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The authors examine the factors influencing mathematics homework interest for Chinese students and compare the findings with a recent study involving U.S. students. The findings from multilevel analyses revealed that some predictors for homework interest functioned similarly (e.g., affective attitude toward homework, learning-oriented reasons, monitoring motivation) across the two cultural samples, while some others did not (e.g., parent education, family homework help, and teacher feedback), suggesting that cultural differences influence students' interest in homework. In addition, this study bridges a gap in previous research on homework, by revealing that mathematics homework interest was positively related to mathematics self-concept. These findings were discussed relating to previous research in the field, and in the context of cultural values and societal influences.

RFID technology is no stranger to more or less have contacted various of industries in nowadays. Premier wen Jiabao put forward the “experience China” in 2009, after the application of IOT (Internet of things) is rocketed development and RFID technology is absolutely necessary as the core of IOT. On the experiment background of campus supermarket, this paper briefly introduces the application of RFID technology in the intelligent supermarket that mainly included four parts. Through analysis of system, it detailed interpretation that IOT bring changes to campus supermarket.

Hesitant fuzzy linguistic preference relations (HFLPRs) can efficiently denote the hesitant qualitative judgments of decision makers. Consistency and consensus are two critical topics in group decision making (GDM) with preference relations. This paper uses the additively consistent concept for linguistic fuzzy preference relations (LFPRs) to give an additive consistency definition for HFLPRs. To judge the additive consistency of HFLPRs, 0‐1 mixed programming models (0‐1‐MPMs) are constructed. Meanwhile, additive‐consistency‐based 0‐1‐MPMs to ascertain missing values in incomplete HFLPRs are established. Following the consistent probability of LFPRs, an algorithm to calculate the linguistic priority weighting vector is presented. In consideration of the consensus of GDM, a consistency‐probability‐distance‐measure‐based consensus index is defined, and an interactive improving consensus method is provided. Finally, a method for GDM with HFLPRs is offered that can address incomplete and inconsistent cases. Meanwhile, numerical examples are offered, and comparative analysis is made. [ABSTRACT FROM AUTHOR]

A Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order fulfillment system where multiple robots coordinate to complete a large number of order picking tasks. The multi-robot task allocation (MRTA) problem in RMFS is complex and dynamic, and it cannot be well solved by traditional MRTA methods. This paper proposes a task allocation method for multiple mobile robots based on multi-agent deep reinforcement learning, which not only has the advantage of reinforcement learning in dealing with dynamic environment but also can solve the task allocation problem of large state space and high complexity utilizing deep learning. First, a multi-agent framework based on cooperative structure is proposed according to the characteristics of RMFS. Then, a multi agent task allocation model is constructed based on Markov Decision Process. In order to avoid inconsistent information among agents and improve the convergence speed of traditional Deep Q Network (DQN), an improved DQN algorithm based on a shared utilitarian selection mechanism and priority empirical sample sampling is proposed to solve the task allocation model. Simulation results show that the task allocation algorithm based on deep reinforcement learning is more efficient than that based on a market mechanism, and the convergence speed of the improved DQN algorithm is much faster than that of the original DQN algorithm.