In the context of carbon peak and carbon neutral policies, low-carbon construction has been the focus of most countries worldwide. As one of the most effective ways to achieve green construction, many countries have launched low-carbon policies to promote the development of prefabrication. However, the effectiveness and influencing factors of low-carbon policies on prefabrication need to be further verified under the dynamic game between the government and the construction enterprise. Therefore, this study considered subsidy and carbon tax policies and developed an evolutionary game model to promote the development of the prefabricated construction market. The evolutionary stable strategy of the government and construction enterprise under different scenarios was obtained. Subsequently, a numerical analysis was conducted to further investigate the impact of the key factors on the stable strategy. The results showed that an appropriate hybrid policy of subsidies and taxes could positively promote the prefabrication implementation of the construction enterprise. The government should adopt an appropriate policy intensity according to the maturity of the market. This study can provide effective guidance and practical enlightenment for the government to achieve low-carbon, green, and sustainable construction. @en

Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sørensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results: Different ML algorithms result in similar test mean absolute errors: ∼8 mm for liver LR, IS, and spleen AP, IS; ∼5 mm for liver AP and spleen LR; ∼80 % for abdomen sDSC; and ∼60 % to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially (+5-mm error for spleen IS,-10 % sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.

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The advent of Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To capture the effects of radiation treatment, treatment plans are typically simulated on virtual surrogates of patient anatomy called phantoms. Currently, phantoms are built to represent categories of patients based on reasonable yet simple criteria. This often results in phantoms that are too generic to accurately represent individual anatomies. We present a novel approach that combines imaging data and ML to build individualized phantoms automatically. We design a pipeline that, given features of patients treated in the pre-3D planning era when only 2D radiographs were available, as well as a database of 3D Computed Tomography (CT) imaging with organ segmentations, uses ML to predict how to assemble a patient-specific phantom. Using 60 abdominal CTs of pediatric patients between 2 to 6 years of age, we find that our approach delivers significantly more representative phantoms compared to using current phantom building criteria, in terms of shape and location of two considered organs (liver and spleen), and shape of the abdomen. Furthermore, as interpretability is often central to trust ML models in medical contexts, among other ML algorithms we consider the Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA), that learns readable mathematical expression models. We find that the readability of its output does not compromise prediction performance as GP-GOMEA delivered the best performing models.@en

3D dose reconstruction for radiotherapy (RT) is the estimation of the 3D radiation dose distribution patients received during RT. Big dose reconstruction data is needed to accurately model the relationship between the dose and onset of adverse effects, to ultimately gain insights and improve today's treatments. Dose reconstruction is often performed by emulating the original RT plan on a surrogate anatomy for dose estimation. This is especially essential for historically treated patients with long-Term follow-up, as solely 2D radiographs were used for RT planning, and no 3D imaging was acquired for these patients. Performing dose reconstruction for a large group of patients requires a large amount of manual work, where the geometry of the original RT plan is emulated on the surrogate anatomy, by visually comparing the latter with the original 2D radiograph of the patient. This is a labor-intensive process that for practical use needs to be automated. This work presents an image-processing pipeline to automatically emulate plans on surrogate computational tomography (CT) scans. The pipeline was designed for childhood cancer survivors that historically received abdominal RT with anterior-To-posterior and posterior-To-Anterior RT field set-up. First, anatomical landmarks are automatically identified on 2D radiographs. Next, these landmarks are used to derive parameters needed to finally emulate the plan on a surrogate CT. Validation was performed by an experienced RT planner, visually assessing 12 cases of automatic plan emulations. Automatic emulations were approved 11 out of 12 times. This work paves the way to effortless scaling of dose reconstruction data generation.

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Rare-earth zirconates (RE₂Zr₂O₇, RE = rare earth elements) are considered as promising thermal barrier coating (TBCs) materials. However, their thermal expansion coefficient (TEC) is relatively low comparing with that of either the state-of-the-art TBCs material Yttria-stabilized Zirconia (YSZ), or the metallic bond-coat. In present research, Cerium was introduced as the substitution of Zirconium to improve the TEC of rare-earth zirconates. A series of Yb₂(Zr_1−xCe_x)₂O₇ solid solutions were synthesized and investigated. Experimental results revealed that their TEC is remarkably increased with the Cerium substitution content. Meanwhile, their thermal conductivity shows no obvious increase compared with that of Yb₂Zr₂O₇due to the phonon scattering effect.

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We present a locally-adaptive approach to segment the fluid and the interfaces between retinal layers in eyes affected by central serous retinopathy based on loosely-coupled level sets. The approach exploits the local attenuation coefficient differences of layers around an interface and introduces auxiliary interfaces to delineate the fluid. Thus, it can handle abrupt attenuation coefficient variations and topology-disrupting anomalies. The method was applied to in-vivo images of retinas acquired by optical coherence tomography. A quantitative comparison with manual annotations shows the method's high accuracy: we obtained a mean absolute deviation for the interfaces of 3.7-8.9 βm (1-2 pixels) and a Dice coefficient for the fluid segmentation of 0.96.

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Optical coherence tomography (OCT) is used to produce high-resolution three-dimensional images of the retina, which permit the investigation of retinal irregularities. In dry age-related macular degeneration (AMD), a chronic eye disease that causes central vision loss, disruptions such as drusen and changes in retinal layer thicknesses occur which could be used as biomarkers for disease monitoring and diagnosis. Due to the topology disrupting pathology, existing segmentation methods often fail. Here, we present a solution for the segmentation of retinal layers in dry AMD subjects by extending our previously presented loosely coupled level sets framework which operates on attenuation coefficients. In eyes affected by AMD, Bruch’s membrane becomes visible only below the drusen and our segmentation framework is adapted to delineate such a partially discernible interface. Furthermore, the initialization stage, which tentatively segments five interfaces, is modified to accommodate the appearance of drusen. This stage is based on Dijkstra's algorithm and combines prior knowledge on the shape of the interface, gradient and attenuation coefficient in the newly proposed cost function. This prior knowledge is incorporated by varying the weights for horizontal, diagonal and vertical edges. Finally, quantitative evaluation of the accuracy shows a good agreement between manual and automated segmentation. @en