1-[4-(4-Nitro­phen­yl)piperazin-1-yl]-2-(4,5,6,7-tetra­hydro­thieno[3,2-c]pyridin-5-yl)ethanone

The title compound, C19H22N4O3S, comprises a thienopyridine moiety which is characteristic for anti­platelet agents of the clopidogrel class of compounds. In the crystal, inversion dimers are formed through pairs of C—H⋯O inter­actions. The benzene ring plane and the nitro plane are almost coplanar, with a dihedral angle of 0.83 (2)°. The piperazine ring adopts a chair conformation

(meso-5,7,7,12,14,14-Hexamethyl-1,4,8,11-tetra­azacyclo­tetra­deca-4,11-diene)nickel(II) bis­[O,O′-bis(4-methyl­phen­yl) dithio­phosphate]

In the title compound, [Ni(C16H32N4)](C14H14O2PS2)2 or [Ni(trans[14]dien)][S2P(OC6H4Me-4)2]2, where trans[14]dien is meso-5,7,7,12,14,14-hexa­methyl-1,4,8,11-tetra­azacyclo­tetra­deca-4,11-diene, the NiII ion lies across a centre of inversion and is four-coordinated in a relatively undistorted square-planar arrangement by the four N atoms of the macrocyclic ligand trans[14]dien. The two O,O′-di(4-methyl­phen­yl)dithio­phos­phates act as counter-ions to balance the charge. Important geometric data include Ni—N = 1.9135 (16) and 1.9364 (15) Å

Improving the Performance of R17 Type-II Codebook with Deep Learning

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.Comment: Accepted by IEEE GLOBECOM 2023, conference version of Arxiv:2305.0808

Seasonal fluxes and sources apportionment of dissolved inorganic nitrogen wet deposition at different land-use sites in the Three Gorges reservoir area.

To identify seasonal fluxes and sources of dissolved inorganic nitrogen (DIN) wet deposition, concentrations and δ15N signatures of nitrate (NO3−) and ammonium (NH4+) in wet precipitation were measured at four typical land-use types in the Three Gorges reservoir (TGR) area of southwest China for a one-year period. Higher DIN fluxes were recorded in spring and summer and their total fluxes (averaged 7.58 kg N ha−1) were similar to the critical loads in aquatic ecosystems. Significant differences of precipitation δ15N were observed for NH4+-N between town and wetland sites in spring and between urban and rural sites in summer. For NO3−-N, significant differences of precipitation δ15N were observed between town and rural sites in spring and between urban and town sites in autumn, respectively. Quantitative results of NO3−-N sources showed that both biomass burning and coal combustion had higher fluxes at the urban site especially in winter (0.18 ± 0.09 and 0.19 ± 0.08 kg N ha−1), which were about three times higher than those at the town site. A similar finding was observed for soil emission and vehicle exhausts in winter. On the whole, DIN wet deposition averaged at 12.13 kg N ha−1 yr−1 with the urban site as the hotspot (17.50 kg N ha−1 yr−1) and regional NO3−-N fluxes had a seasonal pattern with minimum values in winter. The contribution to NO3−-N wet deposition from biomass burning was 26.1 ± 14.1%, which is the second dominant factor lower than coal combustion (26.5 ± 12.6%) in the TGR area during spring and summer. Hence N emission reduction from biomass burning, coal combustion and vehicle exhausts should be strengthened especially in spring and summer to effectively manage DIN pollution for the sustainable development in TGR area

{N′-[(E)-1-(5-Bromo-2-oxidophen­yl)ethyl­idene]-4-chloro­benzohydrazidato}pyridinenickel(II)

The title complex, [Ni(C15H10BrClN2O2)(C5H5N)], displays a square-planar coordination geometry around the NiII ion, formed by the tridentate hydrazone and monodentate pyridine ligands, with the N atoms in a trans arrangement about the Ni center