Spectroscopic and Computational Characterization of Lanthanum-Mediated C-H and N-H Bond Activation of Amines

Metal-mediated bond activation of small organic and inorganic molecules plays critical roles in chemical transformation of small molecules into value-added products. This is because few of such chemical reactions would occur under mild conditions without the metal activation. In this work, lanthanum atom reactions with alkylamines are carried out in a laser-ablation supersonic molecular beam source; C-H and N-H bond activation in these species is investigated. The reaction products are observed with photoionization time-of-flight mass spectrometry and characterized by mass-analyzed threshold ionization (MATI) spectroscopy and theoretical calculations. Adiabatic ionization energy and metal-ligand and ligand-based vibrational frequencies of several short-lived lanthanum complexes are measured from MATI spectra. Molecular structures, electronic states, and formation mechanisms of these complexes are identified by combining the spectroscopic measurements with density functional theory calculations and spectral simulations

Mass-Analyzed Threshold Ionization of Lanthanide Imide LnNH (Ln = La and Ce) Radicals from N–H Bond Activation of Ammonia

Ln (Ln = La and Ce) atom reactions with ammonia are carried out in a pulsed laser vaporization supersonic molecular beam source. Lanthanide-containing species are observed with time-of-flight mass spectrometry, and LnNH molecules are characterized by mass-analyzed threshold ionization (MATI) spectroscopy and quantum chemical calculations. The theoretical calculations include density functional theory for both Ln species and a scalar relativity correction, electron correlation, and spin-orbit coupling for the Ce species. The MATI spectrum of LaNH exhibits a single vibronic band system with a strong origin band and two weak vibronic progressions, whereas the spectrum of CeNH displays two band systems separated by 75 cm−1 with each being like the LaNH spectrum. By comparing with the theoretical calculations, both LaNH and CeNH are identified as linear molecules with C∞v symmetry, and the two vibronic progressions are attributed to the excitations of Ln–N stretching and Ln–N–H bending modes in the ions. The additional band system observed for CeNH is due to the spin-orbit splitting from the interactions of triplet and singlet states. The ground valence electron configurations of LaNH and CeNH are La 6s1 and Ce 4f16s1, and the ionization of each species removes the Ln 6s1 electron. The remaining two electrons that are associated with the isolated Ln atoms or ions are in a doubly degenerate molecular orbital that is a bonding combination between Ln 5dπ and N pπ orbitals

Spectroscopy and Formation of Lanthanum-Hydrocarbon Radicals Formed by C—H and C—C Bond Activation of 1-Pentene and 2-Pentene

La atom reactions with 1-pentene and 2-pentene are carried out in a laser-vaporization molecular beam source. The two reactions yield the same metal-hydrocarbon products from the dehydrogenation and carbon–carbon bond cleavage of the pentene molecules. The dehydrogenated species La(C5H8) is the major product, whereas the carbon–carbon bond cleaved species La(C2H2) and La(C3H4) are the minor ones. La(C10H18) is also observed and is presumably formed by La(C5H8) addition to a second pentene molecule. La(C5H8) and La(C2H2) are characterized with mass-analyzed threshold ionization (MATI) spectroscopy and quantum chemical computations. The MATI spectra of each species from the two reactions exhibit the same transitions. Adiabatic ionization energies and metal-ligand stretching frequencies are determined for the two species, and additional methyl bending and torsional frequencies are measured for the larger one. Five possible isomers are considered for La(C5H8), and a C1 metallacyclopentene (Iso A) is identified as the most possible isomer. La(C2H2) is confirmed to be a C2v metallacyclopropene. The ground electronic state of each species is a doublet with a La 6s1-based electron configuration, and ionization yields a singlet state. The formation of the lanthanacyclopentene includes La addition to the C=C double bond, La insertion into two C(sp3)—H bonds, and concerted dehydrogenation. For the 2-pentene reaction, the formation of the five-membered ring may also involve 2-pentene to 1-pentene isomerization. In addition to the metal addition and insertion, the formation of the three-membered metallacycle from 1-pentene includes C(sp3)—C(sp3) bond breakage and hydrogen migration from La to C(sp3), whereas its formation from 2-pentene may involve the ligand isomerization

THE HYDRAULICS OF NATURE-LIKE FISHWAYS

Nature-like fishway arrangements are commonly used because these structures imitate the characteristics of natural rivers and effectively allow fish to migrate past river sections blocked by hydraulic structures. In this paper, physical models were analyzed, and the velocity distributions of two different fishway structures (Types I and II) were compared. Results showed that the maximum mainstream velocity of the Type I structure was 5.3% lower than that of the Type II structure. However, the average mainstream velocity of the Type I structure was 21.1% greater than that of the Type II structure. The total per-cycle length of the mainstream path in the Type II structure was 2.1 times greater than that of the Type I structure, which indicated that the length of the mainstream path was somewhat proportional to the average velocity of the mainstream. When the flow rate was kept constant, increases in the velocity of the main flow associated with changes in the internal structure of the fishway decreased the average velocity of the main flow, while decreases in the total length of the flow path led to increases in the average velocity of the main flow. Due to frictional head loss along the fishway and local head loss, as well as the overlaps between these factors, the overall flow rate gradually decreased every cycle, despite periodic fluctuations

GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs

Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing deep graph learning frameworks assume pre-stored static graphs and do not support training on graph streams. In contrast, many real-world graphs are dynamic and contain time domain information. We introduce GNNFlow, a distributed framework that enables efficient continuous temporal graph representation learning on dynamic graphs on multi-GPU machines. GNNFlow introduces an adaptive time-indexed block-based data structure that effectively balances memory usage with graph update and sampling operation efficiency. It features a hybrid GPU-CPU graph data placement for rapid GPU-based temporal neighborhood sampling and kernel optimizations for enhanced sampling processes. A dynamic GPU cache for node and edge features is developed to maximize cache hit rates through reuse and restoration strategies. GNNFlow supports distributed training across multiple machines with static scheduling to ensure load balance. We implement GNNFlow based on DGL and PyTorch. Our experimental results show that GNNFlow provides up to 21.1x faster continuous learning than existing systems

Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of ne-onates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 682 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 60 infants who had longitudinal scans. The model was validated on 30 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 71.37% and 84.10% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, the folding morphology demonstrated greater discriminative capability than the cortical thickness, which could serve as the morphological fingerprint in perinatal brains. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of in-dividual uniqueness in the brain during early development

pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting

Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on. Due to the fact that the probability distributions cannot be averaged over different models straightforwardly, the current time series model ensemble methods cannot be directly applied to improve the robustness and accuracy of forecasting. To address this issue, we propose pTSE, a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM). pTSE only takes off-the-shelf outputs from member models without requiring further information about each model. Besides, we provide a complete theoretical analysis of pTSE to prove that the empirical distribution of time series subject to an HMM will converge to the stationary distribution almost surely. Experiments on benchmarks show the superiority of pTSE overall member models and competitive ensemble methods.Comment: The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023