Abnormal
activation of fibroblast growth factor receptors (FGFRs)
results in the development and progression of human cancers. FGFR2
is frequently amplified or mutated in cancers; therefore, it is an
attractive target for tumor therapy. Despite the development of several
pan-FGFR inhibitors, their long-term therapeutic efficacy is hindered
by acquired mutations and low isoform selectivity. Herein, we report
the discovery of an efficient and selective FGFR2 proteolysis-targeting
chimeric molecule, LC-MB12, that incorporates an essential rigid linker.
LC-MB12 preferentially internalizes and degrades membrane-bound FGFR2
among the four FGFR isoforms; this may promote greater clinical benefits.
LC-MB12 exhibits superior potency in FGFR signaling suppression and
anti-proliferative activity compared to the parental inhibitor. Furthermore,
LC-MB12 is orally bioavailable and shows significant antitumor effects
in FGFR2-dependent gastric cancer in vivo. Taken together, LC-MB12
is a candidate FGFR2 degrader for alternative FGFR2-targeting strategies
and offers a promising starting point for drug development.
With the development of wind resources under flat terrain, wind farms in extreme wind conditions are developed, and the size of the WT’s rigid-flexible coupling components increases. Therefore, accurately understanding the load characteristics and transmission mechanism of each component plays an important scientific role in improving the reliability of WT (WT) design and operation. Through the collation and analysis of the literature, this review summarizes the research results of large-scale WT load under source–grid coupling. According to the classification of sources, the variation characteristics of different loads are analyzed, and different research methods for different loads are summarized. In addition, the relative merits of the existing improvement schemes are analyzed, and the existing problems are pointed out. Finally, a new research idea of ‘comprehensively considering the coupling effects of source and network factors, revealing WT load characteristics and transmission mechanism’ is summarized. This paper provides important implications for the safety design and reliable operation research of large WTs with complex terrain.
A random wind speed time history model based on Weibull distribution is constructed. According to the known mean and variance of wind speed, the time fitting curve of random wind speed is obtained through MATLAB simulation method. According to the wind speed range of the actual operation of the wind turbine, the obtained wind speed curve is transformed, and then according to the obtained relationship between the wind speed of the wind turbine and the input torque, the input time-varying torque of the actual operation of the wind turbine is obtained. Establish the rigid shaft model of the wind turbine gearbox, calculate the gearbox transmission ratios at all levels, and then obtain the torque output curves of the gearbox planetary stage, medium-speed stage, and high-speed stage under random wind speed input to study wind power the torque transmission law of the machine drive system. The ability to better analyse the transmission structure of the gearbox and the torque transmission law of key components has a certain significance for the design and manufacture of the wind turbine transmission chain.
Under the background of the “strong direct current and weak alternating current” large power grid, the synchronous condenser with dynamic reactive power support capability becomes more important. Due to factors such as manufacturing, installation, and changes in operating conditions, there are many faults associated with the synchronous condenser. This paper studies a fault diagnosis method based on multi-scale zooming learning framework. First, through the energy fully connected (energy FC) layer, the synchronous condenser feature components of the fault signal of the camera are learned, and the transient features of the signal are enhanced. At the same time, the data is adaptively compressed and the effective features are mapped in a distributed manner. The faults are effectively diagnosed and isolated in advance. Secondly, a multi-scale learning framework is constructed to learn the multi-frequency features in the vibration signal. Finally, experiments show that the proposed method has certain advantages over the existing excellent models. The accuracy rate of diagnosis is higher than 99%.
This study examined whether time slack would influence the prediction of task completion time and whether need for cognition would influence prediction accuracy if time scenarios were manipulated. With a laboratory experiment in which 140 subjects were involved, two major results were obtained: (1) People expected time slack to be greater in the future than in the present, but the operation of time slack scenarios did not reduce the participants' optimism about when their tasks would be completed. Time constraint scenarios enhanced their prediction optimism. (2) Individual differences in need of cognition would influence the effects of operation of time scenarios.
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