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The Kuroshio and Gulf Stream, the subtropical western boundary currents of the North Pacific and North Atlantic, play important roles in meridional heat transport and ocean–atmosphere interaction processes. Using a multimodel ensemble of future projections, we show that a warmer climate intensifies the upper-layer Kuroshio, in contrast to the previously documented slowdown of the Gulf Stream. Our ocean general circulation model experiments show that the sea surface warming, not the wind change, is the dominant forcing that causes the upper-layer Kuroshio to intensify in a warming climate. Forced by the sea surface warming, ocean subduction and advection processes result in a stronger warming to the east of the Kuroshio than to the west, which increases the isopycnal slope across the Kuroshio, and hence intensifies the Kuroshio. In the North Atlantic, the Gulf Stream slows down as part of the Atlantic meridional overturning circulation (AMOC) response to surface salinity decrease in the high latitudes under global warming. The distinct responses of the Gulf Stream and Kuroshio to climate warming are accompanied by different regional patterns of sea level rise. While the sea level rise accelerates along the northeastern U.S. coast as the AMOC weakens, it remains close to the global mean rate along the East Asian coast as the intensifying Kuroshio is associated with the enhanced sea level rise offshore in the North Pacific subtropical gyre.

The annual cycle of sea surface temperature (SST) in the North Pacific Ocean is examined in terms of its response to global warming based on climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). As the global ocean warms up, the SST in the North Pacific generally tends to increase and the warming is greater in summer than in winter, leading to a significant intensification of SST annual cycle. The mixed layer temperature equation is used to examine the mechanism of this intensification. Results show that the decrease of mixed layer depth (MLD) in summer is the main reason behind the intensification of SST annual cycle. Because the MLD in summer is much shallower than that in winter, the incoming net heat flux is trapped in a thinner surface layer in summer, causing a warmer summer SST and the amplification of SST annual cycle. The change of the SST annual cycle in the North Pacific may have profound ecological impacts.

We analyze the projected sea level rise (SLR) for the 21st century for the China Seas (the Bohai Sea, Yellow Sea, East China Sea, and South China Sea) using the Coupled Model Intercomparison Project Phase 5 dataset. We find that the projected SLR over the shallow continental shelves of the China Seas is nearly the same as the global mean sea level change in all future emission scenarios, with a magnitude of 43.6 cm (20.8–67.7 cm, 90% confidence interval) in RCP2.6 and 74.5 cm (41.7–112.8 cm, 90% confidence interval) in RCP8.5 by the year 2100 relative to 1986–2005. We further analyze the causes of SLR and find that more than 90% of the total projected SLR over the continental shelves of the China Seas will result from mass contributions and only a minor contribution will result from local steric height adjustments. This increase in water mass over the continental shelves is not only caused by the loss of land ice, but also from the change in sterodynamic, which tends to push water mass onto the continental shelves from the open oceans.

Since the 1950s, observations and climate models show an amplification of sea surface temperature (SST) seasonal cycle in response to global warming over most of the global oceans except for the Southern Ocean (SO), however the cause remains poorly understood. In this study, we analyzed observations, ocean reanalysis, and a set of historical and abruptly quadrupled CO2 simulations from the Coupled Model Intercomparison Project Phase 6 archive and found that the weakened SST seasonal cycle over the SO could be mainly attributed to the intensification of summertime westerly winds. Under the historical warming, the intensification of summertime westerly winds over the SO effectively deepens ocean mixed layer and damps surface warming, but this effect is considerably weaker in winter, thus weakening the SST seasonal cycle. This wind‐driven mechanism is further supported by our targeted coupled model experiments with the wind intensification effects being removed. Plain Language Summary The Southern Ocean (SO) sea surface temperature (SST) has experienced a decreased seasonal cycle since the 1950s, in contrast with the overall amplified seasonal cycle in the rest of global oceans. Here we investigated observations and climate model simulations and found that the decrease of SST seasonal cycle was associated with less warming in summer than in winter during the historical period. The suppressed warming in summer is accompanied by a deepened ocean mixed layer due to the intensification of surface westerlies. An enhanced mixed layer depth means the incoming heat flux will be trapped in a deeper mixed layer in summer, causing a cooler summer SST, which then leads to a weaker SST annual cycle. When the wind intensification effect is removed in our model simulations, the decreased SST seasonal cycle in the SO disappears. Key Points Observations and climate models show a weakening sea surface temperature (SST) seasonal cycle induced by the delayed warming of summer SST over Southern Ocean (SO) The summer mixed layer depth and wind stress show increasing trends during the same period over the SO The summer wind‐induced mixed layer deepening suppresses SO surface warming and thus weakening the SST seasonal cycle

Spiking Neural Networks (SNNs) possess excellent computational energy efficiency and biological credibility. Among them, Spiking Convolutional Neural Networks (SCNNs) have significantly improved performance, demonstrating promising applications in low-power and brain-like computing. To achieve hardware acceleration for SCNNs, we propose an efficient FPGA accelerator architecture with multi-structure compatibility. This architecture supports both traditional convolutional and residual topologies, and can be adapted to diverse requirements from small networks to complex networks. This architecture uses a clock-driven scheme to perform convolution and neuron updates based on the spike-encoded image at each timestep. Through hierarchical pipelining and channel parallelization strategies, the computation speed of SCNNs is increased. To address the issue of current accelerators only supporting simple network, this architecture combines configuration and scheduling methods, including grouped reuse computation and line-by-line multi-timestep computation to accelerate deep networks with lots of channels and large feature map sizes. Based on the proposed accelerator architecture, we evaluated two scales of networks, named small-scale LeNet and deep residual SCNN, for object detection. Experiments show that the proposed accelerator achieves a maximum recognition speed of 1, 605 frames/s at a 100 MHz clock for the LeNet network, consuming only 0.65 mJ per image. Furthermore, the accelerator, combined with the proposed configuration and scheduling methods, achieves acceleration for each residual module in the deep residual SCNN, reaching a processing speed of 2.59 times that of the CPU with a power consumption of only 16.77% of the CPU. This demonstrates that the proposed accelerator architecture can achieve higher energy efficiency, compatibility, and wider applicability.

The subsurface ocean response to anthropogenic climate forcing remains poorly characterized. From the Coupled Model Intercomparison Project (CMIP), a robust response of the lower thermocline is identified, where the warming is considerably weaker in the subtropics than in the tropics and high latitudes. The lower thermocline change is inversely proportional to the thermocline depth in the present climatology. Ocean general circulation model (OGCM) experiments show that sea surface warming is the dominant forcing for the subtropical gyre change in contrast to natural variability for which wind dominates, and the ocean response is insensitive to the spatial pattern of surface warming. An analysis based on a ventilated thermocline model shows that the pattern of the lower thermocline change can be interpreted in terms of the dynamic response to the strengthened stratification and downward heat mixing. Consequently, the subtropical gyres become intensified at the surface but weakened in the lower thermcline, consistent with results from CMIP experiments.

This study utilized a three-dimensional ocean general circulation model to investigate the intensity, thickness, and width of the three-dimensional deep western boundary current (DWBC) in the South China Sea (SCS). The numerical results show that the DWBC begins near the inlet of the Luzon overflow, flows westward along the northern boundary, proceeds southward along the western boundary, and ultimately terminates at the southern boundary. The mean DWBC’s velocity, thickness, and width is 4.78 cm/s, 1645 m, and 140 km, respectively. Combined with the dynamic results, it is evident that the three-dimensional structure of the DWBC appears to have been visibly weakened after the closure of the deep Luzon overflow. Strong deep mixing has a significantly stronger, thicker, and wider effect on the intensity, thickness, and width of the DWBC. Both the bottom and lateral friction coefficients negatively impact the DWBC in the SCS.

