Diacenaphthylene-fused benzo[1,2-b:4,5-b‘]dithiophenes: polycyclic heteroacenes containing full-carbon five-membered aromatic rings

We herein report on an efficient synthesis of diacenaphthylenefused benzo[1,2-b:4,5-b’]dithiphenes and demonstrate that their packing structure in the solid state depends on the substituent groups. These compounds form dimers with their radical cations in high solution concentration and display good field-effect mobility

Exploring the Characteristics of Green Travel and the Satisfaction It Provides in Cities Located in Cold Regions of China: An Empirical Study in Heilongjiang Province

Green travel can decrease energy consumption and air pollution. Many cities in China have implemented measures encouraging residents to take public transport, ride bicycles, or walk. However, non-green travel is still popular in some northern cities due to prolonged cold weather. In order to understand the characteristics of green travel and its use by urban residents in Heilongjiang Province, a typically cold region, this study conducted traffic surveys in 13 cities in Heilongjiang Province. Through investigation and calculation of the data, we obtained key indicators such as the share rate of motorized travel for public transit and the satisfaction derived from green travel. According to the results of the data analysis, green energy buses are becoming increasingly popular in most cities in Heilongjiang Province. However, green travel infrastructure has failed to be updated on time, resulting in low satisfaction with travel in some cities, especially in terms of the waiting environment in winter. Results indicate the level of exploration and development of green transportation resources significantly differed across cities in Heilongjiang Province. By implementing targeted policies such as developing NEBs, obsoleting TEBs and old NEBs, and optimizing the bus network, old industrial cities can be reinvigorated. This will support governmental decisions and contribute to reducing carbon emissions

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

The traditional grey wolf algorithm is widely used for feature selection. However, within complex feature multi-dimensional problems, the grey wolf algorithm is prone to reach locally optimal solutions and premature convergence. In this paper, a threshold binary grey wolf optimizer based on multi-elite interaction for feature selection (MTBGWO) is proposed. Firstly, the multi-population topology is adopted to enhance the population’s diversity for improving search space utilization. Secondly, an information interaction learning strategy is adopted for the update of sub-population elite wolf position (optimal position) via learning better position from other elite wolves; in order to improve the local exploitation ability of the sub-population. At the same time, the command of $\beta$ and $\delta$ wolves (second and third best positions) for population position updates is removed. Finally, a threshold approach is employed to convert the continuous position of grey wolf individuals into binary one to apply in the feature selection problem. Further, The MTBGWO algorithm proposed in this paper is compared with the traditional binary grey wolf algorithm (BGWO), binary whale algorithm (BWOA), as well as some recently developed novel algorithms to exhibit its superiority and robustness. Totally 16 classification datasets, from the UCI Machine Learning Repository, are chosen for comparison. The Wilcoxon’s rank-sum non-parametric statistical test is carried out at 5% significance level to evaluate whether the results of the proposed algorithms significantly differs from those of the other algorithms. In the experimental results for all datasets, the overall average accuracy of the MTBGWO algorithm is 94.7%, while the highest of the other algorithms is 92.8% and the selected feature subset is 25% of the total dataset. The MTBGWO algorithm selects much smaller subset of features than other algorithms. In terms of computational efficiency, the overall processing time of MTBGWO is 24.2 seconds, whereas HSGW is 44.1 seconds. The results reveal that the MTBGWO has shown its superiority in solving the feature selection problem

A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications

The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population’s diversity and accelerates the algorithm’s convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance

Enhancement of Question Answering System Accuracy via Transfer Learning and BERT

Entity linking and predicate matching are two core tasks in the Chinese Knowledge Base Question Answering (CKBQA). Compared with the English entity linking task, the Chinese entity linking is extremely complicated, making accurate Chinese entity linking difficult. Meanwhile, strengthening the correlation between entities and predicates is the key to the accuracy of the question answering system. Therefore, we put forward a Bidirectional Encoder Representation from Transformers and transfer learning Knowledge Base Question Answering (BAT-KBQA) framework, which is on the basis of feature-enhanced Bidirectional Encoder Representation from Transformers (BERT), and then perform a Named Entity Recognition (NER) task, which is appropriate for Chinese datasets using transfer learning and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. We utilize a BERT-CNN (Convolutional Neural Network) model for entity disambiguation of the problem and candidate entities; based on the set of entities and predicates, a BERT-Softmax model with answer entity predicate features is introduced for predicate matching. The answer ultimately chooses to integrate entities and predicates scores to determine the definitive answer. The experimental results indicate that the model, which is developed by us, considerably enhances the overall performance of the Knowledge Base Question Answering (KBQA) and it has the potential to be generalizable. The model also has better performance on the dataset supplied by the NLPCC-ICCPOL2016 KBQA task with a mean F1 score of 87.74% compared to BB-KBQA

