Raining Dream

This work has a very unique and outstanding effect when viewed. Raining Dream tells a story of colored raindrops in an imaginary scene. Raindrops are traceless and instant, but what if we colored them and traced their steps

Bending behavior test and assessment for full-scale PC box girder reinforced by prestressed CFRP plate

This paper focuses on behavior of full scale prestressed concrete (PC) box girder with difference degrees of damage derived from service stage. According to typical structural damage, strengthening measures are proposed, including gluing steel plate, gluing prestressed CFRP plate and so on. In order to testify the effectiveness of reinforced method, bending behavior test are conducted for full scale PC box girder both before and after strengthening. After the test, the bending behaviors of test girder are comparatively analyzed, and the failure mechanism of test girder reinforced by prestressed CFRP plate is studied. What’s more, the strengthening method of gluing prestressed CFRP plate is applied in in-situ prestressed concrete box girder bridge with obvious damage. The static and dynamic testing of this reinforced bridge shows the feasibility and effectiveness of gluing prestressed CFRP plate strengthening method. Studies in this paper provide reliable guidance for engineering application

Marginally jammed states of hard disks in a one-dimensional channel

We have studied a class of marginally jammed states in a system of hard disks confined in a narrow channel---a quasi-one-dimensional system---whose exponents are not those predicted by theories valid in the infinite dimensional limit. The exponent $\gamma$ which describes the distribution of small gaps takes the value $1$ rather than the infinite dimensional value $0.41269\dots$. Our work shows that there exist jammed states not found within the tiling approach of Ashwin and Bowles. The most dense of these marginal states is an unusual state of matter that is asymptotically crystalline.Comment: 7 pages, 8 figures, includes Supplemental Materia

Ghost field realizations of the spinor W2,sW_{2,s} strings based on the linear W(1,2,s) algebras

It has been shown that certain W algebras can be linearized by the inclusion of a spin-1 current. This Provides a way of obtaining new realizations of the W algebras. In this paper, we investigate the new ghost field realizations of the W(2,s)(s=3,4) algebras, making use of the fact that these two algebras can be linearized. We then construct the nilpotent BRST charges of the spinor non-critical W(2,s) strings with these new realizations.Comment: 10 pages, no figure

Examinees’ Affective Preference for Online Speaking Assessment: Synchronous VS Asynchronous

With technological advancement and the COVID pandemic, online speaking assessment is increasingly used in language teaching. Two modes are developed: online synchronous testing (direct human-to-human interview) and online asynchronous testing (semi-direct human-to-machine interview). Ample literature has explored how each of the two online modes differs from traditional face-to-face speaking assessments. However, few studies have investigated the differences between the two modes, especially in terms of examinees’ affective preferences. This study, therefore, compares the extent to which each mode is accepted and favored by test takers and explores why such an affective preference emerges. The participants are 46 college students enrolled in an Elementary Chinese course. They completed a survey that investigates their level of motivation, self-confidence, and anxiety in the two types of online speaking tests. An open-ended question item solicited further explanations from test-takers. Results showed a strong affective preference for synchronous assessment, as manifested by a higher level of motivation and self-confidence and a lower level of anxiety. Possible reasons are discussed based on students’ written responses. The study is theoretically significant as it identifies factors on student experience and performance in online speaking assessments. It also provides practical guidance for language teachers in optimizing online oral tests

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Funding Information: Funding: This research was funded by the National Natural Science Foundation of China under Grant U1964201 and Grant U21B6001, the Major Scientific and Technological Special Project of Hei-longjiang Province under Grant 2021ZX05A01, the Heilongjiang Natural Science Foundation under Grant LH2019F020, and the Major Scientific and Technological Research Project of Ningbo under Grant 2021Z040. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.publishersversionpublishe