ISSN 0253-2778

CN 34-1054/N

2022 Vol. 52, No. 12

2022-12 Contents
2022, 52(12): 1-2.
2022-12 Abstract
2022, 52(12): 1-2.
Nickel-catalyzed alkene ipso-selective reductive hydroamination with nitroarenes
Zhen Li, Jiawang Wang, Xi Lu, Yao Fu
2022, 52(12): 1. doi: 10.52396/JUSTC-2022-0119
Aromatic amine synthesis via reductive coupling between alkenes and nitroarenes is attractive; however, it remains underdeveloped. Herein, we report a nickel-catalyzed alkene hydroamination with nitroarenes under mild reductive conditions. This reaction exhibited an ipso-selectivity and enabled repaid preparation of aromatic amines with primary and secondary alkyl groups. Many functional groups were well tolerated, providing an efficient approach for drug-like arylamine synthesis.
Q2Chemistry: A quantum computation platform for quantum chemistry
Yi Fan, Jie Liu, Xiongzhi Zeng, Zhiqian Xu, Honghui Shang, Zhenyu Li, Jinlong Yang
2022, 52(12): 2. doi: 10.52396/JUSTC-2022-0118
Quantum computers provide new opportunities for quantum chemistry. In this article,we present a versatile, extensible, and efficient software package, named Q2Chemistry, for developing quantum algorithms and quantum inspired classical algorithms in the field of quantum chemistry. In Q2Chemistry, the wave function and Hamiltonian can be conveniently mapped into the qubit space, then quantum circuits can be generated corresponding to a specific quantum algorithm already implemented in the package or newly developed by the users. The generated circuits can be dispatched to either a physical quantum computer, if available, or to the internal virtual quantum computer realized by simulating quantum circuits on classical computers. As demonstrated by our benchmark simulations, Q2Chemistry achieves excellent performance in simulating medium scale quantum circuits using the matrix product state algorithm. Applications of Q2Chemistry to simulate molecules and periodic systems are given with performance analysis.
A black phosphorus-graphite hybrid as a Li-ion regulator enabling stable lithium deposition
Huanyu Xie, Chaonan Wang, En Zhou, Hongchang Jin, Hengxing Ji
2022, 52(12): 3. doi: 10.52396/JUSTC-2022-0105
Lithium (Li) metal anodes have been regarded as the most promising candidates for high energy density secondary lithium batteries due to their high specific capacity and low redox potential. However, the issues of Li dendrites caused by nonuniform lithium deposition during battery cycling severely hinder the practical applications of Li metal anodes. Herein, a hybrid of black phosphorus-graphite (BP-G) is introduced to serve as an artificial protective layer for the Li metal anode. The two-dimensional few-layer BP, which is lithophilic, combined with the high electronic conductive graphite can act as a regulator to adjust the migration of Li ions, delivering a uniform and stable lithium deposition. As the growth of lithium dendrites is inhibited, the utilization of Li metal achieves > 98.5% for over 500 cycles in Li||Cu half cells, and the life span is maintained over 2000 h in Li||Li symmetric cells with a low voltage hysteresis of 50 mV. Moreover, the LiFePO4||Li full cell with a BP-G Li-ion regulator presents significantly better specific capacity and cycling stability than that with the bare Li metal anode. Therefore, the introduction of the BP-G Li-ion regulator is demonstrated to be an effective approach to enable stable lithium deposition for rechargeable Li metal batteries.
A deformable spinel-type chloride cathode with high ionic conductivity for all-solid-state Li batteries
Jipeng Hao, Cheng Ma
2022, 52(12): 4. doi: 10.52396/JUSTC-2022-0071
All-solid-state Li batteries (ASSLBs) are now considered to be next-generation energy storage devices due to their advantages in safety and energy density. With liquid electrolytes replaced by solid electrolytes, novel cathode active materials (CAMs) with different characteristics are needed. The solid-solid contact in ASSLBs requires CAMs to have good deformability. In addition, higher ionic conductivity is also essential to reduce the mass of the Li-ion conductive agent, thus accessing a higher overall capacity. Herein, we report a spinel-type chloride cathode Li2−2xMn1−xZrxCl4, which has good deformability and high ionic conductivity (up to 0.16 mS∙cm−1 at 25 °C). The ASSLB using the optimal composition of LiMn0.5Zr0.5Cl4 as the cathode exhibits promising cycling stability for 200 cycles at room temperature.
Machine learning in data envelopment analysis: A smart mechanism for indicator selection
