边缘计算中云边任务下发策略研究

目前边缘计算的相关研究大部分着眼于如何将设备端数据卸载至边缘端进行处理,而未考虑云中心如何高效率、低延时地将不同任务下发至边缘节点的问题.针对该问题,文中提出了一种边缘计算架构模型,通过对任务进行统一建模,使用改进的Dijkstra算法得到任务下发最优路径,减少所需计算节点数量和提升计算性能,使其能在最短的时间内下发到...     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Characterization of Roasting Time on Sensory Quality,Color, Taste,and Nonvolatile Compounds of Yuan An Yellow Tea

Roasting is crucial for producing Yuan An yellow tea (YAYT) as it substantially affects sensory quality. However, the effect of roasting time on YAYT flavor quality is not clear. To investigate the effect of roasting time on the sensory qualities, chemical components, odor profiles, and metabolic profile of YAYTs produced with 13 min roasting, 16 min roasting, 19 min roasting, 22 min roasting, and 25 min roasting were determined. The YAYTs roasted for 22 min got higher sensory scores and better chemical qualities, such as the content of gallocatechin (GC), gallocatechin gallate (GCG), free amino acids, solutable sugar, meanwhile the lightness decreased, the hue of tea brew color (b) increased, which meant the tea brew got darker and yellower. YAYTs roasted for 22 min also increased the contents of key odorants, such as benzaldehyde, nonanal, β-cyclocitral, linalool, nerol, α-cedrol, β-ionone, limonene, 2-methylfuran, indole, and longiborneol. Moreover, non-targeted metabolomics identified up to 14 differentially expressed metabolites through pair-wise comparisons, such as flavonoids, phenolic acids, sucrose, and critical metabolites, which were the main components corresponding to YAYT roasted for 22 min. In summary, the current results provide scientific guidance for the production of high quality YAYT.     

Investigation Driven by Network Pharmacology on Potential Components and Mechanism of DGS,a Natural Vasoprotective Combination,for the Phytotherapy of Coronary Artery Disease

Phytotherapy offers obvious advantages in the intervention of Coronary Artery Disease (CAD), but it is difficult to clarify the working mechanisms of the medicinal materials it uses. DGS is a natural vasoprotective combination that was screened out in our previous research, yet its potential components and mechanisms are unknown. Therefore, in this study, HPLC-MS and network pharmacology were employed to identify the active components and key signaling pathways of DGS. Transgenic zebrafish and HUVECs cell assays were used to evaluate the effectiveness of DGS. A total of 37 potentially active compounds were identified that interacted with 112 potential targets of CAD. Furthermore, PI3K-Akt, MAPK, relaxin, VEGF, and other signal pathways were determined to be the most promising DGS-mediated pathways. NO kit, ELISA, and Western blot results showed that DGS significantly promoted NO and VEGFA secretion via the upregulation of VEGFR2 expression and the phosphorylation of Akt, Erk1/2, and eNOS to cause angiogenesis and vasodilation. The result of dynamics molecular docking indicated that Salvianolic acid C may be a key active component of DGS in the treatment of CAD. In conclusion, this study has shed light on the network molecular mechanism of DGS for the intervention of CAD using a network pharmacology-driven strategy for the first time to aid in the intervention of CAD.     

Cu(ii)/SPDO complex catalyzed asymmetric Baeyer–Villiger oxidation of 2-arylcyclobutanones and its application for the total synthesis of eupomatilones 5 and 6

A novel classical kinetic resolution of 2-aryl-substituted or 2,3-disubstituted cyclobutanones of Baeyer–Villiger oxidation catalyzed by a Cu(ii)/SPDO complex is reported for the first time, producing normal lactones in excellent enantioselectivities (up to 96% ee) and regioselectivities (up to >20/1), along with unreacted ketones in excellent enantioselectivities (up to 99% ee). The current transformation features a wide substrate scope. Moreover, catalytic asymmetric total syntheses of natural eupomatilones 5 and 6 are achieved in nine steps from commercially available 3-methylcyclobutan-1-one.

A novel classical kinetic resolution of Baeyer–Villiger oxidation catalyzed by a Cu(ii)/SPDO complex with excellent enantioselectivity, regioselectivity and wide substrate scope is reported for the first time and explore the synthetic application.     

Ordinal Regression for Information Retrieval

This letter presents a new discriminative model for Information Retrieval （IR）, referred to as Ordinal Regression Model （ORM）. ORM is different from most existing models in that it views IR as ordinal regression problem （i.e. ranking problem） instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine （SVM）. The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference （TREC） sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.     

Light-Responsive Hexagonal Assemblies of Triangular Azo Dyes

The rational design of small building block molecules and understanding their molecular assemblies are of fundamental importance in creating new stimuli-responsive organic architectures with desired shapes and functions. Based on the experimental results of light-induced conformational changes of four types of triangular azo dyes with different terminal functional groups, as well as absorption and fluorescence characteristics associated with their molecular assemblies, we report that aggregation-active emission enhancement (AIEE)-active compound (1) substituted with sterically crowded tert-butyl (t-Bu) groups showed approximately 35% light-induced molecular switching and had a strong tendency to assemble into highly stable hexagonal structures with AIEE characteristics. Their sizes were regulated from nanometer-scale hexagonal rods to micrometer-scale sticks depending on the concentration. This is in contrast to other triangular compounds with bromo (Br) and triphenylamine (TPA) substituents, which exhibited no photoisomerization and tended to form flexible fibrous structures. Moreover, non-contact exposure of the fluorescent hexagonal nanorods to ultraviolet (UV) light led to a dramatic hexagonal-to-amorphous structure transition. The resulting remarkable variations, such as in the contrast of microscopic images and fluorescence characteristics, were confirmed by various microscopic and spectroscopic measurements.     

Comparison of Fatty Acid Contents and MMP-1 Inhibitory Effects of the Two Antarctic Fish,Notothenia rossii and Champsocephalus gunnari

Total fatty-acid (FA) contents of different organs (stomach, liver, brain, and skin) of two Antarctic fish, marbled rockcod (Notothenia rossii) and mackerel icefish (Champsocephalus gunnari), were examined using gas chromatography–mass spectrometry (GC–MS). N. rossii possessed higher contents of total omega-3, where eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), the most represented omega-3 FAs, were distributed throughout all parts of the fish. The highest level of EPA was observed in the skin and that of DHA was observed in the brain of N. rossii. C. gunnari showed organ peculiarity in that most of the omega-3 FAs were found in stomach and skin. Specifically, the highest levels of EPA and DHA were both observed in the stomach. Although N. rossii and C. gunnari both inhabit the Antarctic Southern Oceans, their characteristics in terms of the composition of fatty acids were shown to vary. The extracts were also evaluated for matrix metalloproteinase-1 (MMP-1)-inhibitory activities in UVB-induced human dermal fibroblasts, where extracts of the skin and liver of N. rossii showed the most significant inhibition upon MMP-1 production. These findings provide experimental evidence that the extracts of the Antarctic fish could be utilized as bioactive nutrients, particularly in the enhancement of skin health.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.