The current WSN is vulnerable to a variety of malicious attacks, resulting in the decline of its comprehensive performance. Multihop routing involves a number of safety and privacy issues. Problems such as snooping, sinkhole, manipulation, cloning, wormhole, spoofing, and so on affect the integrity, availability, and confidentiality of the WSNs. Therefore, this paper mainly studies the mathematical modeling of WSN multiattack behavior discrimination based on the incidence matrix. The WSN node model is used to collect relevant data and mark and (...) map the disguised data, so as to determine the characteristics of multiattack behavior and establish the WSN multiattack behavior discrimination model based on the incidence matrix. The experimental results show that the designed multiattack behavior discrimination model can distinguish the attack type according to the characteristics, has high recognition ability, can spend a short time to effectively distinguish the attack behavior, has a low false positive rate, and can effectively improve the antiattack ability of the WSN. (shrink)

The category \(\mathbb {DRDL}{'}\), whose objects are c-differential residuated distributive lattices satisfying the condition \(\textbf{CK}\), is the image of the category \(\mathbb {RDL}\), whose objects are residuated distributive lattices, under the categorical equivalence \(\textbf{K}\) that is constructed in Castiglioni et al. (Stud Log 90:93–124, 2008). In this paper, we introduce weak monadic residuated lattices and study some of their subvarieties. In particular, we use the functor \(\textbf{K}\) to relate the category \(\mathbb {WMRDL}\), whose objects are weak monadic residuated distributive lattices, (...) and the category \(\mathbb {WMDRDL}{'}\), whose objects are pairs formed by an object of \(\mathbb {DRDL}{'}\) and a center weak universal quantifier. (shrink)

Unethical research practices are prevalent in China, but little research has focused on the causes of these practices. Drawing on the criminology literature on organisational deviance, as well as the concept of cengceng jiama, which illustrates the increase of pressure in the process of policy implementation within a top-down bureaucratic hierarchy, this article develops an institutional analysis of research misconduct in Chinese universities. It examines both universities and the policy environment of Chinese universities as contexts for research misconduct. Specifically, this (...) article focuses on China’s Double First-Class University Initiative and its impact on elite universities that respond to the policy by generating new incentive structures to promote research quality and productivity as well as granting faculties and departments greater flexibility in terms of setting high promotion criteria concerning research productivity. This generates enormous institutional tensions and strains, encouraging and sometimes even compelling individual researchers who wish to survive to decouple their daily research activities from ethical research norms. This article is written based on empirical data collected from three elite universities as well as a review of policy documents, universities’ internal documents, and news articles. (shrink)

ObjectivesAimed to analyze the mediating effect of executive function between physical activity level and trait anxiety in college students.MethodsThe International Physical Activity Questionnaire, State-Trait Anxiety Inventory, Stroop task, 1-back task, and More-odd shifting task were used to analyze 248 college students.ResultsTrait anxiety were significantly correlated with shifting function (r = 0.182, P = 0.004) and inhibition function (r = 0.163, P = 0.010) and not with working memory (r = 0.056, P = 0.385). Vigorous physical activity (VPA) was most highly (...) correlated with inhibition function (Beta = −0.144, P = 0.024) and working memory (Beta = −0.208, P = 0.001), and light physical activity (LPA) was most highly correlated with shifting function (Beta = −0.211, P = 0.001). Physical activity had a 72.31% association with trait anxiety (B = −0.195), with 11.79% mediated by inhibition function (B = −0.023) and 15.90% by shifting function (B = −0.031).ConclusionCollege students' physical activity promotes both inhibition and shifting functions, which in turn affect trait anxiety. VPA had a direct effect, while the effect of moderate physical activity (MPA) and LPA was completely mediated exclusively through executive functions, and the mediating effect of shifting function was the highest. It is recommended that college workers should motivate students with high trait anxiety to engage in more VPA and pay attention to changes in their inhibition and shifting functions. (shrink)

This study aimed to explore the mediating effects of internet gaming disorder, social network use, and generalized pathological internet use on the association between loneliness and depression. A total of 2211 junior high school students completed questionnaires regarding loneliness, internet gaming disorder, social network use, GPIU, and depression. The results of a structural equation model revealed that the path coefficient of loneliness to depression was significantly positive, loneliness could not predict depression through GPIU directly, but loneliness could predict depression through (...) internet gaming disorder to GPIU, loneliness could predict depression through social network use to GPIU, and loneliness could not predict depression through internet gaming disorder to social network use to GPIU. These results provided significant implications for the prevention and reduction of depression in Chinese junior high school students. (shrink)

