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Automatic text summarization can help people to filter and identify information quickly,grasp the key content of news,and alleviate the problem of information overload.The mainstream abstractive summarization model is mainly based on the encoder-decoder architecture.In view of the fact that the decoder does not fully consider the text topic information when predicting the target word,and the traditional Word2Vec static word vector cannot solve the polysemy problem,an automatic summarization model for Chinese short news is proposed,which integrates the BERT word embedding representation and topic information enhancing.The encoder combines unsupervised algorithm to obtain text topic information and integrates it into the attention mechanism to improve the decoding effect of the model.At the decoder side,the BERT sentence vector extracted from the BERT pre-trained language model is used as the supplementary feature to obtain more semantic information.Meanwhile,pointer mechanism is introduced to solve the problem of out of vocabulary,and coverage mechanism is used to suppress repetition effectively.Finally,in the training process,reinforcement learning method is adopted to optimize the model for non-differentiable index ROUGE to avoid exposing bias.Experimental results on two datasets of Chinese short news summarization show that the proposed model can significantly improve the ROUGE evaluation index,effectively integrate text topic information,and generate fluent and concise summaries.

Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1−xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials. [ABSTRACT FROM AUTHOR]

Spirotryprostatins are representative members of medicinally interesting bioactive molecules of the spirooxindole natural products. In this communication, we present a novel enantioselective total synthesis of the spirooxindole alkaloid dihydrospirotryprostatin B. The synthesis takes advantage of copper-catalyzed tandem reaction of o-iodoanilide chiral sulfinamide derivatives with alkynone to rapidly construct the key quaternary carbon stereocenter of the natural product dihydrospirotryprostatin B. [ABSTRACT FROM AUTHOR]

By grouping nodes with similar attributes into clusters, the energy efficiency and lifespan of wireless sensor networks(WSNs) can be improved effectively. However, the number of clusters needs to be set in advance, and the optimal cluster heads (CHs) are difficult to determine, which will undoubtedly reduce the network’s overall performance. Therefore, a new clustering method using Affinity Propagation and Chaotic Lion swarm Optimization is proposed in this paper to form optimal clusters, which is called APCLO. In APCLO, Affinity Propagation (AP) is used to construct a cluster topology, forming the initial clustering according to the remaining energies of the nodes and the similarity of the distances between nodes. Moreover, the initial CHs are also determined simultaneously by AP. In order to eliminate the outliers from the initial CHs, Chaotic Lion Optimization(CLO) is presented to find the best CHs, in which a fitness function is set according to residual energy and distance to the BS, and chaotic map is used to speed up the convergence of CLO. Simulation results show that the APCLO protocol is superior to the comparison protocols in terms of energy consumption, network throughput, convergence speed, and lifetime. For network lifespan, it increases by 20.1%, 11.2%, and 13.5% respectively.

Objective To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered. [ABSTRACT FROM AUTHOR]

Recently, composting cultivation method is widely used in oyster mushroom production. In this study, we focused on the effects of composting processes on nutritional qualities and antioxidant activity of Pleurotus floridanus mushroom fruiting bodies. Three treatments of different composting time (2, 4, and 5 days) were performed with an atmospheric sterilization treatment as the control. The results showed that the pH value, total carbon content, and total nitrogen content of substrate were critical parameters which would significantly affect mushroom qualities and bioactivities. Fruiting bodies of the control demonstrated significantly higher crude protein content, total amino acid content, and essential amino acid content than that of composting treatments. Moreover, fruiting bodies of treatment D4 and D5 manifested significantly higher crude polysaccharide contents. Crude polysaccharide of treatment D4 represented the highest scavenging ability toward both radical 3‐ethylbenzthiazoline‐6‐sulfonic acid (ABTS·+) and Hydroxyl radical (OH·). It suggests that composting processes is suitable for oyster mushroom cultivation based on nutritional and antioxidant qualities of fruiting bodies. [ABSTRACT FROM AUTHOR]