The 2008–2012 global financial crisis began with the global recession in December 2007 and exacerbated in September 2008, during which the U.S. stock markets lost 20% of value from its October 11 2007 peak. Various studies reported that financial crisis are associated with increase in both cross-correlations among stocks and stock indices and the level of systemic risk. In this paper, we study 10 different Dow Jones economic sector indexes, and applying principle component analysis (PCA) we demonstrate that the rate of increase in principle components with short 12-month time windows can be effectively used as an indicator of systemic risk—the larger the change of PC1, the higher the increase of systemic risk. Clearly, the higher the level of systemic risk, the more likely a financial crisis would occur in the near future.

Nitrous oxide (N2O) emissions from soil contribute to global warming and are in turn substantially affected by climate change. However, climate change impacts on N2O production across terrestrial ecosystems remain poorly understood. Here, we synthesized 46 published studies of N2O fluxes and relevant soil functional genes (SFGs, that is, archaeal amoA, bacterial amoA, nosZ, narG, nirK and nirS) to assess their responses to increased temperature, increased or decreased precipitation amounts, and prolonged drought (no change in total precipitation but increase in precipitation intervals) in terrestrial ecosystem (i.e. grasslands, forests, shrublands, tundra and croplands). Across the data set, temperature increased N2O emissions by 33%. However, the effects were highly variable across biomes, with strongest temperature responses in shrublands, variable responses in forests and negative responses in tundra. The warming methods employed also influenced the effects of temperature on N2O emissions (most effectively induced by open‐top chambers). Whole‐day or whole‐year warming treatment significantly enhanced N2O emissions, but daytime, nighttime or short‐season warming did not have significant effects. Regardless of biome, treatment method and season, increased precipitation promoted N2O emission by an average of 55%, while decreased precipitation suppressed N2O emission by 31%, predominantly driven by changes in soil moisture. The effect size of precipitation changes on nirS and nosZ showed a U‐shape relationship with soil moisture; further insight into biotic mechanisms underlying N2O emission response to climate change remain limited by data availability, underlying a need for studies that report SFG. Our findings indicate that climate change substantially affects N2O emission and highlights the urgent need to incorporate this strong feedback into most climate models for convincing projection of future climate change.

The artificial potential field approach is an efficient path planning method. However, to deal with the local-stable-point problem in complex environments, it needs to modify the potential field and increases the complexity of the algorithm. This study combines improved black-hole potential field and reinforcement learning to solve the problems which are scenarios of local-stable-points. The blackhole potential field is used as the environment in a reinforcement learning algorithm. Agents automatically adapt to the environment and learn how to utilize basic environmental information to find targets. Moreover, trained agents adopt variable environments with the curriculum learning method. Meanwhile, the visualization of the avoidance process demonstrates how agents avoid obstacles and reach the target. Our method is evaluated under static and dynamic experiments. The results show that agents automatically learn how to jump out of local stability points without prior knowledge.

Lateral input of dissolved organics may play a significant role to support productivity in oligotrophic ocean although associated biogeochemical evidences are lacking in the field. Ammonia oxidation (AO), the first step of nitrification that bridges organic remineralization and nitrate, is potentially an immediate responder. By using 15N‐NH4+, the spatial distribution of AO was investigated in the northern South China Sea, where Kuroshio Current intrudes frequently. AO ranged widely (0.001 to 134 nmol · L−1 · day−1) in space and the depth‐integrated (200 m) AO peaked where the Kuroshio influence is moderate suggesting that enhanced AO had occurred due to lateral mixing. Since oligotrophic Kuroshio is characterized by high dissolved organic nitrogen (DON), such lateral mixing not only introduces external DON into the northern South China Sea but also enhances NH4+ regeneration and subsequent oxidation to complicate the conventional new production in the boundary zone with DON gradient.