Due to the complexity of the network structure, log analysis is usually necessary for the maintenance of network-based distributed systems since logs record rich information about the system behaviors. In recent years, numerous works have been proposed for log analysis; however, they ignore temporal relationships between logs. In this paper, we target on the problem of mining informative patterns from temporal log data. We propose an approach to discover sequential patterns from event sequences with temporal regularities. Discovered patterns are useful (...) for engineers to understand the behaviors of a network-based distributed system. To solve the well-known problem of pattern explosion, we resort to the minimum description length principle and take a step forward in summarizing the temporal relationships between adjacent events of a pattern. Experiments on real log datasets prove the efficiency and effectiveness of our method. (shrink)

Efficient knowledge sharing is an important support for the continuous innovation and sustainable development of scientific research teams. However, in realistic management situations, the knowledge sharing of scientific research teams always appears to be unsustainable, and the reasons for this are the subject of considerable debate. In this study, an attempt was made to explore the interactive mechanism of knowledge hiding behaviors in scientific research teams between individual and collective knowledge hiding behaviors and its impact on knowledge sharing by adopting (...) grounded theory to comprehensively understand this situation. The results show that knowledge hiding behavior in the scientific research team is a two-phase interactive process and is capable of affecting sustainable knowledge sharing by reducing the supply of knowledge, creating a poor knowledge sharing atmosphere, and forming an interpersonal distrust relationship. This research may provide a strong basis for a deeper understanding of the interaction mechanism of knowledge hiding behavior and its impact on knowledge sharing. (shrink)

Entrepreneurship is a sustainable development tool that supports the alleviation of poverty and unemployment. Focusing on the promotion of entrepreneurial intention under the background of entrepreneurship education, this study used a structural equation model to examine the role of entrepreneurship policy, entrepreneurial practice, and entrepreneurial spirit on the EI of 384 college students from 22 universities in Guangdong Province. The test results show that there are significant positive correlations between EPo and EI; EPo and EPr; EPo and ES; and EPr (...) and EI. They also support the hypothesis that EPr enhances the positive effect that EPo has on EI. This study puts forth measures to improve EI and makes contributions to future research on EE. (shrink)

Scholars are the main force behind academic entrepreneurship. The method of how to stimulate scholars’ academic entrepreneurial intention and how to further promote social and economic development are important questions for the academic community. Research on the “net effect” of the factors affecting academic entrepreneurial intention has achieved some theoretical results. However, the results that affect academic entrepreneurial intention are complex and not influenced by a single factor, but rather by the interaction between various factors. Therefore, this study used a (...) fuzzy set qualitative comparative analysis research method to explore how various factors can affect scholars’ academic entrepreneurial intention from two dimensions: the Big Five personality traits and academic entrepreneurial motivation. Our findings showed two configurations that affect high academic entrepreneurial intention of university scholars: the openness to experience—ribbon—dominant path, and the ribbon—dominant path. Additionally, two configurations were revealed for the formation of not-high academic entrepreneurial intention: extraversion—conscientiousness—inhibition and extraversion—agreeableness—gold—hindrance paths. Moreover, this study revealed that a causal asymmetry exists between the high and the not-high academic entrepreneurial intention configurations. This study broadens the application of the fuzzy set qualitative comparative analysis method in the research of academic entrepreneurial intention and provides theoretical and practical insights for researchers and practitioners on how to effectively stimulate scholars’ academic entrepreneurial intention. (shrink)

Value co-creation goes through the stage of co-production, customer experience, service-dominant logic, and service ecosystem. The integration of science and technology has become a key factor to the process of VCC. The rise and application of artificial intelligence technology has added a new driving force to VCC and began to affect its original practical logic. Based on the consumer perspective, this study uses Q-methodology to measure consumer cognitive attitude toward the use of AI technology in VCC, aiming to explore the (...) key factors that affect VCC. The study found that content quality, information security, efficiency concern, and degree of manipulation have become the important concerns of consumers for VCC under AI integration. Moreover, their different statements have their specific focus and direction. The study demonstrates and analyzes the importance of the four factors and proposes the combination of human and non-human actors to shape the new model of VCC in the future, which is worth further deepening and exploring for academia and enterprises. (shrink)

