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While intensively studied, it remains unclear how the star formation (SF) in infrared dark clouds (IRDCs) compares to that of nearby clouds. We study G351.77-0.53 (henceforth G351), a cluster-forming filamentary IRDC. We begin by characterizing its young stellar object (YSO) content. Based on the average parallax of likely members, we obtain a Gaia distance of $\sim \, 2.0\pm 0.14$ kpc, resolving the literature distance ambiguity. Using our Herschel-derived N(H2) map, we measure a total gas mass of 10 200 M⊙ (within 11 pc2) and the average line-mass profile of the entire filament, which we model as $\lambda =~1660 (w/\rm pc)^{0.62}\, \, {\rm M}_{\odot }\, \rm {pc}^{-1}$. At w < 0.63 pc, our λ profile is higher and has a steeper power-law index than λ profiles extracted in Orion A and most of its substructures. Based on the YSOs inside the filament area, we estimate the SF efficiency (SFE) and SF rate (SFR). We calculate a factor of 5 incompleteness correction for our YSO catalogue relative to Spitzer surveys of Orion A. The G351 SFE is ∼1.8 times lower than that of Orion A and lower than the median value for local clouds. We measure SFR and gas masses to estimate the efficiency per free-fall time, ϵff. We find that ϵff is ∼1.1 dex below the previously proposed mean local relation, and $\sim \, 4.7\times$ below Orion A. These observations indicate that local SF-relations do not capture variations present in the Galaxy. We speculate that cloud youth and/or magnetic fields might account for the G351 inefficiency.

 

Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstruction method based on learned descent algorithm, which reconstructs high-quality PET images from sinograms without the need for image labels. Specifically, we unroll the proximal gradient method with a learnable norm for PET image reconstruction problem. The training is unsupervised, using measurement domain loss based on deep image prior as well as image domain loss based on rotation equivariance property. The experimental results demonstrate the superior performance of proposed method compared with maximum-likelihood expectation-maximization (MLEM), total-variation regularized EM (EM-TV) and deep image prior based method (DIP). 

Objective. Dynamic positron emission tomography (PET) imaging, which can provide information on dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited counts received in individual frame, especially in ultra short frames. Recently, the unrolled modelbased deep learning methods have shown inspiring results for low-count PET image reconstruction with good interpretability. Nevertheless, the existing model-based deep learning methods mainly focus on the spatial correlations while ignore the temporal domain. Approach. In this paper, inspired by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network (STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability. Main results. The experiments of both simulation data and real rat scan data have shown that the proposed method can achieve substantial noise reduction in both temporal and spatial domains and outperform the maximum likelihood expectation maximization, spatio-temporal kernel method, LPD and FBPnet. Significance. Experimental results show STPDnet better reconstruction performance in the low count situation, which makes the proposed method particularly suitable in whole-body dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer from high level of noise. 

We show that atmospheric gravity waves can generate plasma ducts and irregularities in the plasmasphere using the coupled SAMI3/WACCM‐X model. We find the equatorial electron density is irregular as a function of longitude which is consistent with CRRES measurements (Clilverd et al., 2007,https://doi.org/10.1029/2007ja012416). We also find that plasma ducts can be generated forL‐shells in the range 1.5–3.0 with lifetimes of ∼ 0.5 hr; this is in line with observations of ducted VLF wave propagation with lifetimes of 0.5–2.0 hr (Clilverd et al., 2008,https://doi.org/10.1029/2007ja012602; Singh et al., 1998,https://doi.org/10.1016/s1364-6826(98)00001-7).

 

Co-creation in academe can take multiple forms. In this research, the co-creation focus is on collaboration between faculty and graduate students to develop educational modules. This activity is designed to improve graduate education and prepare students for conducting graduate research. In previous work presented at ASEE 2022, we discussed benefits and challenges of participating in the co-creation process. This current paper focuses on how we took lessons from our first year and transformed them into a structure to better support interdisciplinary research, collaboration, and community building. We will discuss how we supported the process of co-creation by developing a series of workshops to scaffold student learning. Scaffolds are instructional methods and interventions that are designed to foster skill development by allowing for interactions between what students already know and what they have yet to learn. These workshops were designed using the tenets of the gold standard project-based learning (PjBL). The PjBL framework is itself a scaffold that is designed to build research competencies. Specifically, to introduce a challenging problem or question, we created multiple technical overviews of the cyber-physical system theme of interest that would constitute the eventual educational modules. We scaffolded sustained inquiry by developing a workshop using techniques from the Right Question Institute, and also through a workshop about crafting your message for different audiences. To support the PjBL idea of authenticity, we developed a workshop about core values to help students connect personally to their project topics. To further support collaboration and community building, we developed a workshop to introduce ideas of interdisciplinary collaboration, including developing community agreements and recognizing and responding to microaggressions. Periodic reinforcements of these topics were incorporated as students progressed in their co-creation project. We assessed how students applied these topics through student reflections. Scaffolding students’ learning helped to address co-creation challenges that were expressed by our pilot group, including not understanding the goals of the project and not feeling connected to the research. Observational data of the current groups suggests that students have better understanding of the co-creation process and are collaborating more effectively than our pilot group students, and focus group data confirmed these observations. We also collected feedback from students about the workshops to evaluate what is effective about them and what can be improved. Students felt skills taught in the workshops such as how to prioritize research questions, construct messages for specific audiences, and perform literature searches and reviews, were all effective and useful as they worked on their projects. For improvement, they suggested clearer objectives and more workshops that focus on technical aspects of the project work would be helpful.