Enhancement of Question Answering System Accuracy via Transfer Learning and BERT

Entity linking and predicate matching are two core tasks in the Chinese Knowledge Base Question Answering (CKBQA). Compared with the English entity linking task, the Chinese entity linking is extremely complicated, making accurate Chinese entity linking difficult. Meanwhile, strengthening the correlation between entities and predicates is the key to the accuracy of the question answering system. Therefore, we put forward a Bidirectional Encoder Representation from Transformers and transfer learning Knowledge Base Question Answering (BAT-KBQA) framework, which is on the basis of feature-enhanced Bidirectional Encoder Representation from Transformers (BERT), and then perform a Named Entity Recognition (NER) task, which is appropriate for Chinese datasets using transfer learning and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. We utilize a BERT-CNN (Convolutional Neural Network) model for entity disambiguation of the problem and candidate entities; based on the set of entities and predicates, a BERT-Softmax model with answer entity predicate features is introduced for predicate matching. The answer ultimately chooses to integrate entities and predicates scores to determine the definitive answer. The experimental results indicate that the model, which is developed by us, considerably enhances the overall performance of the Knowledge Base Question Answering (KBQA) and it has the potential to be generalizable. The model also has better performance on the dataset supplied by the NLPCC-ICCPOL2016 KBQA task with a mean F1 score of 87.74% compared to BB-KBQA

Tailoring Electrostatic Attraction Interactions to Activate Persistent Room Temperature Phosphorescence from Doped Polyacrylonitrile Films

Amorphous organic materials exhibiting room temperature phosphorescence (RTP) are good candidates for optoelectronic and biomedical applications. In this proof-of-concept work, we present a rational strategy to activate persistent RTP with a wide range of color from doped films in which electron-rich organic phosphor as donor while electron-deficient polymer matrix as acceptor through electrostatic attraction interactions. By tailoring electrostatic attraction interactions between the donor and acceptor, an ultralong lifetime of 968.1 ms is achieved for doped film TBB-6OMe@PAN. Control experiments combined with theoretical calculations demonstrate that the electrostatic attraction interactions between organic phosphor and polymer matrix should be responsible for the persistent RTP of doped films. Besides, doped films show reversible thermal response and excellent stability in water, indicating an advantage of electrostatic attraction over hydrogen bond in terms of practical application.</p

Achieving Diversified Emissive Behaviors of AIE, TADF, RTP, Dual-RTP and Mechanoluminescence from Simple Organic Molecules by Positional Isomerism

Organic emitters with multiple emissions such as aggregation-induced emission (AIE), thermally activated delayed fluorescence (TADF), room-temperature phosphorescence (RTP), and mechanoluminescence (ML) are promising candidates for various high-tech applications but still very rare. Here, for the first time, an effective strategy of positional isomerism is applied to obtain simple organic isomers featuring diversified emissive behaviors of AIE, TADF, RTP, dual-RTP, and ML. In Particular, simultaneous AIE, TADF, and ML are successfully realized in the ortho-isomer o-ClTPA while typical AIE and unique dual-RTP coexist in the para-isomer p-ClTPA. Crystal structure analysis shows that the subtle variation in the substitution position can significantly alter intermolecular interactions and molecular packings, which in turn exerts remarkable effects on crystal-state emissive properties. Therefore, diversified emissive behaviors of TADF, RTP, dual-RTP, and ML are consequently observed from these positional isomers. Furthermore, theoretical calculations not only afford deep insight into the mechanism of different luminescent properties but also elaborate the relationship between positional isomers and luminescent properties from molecular and crystalline perspectives. These simple isomers featuring diversified emissive behaviors of AIE, TADF, RTP, dual-RTP, and ML are reported for the first time, which will deepen the understanding of the relationship between molecule structures and multiple emissions