Temperature is one of the fundamental environmental variables governing microbially mediated denitrification and anaerobic ammonium oxidation (anammox) in sediments. The GHG nitrous oxide (N 2 O) is produced during denitrification, but not by anammox, and knowledge of how these pathways respond to global warming remains limited. Here, we show that warming directly stimulates denitrification-derived N 2 O production and that the warming response for N 2 O production is slightly higher than the response for denitrification in subtropical sediments. Moreover, denitrification had a higher optimal temperature than anammox. Integrating our data into a global compilation indicates that denitrifiers are more thermotolerant, whereas anammox bacteria are relatively psychrotolerant. Crucially, recent summer temperatures in low-latitude sediments have exceeded the optimal temperature of anammox, implying that further warming may suppress anammox and direct more of the nitrogen flow towards denitrification and associated N 2 O production, leading to a positive climate feedback at low latitudes.

A rod-shaped, Gram-stain-positive, obligately anaerobic, xylan-degrading bacterium, SK-Y3, was isolated from oily-sludge of Shengli oilfield, China. Optimum growth occurred at 50 °C, at pH 7.5 and without addition of NaCl. The predominant cellular fatty acids of strain SK-Y3 were iso-C15 : 0, anteiso-C15 : 0 and iso-C17 : 0, and the main polar lipids were glycolipids (GL), lipids (L), phosphatidylglycerol (PG) and diphosphatidylglycerol (DPG); no respiratory quinones were detected. The genomic DNA G+C content was 37.2 mol%. Phylogenetic analysis of 16S rRNA gene sequences showed that strain SK-Y3 belongs to clostridial cluster III, exhibiting 91-92% sequence similarity to the most closely related species, namely Clostridium clariflavum, Clostridium straminisolvens and Acetivibrio cellulolyticus. Based on distinct physiological and phylogenetic differences from the aforementioned described taxa, strain SK-Y3 (=DSM 103557=ACCC 19952) is proposed as the type strain of a novel species of a new genus, Petroclostridium xylanilyticum gen. nov., sp. nov. Furthermore, analysis through 16S rRNA gene, ribosomal protein and whole genome sequences indicated that clostridial cluster III members should be reclassified into four novel genera for which the names Hungateiclostridium gen. nov., Thermoclostridium gen. nov., Ruminiclostridium gen. nov. and Pseudoclostridium gen. nov. are proposed. In combination with the genera Anaerobacterium, Cellulosibacter, Ercella, Fastidiosipila, Mageeibacillus, Pseudobacteroides, Petroclostridium and Saccharofermentans, clostridial cluster III members formed a monophyletic clade within the order Clostridiales but that was clearly distinguished from other Ruminococcaceae members, which is proposed as a novel family, Hungateiclostridiaceae fam. nov.

The machine learning regression model is based on the assumption of normal distribution. In this paper, we mainly study the probability distribution of the machine learning model and the effect of the convergence values of different loss functions on the probability distribution model. Based on the idea of robust regression and the assumption of homogeneous variance of the model, we solved the statistical solution of two-dimensional regression problem by using least square method. The maximum likelihood estimation parameters of the probabilistic model are obtained by using the maximum likelihood estimation method. In order to compare the solving parameters of the two methods, the convergence values of L1 loss function and L2 loss function are used for the regression verification. Through the mathematical and statistical rigorous derivation, obtained two important conclusions; First, under the condition that the data satisfies normal distribution and is based on the assumption of homogeneous variance, the probability model conforms to the multivariate gaussian distribution. Secondly, the model satisfying the multi-gaussian distribution has little influence on the parameter estimation under the condition of the large number theorem, that is, the multi-gaussian distribution model has good tolerance to the loss function.

Developing materials that possess colorimetric responses to external stimuli is a promising strategy for addressing the current challenges in radiation dosimetry. Currently, colorimetric ionizing-radiation-responsive materials remain underexplored, and those with multistimuli response are rare. Herein, the integration of thorium cation and photoresponsive terpyridine carboxylate ligand gives rise to a thorium nanocluster, Th-101, which displays the second case of fluorochromic response and unprecedented piezochromic behavior among all actinide materials. The emission color of Th-101 exhibits a gradual transition from blue to cyan to green upon irradiation with accumulated dose, which renders colorimetric dosimetry of ionizing radiation based on a red−green−blue (RGB) concept. Further fabricating Th-101 into a custom-built optoelectronic device allows for on-site quantification of radiation dose with merits of ease of operation, rapid readout, and cost-effectiveness.