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In this study, a novel spatiotemporal hydrodynamic prediction task framework, named single frame prediction, was developed. The framework could generate results based on boundary conditions and a single flood map from the last time step, relying on hydrodynamic principles rather than historical trends, and doesn't require the assistance of traditional hydrodynamic models. Moreover, a post‐processing method based on physical laws was developed to refine the outputs of deep learning models at each time step, aiming to reduce accumulated errors in long‐term predictions. The performance of a widely used convolutional neural network‐based model, U‐Net, was evaluated to assess the feasibility of single frame prediction and the impact of the proposed post‐processing method. The experiments showed that single frame prediction could produce accurate flood maps, demonstrating the feasibility of the novel framework. Furthermore, the results indicated that the physics‐based post‐processing method could mitigate errors at each step, thereby enhancing prediction accuracy across entire flood event, showing strong effectiveness and applicability in flood prediction. Additionally, an ablation experiment was conducted to assess the effectiveness of each step in the method. The single frame prediction provided a more comprehensive and interpretable depiction of flood prediction processes with essential hydrodynamic variables, including water depth and unit discharge on all grid cells. The post‐processing method significantly reduced the accumulated error in the later stages of single frame prediction to an acceptable range with an average root‐mean‐square error of 0.041 m for water depth and 0.003 m2/s for unit discharge, suggesting a new technique for long‐term flood predictions.

The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device. This work followed a methodical experimental protocol to acquire data from the two devices simultaneously. Four physiological signals were acquired: electrocardiography, electromyography, electrodermal activity and electroencephalography. Root mean square error and coefficient of determination were computed to analyse differences between BITalino and BioPac. Electrodermal activity signals were very similar for the two devices, even without applying any major signal processing techniques. For electrocardiography, a simple morphological comparison also revealed high similarity between devices, and this similarity increased after a common segmentation procedure was followed. Regarding electromyography and electroencephalography data, the approach consisted of comparing features extracted using common post-processing methods. The differences between BITalino and BioPac were again small. Overall, the results presented here show a close similarity between data acquired by the BITalino and by the reference device. This is an important validation step for all researchers working with this multimodal platform.

Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively. The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post-processing operations are self-driven which enhance segmentation results adaptively. The experimental results are compared with four state-of-the-art techniques: fuzzy C-means, spatial fuzzy C-means, spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.

Background In the implementation of Digital Image Correlation (DIC), several post-processing methods have been developed to calculate reliable strain field. Nevertheless, achieving effective and easy-to-implement strain measurement for full-gradient strain fields continues to be a challenge. Objective The widely used pointwise least square (PLS) method is hard to get a balance between smoothing and accuracy when dealing with different deformation fields. A large strain calculation window may lead to over-smoothing, whereas a small strain calculation window may be insufficient to suppress noise. Methods A new exponential function and the exponential function weighted averaging (EFWA) method are proposed. The shape of the exponential function can be either sharp-topped or flat-topped, allowing the EFWA method to either preserve or smooth the original strain results. A straightforward and effective selection strategy for parameters of the exponential function is also provided, enabling the EFWA method to achieve self-adaptive post-processing. Results The calculation examples of synthetic images indicate that, the proposed EFWA method can consistently yield high measurement accuracy for unidirectional and multi-directional complex deformation fields and exhibits superior spatial resolution compared to the PLS method. The minimum Metrological Efficiency Indicator (MEI) value for the EFWA method is 1.72, compared to 4.67 for original results and 5.10 for the PLS method. The results of a tensile experiment carried out on an open-hole specimen indicate that, after the EFWA method is implemented, the strain results in areas away from the hole are effectively smoothed and the strain results in areas around the hole are preserved. Conclusions The proposed EFWA method can achieve effective and easy-to-implement strain measurement for full-gradient strain fields.

Breast cancer is the most common cancer affecting women globally. Despite the significant impact of deep learning models on breast cancer diagnosis and treatment, achieving fairness or equitable outcomes across diverse populations remains a challenge when some demographic groups are underrepresented in the training data. We quantified the bias of models trained to predict breast cancer stage from a dataset consisting of 1000 biopsies from 842 patients provided by AIM-Ahead (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity). Notably, the majority of data (over 70%) were from White patients. We found that prior to post-processing adjustments, all deep learning models we trained consistently performed better for White patients than for non-White patients. After model calibration, we observed mixed results, with only some models demonstrating improved performance. This work provides a case study of bias in breast cancer medical imaging models and highlights the challenges in using post-processing to attempt to achieve fairness.

As a generalization of the short-time Fourier transform (STFT), the novel short-time fractional Fourier transform (NSTFRFT) has been introduced recently. In order to improve the concentration of the time–frequency representation (TFR) generated by the NSTFRFT, two post-processing time–frequency analysis methods, two synchroextracting transforms based on the NSTFRFT with two different fractional Fourier transform (FRFT) angles, are proposed in this paper. One is achieved via an equation where the instantaneous frequency satisfies the condition where the FRFT angle takes π2, and the other one is obtained using the instantaneous frequency estimator in the case that the FRFT angle takes a value related to the chirp rate of the signal. Although the conditions of the two synchroextracting transforms are different, their implementation can be unified into the same algorithm. The proposed synchroextracting transforms supplement existing post-processing time–frequency analysis methods which are based on the NSTFRFT. Experiments are conducted to verify the performance and superiority of the proposed methods.

