GCR-Net:3D Graph convolution-based residual network for robust reconstruction in cerenkov luminescence tomography

Cerenkov Luminescence Tomography(CLT)is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes.However,due to severe ill-posed inverse problem,obtaining accurate reconstruction results is still a challenge for traditional model-based methods.The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source,which effectively improves the performance of CLT reconstruction.However,the previously proposed deep learning-based methods cannot work well when the order of input is disarranged.In this paper,a novel 3D graph convolution-based residual network,GCR-Net,is proposed,which can obtain a robust and accurate reconstruction result from the photon intensity of the surface.Additionally,it is proved that the network is insensitive to the order of input.The performance of this method was evaluated with numerical simulations and in vivo experiments.The results demonstrated that compared with the existing methods,the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing threedimensional information.

The impact of climate change on fire risk in Daxing'anling,China

Daxing’anling is a key region for forest fire prevention in China. Assessing changes in fire risk in the future under multiple climatic scenarios will contribute to our understanding of the influences of climate change for the region and provide a reference for applying adaptive measures for fire management. This study analyzed the changes in fire weather indices and the fire season under four climate scenarios (RCP2.6, RCP4.5, RCP6.0, RCP8.5) for 2021-2050 using data from five global climate models together with observation data. The results showed that the analog data could project the average state of the climate for a given period but were not effective for simulating extreme weather conditions. Compared with the baseline period (1971-2000), the period 2021-2050 was predicted to have an increase in average temperature of 2.02-2.65 °C and in annual precipitation 25.4-40.3 mm, while the fire weather index (FWI) was predicted to increase by 6.2-11.2% and seasonal severity rating (SSR) by 5.5-17.2%. The DMC (Duff moisture code), ISI (initial spread index), BUI (build-up index), FWI and SSR were predicted to increase significantly under scenarios RCP4.5, RCP6.0, and RCP8.5. Furthermore, days with high or higher fire danger rating were predicted to be prolonged by 3-6 days, with the change in the southern region being greater under scenarios RCP4.5, RCP6.0, and RCP8.5.

Supercritical extracts of forest fuels in Great Xing＇an Mountains

Extracts are important components of fuels. Fatty-extracts with high heating value （HV） are hypothe- sized by researchers as positively related to the HV of fuels. The Soxhlet extractor is typically used to extract fatty-extracts but it has shortcomings, including long processing time （8-10 h） and the requirement for large amounts of organic solvent. Supercritical extraction is an alternate and useful technique for extraction of natural products. However, published studies rarely discuss the relationship between extracts and HV. In this study, we assessed the supercritical extracts （SUE） of forest fuels in the Great Xing＇an Mountains. Our results indicated that the optimum conditions for extraction of SuEs were 40-60 mesh, 40-50 MPa, 45℃, 80 min and a CO2 flow rate of 1.5-2.0 dm3/min. The Soxhlet extracts contents and the SuE contents were all related to HV. However, R2 of the coniferous samples （0.8499） and needle samples （0.9722） demonstrated that the correlation between HV and the SuE content was closer. We conclude that supercritical fatty-extracts provide a useful index of the HV of fuels, especially coniferous fuels. SuE data can be used in fire management, for example to estimate the rate of fire spread or fire intensity.

Performance evaluation of the simpli¯ed spherical harmonics approximation for cone-beam X-ray luminescence computed tomography imaging

As an emerging molecular imaging modality,cone-beam X-ray luminescence computed tomog-raphy(CB-XLCT)uses X-ray-excitable probes to produce near-infrared(NIR)luminescence and then reconst ructs three-dimensional(3D)distribution of the probes from surface measurements.A proper photon-transportation model is critical to accuracy of XLCT.Here,we presented a systematic comparison between the common-used Monte Carlo model and simplified spherical harmonics(SPN).The performance of the two methods was evaluated over several main spec-trums using a known XLCT material.We designed both a global measurement based on the cosine similarity and a locally-averaged relative error,to quantitatively assess these methods.The results show that the SP_(3) could reach a good balance between the modeling accuracy and computational efficiency for all of the tested emission spectrums.Besides,the SP_(1)(which is equivalent to the difusion equation(DE))can be a reasonable alternative model for emission wavelength over 692nm.In vivo experiment further demonstrates the reconstruction perfor-mance of the SP:and DE.This study would provide a valuable guidance for modeling the photon-transportation in CB-XLCT.

A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion

With widely availed clinically used radionuclides,Cer enkov luminescence imaging(CLI)has become a potential tool in the field of optical molecular imaging.However,the impulse noises introduced by high-energy gamma rays that are generated during the decay of radionuclide reduce the image quality significantly,which affects the acauracy of quantitative analysis,as well as the three dimensional reconstruction.In this work,a novel denoising framework based on fuzzy dlustering and curvat ure driven difusion(CDD)is proposed to remove this kind of impulse noises.To improve the accuracy,the F u1zzy Local Information C-Means algorithm,where spatial information is evolved,is used.We evaluate the per formance of the proposed framework sys-tematically with a series of experiments,and the corresponding results demonstrate a better denoising effect than those from the commonly used median filter method.We hope this work may provide a useful data pre processing tool for CLI and its following studies.

The Effect of Shot-Peening Treatment on Microstructure and Corrosion Behavior of Closed-Cell Aluminum Foam

Closed-cell aluminum foam was shot peened at different processing time (0 s, 5 s, 10 s, and 20 s), the intensity was the 0.12 mmA. The X-ray diffraction results showed that the reflections became weakened obviously with the shot peened time increased. Combined with Popa model and lognormal distribute model, the surface microstructure of closed-cell aluminum foam was inves-tigated by using the Rietveld whole pattern fitting analysis method. The results revealed that domain size and microstrain fluctuated along different reflection directions after shot peened, which attributed to the random and anisotropic deformation direction. With the shot peened processing time prolonged, a decrease in domain size and an increase in microstrain were also observed. Moreover, the corrosion behavior of closed-cell aluminum foam was studied by weight-loss test. The results indicated that corrosion properties of specimen subjected to shot peened processing was better than the unpeened specimens.

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive services.The task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)devices.This study investigates task offloading scheduling in a dynamic MEC system.By integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply model.We jointly optimize local computing,offloading duration,and edge computing decisions to minimize system cost.On the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME.DTOME can make task offloading decisions by weighing system cost and queue stability.We quote dynamic programming theory to obtain the optimal task offloading strategy.Simulation results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.