Drought impacts on ecosystem functions of the U.S. National Forests and Grasslands: Part I evaluation of a water and carbon balance model

Understanding and quantitatively evaluating the regional impacts of climate change and variability (e.g., droughts) on forest ecosystem functions (i.e., water yield, evapotranspiration, and productivity) and services (e.g., fresh water supply and carbon sequestration) is of great importance for developing climate change adaptation strategies for National Forests and Grasslands (NFs) in the United States. However, few reliable continental-scale modeling tools are available to account for both water and carbon dynamics. The objective of this study was to test a monthly water and carbon balance model, the Water Supply Stress Index (WaSSI) model, for potential application in addressing the influences of drought on NFs ecosystem services across the conterminous United States (CONUS). The performance of the WaSSI model was comprehensively assessed with measured streamflow (Q) at 72 U.S. Geological Survey (USGS) gauging stations, and satellite-based estimates of watershed evapotranspiration (ET) and gross primary productivity (GPP) for 170 National Forest and Grassland (NFs). Across the 72 USGS watersheds, the WaSSI model generally captured the spatial variability of multi-year mean annual and monthly Q and annual ET as evaluated by Correlation Coefficient (R = 0.71–1.0), Nash–Sutcliffe Efficiency (NS = 0.31–1.00), and normalized Root Mean Squared Error (0.06–0.48). The modeled ET and GPP by WaSSI agreed well with the remote sensing-based estimates for multi-year annual and monthly means for all the NFs. However, there were systemic discrepancies in GPP between our simulations and the satellite-based estimates on a yearly and monthly scale, suggesting uncertainties in GPP estimates in all methods (i.e., remote sensing and modeling). Overall, our assessments suggested that the WaSSI model had the capability to reconstruct the long-term forest watershed water and carbon balances at a broad scale. This model evaluation study provides a foundation for model applications in understanding the impacts of climate change and variability (e.g., droughts) on NFs ecosystem service functions

2,6-Diphenyl-4-(2-thien­yl)-1,4-dihydro­pyridine-3,5-dicarbonitrile

The asymmetric unit of the title compound, C23H15N3S, contains two crystallographically independent mol­ecules. The pyridine rings adopt envelope conformations. The thio­phene rings are oriented at dihedral angles of 77.97 (4)/53.53 (4) and 78.44 (4)/57.11 (4)° with respect to the phenyl rings, while the dihedral angles between the phenyl rings are 48.51 (4) and 44.49 (4)°. In the crystal structure, inter­molecular N—H⋯N hydrogen bonds link the mol­ecules into chains along the c axis. The S, C and H atoms of one of the thio­phene rings are disordered over two orientations, with occupancy ratios of 0.314 (15):0.686 (15)

Bis[4-(2-hydroxy­benzyl­amino)phen­yl] ether

The title compound, C26H24N2O3, was synthesized by reduction of the corresponding Schiff base. The mol­ecule does not possess crystallographic or non-crystallographic symmetry. The dihedral angle between the oxygen-bridged benzene rings is 67.98 (8)°. Both hydroxyl groups are involved in O—H⋯O intra­molecular hydrogen bonding. The mol­ecules are linked into a two-dimensional network parallel to the (010) plane by N—H⋯O hydrogen bonds

Observation of electric current induced by optically injected spin current

A normally incident light of linear polarization injects a pure spin current in a strip of 2-dimensional electron gas with spin-orbit coupling. We report observation of an electric current with a butterfly-like pattern induced by such a light shed on the vicinity of a crossbar shaped InGaAs/InAlAs quantum well. Its light polarization dependence is the same as that of the spin current. We attribute the observed electric current to be converted from the optically injected spin current caused by scatterings near the crossing. Our observation provides a realistic technique to detect spin currents, and opens a new route to study the spin-related science and engineering in semiconductors.Comment: 15 pages, 4 figure

Self-Supervised Transformer with Domain Adaptive Reconstruction for General Face Forgery Video Detection

Face forgery videos have caused severe social public concern, and various detectors have been proposed recently. However, most of them are trained in a supervised manner with limited generalization when detecting videos from different forgery methods or real source videos. To tackle this issue, we explore to take full advantage of the difference between real and forgery videos by only exploring the common representation of real face videos. In this paper, a Self-supervised Transformer cooperating with Contrastive and Reconstruction learning (CoReST) is proposed, which is first pre-trained only on real face videos in a self-supervised manner, and then fine-tuned a linear head on specific face forgery video datasets. Two specific auxiliary tasks incorporated contrastive and reconstruction learning are designed to enhance the representation learning. Furthermore, a Domain Adaptive Reconstruction (DAR) module is introduced to bridge the gap between different forgery domains by reconstructing on unlabeled target videos when fine-tuning. Extensive experiments on public datasets demonstrate that our proposed method performs even better than the state-of-the-art supervised competitors with impressive generalization