Precise global solar radiation data are indispensable to the design, planning, operation, and management of solar radiation utilization equipment. Some examples prove that the uncertainty of the prediction of solar radiation provides more value than deterministic ones in the management of power systems. This study appraises the potential of random forest, V-support vector regression, and a resilient backpropagation artificial neural network for daily global solar radiation point prediction from average relative humidity, daily average temperature, and daily sunshine duration. To acquire (...) more accurate predictions of DGSR and examine the influence of historical DGSR on the performance of point prediction models, two different model inputs are considered: three meteorological variables and the lags of DGSR and three meteorological variables. Then, two interval prediction methods are developed by introducing the KDE to out-of-bag, introducing kernel density estimation to split conformal based on the three machine learning models. The two methods for interval prediction are denoted as OOB-KDE and SC-KDE. The mean absolute error, mean relative error, and Kendall rank correlation are used to assess the point prediction models. The performance of interval prediction methods is evaluated by the prediction interval coverage probability, prediction interval normalized average width, and coverage width criteria. The following conclusions are drawn from this study. First, the V-SVR model performs best with the lowest mean absolute error of 0.016 and mean relative error of 0.001. Second, the lags of DGSR improve the prediction accuracy by about 30%. Third, the OOB-KDE and SC-KDE methods improved the quality of the prediction interval. OOB-KDE improved CWC by 81%, and SC-KDE improved CWC by 99.99%. Fourth, the best interval prediction result is obtained using the SC-KDE method using the V-SVR model. The average difference between its PICP and prediction interval nominal coverage is only 3% of the PINC, and its PINAW is less than 0.007. (shrink)

Student employability is a key aspect of any university degree. The relationship between high student learning outcomes and high employability is a problem that needs to be addressed and improved by colleges and universities. Students with high employability can find good jobs after graduation and perform well in the workplace. Employability is associated with the success of university education, thus giving the university a good reputation. This study explores the development of employability, alongside teaching and student learning abilities to examine (...) how these variables affect student employability. The study collected 442 responses to a questionnaire to investigate the relationship between pedagogy for employability, problem-based learning, absorptive capacity, and student employability based on a structural model. The SEM results show that pedagogy for employability and problem-based learning positively correlate to absorptive capacity; pedagogy for employability and absorptive capacity positively correlate to student employability. We then analyzed a case study teaching intervention strategy with 221 students from the school of management and discuss the differential results of all variables. Results showed that the test group was superior to the control group in each variable performance, indicating that the intervention strategy proved effective. Based on these findings, this study proposes suggestions for future research. (shrink)

Self-serving leadership is a form of unethical leadership behavior that has destructive effect on its targets and the overall organization. Adopting a social cognition perspective, this study expands our knowledge of its adverse effect and the way to mitigate the effect. Integrating two sub-theories of social cognition, we propose a theoretical model wherein self-serving leadership hinders team creativity through psychological safety as well as knowledge hiding, with task interdependence acting as a contextual condition. Results from a sample of 107 R&D (...) teams revealed that self-serving leadership not only reduced team psychological safety, but also induced team knowledge hiding, both of which ultimately affected team creativity. The presence of high task interdependence buffered the destructive effect of self-serving leadership on team creativity via team psychological safety as well as the indirect effect via knowledge hiding. (shrink)

In this paper, the fixed-time practical bipartite tracking problem for the networked robotic systems with parametric uncertainties, input disturbances, and directed signed graphs is investigated. A new fixed-time estimator-based control algorithm for the NRSs is presented to address the abovementioned problem. By applying a sliding surface and the time base generator approach, a new stability analysis method is proposed to achieve the fixed-time practical bipartite tracking for the NRSs. We also derive the upper bound of the convergence time for employing (...) the presented control algorithm to solve the practical bipartite tracking problem and further demonstrate that the convergence time is independent of the initial value. Finally, the simulation examples are given to verify the effectiveness of the presented algorithms. (shrink)

Type 2 diabetes mellitus is related to a variety of cognitive impairments that may even progress to dementia. Studies have found the angular gyrus is a cross-modal integration hub that is involved in a variety of cognitive processes. However, few studies have focused on the patterns of resting-state functional connections of the AG in patients with T2DM. This study explored the functional connection between the AG and the whole brain and the relationship between the FC and clinical/cognitive variables in patients (...) with T2DM. 44 patients with T2DM and 43 sex-, age-, and education-matched healthy controls underwent resting-state fMRI and received neuropsychological assessments. Compared with the control group, the T2DM group showed abnormal rsFCs between the AG and multiple brain regions. The FC between the left AG and the left medial temporal lobe in the T2DM group was positively correlated with scores on the Montreal Cognitive Assessment, after a Bonferroni correction. Collectively, patients with T2DM have abnormal FCs between the AG and extensive brain regions that may be related to various cognitive processes. (shrink)

