Search

In today's society, there is an urgent need to help users better access information that they are interested in, as there is an increasing amount of news and messages available with the development of the Internet. Many existing methods involve directly inputting text into a pre-trained model, which limits the effectiveness of text feature extraction. The personalized news recommendation model discussed in this article is a model that can enhance feature extraction from news articles. It consists of a candidate news module, a historically accessed news module, and an access prediction module. Using news titles that accurately summarize news content, a model with double multi-head attention mechanisms and double residual structures (DDM) is utilized to better capture the features of news articles historically accessed by users, thereby achieving an improved recommendation functionality. The candidate news module aims to help the model learn representations of news that users are likely to select from the news titles. The user historical click news module primarily serves to enable the model to learn personalized representations of users from news they have previously browsed. The model has been tested on MIND-small. The AUC reached 0.6665, the MRR reached 0.3205, the nDCG@5 reached 0.3532, and the nDCG@10 reached 0.4158. The results indicate this model has achieved good results in the downstream tasks of preprocessing news-title texts. [ABSTRACT FROM AUTHOR]

Performance of gear transmissions affects the performance of mechanical equipment. It is necessary to develop more reliable gear pitch and tooth thickness accuracy detection methods in order to evaluate gear transmission performance and detect gear pitch and tooth thickness accuracy more accurately. Based on the basic theory of gears, binocular vision technology, and statistical principles, a new method that measures gear pitch and tooth thickness using machine vision is proposed: coaxial multi-ring detection (MCD). There is no contact, no damage, and a high degree of efficiency with this method. Using this method, we are able to detect the pitch and thickness accuracy of each gear tooth multiple times within the tooth width range and in multiple directions perpendicular to the gear axis. We statistically analyze the measurement results to determine the gear's most accurate detection results. The measurement method for gear machining accuracy is investigated using the coaxial multi-ring detection method. The statistical analysis of multiple measurement results is carried out, and the measurement results obtained are highly consistent with those of the gear detection center. In conclusion, the measurement results of this method are highly reliable, and they can be used as a reliable basis for evaluating gear transmission performance. [ABSTRACT FROM AUTHOR]

HY-2C is the third satellite in China's ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (TB) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR's antenna temperatures and a model based on the Whale Optimization Algorithm (WOA), Levenberg–Marquardt (LM) algorithm, and Back-Propagation neural network (WOA–LM–BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated TB at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3. [ABSTRACT FROM AUTHOR]

Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques, while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper, we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as greenhouses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environments, especially for traits that are difficult to be tested in multi-environment trials (MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer's cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.