RGBT (visible-thermal) object tracking holds significant value in complex scenarios such as low-light and hazy environments, enabling robust all-weather tracking by leveraging the complementary strengths of visible and thermal infrared modalities. However, challenges such as target appearance variations, similar object interference, and camera motion often lead to tracking drift. This paper proposes RecheckTrack, a robust RGBT tracking framework that addresses these issues through the enhancement of temporal information and a backward trajectory verification mechanism. The dual-branch fusion network adaptively learns target dynamics using appearance tokens and modality tokens. Modality tokens focus on high-quality features and target-probable regions, while appearance tokens track dynamic changes in target appearance, improving robustness against deformation, occlusion, and scale variations. To mitigate drift caused by sudden target or camera motion, a recheck network is introduced, which employs a two-stage candidate box selection method and jointly matches targets using bidirectional tracking consistency and appearance similarity. Additionally, for long-term tracking scenarios where targets may be lost, the recheck network is improved with a path-consistency-based backward trajectory selection method and an approximate global search strategy, efficiently recovering lost targets. Experiments on the VTUAV, LasHeR, and RGBT234 datasets demonstrate that RecheckTrack significantly reduces tracking drift and improves accuracy, providing an effective solution for RGBT tracking in complex scenarios.

Vanadium flow battery (VFB) is one of the most promising candidates for large-scale energy storage. A modified polyacrylonitrile (PAN) porous membrane is successfully applied in VFB. Herein, a simple solvent post-processing method is presented to modify PAN porous membranes prepared by the traditional nonsolvent induced phase separation (NIPS) method. In the design, polymer PAN is chosen as the membrane material owing to its low cost and high stability. The large-size pores from NIPS method are well optimized by the solvent swelling and shrinking during the solvent post-processing. Meanwhile, the interconnectivity of pores is maintained well. As a result, the ion selectivity of PAN porous membranes is dramatically improved, and the CE of a VFB with PAN porous membranes rises from 68% to 93% after the solvent post-processing process. A VFB with the modified PAN porous membranes is capable of delivering a limiting current density of 900 mA cm−2, and a high peak power density of 650 mW cm−2, which is very competitive among the various flow batteries.

Traditional photogrammetry techniques require the use of Ground Control Points (GCPs) to accurately georeference aerial images captured by unmanned aerial vehicles (UAVs). However, the process of collecting GCPs can be time-consuming, labor-intensive, and costly. Real-time kinematic (RTK) georeferencing systems eliminate the need for GCPs without deteriorating the accuracy of photogrammetric products. In this study, a statistical comparison of four RTK georeferencing systems (continuously operating reference station (CORS)-RTK, CORS-RTK + post-processed kinematic (PPK), RTK + dynamic RTK 2 (DRTK2), and RTK + DRTK2 + GCP) is presented. The aerial photo was acquired using a Dà-Jiāng Innovation Phantom 4 RTK. The digital photogrammetric processing was performed in Agisoft Metashape Professional software. A pair of global navigation satellite systems (GNSSs) receiving antennas model CHC x900 were used for the establishment of check points (CPs). The accuracy of photogrammetric products was based on a comparison between the modeled and CP coordinates. The four methods showed acceptable planimetric accuracies, with a root mean square error (RMSE) ranging from 0.0164 to 0.0529 m, making the RTK-CORS + PPK method the most accurate (RMSE = 0.0164 m). RTK-CORS + PPK, RTK-DRTK2, and RTK-DRTK2 + GCP methods showed high altimetric accuracies, with RMSE values ranging from 0.0201 to 0.0334 m. In general, RTK methods showed a high planimetric and altimetric accuracy, similar to the accuracy of the photogrammetric products obtained using a large number of GCPs.

Meteorological Ensemble Streamflow Prediction (ESP), which uses Ensemble Weather forecasts (EWFs) to drive hydrological models, is a useful methodology for extending forecast periods and to provide valuable uncertainty information to improve the operation of future water resources. However, raw EWFs are usually biased and under-dispersive and so cannot be directly used in ESP, leading to the development of several post-processing methods. The performance of these methods needs to be evaluated/compared in building ESP based on deterministic and probabilistic criteria. In addition, likely influencing factors also need to be identified. This study evaluated the performance of four state-of-the-art methods: the Generator-based Post-Processing (GPP) method, Extended Logistic Regression (ExLR), Bayesian Model Averaging (BMA) and Affine Kernel Dressing (AKD), using a simple bias correction (BC) method as a benchmark. The evaluation was carried out over four watersheds with different basin areas in the humid region of central-south China based on the weather reforecasts from the Global Ensemble Forecasting System (GEFS). The results show that the performance of the post-processing methods varies with the forecast variable (precipitation, or air temperature or streamflow), but all of them outperform the BC and GEFS. For the four post-processing methods, the advantage of the generator-based methods (GPP and ExLR) lies in their probabilistic performance, which outperforms the distribution-based methods (BMA and AKD) by about 10% in precipitation forecasts and about 20% in streamflow forecasts, while the distribution-based methods (BMA and AKD) are better at their deterministic performance for precipitation forecasts, with a benefit of about 15%. Meanwhile, the post-processing methods generally perform better for precipitation and streamflow forecasts, but worse for air temperature forecasts for a bigger basin compared to the distribution-based methods. The results of this study emphasize the importance of considering the uncertainty of post-processing methods in ESP.