Bacteria present in natural environment especially those in cold regions would experience freeze-thaw (FT) process during day-night and season turns. However, knowledge about the influence of FT on bacteria release behaviors in porous media was limited. In present study, the bacteria release behaviors from quartz sand columns without and with 1 and 3 FT treatment cycles under three water saturations (θ=100%, 90%, and 60%) were investigated. We found that for all three water saturated columns without FT treatment, negligible bacteria released from columns via background salt solution elution, while the subsequent release of bacteria from sand columns via low ionic strength (IS) solution elution decreased with decreasing column water saturations. More importantly, we found unlike the negligible bacteria release in columns without FT treatment, for columns with high saturations (θ=100% and 90%), FT treatment could promote bacteria release with background salt solution elution. Moreover, for high saturated columns, FT treatment would decrease subsequent bacteria release with low IS solution elution. This phenomenon was more obvious with increasing FT treatment cycles. In contrast, FT treatment had negligible influence on bacteria release from columns with lower saturation (θ=60%). The decreased bacterial sizes, the loss of bacterial flagella, as well as the change of local configuration of porous media (via changing water into ice and ice back into water) during the FT processes contributed to increased bacteria release via background salt solution elution from high saturated sand columns. While, the reduced amount of bacteria being retained at secondary energy minima drove to the subsequently decreased bacteria release via low IS solution elution. The results of this study clearly showed that for porous media with high saturations, FT cycles would increase the risk of bacteria detaching from porous media with flushing by the background solution.
Strontium isotope composition is a powerful proxy for tracking the source and evolution of a range of geological processes in magmatic, sedimentary and metamorphic environments. Owing to the mobility of Sr with fluids, Sr isotope is also an ideal proxy to investigate, and has been extensively used to tackle the history of magmatic-hydrothermal systems and associated water-rock interactions. Apatite is an excellent choice for such a purpose as it is ubiquitous in almost all these environments, and contains abundant Sr but exceptionally limited Rb. Microanalytical techniques are preferred for apatite Sr isotope studies since most apatites are small in size and may have a complicated growth history to be decoded. Matrix-matched reference materials are a prerequisite for microanalysis, and although variable F- and OH-rich apatite Sr isotope reference materials have been developed, chlorine-rich ones are lacking. Here, we further evaluate Eppawala-AP, a Cl-rich apatite, as a potential Sr isotope reference material. Our extensive Sr isotope microanalysis at three laboratories confirmed that the Eppawala-AP was homogeneous in terms of Sr isotope composition, with a recommended Sr-87/Sr-86 ratio of 0.704989 +/- 0.000017 (2 SD, n = 9) determined by thermal ionization mass spectrometry from another two laboratories. The Eppawala-AP also had homogeneous trace element contents and Nd isotope composition. Therefore, Eppawala-AP can be used as a Sr isotope reference material, and also as a working reference to monitor the Nd isotope and trace element analysis, and thus could be expected to fill the much-needed gap for Cl-rich apatite reference materials and facilitate more precise and accurate analysis.
The effects of contemporary climate, habitat heterogeneity, and long-term climate change on species richness are well studied for woody plants in forest ecosystems, but poorly understood for herbaceous plants, especially in alpine-arctic ecosystems. Here, we aim to test if the previously proposed hypothesis based on the richness-environment relationship could explain the variation in richness patterns of the typical alpine-arctic herbaceous genus Saxifraga.Using a newly compiled distribution database of 437 Saxifraga species, we estimated the species richness patterns for all species, narrow- and wide-ranged species. We used generalized linear models and simultaneous autoregressive models to evaluate the effects of contemporary climate, habitat heterogeneity, and historical climate on species richness patterns. Partial regressions were used to determine the independent and shared effects of different variables. Four widely used models were tested to identify their predictive power in explaining patterns of species richness.We found that temperature was negatively correlated with the richness patterns of all and wide-ranged species, and that was the most important environmental factor, indicating a strong conservatism of its ancestral temperate niche. Habitat heterogeneity and long-term climate change were the best predictors of the spatial variation of narrow-ranged species richness. Overall, the combined model containing five predictors can explain ca. 40~50% of the variation in species richness. We further argued that additional evolutionary and biogeographical processes might have also played an essential role in shaping the Saxifraga diversity patterns and should be considered in future studies.
Accurate determination of CO2-hydrocarbon minimum miscibility pressure (MMP) is critically important for CO2 geological storage and utilization in oil and gas reservoirs. Here we propose a machine-learning framework, the conditional generative adversarial network, together with the Bayesian optimization algorithm, to calculate the CO2-hydrocarbon MMPs. A total of 180-set MMP data are collected from the public resources to facilitate and validate the proxy model. Also, 21 MMP-influential factors covering fluid compositions and operating conditions are specifically evaluated to analyse their effects on the MMP. In comparison with the existing artificial neural network as well as support vector regression models based on radial basis function kernel and polynomial function kernel, the newly-proposed model does not only outperform with the lowest calculation error (MAPE of 6.81% and MSE of 3.2006), also vividly reflect the interactive relationships of each influential factor and the MMP
Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then, hierarchical reconciliation can be used to improve the overall performance further. In this paper, we present a hierarchical-forecasting-with-alignment approach that treats the bottom-level forecasts as mutable to ensure higher forecasting accuracy on the upper levels of the hierarchy. We employ a pure deep learning forecasting approach, N-BEATS, for continuous time series at the top levels, and a widely used tree-based algorithm, LightGBM, for intermittent time series at the bottom level. The hierarchical-forecasting-with-alignment approach is a simple yet effective variant of the bottom-up method, accounting for biases that are difficult to observe at the bottom level. It allows suboptimal forecasts at the lower level to retain a higher overall performance. The approach in this empirical study was developed by the first author during the M5 Accuracy competition, ranking second place. The method is also business orientated and can be used to facilitate strategic business planning.
Very recently, the septuple-atomic-layer MoSi2N4 has been successfully synthesized by a chemical vapor deposition method. However, pristine MoSi2N4 exhibits some shortcomings, including poor visible-light harvesting capability and a low separation rate of photo-excited electron–hole pairs, when it is applied in water splitting to produce hydrogen. Fortunately, we find that MoSi2N4 can be considered as a good co-catalyst to be stacked with InSe forming an efficient heterostructure photocatalyst. Here, the electronic and photocatalytic properties of the two-dimensional (2D) InSe/MoSi2N4 heterostructure have been systematically investigated by density functional theory for the first time. The results demonstrate that 2D InSe/MoSi2N4 has a type-II band alignment with a favourable direct bandgap of 1.61 eV and exhibits suitable band edge positions for overall water splitting. Particularly, 2D InSe/MoSi2N4 has high electron mobility (104 cm2 V−1 s−1) and shows a noticeable optical absorption coefficient (105 cm−1) in the visible-light region of the solar spectrum. These brilliant properties declare that 2D InSe/MoSi2N4 is a potential photocatalyst for overall water splitting.
