional network (TCN), have been applied to wave prediction. In this study, a prediction model based on improved TCN-Attention (ITCN-A) is proposed. This model incorporates improvements in two aspects. Firstly, to address the difficulty of calibrating hyperparameters in traditional TCN models, a whale optimization algorithm (WOA) has been introduced to achieve global optimization of hyperparameters. Secondly, we integrate dynamic ReLU to implement an adaptive activation function. The improved TCN is then combined with the attention mechanism to further enhance the extraction of long-term features of wave height. We conducted experiments using data from three buoy stations with varying water depths and geographical locations, covering prediction lead times ranging from 1 h to 24 h. The results demonstrate that the proposed integrated model reduces the $RMSE$ of prediction by 12.1% and $MAE$ by an 18.6% compared with the long short-term memory (LSTM) model. Consequently, this model effectively improves the accuracy of wave height predictions at different stations, verifying its effectiveness and general applicability. Full article

Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF₇₆₀) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF₇₆₀ improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF₇₆₀ were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF₇₆₀-GPP relationship weakened while the SIF₇₆₀-ET strengthened. The range of significant correlations between SIF₇₆₀, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF₇₆₀-GPP and SIF₇₆₀-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF₇₆₀ with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain. Full article

The rise in inexpensive Unmanned Aerial Systems (UAS) and accessible processing software offers several advantages in forest ecosystem monitoring and management. The increase in usability of such tools can result in the simplification of workflows, potentially impacting the quality of the generated data. This study offers insights into the precision and reliability of the DJI Phantom 4 Multispectral (P4MS) UAS for mapping shrublands using the Agisoft Metashape (AM) for image processing. Geometric accuracy was evaluated using ground control points (GCPs) and different configurations. The best configuration was then used to produce orthomosaics. Subsequently, the orthomosaics were transformed into reflectance orthomosaics using various radiometric correction methods. These methods were further assessed using reference panels. The method producing the most accurate reflectance values was then chosen to create the final reflectance and Normalised Difference Vegetation Index (NDVI) maps. Radiometric accuracy was assessed through a multi-step process. Initially, precision was measured by comparing reflectance orthomosaics and NDVI derived from images taken on consecutive days. Finally, reliability was evaluated by comparing the NDVI with NDVI from a reference camera, the MicaSense Altum AL0, produced with images acquired on the same days. The results demonstrate that the P4MS is both precise and reliable for shrubland mapping. Reflectance maps and NDVI generated in AM exhibit acceptable geometric and radiometric accuracy when geometric calibration is performed with at least one GCP and radiometric calibration utilises images of reflectance panels captured at flight height, without relying on incident light sensor (ILS) data. Full article

Recent advances in the use of portable/handheld laser-induced breakdown spectroscopy (LIBS) instrumentation have allowed its use directly in the field. In this study, a portable/handheld LIBS demo kit was tested to detect the titanium (Ti) content in some quartz veinlets hosted by hornfels collected from the contact aureole of a Pliocene granite from Italy. Results of the present study demonstrate the promising potential of LIBS in the rapid detection of low and very variable Ti contents in quartz, which can be used as a preliminary test for the estimation of the temperature of quartz crystallization in the laboratory. However, to date, the limited availability of matrix-matched calibration samples, the refinement of sampling protocols, as well as the development of suitable algorithms for data processing and spectral analysis still require further investigation. Full article

The Joint Polar Satellite System 3 (JPSS-3) and 4 (JPSS-4) Visible Infrared Imaging Radiometer Suite (VIIRS) are the fourth and fifth in its series of instruments designed to provide high-quality data products for environmental and climate data records. The VIIRS instrument must be calibrated and characterized prior to launch to meet the data product needs. A comprehensive test program was conducted at the Raytheon Technologies facility in 2020 (JPSS-3) and 2023 (JPSS-4) that included extensive functional and environmental testing. The thermal band radiometric pre-launch performance and stability are the focus of this article, which also compares several instrument performance metrics to the design requirements. Brief comparisons with the JPSS-1 and -2 VIIRS instrument performance will also be discussed. Full article

Concrete columns are considered critical elements with respect to the stability of buildings during earthquakes. To improve the accuracy of column damage and collapse risk estimates using numerical simulations, it is important to develop a methodology to quantify the effect of displacement history on column force–deformation modeling parameters. Addressing this knowledge gap systematically and comprehensively through experimentation is difficult due to the prohibitive cost. The primary objective of this study was to develop guidelines to simulate the lateral cyclic behavior and axial collapse of concrete columns with different modes of failure using continuum finite element (FE) models, such that wider parametric studies can be conducted numerically to improve the accuracy of assessment methodologies for critical columns. This study expands on existing FEM research by addressing the complex behavior of columns that experience multiple failure modes, including axial collapse following flexure–shear, shear, and flexure degradation, a topic which has been underexplored in previous works. Nonlinear FE models were constructed and calibrated to experimental tests for 21 columns that sustained flexure, flexure–shear, and shear failures, followed by axial failure, when subjected to cyclic and monotonic lateral displacement protocols. The selected columns represented a range of axial loads, shear stresses, transverse reinforcement ratios, longitudinal reinforcement ratios, and shear span-to-depth ratios. Recommendations on optimal material model parameters obtained from a parametric study are presented. Metrics used for optimization include crack widths, damage in concrete and reinforcement, drift at initiation of axial and lateral strength degradation, and peak lateral strength. The capacities of shear–critical columns calculated with the optimized numerical models are compared with experimental results and standard equations from ASCE 41-17 and ACI 318-19. The optimized finite element models were found to reliably predict peak strength and deformation at the onset of both lateral and axial strength failure, independent of the mode of lateral strength degradation. Also, current standard shear capacity provisions were found to be conservative in most cases, while the FE models estimated shear strength with greater accuracy. Full article