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This paper studies a combination forecasting model with strong adaptability and high dimension to evaluate value of patents, and verifies the model empirically in the research. Through the AHP analysis, five necessary factors that affect the value of patents are discovered, which come from both the characteristic factors of the patent itself and the institutional characteristic factors that attempt to transform the patent. We’ve constructed a state space model through some data that can already obtain the added value of its patent through the Hejun value evaluation model and deployed the state space model in the artificial intelligence data space to have the neural network training. Another part of the known data is used to empirically verify this model, and it is found that the data fits well. Not only can the model be used for patent conversion institutions to evaluate patents, but also for patent holders to evaluate patent conversion institutions.

Based on the strong classification feature recognition algorithm, the calculation algorithm of a text semantic similarity is studied with the performance evaluation in this paper. In order to achieve a general algorithm for this function, the semantic function library based on a semantic recognition code as a comparison object is designed. It drives the algorithm modules of two fuzzy neuron deep convolution machine learning, and between these two processes of machine learning, a rigid algorithm based on Fourier transform frequency domain feature is extracted. Finally, a more complex machine learning general algorithm is realized by the use of external data fuzzy algorithm and de-fuzzy algorithm before and after the algorithm module. It is also a technical innovation in this paper. Through the performance evaluation based on the subjective evaluation of volunteers, it is found that the system focuses on the text semantic similarity evaluation of the Chinese language, and achieves a comparison result of 81.78% of the artificial judgment accuracy rate, and only 5.52% of the volunteers believe that the system judgment result is completely different from that of manual judgment.

Glycolysis is vital for the excessive proliferation of keratinocytes in psoriasis, and uridine phosphorylase‐1 (UPP1) functions as an enhancer of cancer cell proliferation. However, little is known about whether UPP1 promotes keratinocyte proliferation and accelerates psoriasis development. This study revealed that UPP1 facilitates cell viability and cell‐cycle progression in human epidermal keratinocytes (HEKs) by modulating the glycolytic pathway. Bioinformatics analysis of UPP1 gene expression and its correlation with the Reactome revealed that UPP1 mRNA expression, cell‐cycle progression, the interleukin‐6 (IL‐6)/Janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) pathway and glycolysis were positively associated with psoriasis. Cell proliferation, the cell cycle and glycolysis were evaluated after UPP1 was silenced or overexpressed. The results showed that UPP1 overexpression increased cell proliferation, cell‐cycle progression and glycolysis, which was contrary to the effects of UPP1 silencing. However, the STAT3 inhibitor diminished UPP1 expression because STAT3 can bind to the UPP1 promoter. In conclusion, UPP1 was significantly activated by the IL‐6/STAT3 pathway and could modulate glycolysis to regulate cell proliferation and cell‐cycle progression in keratinocytes during the development of psoriasis. [ABSTRACT FROM AUTHOR]

The state-norm estimation problem for the switched nonlinear system under a more generalized type of switching signals is investigated in this paper. The component subsystems consist of input/output-to-state stability (IOSS) and non-IOSS continuous systems simultaneously, whose activation time is lower and upper bounded by mode-dependent time-varying functions, respectively. The considered switching signals satisfy mode-dependent time-varying constraints, whose switching number can grow faster than those with constant average dwell time condition. Then based on multiple IOSS Lyapunov functions method, the existence condition and design method for the switched state-norm estimator are proposed. The obtained results present extensions compared with the existing results, which are applicable for the more general situations. Finally, the effectiveness and improvement of the proposed approach are illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper considers the event-triggered H ∞ filtering problem for continuous-time switched linear systems. It is assumed that only the information of system output and switching signal is available to the filter at each event-triggered sampling instant. Based on a mode dependent event-triggered sampling scheme, the filtering error system is modeled as a switched system with augmented switching signal. Then, by the average dwell time technique and multiple Lyapunov function method, a new sufficient condition is obtained such that the filtering error system is exponentially asymptotically stable and satisfies the weighted H ∞ performance. The design method for the filter and event-triggered parameters is formulated by solving a set of linear matrix inequalities. Finally, the effectiveness of the proposed approach is illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]