According to the weakness of building ontology needs manually designated concepts and instances from the basic information of web,an automatic ontology learning approach based on web information items is designed.Utilizing pre-research that an arithmetic of an inductive learning based on DOM for the similar path of information items and an approach for identifying automatic Key words based on PAT-tree,the learning for the concepts and the relation between concepts is implemented by using an approved TFIDF statistic method and an algorithm of composite event association rule,the information item ontology is built,the manual workload for building ontology is reduce
Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments.In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP.A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.
Development of efficient catalyst with reduced noble metal usage is extremely desirable for selective hydrogenations of furan-containing bio-based feedstocks, which represents an attractive and sustainable alternative to petrochemical resources.
The effect of digital transformation on enterprise technological innovation is reflected in quantity and quality, which may show heterogeneity. In this regard, this paper uses the data of China’s A-share agricultural listed companies from 2015 to 2020 to compare the differential impact of enterprise digital transformation from the perspective of quantity and quality of technological innovation. Firstly, the Tobit model is used to test whether there are differences in the impact of digital transformation on the quantity and quality of technological innovation of agricultural enterprises, and heterogeneity is tested according to the nature of enterprises. Secondly, this paper explores the reasons digital transformation has different effects on the quantity and quality of technological innovation through mechanism analysis. Finally, according to the threshold model, the conditions for digital transformation to promote the quantity and quality of technological innovation of agricultural enterprises are discussed. The empirical results show that, first, the digital transformation of agricultural enterprises only promotes the number of technological innovations, and there is heterogeneity in the nature of enterprises, but the innovation efficiency is not affected. Second, the period expense rate will lead to digital transformation, having different effects on the quantity and efficiency of technological innovation of agricultural enterprises. Third, the impact of digital transformation on the technological innovation efficiency of agricultural enterprises has a significant single threshold effect, and when the period expense rate is less than the threshold, the digital transformation has a significant role in promotion.
This paper from the perspective of productivity changes examines the impact of innovation activities and foreign direct investment (FDI) on improved green productivity (IGP) in developing countries. We divide the sample into two sub-groups; the BRICS and the other developing countries so as to account for underlying country heterogeneity. The analysis follows a panel data approach over the period 1991 to 2014, and used the global Malmquist-Luenberger productivity index to measure IGP. The results indicate that IGP in developing countries has declined. Innovation activities have a positive impact on IGP. FDI has a significant negative impact on IGP. Further study finds that there are threshold effects between FDI and IGP based on innovation activities, when the developing countries with a low-level of innovation, FDI has a negative impact on IGP; when the developing countries innovation activities above the threshold, innovation activities and FDI both can promote IGP.
<abstract><p>Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.</p></abstract>
When the modulation algorithm of the space vector in a multilevel inverter was rotated by 45°,the concepts of characteristic network using basic vectors to track the reference vectors,characteristic quadrangle and vectors basic switching were obtained.On the basis of the switching relation between the closed routes of characteristic quadrangles,we chose the per-vertex of each sector in the basic switching traversal characteristic network.Then,we got the least switching times in the modulation algorithm of space vectors in multilevel inverters.Based on a 2-cell 5-level inverter,according to the reference voltage variation,we have found that the characteristic network can be divided into two types and have given the corresponding algorithms of switching times.Compared with the simulation result,it has been shown that this algorithm is correct.And compared with the phase disposition sinusoidal modulation(PD SPWM) method,the total switching times in one reference voltage period of the proposed modulation algorithm is only 38% of PD SPWM.The simulation output voltage waveform is quite close to the reference voltage.
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