O-Regioselective Synthesis with the Silver Salt Method - Wang, Y.-G.; Barnes, E.C. (ACS Omega, 2018)
The excellent O-regioselectivity of the glycosidation of the ambident 2-O-substituted 5-fluorouracil (5-FU) via the silver salt method is computationally investigated at the MP2/6-311++G(2d,p):DZP//B3LYP/6-31+G(d):DZP level of theory. The reactions studied are those between 1-bromo-1-deoxy-2,3,4,6-tetra-O-acetyl-α-d-glucopyranose and the silver salts of 5-FU, 2-O-butyl-5-FU, and 2-O-benzyl-5-FU. Two pathways are considered as follows: (A) one where the silver and bromide ion do not interact, and (B) another where the silver and bromide ion interact in the transition states. Because the O-reaction barriers are much lower (by 13.3-22.2 kcal/mol) than N-reaction barriers in both pathways, the O-regioselectivity of the silver salt method can be satisfactorily explained by either path A or path B. Furthermore, path B, where Ag and Br interact consistently, has lower activation barriers than the corresponding path A (by 6.8-17.4 kcal/mol) in both N- and O-reactions. This computational result can be attributed to the following reasons: (1) the speeding-up effect in Koenigs-Knorr reactions due to the addition of silver carbonate into the reaction mixture; (2) the halogens being pulled away by silver ions from halides, as proposed by Kornblum and co-workers; and (3) the oxocarbenium ion involvement in the glycosidation reactions. The large energy difference between N- and O-transition states originates from the association between Ag and N-(O-) of the ambident unit (-N3-C4=O4) that shows significant covalent character so that the O-reaction transition states of the silver salt method benefit from favorable ionic interaction (C+···O-) and favorable covalent interaction (Ag···N). These two favorable interactions are in agreement with the hard and soft acids and bases principle; the former is a hard-hard interaction and the latter is a soft-soft interaction. © 2018 American Chemical Society.
Keywords: dept.che; peer-reviewed

Service Mode of Smart Recommendation Based on Knowledge Discovery/基于知识挖掘的图书馆智慧推荐服务模式 - Wang, Y.; Dong, X.; Liu, Y. Q. (Research on Library Science/图书馆学研究, 2018)

The Latest Study of Workplace Bullying and Anti-Bullying in American Libraries/美国图书馆工作场所欺凌和反欺凌现状最新调研 - He, J; Wang, Y; Liu, Y. Q. (Library & Information/图书与情报, 2015)
Keywords: dept.ils

The latest study of digital virtual reference service in Chinese top university libraries - He, X; Wang, Y; Liu, Yan Quan (Journal of Intelligence, 2017)
Keywords: dept.ils; ⛔ No DOI found

The latest study of reading promotion activities in American public libraries - He, X; Wang, Y; Liu, Yan Quan (Library and Information, 2017)
Keywords: dept.ils; ⛔ No DOI found

Research on the current situation and countermeasures of the mobile information service in Chinese academic journals of library and information science - Zhao, F; Liu, Yan Quan; Wang, Y (Library and Information Service, 2017)
Keywords: dept.ils

Development path research on library smart service mode - Wang, Y; He, X; Liu, Yan Quan (Library Work and Study, 2018)
Keywords: dept.ils; ⛔ No DOI found

The present situation of mobile service in public libraries of China and the comparative analysis of public libraries in Eastern-Central-Western regions - Wang, Y; He, X; Liu, Yan Quan (Library, 2018)
Keywords: dept.ils; ⛔ No DOI found

Service mode of smart recommendation based on knowledge discovery - Wang, Y; Dong, X; Liu, Yan Quan (Research on Library Science, 2018)
Keywords: dept.ils; peer-reviewed; ⛔ No DOI found

An Analytical Study of Recommendation Systems in Chinese City Libraries / 城市图书馆馆藏资源推荐系统调查分析 - Guo, Q; Liu, Yan Quan; Wang, Y (Research on Library Science, 2019)
Keywords: dept.ils; peer-reviewed; ⛔ No DOI found

Investigation and Research on the Application of Artificial Intelligence Technology in Library Service / 图书馆服务中人工智能技术应用的调查研究 - Yang, Z.; Wang, Y.; Liu, Yan Quan; Qu, Z. (Library/TU Shu Guan, 2019)
Keywords: dept.ils; ⛔ No DOI found

