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This paper presents repairs to rural bridges in North Carolina that deteriorated as a result variously of aging, overweight traffic, and exposure to salts and sulfates. The prestressed concrete C-channel superstructures exhibited prestressing strand loss and displayed significant concrete spalling, with one structure having to be closed to traffic after a routine inspection. Analysis conducted using the American Association of State Highway and Transportations Officials (AASHTO) bridge load rating criteria concluded that repair techniques which strengthen deteriorated flexural elements without also restoring lost prestressing forces are insufficient to maintain load ratings in C-channel structures with heavily damaged prestressing tendons. A prestressed mechanically-fastened fiber-reinforced polymer (MF-FRP) retrofit solution was developed and successfully installed on three structures by the authors and North Carolina Department of Transportation maintenance crews. The most extensive of these three repairs is presented here in detail. The field applications and associated analysis show the temporary MF-FRP repair system is capable of restoring lost prestressing forces, allowing original inventory and operating ratings to remain in place until a permanent superstructure replacement can be scheduled. The most heavily repaired bridge remains in service after 23 months, its performance demonstrated by long-term monitoring data. As currently implemented, the MF-FRP repair is a viable temporary solution for maintaining traffic on a degraded structure while a replacement structure is designed, programmed, and implemented. Efforts to expand the MF-FRP repair into a longer-term solution are underway.

 

The roles of unforgiving H 2 SO 4 solvent in CH 4 activation with molecular catalysts have not been experimentally well-illustrated despite computational predictions. Here, we provide experimental evidence that metal-bound bisulfate ligand introduced by H 2 SO 4 solvent is redox-active in vanadium-based electrocatalytic CH 4 activation discovered recently. Replacing one of the two terminal bisulfate ligands with redox-inert dihydrogen phosphate in the pre-catalyst vanadium (V)-oxo dimer completely quenches its activity towards CH 4 , which may inspire environmentally benign catalysis with minimal use of H 2 SO 4 . 

The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We present a transfer-learning approach to estimate cluster masses using the ugriz-band images in the SDSS Data Release 12. The target masses are derived from X-ray or SZ measurements that are only available for a small subset of the clusters. We designed a semisupervised deep learning model consisting of two convolutional neural networks. In the first network, a feature extractor is trained to classify the SDSS photometric bands. The second network takes the previously trained features as inputs to estimate their total masses. The training and testing processes in this work depend purely on real observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232 dex on average and 0.214 dex for the best fold. The performance is comparable to that given by redMaPPer, 0.192 dex. We have further applied a joint integrated gradient and class activation mapping method to interpret such a two-step neural network. The performance of our algorithm is likely to improve as the size of training data set increases. This proof-of-concept experiment demonstrates the potential of deep learning in maximizing the scientific return of the current and future large cluster surveys.

 

Abstract Understanding how material accretes onto the rotationally supported disk from the surrounding envelope of gas and dust in the youngest protostellar systems is important for describing how disks are formed. Magnetohydrodynamic simulations of magnetized, turbulent disk formation usually show spiral-like streams of material (accretion flows) connecting the envelope to the disk. However, accretion flows in these early stages of protostellar formation still remain poorly characterized, due to their low intensity, and possibly some extended structures are disregarded as being part of the outflow cavity. We use ALMA archival data of a young Class 0 protostar, Lupus 3-MMS, to uncover four extended accretion flow–like structures in C 18 O that follow the edges of the outflows. We make various types of position–velocity cuts to compare with the outflows and find the extended structures are not consistent with the outflow emission, but rather more consistent with a simple infall model. We then use a dendrogram algorithm to isolate five substructures in position–position–velocity space. Four out of the five substructures fit well (>95%) with our simple infall model, with specific angular momenta between 2.7–6.9 × 10 −4 km s −1 pc and mass-infall rates of 0.5–1.1 × 10 −6 M ⊙ yr −1 . Better characterization of the physical structure in the supposed “outflow cavities” is important to disentangle the true outflow cavities and accretion flows. 

The role of the cannabinoid receptor 2 (CNR2) is still poorly described in sensory epithelia. We found strong cnr2 expression in hair cells (HCs) of the inner ear and the lateral line (LL), a superficial sensory structure in fish. Next, we demonstrated that sensory synapses in HCs were severely perturbed in larvae lacking cnr2. Appearance and distribution of presynaptic ribbons and calcium channels (Ca v 1.3) were profoundly altered in mutant animals. Clustering of membrane-associated guanylate kinase (MAGUK) in post-synaptic densities (PSDs) was also heavily affected, suggesting a role for cnr2 for maintaining the sensory synapse. Furthermore, vesicular trafficking in HCs was strongly perturbed suggesting a retrograde action of the endocannabinoid system (ECs) via cnr2 that was modulating HC mechanotransduction. We found similar perturbations in retinal ribbon synapses. Finally, we showed that larval swimming behaviors after sound and light stimulations were significantly different in mutant animals. Thus, we propose that cnr2 is critical for the processing of sensory information in the developing larva. 

Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become promising and important on mobile devices such as smart phones. However, previous RNN compression techniques either suffer from hardware performance overhead due to irregularity or significant accuracy loss due to the preserved regularity for hardware friendliness. In this work, we propose RTMobile that leverages both a novel block-based pruning approach and compiler optimizations to accelerate RNN inference on mobile devices. Our proposed RTMobile is the first work that can achieve real-time RNN inference on mobile platforms. Experimental results demonstrate that RTMobile can significantly outperform existing RNN hardware acceleration methods in terms of both inference accuracy and time. Compared with prior work on FPGA, RTMobile using Adreno 640 embedded GPU on GRU can improve the energy efficiency by 40x while maintaining the same inference time.