Search

Six groups of austenitic 022Cr19Ni10 stainless steel bending specimens with three types of cross-sectional forms were used to study the impact of V-stiffeners on the failure mode and flexural behavior of stainless steel lipped channel beams. These cross-sectional forms included V-stiffeners in the web compression zone at 1/3 height near the compressed flange and no V-stiffeners on the flange, V-stiffeners in the web compression zone at 1/3 height near the compressed flange and V-stiffeners in the middle of the compressed flange, and V-stiffeners on the web at 1/2 height and V-stiffeners in the middle of the compressed and tensioned flange. The findings show that the specimens without a V-stiffener on the flange and with a V-stiffener in the web compression zone at 1/3 height near the compressed flange have a lower ultimate bearing capacity as a result of local–distortional interaction buckling. The test specimen’s flexural bearing capacity will rise with an increase in web height under the same stiffening form and other fixed cross-sectional parameters. Furthermore, the additional V-stiffeners in the middle of the flange effectively lower the width-to-thickness ratio of the stainless steel specimen with a flange that has been V-stiffened, thus increasing the specimen’s bending bearing capacity by 13% and emphasizing the distortional buckling issue. Buckling first manifests in the compression flange and is caused by the stress evolution on the web of the bending specimen, which is constrained by the V-stiffeners. The specimen’s capacity to withstand bending loads can be improved more successfully through the use of V-stiffeners in the web compression zone at 1/3 height close to the compressed flange and V-stiffeners in the middle of the compressed flange. For the test data, a finite element simulation was established, and the results are generally consistent with the test results.

With the development of the Internet, the problem of information overload becomes more and more serious. The personalized recommendation technology can establish user profiles through the user’s behavior and other information, and automatically recommend the items that best match the user’s preferences, thus effectively reducing the information overload problem. Although scholars from all over the world have proposed many solutions to the personalized news recommendation system, there are still problems such as computing redundancy, incomplete data, and the inability to make a personalized recommendation to users. Based on the problem, this paper will focus on selected IHUMCF algorithm in computing the user similarity neighbor reason is inadequate to some extent, and the attention degree impact factor is put forward, and then puts forward the L-HUMCF algorithm, and then this paper based on L-HUMCF recommended to improve, as a result, joined the user of information entropy, EL-HUMCF algorithm is proposed, and experimental verification algorithm is effective. Experimental results show that the proposed algorithm is better than other personalized recommendation algorithms.

Agricultural news usually describes objective facts, and its subjectivity is weak. Conventional sentiment analysis methods which can get high accuracy by definite sentiment trend are difficult to apply to the agricultural news filed. We propose a simple yet effective framework that allows us to accurately analyze the sentiment of Chinese Agricultural News. The basic idea is to create a dictionary of a positive and negative sentiment of agricultural news as prior knowledge and construct a sentiment classification model with a joint emotion-subject model (JST) and the dictionary. The practical merits of our approach are demonstrated in real-world datasets.