The Reactivity of Ambident Nucleophiles: Marcus Theory or Hard and Soft Acids and Bases Principle? - Wang, Y.-G.; Barnes, E.C.; Kaya, S.; Sharma, V. (Journal of Computational Chemistry, 2019)
The model reactions CH3X + (NH—CH=O)M ➔ CH3—NH—NH═O or NH═CH—O—CH3 + MX (M = none, Li, Na, K, Ag, Cu; X = F, Cl, Br) are investigated to demonstrate the feasibility of Marcus theory and the hard and soft acids and bases (HSAB) principle in predicting the reactivity of ambident nucleophiles. The delocalization indices (DI) are defined in the framework of the quantum theory of atoms in molecules (QT-AIM), and are used as the scale of softness in the HSAB principle. To react with the ambident nucleophile NH═CH—O−, the carbocation H3C+ from CH3X (F, Cl, Br) is actually a borderline acid according to the DI values of the forming C…N and C…O bonds in the transition states (between 0.25 and 0.49), while the counter ions are divided into three groups according to the DI values of weak interactions involving M (M…X, M…N, and M…O): group I (M = none, and Me4N) basically show zero DI values; group II species (M = Li, Na, and K) have noticeable DI values but the magnitudes are usually less than 0.15; and group III species (M = Ag and Cu(I)) have significant DI values (0.30–0.61). On a relative basis, H3C+ is a soft acid with respect to group I and group II counter ions, and a hard acid with respect to group III counter ions. Therefore, N-regioselectivity is found in the presence of group I and group II counter ions (M = Me4N, Li, Na, K), while O-regioselectivity is observed in the presence of the group III counter ions (M = Ag, and Cu(I)). The hardness of atoms, groups, and molecules is also calculated with new functions that depend on ionization potential (I) and electron affinity (A) and use the atomic charges obtained from localization indices (LI), so that the regioselectivity is explained by the atomic hardness of reactive nitrogen atoms in the transition states according to the maximum hardness principle (MHP). The exact Marcus equation is derived from the simple harmonic potential energy parabola, so that the concepts of activation free energy, intrinsic activation barrier, and reaction energy are completely connected. The required intrinsic activation barriers can be either estimated from ab initio calculations on reactant, transition state, and product of the model reactions, or calculated from identity reactions. The counter ions stabilize the reactant through bridging N- and O-site of reactant of identity reactions, so that the intrinsic barriers for the salts are higher than those for free ambident anions, which is explained by the increased reorganization parameter Δr. The proper application of Marcus theory should quantitatively consider all three terms of Marcus equation, and reliably represent the results with potential energy parabolas for reactants and all products. For the model reactions, both Marcus theory and HSAB principle/MHP principle predict the N-regioselectivity when M = none, Me4N, Li, Na, K, and the O-regioselectivity when M = Ag and Cu(I). © 2019 Wiley Periodicals, Inc. © 2019 Wiley Periodicals, Inc.
Keywords: Activation energy; Atoms; Calculations; Copper compounds; Delocalization indexes; Electron affinity; Free energy; Hardness; Ionization potential; Ions; Lithium compounds; Localization index; Marcus theory; Maximum hardness principle; Molecules; Nucleophiles; Potential energy; Quantum theory; Regioselectivity; Silver; Sodium compounds; ambident reactivity; atom and molecule hardness; conceptual DFT; delocalization index; dept.chem; electron affinity; hard and soft acids and bases (HSAB) principle; ionization potential; localization index; maximum hardness principle (MHP); peer-reviewed

Deep learning based plant disease detection for smart agriculture - Ale, L.; Sheta, A.; Li, L.; Wang, Y.; Zhang, N. (Institute of Electrical and Electronics Engineers Inc., 2019)
Deep learning is a promising approach for fine- grained disease severity classification for smart agriculture, as it avoids the labor-intensive feature engineering and segmentation-based threshold. In this work, we first propose a Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources. Then, we propose a lightweight Deep Neural Networks (DNN) approach that can run on Internet of Things (IoT) devices with constrained resources. To reduce the size and computation cost of the model, we further simplify the DNN model and reduce the size of input sizes. The proposed models are trained with different image sizes to find the appropriate size of the input images. Experiment results are provided to evaluate the performance of the proposed models based on real- world dataset, which demonstrate the proposed models can accurately detect plant disease using low computational resources. © 2019 IEEE.
Keywords: Agriculture; Classification; Classification (of information); Computational resources; Computing resource; Constrained resources; Convolution; Convolutional Neural Network; Convolutional networks; Convolutional neural networks; Corp Disease; Deep learning; Deep neural networks; Detection; Digital Epidemiology; Error detection; Feature engineerings; Internet of Things (IOT); Internet of things; Learning systems; Plants Disease; Smart agricultures; Transfer learning; Transfer learning methods; dept.csc