Sustainable Oxidative Gold Catalysis: Ligand-Assisted Gold-Catalyzed Alkynylative Cyclization and C(sp)-C(sp) Cross-Coupling Using Hydrogen Peroxide as Oxidant

The thesis is focused on 1,10-phenanthroline (phen)-assisted homogeneous oxidative gold catalysis using hydrogen peroxide (H2O2) as oxidant. It is an unprecedented oxidative gold catalysis strategy with an ideal “benefit balance”, not just as a more attractive substitute of common methodologies, but as an extraordinary reaction system. The efficient constructions of 3-alkynylbenzofurans, 1,3-diynes and polyynes were possible by this catalytic system (Au/phen/H2O2). The thesis considers the significant advantages (ideal “benefit balance”) and challenges (no-report) of H2O2 as an oxidant for oxidative gold catalysis. In the first part (Chapter 2), we focused on exploring the possibility of oxidative gold catalysis using H2O2 as oxidant and the potential application value of this reaction system. We discovered that bidentate N-ligands (phen) can effectively promote the oxidation of AuI to AuIII in the presence of H2O2. Furthermore, a set of experiments with stoichiometric gold(I) complexes demonstrated that this catalytic system can be applied for homogeneous gold-catalyzed C(sp2)-C(sp) and C(sp)-C(sp) cross-coupling reactions. The gold-catalyzed cyclization-functionalization is a powerful approach to construct high-value organic molecules. However, current strategies mainly rely on expensive external oxidants or pre-functionalized substrates, which exhibit low atom economy and high costs. To circumvent these drawbacks, in the second part (Chapter 3), we focused on investigating the use of this catalytic system for efficient gold-catalyzed cyclization-functionalizations. A direct construction of 3-alkynylbenzofurans from terminal alkynes was possible by this gold-catalyzed process. Green and inexpensive oxidants, simple gold catalysts, mild reaction conditions, high atom economy, remarkable selectivity, wide substrate scope, broad functional group compatibility and a facile gram-scale synthesis make this alkynylative cyclization method practical for many forms of cyclization reactions. In contrast to prior methods neither pre-functionalized alkynes nor expensive external oxidants are needed. Conjugated 1,3-diynes are unique carbon frameworks which are widely found in natural products, biologically active molecules and functional materials. Considering the importance of synthetic methods for conjugated diynes, especially unsymmetrical 1,3-diynes, we next focused on investigating the use of this catalytic system for a gold-catalyzed cross-coupling of terminal alkynes. An efficient synthesis of unnsymmetrical 1,3-diynes from terminal alkynes via this new gold catalytic system was developed (Chapter 4). A wide range of substrates, including several complex molecules and marketed drugs, were transferred with excellent functional group tolerance. Furthermore, the catalyst system was applied at a gram scale and an extension towards the synthesis of polyynes via a relay strategy was possible. Considering the importance of polyynes in chemical and materials research, and tedious synthesis procedure of the current strategy. In the fourth part (Chapter 5), we focused on exploring gold-catalyzed C(sp)–C(sp) cross-coupling of alkynylsilanes using H2O2 as oxidant. Through this catalytic system, 1,3-diynes and polyynes can be successfully prepared from ethynyltrimethylsilanes without pre-functionalization or deprotection. Compared with current synthetic strategies towards polyynes, our method greatly improves the synthetic efficiency, provide new ideas for the synthesis of polyynes

Experimental and analytical study on heat generation characteristics of a lithium-ion power battery

This document is the Accepted Manuscript version of the following article: Yongqi Xie, Shang Shi, Jincheng Tang, Hongwei Wu, and Jianzu Yu, ‘Experimental and analytical study on heat generation characteristics of a lithium-ion power battery’, International Journal of Heat and Mass Transfer, Vol. 122: 884-894, July 2018. Under embargo until 20 February 2019. The final, definitive version is available online via: https://doi.org/10.1016/j.ijheatmasstransfer.2018.02.038A combined experimental and analytical study has been performed to investigate the transient heat generation characteristics of a lithium-ion power battery in the present work. Experimental apparatus is newly built and the investigations on the charge/discharge characteristics and temperature rise behavior are carried out at ambient temperatures of 28 °C, 35 °C and 42 °C over the period of 1 C, 2 C, 3 C and 4 C rates. The thermal conductivity of a single battery cell is experimentally measured to be 5.22 W/(m K). A new transient model of heat generation rate based on the battery air cooling system is proposed. Comparison of the battery temperature between simulated results and experimental data is performed and good agreement is achieved. The impacts of the ambient temperature and charge/discharge rate on the heat generation rate are further analyzed. It is found that both ambient temperature and charge/discharge rate have significant influences on the voltage change and temperature rise as well as the heat generation rate. During charge/discharge process, the higher the current rate, the higher the heat generation rate. The effect of the ambient temperature on the heat generation demonstrates a remarkable difference at different charge states.Peer reviewe

