Metal-Free Synthesis of \u3ci\u3eN-Heterocycles via Intramolecular Electrochemical C-H Aminations

N-heterocycles are key structural units in many drugs, biologically interesting molecules and functional materials. To avoid the residues of metal catalysts, the construction of N-heterocycles under metal-free conditions has attracted much research attention in academia and industry. Among them, the intramolecular electrochemical C-H aminations arguably constitute environmentally friendly methodologies for the metal-free construction of N-heterocycles, mainly due to the direct use of clean electricity as the redox agents. With the recent renaissance of organic electrosynthesis, the intramolecular electrochemical C-H aminations have undergone much progress in recent years. In this article, we would like to summarize the advances in this research field since 2019. The emphasis is placed on the reaction design and mechanistic insight. The challenges and future developments in the intramolecular electrochemical C-H aminations are also discussed

Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach

Despite decades of efforts, robot navigation in a real scenario with volatility, uncertainty, complexity, and ambiguity (VUCA for short), remains a challenging topic. Inspired by the central nervous system (CNS), we propose a hierarchical multi-expert learning framework for autonomous navigation in a VUCA environment. With a heuristic exploration mechanism considering target location, path cost, and safety level, the upper layer performs simultaneous map exploration and route-planning to avoid trapping in a blind alley, similar to the cerebrum in the CNS. Using a local adaptive model fusing multiple discrepant strategies, the lower layer pursuits a balance between collision-avoidance and go-straight strategies, acting as the cerebellum in the CNS. We conduct simulation and real-world experiments on multiple platforms, including legged and wheeled robots. Experimental results demonstrate our algorithm outperforms the existing methods in terms of task achievement, time efficiency, and security.Comment: 8 pages, 10 figure

One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water ($\rho_e$) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient $L_0$-norm minimization, for Z and $\rho_e$ maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss

Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. Purpose: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT

A New Overdispersed Integer-Valued Moving Average Model with Dependent Counting Series

A new integer-valued moving average model is introduced. The assumption of independent counting series in the model is relaxed to allow dependence between them, leading to the overdispersion in the model. Statistical properties were established for this new integer-valued moving average model with dependent counting series. The Yule–Walker method was applied to estimate the model parameters. The estimator’s performance was evaluated using simulations, and the overdispersion test of the INMA(1) process was applied to examine the dependence between counting series

The Circumstance-Driven Bivariate Integer-Valued Autoregressive Model

The novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model for non-stationary count time series is proposed. The non-stationarity of the bivariate count process is defined by a joint categorical sequence, which expresses the current state of the process. Additional cross-dependence can be generated via cross-dependent innovations. The model can also be equipped with a marginal bivariate Poisson distribution to make it suitable for low-count time series. Important stochastic properties of the new model are derived. The Yule–Walker and conditional maximum likelihood method are adopted to estimate the unknown parameters. The consistency of these estimators is established, and their finite-sample performance is investigated by a simulation study. The scope and application of the model are illustrated by a real-world data example on sales counts, where a soap product in different stores with a common circumstance factor is investigated

Rubbing of the Fangshan Canon-Mahāprajñāpāramitā-sūtra fascicle twenty-eight (833), Fanyuan Temple (法源寺)

Rubbing of the Fangshan Canon, Fanyuan Temple (法源寺). In the Guest Hall, we get the chance to look at the rubbing of the Fangshan Canon, which is the beginning part of Mahāprajñāpāramitā-sūtra fascicle twenty-eight (833). The length of the rubbing: 1.95m; the width of the rubbing 56cm, the length of each character is 2cm.Non UBCUnreviewedAuthor Affiliations: University of Arizona, Zhejiang University, Tsinghua UniversityGraduat

The stele tablet of a thousand Buddhist statues (千佛碑), Yunju Temple (雲居寺)

The stele tablet of a thousand Buddhist statues (千佛碑) at Yunju Temple (雲居寺). The tablet is full of small shrines on all sides, with more than a thousand carved Buddhist statues inset into the stone. The front of the tablet was engraved in 621 during the Sui Dynasty 隨 (581-618), whose words are now difficult to read. This form of inscription, which is rare in China, was unearthed at Dou Dian 窦店 Ancient City site in Fangshan District 房山 and moved to Yunju Temple 雲居寺 in the 1970s.The texture is turquoise, 2.66 meters high.Non UBCUnreviewedAuthor Affiliations: University of Arizona, Zhejiang University, Tsinghua UniversityGraduat

Inscription of the Clerk of the Ministry of Rites 禮部令史題名記, Fanyuan Temple (法源寺)

The stele with the inscription of the Clerk of the Ministry of Rites (禮部令史題名記) is exhibited in the Minzhong Hall (憫中閣) of Fayuan Temple (法源寺). It is 65cm high, 40cm long and 8cm in wide.Non UBCUnreviewedAuthor Affiliations: University of Arizona, Zhejiang University, Tsinghua UniversityGraduat