Ocean warming-acidification synergism undermines dissolved organic matter assembly.

Understanding the influence of synergisms on natural processes is a critical step toward determining the full-extent of anthropogenic stressors. As carbon emissions continue unabated, two major stressors--warming and acidification--threaten marine systems on several scales. Here, we report that a moderate temperature increase (from 30°C to 32°C) is sufficient to slow--even hinder--the ability of dissolved organic matter, a major carbon pool, to self-assemble to form marine microgels, which contribute to the particulate organic matter pool. Moreover, acidification lowers the temperature threshold at which we observe our results. These findings carry implications for the marine carbon cycle, as self-assembled marine microgels generate an estimated global seawater budget of ~1016 g C. We used laser scattering spectroscopy to test the influence of temperature and pH on spontaneous marine gel assembly. The results of independent experiments revealed that at a particular point, both pH and temperature block microgel formation (32°C, pH 8.2), and disperse existing gels (35°C). We then tested the hypothesis that temperature and pH have a synergistic influence on marine gel dispersion. We found that the dispersion temperature decreases concurrently with pH: from 32°C at pH 8.2, to 28°C at pH 7.5. If our laboratory observations can be extrapolated to complex marine environments, our results suggest that a warming-acidification synergism can decrease carbon and nutrient fluxes, disturbing marine trophic and trace element cycles, at rates faster than projected

Filament formation via collision-induced magnetic reconnection - formation of a star cluster

Funding: RJS gratefully acknowledges an STFC Ernest Rutherford fellowship (grant ST/N00485X/1) and HPC from the Durham DiRAC supercomputing facility (grants ST/P002293/1, ST/R002371/1, ST/S002502/1, and ST/R000832/1.A collision-induced magnetic reconnection (CMR) mechanism was recently proposed to explain the formation of a filament in the Orion A molecular cloud. In this mechanism, a collision between two clouds with antiparallel magnetic fields produces a dense filament due to the magnetic tension of the reconnected fields. The filament contains fiber-like sub-structures and is confined by a helical magnetic field. To show whether the dense filament is capable of forming stars, we use the AREPO code with sink particles to model star formation following the formation of the CMR-filament. First, the CMR-filament formation is confirmed with AREPO. Secondly, the filament is able to form a star cluster after it collapses along its main axis. Compared to the control model without magnetic fields, the CMR model shows two distinctive features. First, the CMR-cluster is confined to a factor of ∼4 smaller volume. The confinement is due to the combination of the helical field and gravity. Secondly, the CMR model has a factor of ∼2 lower star formation rate. The slower star formation is again due to the surface helical field that hinders gas inflow from larger scales. Mass is only supplied to the accreting cluster through streamers.Peer reviewe

Suppressed star formation in circumnuclear regions in Seyfert galaxies

Feedback from black hole activity is widely believed to play a key role in regulating star formation and black hole growth. A long-standing issue is the relation between the star formation and fueling the supermassive black holes in active galactic nuclei (AGNs). We compile a sample of 57 Seyfert galaxies to tackle this issue. We estimate the surface densities of gas and star formation rates in circumnuclear regions (CNRs). Comparing with the well-known Kennicutt-Schmidt (K-S) law, we find that the star formation rates in CNRs of most Seyfert galaxies are suppressed in this sample. Feedback is suggested to explain the suppressed star formation rates.Comment: 1 color figure and 1 table. ApJ Letters in pres

KIT Bus: A Shuttle Model for CARLA Simulator

With the continuous development of science and technology, self-driving vehicles will surely change the nature of transportation and realize the automotive industry\u27s transformation in the future. Compared with self-driving cars, self-driving buses are more efficient in carrying passengers and more environmentally friendly in terms of energy consumption. Therefore, it is speculated that in the future, self-driving buses will become more and more important. As a simulator for autonomous driving research, the CARLA simulator can help people accumulate experience in autonomous driving technology faster and safer. However, a shortcoming is that there is no modern bus model in the CARLA simulator. Consequently, people cannot simulate autonomous driving on buses or the scenarios interacting with buses. Therefore, we built a bus model in 3ds Max software and imported it into the CARLA to fill this gap. Our model, namely KIT bus, is proven to work in the CARLA by testing it with the autopilot simulation. The video demo is shown on our Youtube

A novel attention-based gated recurrent unit and its efficacy in speech emotion recognition

Abstract Notwithstanding the significant advancements in the field of deep learning, the basic long short-term memory (LSTM) or Gated Recurrent Unit (GRU) units have largely remained unchanged and unexplored. There are several possibilities in advancing the state-of-art by rightly adapting and enhancing the various elements of these units. Activation functions are one such key element. In this work, we explore using diverse activation functions within GRU and bi-directional GRU (BiGRU) cells in the context of speech emotion recognition (SER). We also propose a novel Attention ReLU GRU (AR-GRU) that employs attention-based Rectified Linear Unit (AReLU) activation within GRU and BiGRU cells. We demonstrate the effectiveness of AR-GRU on one exemplary application using the recently proposed network for SER namely Interaction-Aware Attention Network (IAAN). Our proposed method utilising AR-GRU within this network yields significant performance gain and achieves an unweighted accuracy of 68.3% (2% over the baseline) and weighted accuracy of 66.9 % (2.2 % absolute over the baseline) in four class emotion recognition on the IEMOCAP database

Human stem cell neuronal differentiation on silk-carbon nanotube composite

Human embryonic stem cells [hESCs] are able to differentiate into specific lineages corresponding to regulated spatial and temporal signals. This unique attribute holds great promise for regenerative medicine and cell-based therapy for many human diseases such as spinal cord injury [SCI] and multiple sclerosis [MS]. Carbon nanotubes [CNTs] have been successfully used to promote neuronal differentiation, and silk has been widely applied in tissue engineering. This study aims to build silk-CNT composite scaffolds for improved neuron differentiation efficiency from hESCs