Multi-Graph Convolution Network for Pose Forecasting

Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose sequences. This task is complex due to modeling spatial and temporal relationships. The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks. However, RNNs have several drawbacks, such as vanishing or exploding gradients. Other researchers have attempted to solve the communication problem in the spatial dimension by integrating Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) models. These works deal with temporal and spatial information separately, which limits the effectiveness. To fix this problem, we propose a novel approach called the multi-graph convolution network (MGCN) for 3D human pose forecasting. This model simultaneously captures spatial and temporal information by introducing an augmented graph for pose sequences. Multiple frames give multiple parts, joined together in a single graph instance. Furthermore, we also explore the influence of natural structure and sequence-aware attention to our model. In our experimental evaluation of the large-scale benchmark datasets, Human3.6M, AMSS and 3DPW, MGCN outperforms the state-of-the-art in pose prediction.Comment: arXiv admin note: text overlap with arXiv:2110.04573 by other author

Active Defense Analysis of Blockchain Forking through the Spatial-Temporal Lens

Forking breaches the security and performance of blockchain as it is symptomatic of distributed consensus, spurring wide interest in analyzing and resolving it. The state-of-the-art works can be categorized into two kinds: experiment-based and model-based. However, the former falls short in exclusiveness since the derived observations are scenario-specific. Hence, it is problematic to abstractly reveal the crystal-clear forking laws. Besides, the models established in the latter are spatiality-free, which totally overlook the fact that forking is essentially an undesirable result under a given topology. Moreover, few of the ongoing studies have yielded to the active defense mechanisms but only recognized forking passively, which impedes forking prevention and cannot deter it at the source. In this paper, we fill the gap by carrying out the active defense analysis of blockchain forking from the spatial-temporal dimension. Our work is featured by the following two traits: 1) dual dimensions. We consider the spatiality of blockchain overlay network besides temporal characteristics, based on which, a spatial-temporal model for information propagation in blockchain is proposed; 2) active defense. We hint that shrinking the long-range link factor, which indicates the remote connection ability of a link, can cut down forking completely fundamentally. To the best of our knowledge, we are the first to inspect forking from the spatial-temporal perspective, so as to present countermeasures proactively. Solid theoretical derivations and extensive simulations are conducted to justify the validity and effectiveness of our analysis.Comment: 10 pages,10 figure

HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using CT Images and Text

Prosthetic Joint Infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients' numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based feature fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4\% and an area under the curve (AUC) of 95.9\%, outperforming recent multimodal approaches by 2.9\% in ACC and 2.2\% in AUC, with a parameter count of only 68M. Notably, the interpretability results highlighted our model's strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice

Proof of User Similarity: the Spatial Measurer of Blockchain

Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternatives, energy-conservation and energy-recycling mechanisms heaved in sight. In this paper, we propose proof of user similarity (PoUS), a distinct energy-recycling consensus mechanism, harnessing the valuable computing power to calculate the similarities of users, and enact the calculation results into the packing rule. However, the expensive calculation required in PoUS challenges miners in participating, and may induce plagiarism and lying risks. To resolve these issues, PoUS embraces the best-effort schema by allowing miners to compute partially. Besides, a voting mechanism based on the two-parties computation and Bayesian truth serum is proposed to guarantee privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes itself in recycling the computing power back to blockchain since it turns the resource wastage to facilitate refined cohort analysis of users, serving as the spatial measurer and enabling a searchable blockchain. We build a prototype of PoUS and compare its performance with PoW. The results show that PoUS outperforms PoW in achieving an average TPS improvement of 24.01% and an average confirmation latency reduction of 43.64%. Besides, PoUS functions well in mirroring the spatial information of users, with negligible computation time and communication cost.Comment: 12 pages,10 figure