本书内容包括:科技时代面对崭新的道德难题,科技伦理新建构的底线,科技共同体内的伦理问题,科技社会中人际伦理问题,科技时代文化人际伦理问题,科技背景下人与自然间伦理问题,科技发展中迫切的道德抉择课题(案 例分析).

本文内容为:演讲中的逻辑诀窍,论辩中的逻辑斗智,中国古代辩论的逻辑艺术和逻辑技巧与亚洲大专辩论会。.

The aerial manipulator is a complex system with high coupling and instability. The motion of the robotic arm will affect the self-stabilizing accuracy of the unmanned aerial vehicles. To enhance the stability of the aerial manipulator, a composite controller combining conventional proportion integration differentiation control, fuzzy theory, and neural network algorithm is proposed. By blurring the attitude error signal of UAV as the input of the neural network, the anti-interference ability and stability of UAV is improved. At the same time, (...) a neural network model identifier based on Maxout activation function is built to realize accurate recognition of the controlled model. The simulation results show that, compared with the conventional PID controller, the composite controller combined with fuzzy neural network can improve the anti-interference ability and stability of UAV greatly. (shrink)

This article explores communicative processes in Chinese involving Chinese American children in order to explain the notion of preference for ambiguity, a characteristic often invoked when describing the Chinese as a group. It also speculates on the impact this notion has on children's socialization. Preference for ambiguity can be defined as making ambiguous something that is otherwise clear-communicating ambiguously or conveying something that is ambiguous-communicating ambiguity. Treating ambiguity as an interaction-centered and activity-bound phenomenon rather than a purely semantic or logical (...) problem, this article locates ambiguity in the intersubjectivity co-constructed by all participants and explores its role in the construction of cultural, situational and interpersonal contexts. It shows that ambiguity is shaped not only by lexico-grammar but also by discourse structure. This article draws data from a total of 30 hours of audio/video recorded classroom interactions involving 4 teachers and about 35 students in two Chinese Heritage Language Schools in the USA as well as from observations and interviews. In the classroom context, children are compelled to make speculations and draw inferences regarding their interlocutors' references, intentions, dispositions and goals. They do so without confronting their interlocutors or holding their interlocutors accountable. In contrast, teachers freely and vocally formulate, clarify, question and interpret children's utterances in an effort to make the children's stances unequivocal. This study suggests that the communicative patterns observed with respect to ambiguity may play an important part in the socialization of cultural knowledge and cognitive styles. (shrink)

In order to obtain a new structure of beam flexure hinge with good performance, the flexure hinge based on the X-lattice structure is researched in this paper. The truss model in the finite element method is used to model the 6-DOF compliance of the flexure hinge based on the X-lattice structure. The influence of structural parameters on the compliance and compliance ratio of flexure hinges is analyzed based on this model, and the performance is compared with the traditional beam flexure (...) hinge of the same size. In order to design a flexure hinge based on the X-lattice structure with good comprehensive performance, this paper proposes an intelligent structure optimization method based on a genetic algorithm. The feasibility of the optimization algorithm is verified by an example. (shrink)

BackgroundParent-mediated intervention has been studied in promoting skill acquisition or behavior change in the children with autism spectrum disorder. Most studies emphasize on the improvement of child’s core symptoms or maladaptive behaviors, making parental perceived competence and self-efficacy secondary. Yet, the evaluations of intervention implementation are under-reported, especially when translating such interventions into a new population or context. This research investigated the intervention implementation of a 12-week parent coaching intervention which was delivered through telehealth and tailored to Chinese population. The (...) intervention was based on the Parent-mediated Early Start Denver Model with culturally adapted lectures, manuals, and demonstration and commentary videos. This study aimed to evaluate the intervention implementation by assessing parents’ satisfaction, acceptability, appropriateness, and feasibility.MethodA randomized controlled trial was conducted with two telehealth conditions: self-directed and web+group therapy. Parents in the self-directed condition received intervention individually through the online learning platform. The web+group therapy condition navigated the same program with weekly 1.5-h group coaching sessions via videoconferencing. This mixed-methods study used a concurrent convergent design to evaluate the intervention implementation at post-intervention. The quantitative data was collected from the Program Evaluation Survey and the qualitative data was collected from five focus groups.ResultsParents in self-directed group reported significantly lower scores in total perceived competence than parents in web+group therapy condition, while there was no group difference on the total self-efficacy. Tailored feedback, demonstration and commentary videos, peer commenting, live coaching, and guided reflection were the top-five acceptable telehealth strategies that were strongly endorsed by parents. Family centered care, home-based intervention, strategies relative to daily activities, the remote learning platform, and the program-based community were elements that parents considered when evaluating the program’s appropriateness. Parent modeling, step-by-step instructions, and tailored feedback were key components in making intervention strategies feasible for parents to implement at home.ConclusionFindings indicate the application of telehealth was acceptable, appropriate, and feasible for Chinese parents. Group-based parent coaching intervention via videoconferencing could be a promising home-based service model to increase parental perceived competence. A large-scale RCT is needed to investigate the effectiveness of group-based PMI via telehealth. (shrink)

This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number (...) of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks. (shrink)

Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding (...) results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones. (shrink)

Diffusion plays an essential role in the distribution of predator and prey. We mainly research the diffusion network’s effect on the predator-prey model through bifurcation. First, it is found that the link probability and diffusion parameter can cause Turing instability in the network-organized predator-prey model. Then, the Turing stability region is obtained according to the sufficient condition of Turing instability and the eigenvalues’ distribution. Finally, the biological mechanism is explained through our theoretical results, which are also illustrated by numerical simulation.

Extreme Learning Machine is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation into the hidden layer of ELM to (...) reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches. (shrink)

Previous studies have paid more attention to the impact of non-balanced reciprocity in the organization on employees’ behaviors and outcomes, and have expected that the reciprocity norm could improve employees’ compliance behavior. However, there are two distinct types of non-balanced reciprocity, and whether generalized reciprocity affects employees’ compliance behavior rather than negative reciprocity and its mechanisms has not been further explored so far. Building on the social exchange theory and cognitive appraisal theory, we established and examined a model in a (...) scenario-based experiment across a two-stage survey of 316 participants. In this article, we propose that generalized reciprocity positively influences employees’ compliance behavior, and thriving at work mediates its relationship. Furthermore, we argue that the positive association between generalized reciprocity and thriving at work is moderated by the perceived cognitive capabilities of artificial intelligence. This association is amplified for people high in the perceived cognitive capabilities of AI. We also propose that the positive association between thriving at work and compliance behavior is moderated by conscientiousness, such that the association is amplified for people high in conscientiousness. These findings have theoretical and practical implications. (shrink)