The COVID-19 pandemic has both exposed and created deep rifts in society. It has thrust us into deep ethical thinking to help justify the difficult decisions many will be called upon to make and to protect from decisions that lack ethical underpinnings. This paper aims to highlight ethical issues in six different areas of life highlighting the enormity of the task we are faced with globally. In the context of COVID-19, we consider health inequity, dilemmas in triage and allocation of (...) scarce resources, ethical issues associated with research, ethical considerations relating to tracing apps, and exit strategies such as immunity passports and COVID-19 vaccines. Finally, we consider environmental issues in light of COVID-19. The paper also offers some ethical reflection on these areas as many parts of the world contemplate the recovery phase. (shrink)
This article examines the mediation effect of brand identification and the moderating effect of service quality (SQ) on the effects of corporate social responsibility (CSR) association on service brand performance. A survey of customers of mobile telecommunications services was conducted. The study finds, first, that both CSR and SQ have direct effects on brand identification and customer satisfaction and indirect effects on customer satisfaction (via brand identification) and on service brand loyalty (via customer satisfaction and via "brand identification/customer satisfaction"). Second, (...) SQ enhances the effect of CSR on brand identification. This study contributes to the literature by incorporating three perspectives of service brand performance — CSR association, SQ, and brand identification - into one general framework that stresses (a) the mediating role of brand identification in predicting customer satisfaction and service brand loyalty; and (b) the interactive effect of CSR and SQ in predicting brand identification. (shrink)
This study examines the impact of corporate social responsibility activities on insider trading. While opponents of insider trading claim that the buying or selling of a security by insiders who have access to non-public information is illegal, proponents argue that insider trading improves economic efficiency and fairness when corporate insiders buy and sell stock in their own companies. Based on extensive U.S. data of insider trading and CSR engagement, we find that both the number of insider transactions and the volume (...) of insider trading are positively associated with CSR activities.We also find that legal insider transactions are positively related to CSR engagement even after controlling for potential endogeneitybias and various firm characteristics. Furthermore, our evidence suggests that firms perceive adjustment to CSR dimension of product as being efficient, while adjustment to diversity and environmental CSR as being inefficient. Our results of bad and illegal insider trading proxies are consistent with the interpretation that firms with high CSR ratings do not attempt to engage in unethical or bad insider trading in a significant fashion. Combined together, we consider our empirical evidence supportive of the fairness and efficiency explanation, but not the unfairness and inefficiency hypothesis. (shrink)
In this paper, the single-machine scheduling problem is studied by simultaneously considering due-date assignment and group technology. The objective is to determine the optimal sequence of groups and jobs within groups and optimal due-date assignment to minimize the weighted sum of the absolute value in lateness and due-date assignment cost, where the weights are position dependent. For the common due-date assignment, slack due-date assignment, and different due-date assignment, an O n log n time algorithm is proposed, respectively, to solve the (...) problem, where n is the number of jobs. (shrink)
In view of the particularity and high risk of coal mining industry, the decision-making behavior of multiple agents inside the coal-mine enterprise plays a very important role in ensuring the safety and sustainable development of coal mining industry. The existing literature studies on coal-mine safety production focus mainly on statically analyzing the game among the external entities such as the government, the enterprises themselves, and the employees inside the enterprise from a macro perspective,are short of research on revealing the dynamic (...) interactions among the actors directly involved in the coal-mine accidents and also on proposals for effective interactions that will lead to improved safety outcomes. Therefore, this paper explores the use of evolutionary game theory to describe the interactions among the stakeholders in China’s coal-mine safety production system, which includes the organization, the first-line miners, and the first-line managers. Moreover, the paper also explores dynamic simulations of the evolutionary game model to analyze the stability of stakeholder interactions and to identify equilibrium solutions. The simulation results show that when certain conditions are met, the decision-making behavior of the organization, miners, and managers can evolve into the unique ideal steady state. In addition, the strategy portfolio with a relatively high initial proportion of three agents converges more quickly to an ideal state than a relatively low strategy portfolio. Moreover, the stable state and equilibrium values are not affected by the initial value changes. Finally, we find that the combination of positive incentive policies and strict penalties policies can make the evolutionary game system converge to desired stability faster. The application of the evolutionary game and numerical simulation when simulating the multiplayer game process of coal-mine safety production is an effective way, which provides a more effective solution to the safety and sustainable development of coal mining industry. (shrink)
Good balance between product and service is the key in the innovative design of product service systems. In this study, the evolution route of the PSS based on Teoriya Resheniya Izobretatelskikh Zadatch ideal final result was provided. The function model of the PSS was constructed according to the service blueprint and function system diagrams. On this basis, an innovation design method of the PSS based on function incentive was established. The function incentive strategies included function synergy, function supplement, and function (...) substitution. Finally, the PSS design process of agricultural machinery based on computer-aided innovation platform was analyzed to verify this method. (shrink)
