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Disadvantaged group members may feel either anger or fear towards unfair treatment of them, usually implying an intent of of approach or avoidance. However, when communicating with ingroup and outgroup members, those disadvantaged group members can express anger and fear consistently or inconsistently for certain purposes. For example, despite expressing fear with their ingroup, disadvantaged group members may inconsistently express anger with outgroup members in order put pressure on the outgroup. In contrast, despite expressing anger with their ingroup, they may express fear with the outgroup in order to seek sympathy and help. The current research aims to investigate the ingroup (disadvantaged) and outgroup (advantaged) reactions to such consistent (vs. inconsistent) expression of anger and fear. We will manipuate a disadvantaged group member's emotion expressions (anger vs. fear; with ingroup vs. outgroup) and ask about both advantaged and disadvantaged group members' willingness to help and participant collective action for him. We will also measure social evaluations and emotions as potential mediators.

BACKGROUND: Soil quality assessment is a critical strategy for determining optimum fertilization in intensive pomelo production. In this study, we evaluated the soil quality status and mapped the spatial distribution of 347 soil samples collected from pomelo orchards in Pinghe County, southern China. We analyzed nine chemical parameters and an altitude indicator. RESULTS: The mean soil quality index (SQI) was 0.355 in the total data set (TDS) and 0.292 in the minimum data set (MDS). Available Ca (Avail-Ca), pH value, organic matter, and altitude were selected as indicators of soil quality in the MDS. The SQI in mature orchards (>10 y) was higher than that in young orchards (

The purpose of this paper is to propose a method to improve the curriculum system in higher education institutions by taking use of the Lean Six Sigma (LSS) framework incorporated with big data analysis. The Lean Six Sigma Define-Measure-Analyze-Improve-Control methodology combined with text analysis, knowledge graph analysis and topology graph analysis are used. The proposed method can improve the curriculum system and better meet the talent market requirements, the technical requirements, and the social requirements. Little study uses the LSS framework and big data analysis to improvement the curriculum system. However, big data is an effective and efficient tool and is the trend to be used to help to make decision. This paper proposed the detailed improvement process using both LSS framework and big data analysis.

In recent years, online learning has become more and more popular. However, because of information overload, learners often find it difficult to retrieve suitable learning resources. Although many scholars have proposed excellent online learning resource recommendation algorithms, the accuracy of personalized recommendation results still needs to be improved. This study proposes an improved hybrid ontology-based approach for online learning resource recommendations, combining collaborative filtering algorithm and sequential pattern mining (SPM) techniques. Ontology can be used effectively for knowledge representation to avoid cold start and data sparsity problems. And the history of learners’ sequential access patterns helps in providing recommendations that are more consistent with the law of learning activities. Experimental results reveal that our improved hybrid approach for learning resource recommendations yields better performance and recommendation quality than other related algorithms. Compared with previous research outcomes, our collaborative filtering engine, with ontology domain knowledge, makes full use of the historical learning paths of similar learners. The ontology construction in this study has a more reliable theoretical basis and the selection of features is more representative. In addition, improvement of the SPM process further improves the efficiency of our recommended algorithm.

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intimin encoding eae gene types

File 1: Intimin encoding eae gene typesFile 2: Three newly identified specific genes of Escherichia albertii.

Aiming at the practical problem of complex parameter estimation in Richards model, the parameter estimation problem of Richards model is transformed into a multi-dimensional nonlinear function optimization problem by following the principle of "least squares". Combined with the deformation monitoring data of foundation pit slope in a certain construction project and through C++ programming, variable random search step size strategy and variable drosophila population size strategy were introduced to realize the adaptive improvement of standard drosophila algorithm (FOA), so as to improve the local search ability and search efficiency of the algorithm. In order to verify the accuracy and effectiveness of the algorithm's estimation parameters, the improved Fruit fly algorithm (IFOA), particle swarm optimization (PSO) and genetic Algorithm (GA) are compared and analyzed. The results show that the improved Fruit fly algorithm (IFOA) is more applicable to the parameter estimation of Richards model.

