ASIRI : an ocean–atmosphere initiative for Bay of Bengal

Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 97 (2016): 1859–1884, doi:10.1175/BAMS-D-14-00197.1.Air–Sea Interactions in the Northern Indian Ocean (ASIRI) is an international research effort (2013–17) aimed at understanding and quantifying coupled atmosphere–ocean dynamics of the Bay of Bengal (BoB) with relevance to Indian Ocean monsoons. Working collaboratively, more than 20 research institutions are acquiring field observations coupled with operational and high-resolution models to address scientific issues that have stymied the monsoon predictability. ASIRI combines new and mature observational technologies to resolve submesoscale to regional-scale currents and hydrophysical fields. These data reveal BoB’s sharp frontal features, submesoscale variability, low-salinity lenses and filaments, and shallow mixed layers, with relatively weak turbulent mixing. Observed physical features include energetic high-frequency internal waves in the southern BoB, energetic mesoscale and submesoscale features including an intrathermocline eddy in the central BoB, and a high-resolution view of the exchange along the periphery of Sri Lanka, which includes the 100-km-wide East India Coastal Current (EICC) carrying low-salinity water out of the BoB and an adjacent, broad northward flow (∼300 km wide) that carries high-salinity water into BoB during the northeast monsoon. Atmospheric boundary layer (ABL) observations during the decaying phase of the Madden–Julian oscillation (MJO) permit the study of multiscale atmospheric processes associated with non-MJO phenomena and their impacts on the marine boundary layer. Underway analyses that integrate observations and numerical simulations shed light on how air–sea interactions control the ABL and upper-ocean processes.This work was sponsored by the U.S. Office of Naval Research (ONR) in an ONR Departmental Research Initiative (DRI), Air–Sea Interactions in Northern Indian Ocean (ASIRI), and in a Naval Research Laboratory project, Effects of Bay of Bengal Freshwater Flux on Indian Ocean Monsoon (EBOB). ASIRI–RAWI was funded under the NASCar DRI of the ONR. The Indian component of the program, Ocean Mixing and Monsoons (OMM), was supported by the Ministry of Earth Sciences of India.2017-04-2

Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction

The application of deep learning (DL) for solving construction safety issues has achieved remarkable results in recent years that are superior to traditional methods. However, there is limited literature examining the links between DL and safety management and highlighting the contributions of DL studies in practice. Thus, this study aims to synthesize the current status of DL studies on construction safety and outline practical challenges and future opportunities. A total of 66 influential construction safety articles were analyzed from a technical aspect, such as convolutional neural networks, recurrent neural networks, and general neural networks. In the context of safety management, three main research directions were identified: utilizing DL for behaviors, physical conditions, and management issues. Overall, applying DL can resolve important safety challenges with high reliability; therein the CNN-based method and behaviors were the most applied directions with percentages of 75% and 67%, respectively. Based on the review findings, three future opportunities aiming to address the corresponding limitations were proposed: expanding a comprehensive dataset, improving technical restrictions due to occlusions, and identifying individuals who performed unsafe behaviors. This review thus may allow the identification of key areas and future directions where further research efforts need to be made with priority

Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction

The application of deep learning (DL) for solving construction safety issues has achieved remarkable results in recent years that are superior to traditional methods. However, there is limited literature examining the links between DL and safety management and highlighting the contributions of DL studies in practice. Thus, this study aims to synthesize the current status of DL studies on construction safety and outline practical challenges and future opportunities. A total of 66 influential construction safety articles were analyzed from a technical aspect, such as convolutional neural networks, recurrent neural networks, and general neural networks. In the context of safety management, three main research directions were identified: utilizing DL for behaviors, physical conditions, and management issues. Overall, applying DL can resolve important safety challenges with high reliability; therein the CNN-based method and behaviors were the most applied directions with percentages of 75% and 67%, respectively. Based on the review findings, three future opportunities aiming to address the corresponding limitations were proposed: expanding a comprehensive dataset, improving technical restrictions due to occlusions, and identifying individuals who performed unsafe behaviors. This review thus may allow the identification of key areas and future directions where further research efforts need to be made with priority

Quantum Mechanical Screening of Single-Atom Bimetallic Alloys for the Selective Reduction of CO₂ to C₁ Hydrocarbons

Electrocatalytic reduction of CO₂ to energy-rich hydrocarbons such as alkanes, alkenes, and alcohols is a very challenging task. So far, only copper has proven to be capable of such a conversion. We report density functional theory (DFT) calculations combined with the Poisson–Boltzmann implicit solvation model to show that single-atom alloys (SAAs) are promising electrocatalysts for CO₂ reduction to C₁ hydrocarbons in aqueous solution. The majority component of the SAAs studied is either gold or silver, in combination with isolated single atoms, M (M = Cu, Ni, Pd, Pt, Co, Rh, and Ir), replacing surface atoms. We envision that the SAA behaves as a one-pot tandem catalyst: first gold (or silver) reduces CO₂ to CO, and the newly formed CO is then captured by M and is further reduced to C₁ hydrocarbons such as methane or methanol. We studied 28 SAAs, and found about half of them selectively favor the CO₂ reduction reaction over the competing hydrogen evolution reaction. Most of those promising SAAs contain M = Co, Rh, or Ir. The reaction mechanism of two SAAs, Rh@Au(100) and Rh@Ag(100), is explored in detail. Both of them reduce CO₂ to methane but via different pathways. For Rh@Au(100), reduction occurs through the pathway *CO → *CHO → *CHOH → *CH + H₂O_(<i>l</i>) → *CH₂ + H₂O_(<i>l</i>) → *CH₃ + H₂O_(<i>l</i>) → * + H₂O_(<i>l</i>) + CH_4(<i>g</i>); whereas, for Rh@Ag(100), the pathway is *CO → *CHO → *CH₂O→ *OCH₃ → *O + CH_4(<i>g</i>) → *OH + CH_4(<i>g</i>) → * + H₂O_(<i>l</i>) + CH_4(<i>g</i>). The minimum applied voltages to drive the two electrocatalytic systems are −1.01 and −1.12 V_RHE for Rh@Au(100) and Rh@Ag(100), respectively, at which the Faradaic efficiencies for CO₂ reduction to CO are 60% for gold and 90% for silver. This suggests that SAA can efficiently reduce CO₂ to methane with as small as 40% loss to the hydrogen evolution reaction for Rh@Au(100) and as small as 10% for Rh@Ag(100). We hope these computational results can stimulate experimental efforts to explore the use of SAA to catalyze CO₂ electrochemical reduction to hydrocarbons