Previous studies have demonstrated that lying can undermine memory and that its memory-undermining effects could be modulated by the cognitive resources required to tell lies. We extended the investigation of the memory-undermining effect by using a daily life setting in which participants were highly involved in a mock shopping task. Participants were randomly assigned to truth-telling, denying or mixed lying conditions. After finishing the shopping task, participants were told that two people wanted to know about their shopping lists and would (...) ask them some questions in an interview. During the interview, participants were asked whether each of ten items were on the shopping list, five of which were randomly selected from the shopping list, while the other five were not sold in the store. In answering the interview questions, the truth-telling group was asked to respond honestly, the denying group was asked to give denial responses, and the mixed lying group was asked to respond deceptively. Thus, the denying group told five lies and the mixed lying group told ten lies in the interview. The item memory test, source memory test and destination memory test were given in an orderly manner 48 h after the interview. We found that the mixed lying group, rather than the denying group, forgot the lies they told in the interview and mistakenly believed they had lied about something that they had not lied about. Moreover, the mixed lying group retained fewer memories about the person they responded to than the honest group. In addition, participants in the mixed lying group had more non-believed memories than those in the truth-telling group in both item and source memory tests. We conclude that more lies could result in more memory disruptions in daily life. (shrink)
Constructing an all-directional, multilevel, and composite interconnection network, accelerating the free flow of producer services elements across regions, and further improving the efficiency of resource integration demand to conduct a comprehensive and systematic analysis of producer services trade. Thus, using bilateral trade data, this paper builds producer services trade network composed of 61 major countries and innovatively combines the methods of social network and economic geography to explore its spatiotemporal evolution and system properties. The results show that, firstly, the producer (...) services trade network has spatial heterogeneity, which is characterized by high-value agglomerations in Western Europe and East Asia, and low-value agglomerations in Southern Europe and Southeast Asia. Secondly, most countries tend to choose trading partners with close geographical locations or common cultures to establish a cohesive subgroup. Thirdly, the producer services trade network has a significant core-periphery structure, the “spaghetti bowl” effect, which leads to a downward trend in the number of core and semi-peripheral countries. Finally, the trade agreement relations, language relations, and differences in economy, geography, institution, and technology all have a significant impact on the evolution of producer services trade network, but this change has little relationship with the population size divergences of different countries. (shrink)
Cognitive diagnostic assessment has been developed rapidly to provide fine-grained diagnostic feedback on students’ subskills and to provide insights on remedial instructions in specific domains. To date, most cognitive diagnostic studies on reading tests have focused on retrofitting a single booklet from a large-scale assessment. Critical issues in CDA involve the scarcity of research to develop diagnostic tests and the lack of reliability and validity evidence. This study explored the development and validation of the Diagnostic Chinese Reading Comprehension Assessment for (...) primary students under the CDA framework. Reading attributes were synthesized based on a literature review, the national curriculum criteria, the results of expert panel judgments, and student think-aloud protocols. Then, the tentative attributes were used to construct three booklets of reading comprehension items for 2–6 graders at three key stages. The assessment was administered to a large population of students in grades 2–6 from 20 schools in a district of Changchun City, China. Q-matrices were compared and refined using the model-data fit and an empirical validation procedure, and five representative cognitive diagnostic models were compared for optimal performance. The fit indices suggested that a six-attribute structure and the G-DINA model were best fitted for the reading comprehension assessment. In addition, diagnostic reliability, construct, internal and external validity results were provided, supporting CDM classifications as reliable, accurate, and useful. Such diagnostic information could be utilized by students, teachers, and administrators of reading programs and instructions. (shrink)
Emotion recognition plays an important part in human-computer interaction. Currently, the main challenge in electroencephalogram -based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are then passed through (...) TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness, anger, and fear on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness, anger, and fear on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition. (shrink)
Atmospheric pollution is deteriorating, which has affected the evolution of respiratory disease for the exposed human worldwide. Thus, exploring the influence of air pollution on the evolution of disease transmission dynamics is a significant issue. In this article, a stochastic susceptible-infective epidemic model in a polluted atmospheric environment is investigated. The existence and uniqueness of the global positive solution are established. In virtue of the aggregation methods and Lyapunov function, the sufficient conditions of disease extinction, persistence, and existence of the (...) stationary distribution are established, respectively. TakingPM2.5concentration as the air pollutant index, numerical simulations are carried out to support these results. Our results indicated that the disease transmission dynamics are significantly associated with the environmental atmospheric pollution and fluctuation. (shrink)
This paper investigates whether local religious beliefs have a significant impact on the practice of earnings management. We extend the existing literature on the role of firm characteristics in mitigating earnings management by showing that local religious beliefs significantly impact the practice of earnings management. Specifically, exploring firms located in the U.S. counties that vary from 2000 through 2010, we document the negative relationship between religiosity and earnings management using multivariate regression analysis. Our results show that firms in counties with (...) strong religious social norms are less likely to engage in earnings management. Furthermore, we attempt to mitigate endogeneity concerns by employing a modified Difference-Differences model and Propensity score matching methods. We find that the negative effects of religion on earnings management still hold. Overall, these findings emphasize the empirical relevance of the association between the local social norms and earnings manipulations. (shrink)
Research has independently highlighted the roles of semantic memory and associative abilities in creative thinking. However, it remains unclear how these two capacities relate to each other, nor ho...