Starting from the practical significance of "New Engineering" to the practical teaching reform of engineering management specialty, this paper analyzes the current situation of practical teaching of engineering management specialty, expounds the construction ideas of practical teaching system of virtual and real integration of engineering management specialty from the perspective of new engineering, and puts forward the integration of practical teaching content, construction of virtual and real integration practice teaching platform and diversified evaluation system, so as to improve young teachers Engineering practice ability and other safeguard measures. Practice shows that practice teaching is the main channel to cultivate engineering practice ability and core competitiveness of engineering management students.

In view of the fact that the factors affecting the subsidence deformation of subway engineering are complex and the subsidence monitoring data are random, this paper constructs an optimized wavelet neural network model with markov chain improvement to improve the accuracy of the predicted data. Firstly, the wavelet neural network is optimized by adding momentum and adjusting learning rate adaptively. Finally, based on markov chain theory, the residual between the predicted value and the actual monitoring value of the optimized wavelet neural network is improved. Taking the settlement data of a settlement monitoring point of xi'an metro line 14 as the research object, the prediction accuracy of the model was compared and analyzed by C++ program. The results of the program show that the additional momentum and the adaptive learning rate are effective for the optimization of the wavelet neural network model, and the prediction accuracy of the optimized wavelet neural network model improved by the markov chain is higher than that of the single model, which is suitable for the prediction of the subway engineering settlement.

In the framework of heterogeneous wireless networks, it is diffcult for every user to obtain QoS-based services anywhere at any time. Due to heterogeneous networks, the dynamic network selection scheme needs to achieve seamless mobility, and also supports the optimization of service quality and load balancing. According to different business characteristics, this paper describes different real-time businesses in utility functions, and solves network selection problems for real-time businesses. Based on auction mechanism, it introduces the upset price in order to maximize online profts. Meanwhile, the network selection scheme is also helpful to control network congestion. The study of real-time business network selection based on auction mechanism can not only meet the demands of service quality of multiple realtime applications, but also achieves load balancing between different networks.

In this paper, we propose a systemic architecture of network selection based on context-awareness services, which gathers contextual information that includes such network information, user information and local information. This network selection strategy considers the Quality of Service (QoS) and user preferences. Also, it perceives contexts such as speed, coverage percentage and location, etc., and it eventually performs network selection decision making and network execution based on multiple factors. From the perspective of network decision, it presents two network selection algorithms, namely the fuzzy mathematics evaluation method and multiple attribute decision making using the TOPSIS evaluation method. System simulations suggest that network selection based on the mathematics evaluation method is much faster than the TOPSIS evaluation method. However, the TOPSIS evaluation method is practically more efficient. The network selection method based on context-awareness provides an effective and flexible network vertical handover strategy, and ensures a good accuracy and efficiency.

This paper firstly analyzed the distribution of nuclear technology application units and the radiation environmental situation in Shanghai. On this basis, several methods including literature review, consultation from experts and experience analysis were adopted to identify and assess ionizing radiation risk in Shanghai. The results indicated that we should pay more attention to radiation accident and nuclear terrorism, as well as the accidents of radioactive sources lost/stolen which were the most serious one among 18 kinds of radiation risk. Furthermore, hazards of nuclear and radiological emergencies were discussed and relative countermeasures were put forward in the end.

People vary considerably in moral reasoning. According to Kohlberg’s theory, individuals who reach the highest level of post-conventional moral reasoning judge moral issues based on deeper principles and shared ideals rather than self-interest or adherence to laws and rules. Recent research has suggested the involvement of the brain’s frontostriatal reward system in moral judgments and prosocial behaviors. However, it remains unknown whether moral reasoning level is associated with differences in reward system function. Here, we combined arterial spin labeling perfusion and blood oxygen level-dependent functional magnetic resonance imaging and measured frontostriatal reward system activity both at rest and during a sequential risky decision making task in a sample of 64 participants at different levels of moral reasoning. Compared to individuals at the pre-conventional and conventional level of moral reasoning, post-conventional individuals showed increased resting cerebral blood flow in the ventral striatum and ventromedial prefrontal cortex. Cerebral blood flow in these brain regions correlated with the degree of post-conventional thinking across groups. Post-conventional individuals also showed greater task-induced activation in the ventral striatum during risky decision making. These findings suggest that high-level post- conventional moral reasoning is associated with increased activity in the brain’s frontostriatal system, regardless of task-dependent or task- independent states.