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.
Global warming-induced melting and thawing of the cryosphere are severely altering the volume and timing of water supplied from High Mountain Asia, adversely affecting downstream food and energy systems that are relied on by billions of people. The construction of more reservoirs designed to regulate streamflow and produce hydropower is a critical part of strategies for adapting to these changes. However, these projects are vulnerable to a complex set of interacting processes that are destabilizing landscapes throughout the region. Ranging in severity and the pace of change, these processes include glacial retreat and detachments, permafrost thaw and associated landslides, rock–ice avalanches, debris flows and outburst floods from glacial lakes and landslide-dammed lakes. The result is large amounts of sediment being mobilized that can fill up reservoirs, cause dam failure and degrade power turbines. Here we recommend forward-looking design and maintenance measures and sustainable sediment management solutions that can help transition towards climate change-resilient dams and reservoirs in High Mountain Asia, in large part based on improved monitoring and prediction of compound and cascading hazards.
FeSx@MoS2-x (FM-x, x implied real Mo/Fe content ratios) in which FeSx derived from MIL-88A deposited on the surface of MoS2 with a tight heterogeneous interface were synthesized for peroxymonosulfate (PMS) activation to degrade atrazine (ATZ). The catalytic performance of FM-0.96 was greatly improved due to the rapid regeneration of Fe2+ resulting from the interfacial interaction. FM-0.96 could completely degrade 10.0 mg/L ATZ within 1.0 min, and the toxicities for most of its intermediates were greatly reduced. The k value of FM-0.96 was 320 and 40 times higher than that of the MoS2 and FeSx, respectively. The SO4·−, ·OH and 1O2 were mainly responsible for ATZ degradation in FM-0.96/PMS system, and the conversion pathway of 1O2 was analyzed. Furthermore, the long-term continuous operation for ATZ degradation was achieved using a fixed membrane reactor. This work provides deep insights into metal sulfide composites derived from metal-organic frameworks for removing pollutants by activating PMS.
The compression of three-dimensional sound field signals has always been a very important issue. Recently, an Independent Component Analysis (ICA) based Higher Order Ambisonics (HOA) compression method introduces blind source separation to solve the shortcomings of discontinuity between frames in the existing Singular Value Decomposition (SVD) based methods. However, ICA is weak to model the reverberant environment, and its target is not to recover original signal. In this work, we replace ICA with autoencoder to further improve the above method’s ability to cope with reverberation conditions and ensure the unanimous optimization both in separation and recovery by reconstruction loss. We constructed a dataset with simulated and recorded signals, and verified the effectiveness of our method through objective and subjective experiments.
An accurate description of 2-D quantum transport in a double-gate metal oxide semiconductor field effect transistor (dgMOSFET) requires a high-resolution solver for a coupled system of the 4-D Wigner equation and 2-D Poisson equation. In this paper, we propose an operator-splitting spectral method to evolve such Wigner–Poisson (WP) system in 4-D phase space with high accuracy. After the operator splitting of the Wigner equation, the resulting two sub-equations can be solved analytically with spectral approximation in phase space. Meanwhile, we adopt a Chebyshev spectral method to solve the Poisson equation. Spectral convergence in phase space and a fourth-order accuracy in time are both numerically verified. Finally, we apply the proposed solver to the simulation of a dgMOSFET, develop the steady states via long-time simulations and obtain numerically converged current–voltage (I–V) curves.
Transient electronics is an emerging class of electronic devices that can physically degrade or disintegrate after a stable period of service, showing a vast prospect in applications of “green” consumer electronics, hardware-secure devices, medical implants, etc. Complementary metal-oxide–semiconductor (CMOS) technology is dominant in integrated circuit design for its advantages of low static power consumption, high noise immunity, and simple design layout, which also work and are highly preferred for transient electronics. However, the performance of complementary transient electronics is severely restricted by the confined selection of transient materials and compatible fabrication strategies. Here, we report the realization of high-performance transient complementary electronics based on carbon nanotube thin films via a reliable electrostatic doping method. Under a low operating voltage of 2 V, on a 1.5 μm-thick water-soluble substrate made of poly(vinyl alcohol), the width-normalized on-state currents of the p-type and n-type transient thin-film transistors (TFTs) reach 4.5 and 4.7 μA/μm, and the width-normalized transconductances reach 2.8 and 3.7 μS/μm, respectively. Meanwhile, these TFTs show small subthreshold swings no more than 108 mV/dec and current on/off ratios above 106 with good uniformity. Transient CMOS inverters, as basic circuit components, are demonstrated with a voltage gain of 24 and a high noise immunity of 67.4%. Finally, both the degradation of the active components and the disintegration of the functional system are continuously monitored with nontraceable remains after 10 and 5 h, respectively.
The overlooked role of high-valent cobalt-oxo species (Co(IV)) in the Co(II)/peroxymonosulfate (PMS) process was uncovered recently using methyl phenyl sulfoxide (PMSO) as the probe. Herein, we further interestingly found that Co(IV) could trigger hydroxyl radical (•OH) formation, resulting in the oxidized products distribution of PMSO heavily relied on the relative concentration of PMSO. More significantly, the generation of a series of 18O-labeled hydroxylated products (i.e., hydroxylated methyl phenyl sulfone, nitrobenzene and 4-nitrobenzoic acid) in H218O conclusively verified that •OH was triggered by Co(IV) species. Density functional theory calculation demonstrated that Co(IV) initiated •OH formation via oxo ligand protonation-induced valence tautomerization. Moreover, the oxidative contribution of Co(IV) and •OH on organic degradation was specifically dependent on the type and concentration of the substrate. This study provided deeper insights into the evolution pathway of •OH mediated by Co(IV) species and enriched the understandings on the collaborative oxidation mechanism in Co(IV)-involved processes.