Fermented vegetable-based foods, renowned for their unique flavors and human health benefits, contain probiotic organisms with reported in vitro antioxidative effects. This study investigates the probiotic properties of Latilactobacillus sakei MS103 (L. sakei MS103) and its antioxidant activities using an in vitro oxidative stress model based on the hydrogen peroxide (H2O2)-induced oxidative damage of RAW 264.7 cells. L. sakei MS103 exhibited tolerance to extreme conditions (bile salts, low pH, lysozyme, H2O2), antibiotic sensitivity, and auto-aggregation ability. Moreover, L. sakei MS103 co-aggregated with pathogenic Porphyromonas gingivalis cells, inhibited P. gingivalis-induced biofilm formation, and exhibited robust hydrophobic and electrostatic properties that enabled it to strongly bind to gingival epithelial cells and HT-29 cells for enhanced antioxidant effects. Additionally, L. sakei MS103 exhibited other antioxidant properties, including ion-chelating capability and the ability to effectively scavenge superoxide anion free radicals, hydroxyl, 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid, and 2,2-diphenyl-1-picrylhydrazyl. Furthermore, the addition of live or heat-killed L. sakei MS103 cells to H2O2-exposed RAW 264.7 cells alleviated oxidative stress, as reflected by reduced malondialdehyde levels, increased glutathione levels, and the up-regulated expression of four antioxidant-related genes (gshR2, gshR4, Gpx, and npx). These findings highlight L. sakei MS103 as a potential probiotic capable of inhibiting activities of P. gingivalis pathogenic bacteria and mitigating oxidative stress.

Based on the satellite observed sea surface temperature (SST), the recovery of SST cooling induced by the tropical cyclones (TCs) over the northwestern Pacific Ocean is investigated. The results show that the passage of a TC induces a mean maximum cooling in the SST of roughly −1.25 °C. It was also found that most of this cooling (~87%) is typically erased within 30 days of TC passage. This recovery time depends upon the degree of cooling, with stronger (weaker) SST cooling corresponding to longer (shorter) recovery time. Further analyses show that the mixed layer depth (MLD) and the upper layer thermocline temperature gradient (UTTG) also play an important role in the SST response to TCs. The maximum cooling increases ~0.1 °C for every 7 m decrease in the MLD or every 0.04 °C/m increase in the UTTG. The combined effects of MLD and TC intensity and translation speed on the SST response are also discussed.

In order to solve the problem of emergency decision-making with incomplete information and deal with the accident information in different time series at the scenes of major accidents, this paper proposes a method of sequential decision-making by utilizing the relevant knowledge of D-S evidence theory and game theory. Firstly, we took an oil tank fire accident as an example and sorted out historical cases and expert experiences to establish a logical relationship between key accident scenes and accident scene symptoms in the accident. Meanwhile, we applied the logistic regression analysis method to obtain the basic probability distribution of each key accident scene in the oil tank fire, and on this basis, we constructed an evidence set of the fire. Secondly, based on the D-S evidence theory, we effectively quantified the knowledge uncertainty and evidence uncertainty, with the incomplete and insufficient information taken as an evidence system of the development of key accident scenes to construct a situation prediction model of these accident scenes. Thirdly, based on the game theory, we viewed emergency decision-makers and major accidents as two sides of the game to compare and analyze accident states at different time points and solve the contradiction between loss costs of decision-making and information collection costs. Therefore, this paper has provided a solution for the optimization of accident schemes at different time stages, thus realizing the sequential decision-making at the scenes of major accidents. Furthermore, we combined the situation prediction model with sequential decision-making, with the basic steps described below: (1) We drew up an initial action plan in the case of an extreme lack of information; then, we (2) started to address the accident and constructed a framework of accident identification, (3) collected and dealt with the continuously added evidence information with the evolution of the accident, (4) calculated the confidence levels of key accident scenarios after evaluating different evidence and then predicted the accident state in the next stage, and (5) calculated the profit–loss ratio between the current decision-making scheme and the decision-making scheme of the next stage. Finally, we (6) repeated steps (3) to (5) until the accident completely vanished. We verified the feasibility of the proposed method with the explosion accident of the Zhangzhou P.X. project in Fujian on 6 April used as an example. Based on the D-S evidence theory, this method employs approximate reasoning on the incomplete and insufficient information obtained at the scenes of major accidents, thus realizing the situation prediction of key scenes of these accidents. Additionally, this method uses the game theory to solve the contradiction between decision-making loss costs and information collection costs, thus optimizing the decision-making schemes at different time stages of major accidents.