Jie Wu, Yumeng Wu
2022, 52(12): 5. doi: 10.52396/JUSTC-2022-0106
Indicator selection has been a compelling problem in data envelopment analysis. With the advent of the big data era, scholars are faced with more complex indicator selection situations. The boom in machine learning presents an opportunity to address this problem. However, poor quality indicators may be selected if inappropriate methods are used in overfitting or underfitting scenarios. To date, some scholars have pioneered the use of the least absolute shrinkage and selection operator to select indicators in overfitting scenarios, but researchers have not proposed classifying the big data scenarios encountered by DEA into overfitting and underfitting scenarios, nor have they attempted to develop a complete indicator selection system for both scenarios. To fill these research gaps, this study employs machine learning methods and proposes a mean score approach based on them. Our Monte Carlo simulations show that the least absolute shrinkage and selection operator dominates in overfitting scenarios but fails to select good indicators in underfitting scenarios, while the ensemble methods are superior in underfitting scenarios, and the proposed mean approach performs well in both scenarios. Based on the strengths and limitations of the different methods, a smart indicator selection mechanism is proposed to facilitate the selection of DEA indicators.
Life Science, Info. & Intelligence
Preoperative diagnosis of hepatocellular carcinoma patients with bile duct tumor thrombus using deep learning method
Jinming Liu, Jiayi Wu, Anran Liu, Yannan Bai, Hong Zhang, Maolin Yan
2022, 52(12): 6. doi: 10.52396/JUSTC-2022-0057
Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. Although dilated bile ducts (DBDs) can act as biomarkers for diagnosing BDTT, it is easy for doctors to ignore DBDs when reporting the imaging scan result, leading to a high missed diagnosis rate in practice. This study aims to develop an artificial intelligence (AI) pipeline for automatically diagnosing HCC patients with BDTT using medical images. The proposed AI pipeline includes two stages. First, the object detection neural network Faster R-CNN was adopted to identify DBDs; then, an HCC patient was diagnosed with BDTT if the proportion of images with at least one identified DBD exceeded some threshold value. Based on 2354 CT images collected from 32 HCC patients (16 with BDTT and 16 without BDTT, 1∶1 matched), the proposed AI pipeline achieves an average true positive rate of 0.92 for identifying DBDs per patient and a patient-level true positive rate of 0.81 for diagnosing BDTT. The AUC value of the patient-level diagnosis of BDTT is 0.94 (95% CI: 0.87, 1.00), compared with 0.71 (95% CI: 0.51, 0.90) achieved by random forest based on preoperative clinical variables. The high accuracies demonstrate that the proposed AI pipeline is successful in the diagnosis and localization of BDTT using CT images.
Info.& intellengence
Unsupervised person re-identification based on removal of camera bias and dynamic updating of the memory bank
Jun Zhang, Xinmei Tian
2022, 52(12): 7. doi: 10.52396/JUSTC-2022-0015
In recent years, unsupervised person reidentification technology has made great strides. The technology retrieves images of interested persons under different cameras from massive repositories of unlabeled images. However, in the current research, there are some existing problems, such as the influence of pedestrians appearing across cameras and pseudo-label noise. To solve these problems, we conduct research in two ways: removing the camera bias and dynamically updating the memory model. In removing the camera bias, based on a learnable channel attention module, the features that are only related to cameras can be extracted from the feature map, thereby removing the camera bias in the global features and obtaining the features that can represent the pedestrians. In regards to dynamically updating the memory model, since the instance features do not necessarily belong to the identity represented by the pseudo-label, we adopt a method to update the memory dynamically according to the distance between the instance features and the category features so that the category features tend to be true. We combine the removal of the camera bias and the dynamic updating of the memory model to better solve problems in this field. Extensive experimentation demonstrates the superiority of our method over the state-of-the-art approaches on fully unsupervised Re-ID tasks.