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Abstract This study focuses on the prediction and management of carbon emissions (CE) under the backdrop of global warming, with a particular emphasis on developing spatial planning strategies for urban clusters. In this context, we integrate artificial intelligence technologies to devise an optimized spatial analysis method based on the attributes of multi-source, urban-level spatio-temporal big data on CE. This method enhances both the accuracy and interpretability of CE data processing. Our objectives are to accurately analyze the current status of CE, predict the future spatial distribution of urban CE in the Yangtze River Delta (YRD), and identify key driving factors. We aim to provide pragmatic recommendations for sustainable urban carbon management planning. The findings indicate that: (1) the algorithm designed by us demonstrates excellent fitting capabilities in the analysis of CE data in the YRD, achieving a fitting accuracy of 0.93; (2) it is predicted that from 2025 to 2030, areas with higher CE in the YRD will be primarily concentrated in the 'Provincial Capital Belt' and the 'Heavy Industry Belt'; (3) the economic foundation has been identified as the most significant factor influencing CE in the YRD; (4) projections suggest that CE in the YRD are likely to peak by 2030.

Abstract Fluid dynamics computations for tube-like geometries are crucial in biomedical evaluations of vascular and airways fluid dynamics. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional computational fluid dynamics (CFD) methods. However, vanilla PINNs often demand longer training times than conventional CFD methods for each specific flow scenario, limiting their widespread use. To address this, multi-case PINN approach has been proposed, where varied geometry cases are parameterized and pre-trained on the PINN. This allows for quick generation of flow results in unseen geometries. In this study, we compare three network architectures to optimize the multi-case PINN through experiments on a series of idealized 2D stenotic tube flows. The evaluated architectures include the ‘Mixed Network’, treating case parameters as additional dimensions in the vanilla PINN architecture; the “Hypernetwork”, incorporating case parameters into a side network that computes weights in the main PINN network; and the “Modes” network, where case parameters input into a side network contribute to the final output via an inner product, similar to DeepONet. Results confirm the viability of the multi-case parametric PINN approach, with the Modes network exhibiting superior performance in terms of accuracy, convergence efficiency, and computational speed. To further enhance the multi-case PINN, we explored two strategies. First, incorporating coordinate parameters relevant to tube geometry, such as distance to wall and centerline distance, as inputs to PINN, significantly enhanced accuracy and reduced computational burden. Second, the addition of extra loss terms, enforcing zero derivatives of existing physics constraints in the PINN (similar to gPINN), improved the performance of the Mixed Network and Hypernetwork, but not that of the Modes network. In conclusion, our work identified strategies crucial for future scaling up to 3D, wider geometry ranges, and additional flow conditions, ultimately aiming towards clinical utility.

Abstract Preventing avoidable venous-thrombo-embolism (VTE) is a priority to improve patient and service outcomes after total hip and total knee arthroplasty (THA, TKA), but compliance with relevant clinical guidelines varies. This study aims to determine the degree to which prophylaxis was compliant with Australian Orthopaedic Association (AOA) VTE prophylaxis guidelines and whether non-compliance is associated with increased risk of VTE. A prospective multi-centre cohort study of adults with osteoarthritis undergoing primary TKA/THA was completed at 19 high-volume public and private hospitals. Data were collected prior to surgery and for one-year post-surgery. Logistic regression was undertaken to explore associations between non-compliance with AOA VTE prophylaxis guidelines and symptomatic 90-day VTE outcomes. Data were analysed for 1838 participants from 19 sites. The rate of non-compliance with all clinical guideline recommendations was 20.1% (N = 369), with 14.1% (N = 259) non-compliance for risk-stratified prophylaxis, 35.8% (N = 658) for duration, and 67.8% (N = 1246) for other general recommendations. Symptomatic VTE was experienced up to 90-days post-surgery by 48 people (2.6%). Overall guideline non-compliance (AOR = 0.93, 95%CI = 0.4 to 1.3, p = 0.86) was not associated with a lower risk of symptomatic 90-day VTE. Results were consistent when people with high bleeding risk were excluded (AOR = 0.94, 95%CI = 0.44 to 2.34, p = 0.89). Non-compliance with the AOA VTE prophylaxis guidelines was not associated with risk of 90-day VTE after arthroplasty. This counterintuitive finding is concerning and necessitates a rigorous review of the AOA VTE prevention clinical guideline.

Abstract Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises a spatio-temporal model of motion, b-splines of Fourier, to fit to displacement fields from pairwise image registration. In the process, it enforces spatial and temporal smoothness and consistency, cyclic nature of cardiac motion, and better adherence to the stroke volume of the heart. Flexibility is further given for inclusion of any set of registration displacement fields. The approach gave high accuracy. When applied to human adult Ultrasound data from a Cardiac Motion Analysis Challenge (CMAC), the proposed method is found to have 10% lower tracking error over CMAC participants. Satisfactory cardiac motion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick embryonic heart ultrasound images, and zebrafish embryonic microscope images, with the average Dice coefficient between estimation motion and manual segmentation at 0.82–0.87. The approach of performing regularisation as an add-on layer after the completion of image registration is thus a viable option for cardiac motion estimation that can still have good accuracy. Since motion estimation algorithms are complex, dividing up regularisation and registration can simplify the process and provide flexibility. Further, owing to a large variety of existing registration algorithms, such an approach that is usable on any algorithm may be useful.