Hesitant Fuzzy DeGroot Opinion Dynamics Model and Its Application in Multi-attribute Decision Making

The research on the evolution law of the opinions can help the decision makers (DMs) improve the decision-making efficiency, predict the trend of events and make the right decision. These opinions are always described by one number, which is inaccurate and incomplete. To solve such a problem, in this paper, the hesitant fuzzy DeGroot (HF-DeGroot) opinion dynamics model is proposed. In order to simulate the transformation of hesitant fuzzy opinions, we introduced the multiplications for real matrix and hesitant fuzzy matrix. Then three kinds of transformation matrices with the consideration of the similarity degree, self-confidence degree and authority degree are constructed based on the hesitant fuzzy data and the consensus condition for the model is discussed as well. Furthermore, the HF-DeGroot opinion dynamics decision-making method is proposed from a prediction perspective and is applied to the emergency decision for the public health events. Finally, the effectiveness, feasibility and practicability of this method are shown by the comparison and simulation results

Rhabdomyolysis and acute kidney injury in the deceased donor: a case report

Rhabdomyolysis is a common complication among brain death donors, affecting the number of organ donations and the quality of donor kidneys. Case report: Male, 17 years old, admitted to the hospital due to a car accident. Subsequently, brown urine appeared, blood myoglobin increased significantly, urine output decreased, and renal function impaired. Treatments including fluid replacement, alkalization of urine, plasma exchange and bedside CRRT were given. The patient's renal function recovered, and the organs were successfully acquired. The renal function recovered well after transplantation. Conclusion: Attention should be paid to rhabdomyolysis. Early diagnosis and treatment of patients with brain death could improve donation success rate and the recovery of postoperative renal function

Improving the security of quantum direct communication with authentication

Two protocols of quantum direct communication with authentication [Phys. Rev. A {\bf 73}, 042305 (2006)] are recently proposed by Lee, Lim and Yang. In this paper we will show that in the two protocols the authenticator Trent should be prevented from knowing the secret message of communication. The first protocol can be eavesdropped by Trent using the the intercept-measure-resend attack, while the second protocol can be eavesdropped by Trent using single-qubit measurement. To fix these leaks, I revise the original versions of the protocols by using the Pauli-Z operation $\sigma_z$ instead of the original bit-flip operation $X$. As a consequence, the protocol securities are improved.Comment: Any suggestion,comment or help is welcome

The extraction of natural essential oils and terpenoids from plants by supercritical fluid

In order to provide guidance for the improvement of supercritical fluid extraction technology in the extraction of natural volatile oil and terpenoids from plants, SFE was compared with steam distillation, solvent extraction, Soxhlet extraction, pressure method and other traditional extraction processes, and the supercritical CO2 extraction conditions of SFE in the extraction of natural volatile oil and terpenoids were studied, including temperature, pressure, extraction time, extraction time, extraction time, extraction time, extraction time, extraction time and so on. The influence of entrainer or co extractant on the extraction effect was discussed to provide optimization parameters for the extraction process of natural volatile oil and terpenoids. SFE technology has advantages in the extraction of natural plant volatile oil and has broad application prospects in industrial production

How Social Capital Affects the Quality Performance of Agricultural Products: Evidence from a Binary Perspective of China

Improving the quality of agricultural products is the key factor in promoting agriculturaldevelopment in the Belt and Road program. Although many studies have investigated therelationship between social capital and performance, the findings are inconsistent. Moreover, themechanism of how social capital affects the quality performance of agricultural products remainsunclear. Accordingly, this study developed a theoretical model with propositions from a socialcapital-quality performance of agricultural products paradigm for examining and comparing thethree dimensions of social capital: The relationships among cognitive (measured by shared values),relational (measured by reciprocity) and structural (measured by communication), and their role inensuring quality performance of agricultural products from the company and farmer perspectives.This study selected the companies and farmers in “A company + farmers” model. The data analysisis based on a sample of 184 companies and 414 farmers. The results show that shared values andcommunication have a significant positive effect on reciprocity. In terms of the influence onreciprocity, communication is higher than shared values from both the corporate and farmerperspectives. The three dimensions of social capital have different effects on quality performance ofagricultural products. On the company side, communication and reciprocity in social capital have asignificant positive effect on the quality performance of agricultural products, with the order ofeffect being communication first followed by reciprocity. On the farmer side, reciprocity and sharedvalues have a significant positive effect on the quality performance of agricultural products, withthe order of effect being reciprocity first followed by shared values. These findings have positivetheoretical and practical significance for companies and farmers aiming to improve the quality ofagricultural products

Polyketides from the Halotolerant Fungus Myrothecium sp. GS-17

Two new polyketides, myrothecol (1) and 5-hydroxy-3-methyl-4-(1- hydroxylethyl)-furan-2(5H)-one (2), were isolated from the fermentation broth of the halotolerant fungus Myrothecium sp. GS-17 along with three known compounds, 5-hydroxyl-3-[(1S)-1-hydroxyethyl]-4-methylfuran-2(5H)-one (3), 3,5-dimethyl-4- hydroxylmethyl-5-methoxyfuran-2(5H)-one (4), and 3,5-dimethyl-4-hydroxymethyl-5- hydroxyfuran-2(5H)-one (5). Compound 1 is the first natural occurring polyketide with a unique furylisobenzofuran skeleton. The structures of these compounds were established via extensive spectroscopic analyses including 1D-, 2D-NMR, HRESI-MS, and crystal X-ray diffraction analysis

Deep Adaptive Graph Clustering via Von Mises-Fisher Distributions

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed Deep Adaptive Graph Clustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρk which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e., (H|ρ) and (ρ|H), respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets