Bis(ferrocene­carboxyl­ato-κO)bis­(2-pyridyl­methanol-κ2 N,O)cobalt(II)

The title complex mol­ecule, [Fe2Co(C5H5)2(C6H4O2)2(C6H7NO)2], has a crystallographic imposed centre of symmetry. The CoII atom displays a distorted octa­hedral coordination geometry, provided by the O atoms of two monodentate ferrocene­carboxyl­ate anions and by the N and O atoms of two 2-pyridyl­methanol mol­ecule. The mol­ecular conformation is stabilized by intra­molecular C—H⋯O hydrogen bonds

Tetra­kis{μ3-2-[(2-hy­droxy­eth­yl)amino]­ethano­lato}tetra­kis­[chloridonickel(II)] methanol solvate

The complex mol­ecule of the title compound, [Ni4(C4H10NO2)4Cl4]·CH3OH, consists of a cubane-like {Ni4O4} core in which each nickel(II) atom is six-coordinated in a distorted octa­hedral geometry by one N and four O atoms of three mono-deprotonated diethano­lamine ligands and by a chloride anion. The mol­ecular conformation is stabilized by intra­molecular O—H⋯Cl bonds. In the crystal structure, complex mol­ecules and methanol solvent mol­ecules are linked into a three-dimensional network by N—H⋯Cl, N—H⋯O and O—H⋯Cl hydrogen-bonding inter­actions

Tandem Hydrogenolysis-Hydrogenation of Lignin-Derived Oxygenates over Integrated Dual Catalysts with Optimized Interoperations.

The efficient hydrodeoxygenation (HDO) of lignin-derived oxygenates is essential but challenging owing to the inherent complexity of feedstock and the lack of effective catalytic approaches. A catalytic strategy has been developed that separates C-O hydrogenolysis and aromatic hydrogenation on different active catalysts with interoperation that can achieve high oxygen removal in lignin-derived oxygenates. The flexible use of tungsten carbide for C-O bond cleavage and a nickel catalyst with controlled particle size for arene hydrogenation enables the tunable production of cyclohexane and cyclohexanol with almost full conversion of guaiacol. Such integration of dual catalysts in close proximity enables superior HDO of bio-oils into liquid alkanes with high mass and carbon yields of 27.9 and 45.0 wt %, respectively. This finding provides a new effective strategy for practical applications

Enhanced stability of charged dendrimer-encapsulated Pd nanoparticles in ionic liquids

Highly stable dendrimer-encapsulated Pd nanoparticles in ionic liquids were prepared for the first time by using charged PAMAM dendrimers as templates, which could maintain hydrogenation efficiency for up to at least 12 recycles

Correction: Li et al. Quantitative Evaluation of China’s Pork Industry Policy: A PMC Index Model Approach. Agriculture 2021, 11, 86

The authors found some omissions and errors in the original paper [...

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end

Quantitative Evaluation of China’s Pork Industry Policy: A PMC Index Model Approach

To ease the fluctuation of hog prices and maintain the hog market’s stability, the central government of China has issued a series of hog price control policies. This paper, supplemented by co-word analysis and LDA thematic modeling, constructed 9 first-level indicators and 36 second-level indicators and used a PMC index model to conduct quantitative research on the selected 74 policies and regulations of China’s pig price regulation policies from July 2007 to April 2020. The research concludes that the research tool system of China’s hog price control is formed. The overall design of the hog price control policy is relatively reasonable, but there are still the following problems: the subject of China’s pig price control policy is singular, so it is difficult to form a resultant force; the policy pays attention to the price regulation in the short term, but ignores the long-term industrial structure adjustment; it emphasizes market supervision, but insufficient support for slaughtering and processing; it focuses on production and management to improve the development quality and efficiency of the pig industry, but does not take social equity into account. Finally, some policy suggestions are put forward: multi-department division of labor and close cooperation; adjusting the industrial structure of hog and carrying out appropriate large-scale breeding; establishing the operation mode of slaughtering and processing in the producing area to reduce the circulation cost of the pig industry; ensuring the consumption of pork by low-income groups and giving consideration to social efficiency and equity

Research on the Disturbance Sources of Vegetable Price Fluctuation Based on Grounded Theory and LDA Topic Model

Vegetables are an important element in people’s dietary structure, and the price fluctuation of vegetables has attracted more and more attention. The disturbance sources of vegetable price fluctuations are characterized by uncertain risks, environmental complexity, nonlinearity, self-organization and mutation. Analyzing the disturbance sources that affect vegetable price fluctuation is helpful to the establishment of early warning and regulation mechanisms of vegetable price risk. To address the problem that existing studies have not comprehensively and objectively clarified the disturbance sources of vegetable price fluctuations, this paper proposes a method of combining the LDA (Latent Dirichlet Allocation) topic model with grounded theory, constructs a system of vegetable price volatility disturbance source indicators and relationship matrix by improved conceptual lattice-weighted cluster method, obtains 23 disturbance sources indicators affecting vegetable price fluctuations in four aspects of supply, demand, natural environment and economic policy environment, and identifies six key factors through calculation and analysis. Through the research of this paper, a system of disturbance source indicators affecting vegetable price fluctuations is constructed, the internal connection of many disturbance sources of vegetable price fluctuations in a complex and uncertain environment is clarified, and key influencing factors are selected, thus facilitating the establishment of vegetable price risk warning models and regulation mechanisms

A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting

This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P) filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others

Construction of an Early-Warning System for Vegetable Prices Based on Index Contribution Analysis

An early-warning indicator screening method is proposed in order to construct an early-warning system for vegetable prices. Through index contribution analysis and the application of a support vector regression algorithm, we compare the results of early warning before and after index optimization. Experimental results show that the proposed early-warning system was significantly improved after indicator optimization by using index contribution analysis