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Abstract When multiple electric vehicles need to be charged, it will take more time and money for the electric vehicles to randomly enter the charging stations during the disorderly scheduling process. In the meantime, the utilization rate of charging piles is different, and the load of power grid is heavier. In this paper, a charging scheduling strategy is designed considering of the requests of multiple electric vehicles, which schedule in a way of overall parallel. In this charging scheduling strategy, electric vehicles will cost less time and money, the utilization rate of charging piles is more equal, and the power grid has minimum load. According to the charging scheduling strategy, a vehicle charging scheduling model is established based on multi-objective optimization. Technique for order preference by similarity to ideal solution is used to eliminate the dimensions of multiple objectives, and the genetic algorithm is used to solve the model. The simulation results show that the charging scheduling strategy can select appropriate charging stations for electric vehicles and achieve the goal of multi-objective optimization.

The current study employed an eye-movement technique with an attempt to explore the reading patterns for the two types of Chinese relative clauses, subject-extracted relative clauses (SRCs) and object-extracted relative clauses (ORCs), by native speakers (L1), and Japanese learners (L2) of Chinese. The data were analyzed in terms of gaze duration, regression path duration, and regression rate on the two critical regions, head noun, and embedded verb. The results indicated that both the L1 and L2 participants spent less time on the head nouns in ORCs than in SRCs. Also, the L2 participants spent less time on the embedded verbs in ORCs than in SRCs and their regression rate for embedded verbs was generally lower in ORCs than in SRC. The findings showed that the participants experienced less processing difficulty in ORCs than SRCs. These results suggest an ORC preference in L1 and L2 speakers of Chinese, which provides evidence in support of linear distance hypothesis and implies that the syntactic nature of Chinese is at play in the RC processing.

Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. However, most of these methods have only considered single type textual feature, e.g., term frequency, or using word embeddings. Such approaches neglect the other rich textual information that could be utilized to improve hate speech detection. In this paper, we propose DeepHate, a novel deep learning model that combines multi-faceted text representations such as word embeddings, sentiments, and topical information, to detect hate speech in online social platforms. We conduct extensive experiments and evaluate DeepHate on three large publicly available real-world datasets. Our experiment results show that DeepHate outperforms the state-of-the-art baselines on the hate speech detection task. We also perform case studies to provide insights into the salient features that best aid in detecting hate speech in online social platforms. Comment: Paper Accepted for 12th International ACM Conference on Web Science

The first issue of Nature Reviews Neuroscience was published 20 years ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selection of researchers from across the field who have authored pieces published in the journal in recent years for their thoughts on notable and interesting developments in neuroscience, and particularly in their areas of the field, over the past two decades. They also provide some thoughts on current lines of research and questions that excite them.

The Porto settlement (Northwest Portugal, Iberian Peninsula) was originally built in the twelfth century and has been developed on granitic hill slopes of the Douro riverside, being one of the oldest cities in Europe. In the urban area of Porto, the second most important city of the Portuguese mainland, there is a population of about 216,000 inhabitants. This study highlights the importance of urban speleological mapping applied to groundwater and geo-engineering studies. All the water that flows from the so-called Paranhos or Arca D’Água springs is captured by catchwork galleries and their utilization date back around 1120 AD. Paranhos spring galleries catchworks (c. 3,3 km extension and a -21m below ground level) was one of the main water supplies to Porto City for more than six centuries and, nowadays, these waters are still appropriate for irrigation uses. Topographical, geological, geophysical and hydrogeological data were collected and interpreted, allowing the definition of a hydrogeotechnical zoning. All these features were mapped and overlaid using GIS mapping techniques. This multidisciplinary approach offers a good potential for reliable urban speleological and geo-engineering studies of Arca D’Água site.

Abstract Target detection has experienced a rapid development stage from the concept to the practical application, the core technology has been broken through, many difficult problems have been solved, and it has been widely used in various fields. However, the existing target detection models still have some technical problems such as incomplete detection and low accuracy. Therefore, this paper proposes a target detection algorithm based on V3 model to detect occluded targets. This paper makes an in-depth investigation and Research on the existing target detection model, analyzes the shortcomings of the current traditional target detection model through the test results, and the advantages of the network detection model based on YOLOv3 in target detection. Aiming at the main problems existing in the current detection technology, this paper puts forward the optimization and improvement scheme. By reconstructing the whole framework of multi-target tracking algorithm, optimizing the feature fusion algorithm, and improving the NMS algorithm, the scheme greatly improves the accuracy of detecting the occluded objects. The simulation results show that the improved scheme can improve the accuracy by 12.3% compared with the traditional scheme. The analysis shows that the target detection model based on YOLOv3 network can effectively improve the detection accuracy of the model.

This paper It shows the interconnections between the museological display and the poetics of Manoel de Barros (1916-2014), Brazilian writer whose proposal is to disinvent objects and distort the look. In this respect, as the poet strategy, the exhibition approaches different things, fragmented trajectories and taken from their original function are placed in a new context resulting from a poetic gesture (syntax of things). Our aim is to investigate to what extent the poetic alchemy promoted by exhibitions and the poetics and politics of museums promoted by Sociomuseology consist of ways to institute a new dramaturgy of the memory. O artigo aproxima a exposição museológica da poética de Manoel de Barros (1916-2014), escritor brasileiro cuja proposta consiste em desinventar objetos e distorcer o olhar. Nesse aspecto, assim como a estratégia do poeta, a exposição aproxima coisas distintas, de trajetórias fragmentadas e que retiradas de sua função original são inseridas em um novo contexto, resultante de um gesto poético (sintaxe das coisas). Nosso intuito é investigar em que medida a alquimia poética promovida pelas exposições e a poética e a política dos museus promovida pela Sociomuseologia consistem em formas de instituir novas dramaturgias da memória. Cadernos de Sociomuseologia