Effect of porosity variation strategy on the performance of functionally graded Ti-6Al-4V scaffolds for bone tissue engineering

Functionally graded scaffold (FGS) is designed to mimic the morphology, mechanical and biological properties of natural bone closely. Porosity variation strategy between different regions in FGS plays a crucial role in influencing its mechanical and biological performance. A combination of modeling tool and scripting language can effectively enhance the ability to optimize FGS design. This study was aimed at determining the effect of porosity variation strategy on the mechanical performance and permeability of the as-built and as-heat-treated FGSs. Ti-6Al-4V FGSs with sizes of 10 × 10 × 15 mm and diamond lattice structures were designed and fabricated by means of selective laser melting. A wide range of porosities in the FGSs (38–75%) were achieved by applying six different porosity variation strategies. The elastic modulus (3.7–5.7 GPa) and yield strength (27.1–84.7 MPa) of the as-built FGSs were found to vary between the corresponding mechanical properties of cancellous bone and cortical bone. Heat treatment reduced the strengths by 13–56%. Porosity variation strategy strongly affected the deformation behavior and failure mechanisms of the FGSs. The sigmoid function-controlled FGSs showed gradual failure behavior and sample Sigk0.5b8 showed superior overall performance. The results demonstrated that porosity variation strategy is a feasible means for tailor design of FGS.Accepted Author ManuscriptBiomaterials & Tissue Biomechanic

Sub-second photon dose prediction via transformer neural networks

Background: Fast dose calculation is critical for online and real-time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computation times, deep learning models can potentially perform dose prediction tasks with both high fidelity and speed. Purpose: We present a deep learning algorithm that, exploiting synergies between transformer and convolutional layers, accurately predicts broad photon beam dose distributions in few milliseconds. Methods: The proposed improved Dose Transformer Algorithm (iDoTA) maps arbitrary patient geometries and beam information (in the form of a 3D projected shape resulting from a simple ray tracing calculation) to their corresponding 3D dose distribution. Treating the 3D CT input and dose output volumes as a sequence of 2D slices along the direction of the photon beam, iDoTA solves the dose prediction task as sequence modeling. The proposed model combines a Transformer backbone routing long-range information between all elements in the sequence, with a series of 3D convolutions extracting local features of the data. We train iDoTA on a dataset of 1700 beam dose distributions, using 11 clinical volumetric modulated arc therapy (VMAT) plans (from prostate, lung, and head and neck cancer patients with 194–354 beams per plan) to assess its accuracy and speed. Results: iDoTA predicts individual photon beams in ≈50 ms with a high gamma pass rate of (Formula presented.) (2 mm, 2%). Furthermore, estimating full VMAT dose distributions in 6–12 s, iDoTA achieves state-of-the-art performance with a (Formula presented.) (2 mm, 2%) pass rate and an average relative dose error of 0.75 ± 0.36%. Conclusions: Offering the millisecond speed prediction per beam angle needed in online and real-time adaptive treatments, iDoTA represents a new state of the art in data-driven photon dose calculation. The proposed model can massively speed-up current photon workflows, reducing calculation times from few minutes to just a few seconds.RST/Medical Physics & TechnologyRST/Reactor Physics and Nuclear Material

Assessing oil spill risk in the Chinese Bohai Sea: A case study for both ship and platform related oil spills

Many oil spill accidents have occurred in the Chinese Bohai Sea recently because of extensive offshore oil exploitation and maritime transportation. In this study we made an attempt towards the development of a probabilistic model assessing the oil spill risks for the Bohai Sea. We utilized satellite images together with historic accident data from 1973 to 2002 and information from literature to establish probability of oil spills, which was then adjusted for the consequence of spill impacts. The assessment represented a distribution of oil spill risk in the study area. Totally, seven high risk zones have been identified, providing useful information on the optimal locations for oil spill recovery and cleanup equipment to improve spill-response readiness in the region. We anticipated that the results would contribute to the mitigation of the harmful effects of future oil spills in the Bohai Sea, if the presented model was used with care. (C) 2014 Elsevier Ltd. All rights reserved.Many oil spill accidents have occurred in the Chinese Bohai Sea recently because of extensive offshore oil exploitation and maritime transportation. In this study we made an attempt towards the development of a probabilistic model assessing the oil spill risks for the Bohai Sea. We utilized satellite images together with historic accident data from 1973 to 2002 and information from literature to establish probability of oil spills, which was then adjusted for the consequence of spill impacts. The assessment represented a distribution of oil spill risk in the study area. Totally, seven high risk zones have been identified, providing useful information on the optimal locations for oil spill recovery and cleanup equipment to improve spill-response readiness in the region. We anticipated that the results would contribute to the mitigation of the harmful effects of future oil spills in the Bohai Sea, if the presented model was used with care. (C) 2014 Elsevier Ltd. All rights reserved

Thermal properties of biochars derived from waste biomass generated by agricultural and forestry sectors

Waste residues produced by agricultural and forestry industries can generate energy and are regarded as a promising source of sustainable fuels. Pyrolysis, where waste biomass is heated under low-oxygen conditions, has recently attracted attention as a means to add value to these residues. The material is carbonized and yields a solid product known as biochar. In this study, eight types of biomass were evaluated for their suitability as raw material to produce biochar. Material was pyrolyzed at either 350 °C or 500 °C and changes in ash content, volatile solids, fixed carbon, higher heating value (HHV) and yield were assessed. For pyrolysis at 350 °C, significant correlations (p < 0.01) between the biochars’ ash and fixed carbon content and their HHVs were observed. Masson pine wood and Chinese fir wood biochars pyrolyzed at 350 °C and the bamboo sawdust biochar pyrolyzed at 500 °C were suitable for direct use in fuel applications, as reflected by their higher HHVs, higher energy density, greater fixed carbon and lower ash contents. Rice straw was a poor substrate as the resultant biochar contained less than 60% fixed carbon and a relatively low HHV. Of the suitable residues, carbonization via pyrolysis is a promising technology to add value to pecan shells and Miscanthus