Understanding the implication of point cloud is still challenging in the aim of classification or segmentation for point cloud due to its irregular and sparse structure. As we have known, PointNet architecture as a ground-breaking work for point cloud process can learn shape features directly on unordered 3D point cloud and has achieved favorable performance, such as 86% mean accuracy and 89.2% overall accuracy for classification task, respectively. However, this model fails to consider the fine-grained semantic information of local structure (...) for point cloud. Then, a multiscale receptive fields graph attention network by means of semantic features of local patch for point cloud is proposed in this paper, and the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet datasets, and results show it achieves state-of-the-art performance in shape classification tasks, such as it outperforms GAPNet model by 0.1% in terms of OA and compete with DGCNN model in terms of MA. (shrink)
Seismic facies analysis can effectively estimate reservoir properties, and seismic waveform clustering is a useful tool for facies analysis. We have developed a deep-learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. We fuse the two independent processes of feature extraction and clustering, such that the extracted features are modified simultaneously with the results of clustering. We use (...) a convolutional autoencoder for extracting features from seismic data and to reduce data redundancy in the algorithm. At the same time, weights of clustering network are fine-tuned through iteration to obtain state-of-the-art clustering results. We apply our new classification algorithm to a data volume acquired in western China to map architectural elements of a complex fluvial depositional system. Our method obtains superior results over those provided by traditional K-means, Gaussian mixture model, and some machine-learning methods, and it improves the mapping of the extent of the distributary system. (shrink)
The image-to-image translation method aims to learn inter-domain mappings from paired/unpaired data. Although this technique has been widely used for visual predication tasks—such as classification and image segmentation—and achieved great results, we still failed to perform flexible translations when attempting to learn different mappings, especially for images containing multiple instances. To tackle this problem, we propose a generative framework DAGAN that enables domains to learn diverse mapping relationships. We assumed that an image is composed with background and instance domain and (...) then fed them into different translation networks. Lastly, we integrated the translated domains into a complete image with smoothed labels to maintain realism. We examined the instance-aware framework on datasets generated by YOLO and confirmed that this is capable of generating images of equal or better diversity compared to current translation models. (shrink)
The massive social change in urban China today has led to a decline in the adaptive implications of shyness for child adjustment, yet evidence of this trend in young children is limited. Moreover, the underlying mechanisms that help to explain the associations between shyness and maladjustment remains poorly understood. The primary goal of the present study was to explore the moderating role of conflict resolution skills in the links between shyness and socio-emotional and school adjustment among urban Chinese preschoolers. Data (...) were collected from 360 children in kindergartens using parent ratings, teacher ratings, and child interviews. The analyses indicated that the relations between shyness and adjustment were moderated by child conflict resolution skills, which served to buffer shy children from adjustment problems. The results were discussed in terms of the implications of conflict resolution skills for early adjustment of shy preschoolers in the Chinese context. (shrink)
In the traditional scientific research and production activities, due to the lack of sufficient communication and communication between researchers, the phenomenon of waste of scientific research resources occurs from time to time, which hinders the efficiency of scientific research output. Based on the design principle of the semantic knowledge framework, this paper puts forward the definition of ontology and semantic relationship of the collaborative system of scientific researchers. In this paper, a framework of collaborative semantic knowledge among researchers is established (...) through decentralized semantic information exchange architecture. In this article, the simulation is verified by experiments and compared with other exchange architectures. The results of the experiment confirmed the semantic information exchange architecture based on semantic knowledge proposed in this paper is 10.39% faster than the traditional centralized method in terms of data volume; the construction speed under the data node perspective is 12.84% higher than that of the traditional centralized construction method; the subject query speed is 36.84% higher than that of the traditional centralization method; the predicate query speed is 31.58% higher than that of the traditional centralization method. The experimental results confirm that the semantic information exchange architecture based on the semantic knowledge framework is feasible, and it has excellent performance in terms of construction speed and query speed. Under the background that researchers rely more and more on collaborative technology to interact with other members, this paper has a certain reference value and exploration value and proposes a new idea of group collaboration system under the framework of semantic knowledge. (shrink)
This study provided a content analysis of studies aiming to disclose how artificial intelligence has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer, application layer, (...) and integration layer. Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research. (shrink)