A geometric lattice is the lattice of closed subsets of a closure operator on a set which is zero-closure, algebraic, atomistic and which has the so-called exchange property. There are many profound results about this type of lattices, the most recent one of which, due to Czédli and Schimdt, says that a lattice L of finite length is semimodular if and only if L has a cover-preserving embedding into a geometric lattice G of the same length. The goal of our (...) paper is to offer the following result: a lattice of finite length is semimodular if and only if every cell in L is a 4-element Boolean lattice and the 7-element non-distributive atomistic lattice having 3 atoms is not a cover-preserving sublattice of L. (shrink)

The rapid improvement of technologies such as artificial intelligence in recent years has resulted in the development of smart technologies that can influence learning performance in different fields. The purpose of study is to explore the link between smart technology and learning performance. Using the S-O-R model as a framework, the researchers argue that smart technology will increase corporate trust, self-efficacy, and well-being, resulting in improved learning performance. The current model regards corporate trust and self-efficacy as relationship factors and investigates (...) their direct influence on employee well-being and learning performance and the mediating role played by these variables. Additionally, the function of employee well-being in moderating the relationship between corporate trust, self-efficacy, and employee learning performance is also explored. The respondents in the present study are made up of employees from 10 logistics companies located in China. The data analysis is conducted using the AMOS software. The results show that that smart technologies can affect learning performance through corporate trust, self-efficacy, and employee well-being. The implementation of smart technology initiatives by corporations may provide positive workplace outcomes for employees, corporations, and the relationship between employees and the companies that employ them. (shrink)

Much of early western research has focused on identity. A primed identity can inhibit the priming of other alternative identities, and also negatively affect the intention to purchase products related to those alternative identities. In western culture, individuals operate within a cultural framework that makes them more likely to prioritize their own goals and less likely to rely on environmental factors when evaluating others. Individuals are more likely to choose products that fit their primed identity. In this study, we suggest (...) that in collectivist Chinese culture, individuals may give priority to group-level goals and attach more importance to situational factors when evaluating others. Therefore, a primed identity may not necessarily inhibit the priming of alternative identities. In this research, we examine the association between a focal identity and alternative identities, with particular emphasis on the accessibility, discrepancies, and effects on purchase intention of alternative identities. We also examine the intent to purchase products related to the alternative identity vs. the focal identity, developing a model with which to explore this construct. We test four hypotheses through experiments using an online questionnaire and analyzing the resulting data using statistical product service solutions 22.0 and the PROCESS macro modeling tool. The results are as follows: First, the association between a focal identity and alternative identity has a positive impact on the accessibility of the alternative identity. The clearer the alternative identity is, the greater the impact of the association between the focal identity and alternative identity on the latter’s accessibility. Second, the accessibility of the alternative identity has a positive impact on the intent to purchase alternative identity vs. focal identity-related products. The greater the discrepancy between the focal identity and the alternative identity, the greater the impact on the intent to purchase alternative identity vs. focal identity-related products. The results of this study confirmed the interaction between identity association and the clarity of the alternative identity, as well as the influence of the accessibility of the alternative identity on the intent to purchase alternative identity vs. focal identity-related products. We contribute to the development of a theory on intention to purchase identity-related products. (shrink)

On the basis of PM2.5 data of the national air quality monitoring sites, local population data, and baseline all-cause mortality rate, PM2.5-related health economic benefits of the Air Improvement Action Plan implemented in Wuhan in 2013–2017 were investigated using health-impact and valuation functions. Annual avoided premature deaths driven by the average concentration of PM2.5 decrease were evaluated, and the economic benefits were computed by using the value of statistical life (VSL) method. Results showed that the number of avoided premature deaths (...) in Wuhan are 21,384 (95% confidence interval (CI): 15,004 to 27,255) during 2013–2017, due to the implementation of the Air Improvement Action Plan. According to the VSL method, the obtained economic benefits of Huangpi, Wuchang, Hongshan, Xinzhou, Jiang’an, Hanyang, Jiangxia, Qiaokou, Jianghan, Qingshan, Caidian, Dongxihu, and Hannan District were 8.55, 8.19, 8.04, 7.39, 5.78, 4.84, 4.37, 4.04, 3.90, 3.30, 2.87, 2.42, and 0.66 billion RMB (1 RMB = 0.1417 USD On 14 October 2019), respectively. These economic benefits added up to 64.35 billion RMB (95% CI: 45.15 to 82.02 billion RMB), accounting for 4.80% (95% CI: 3.37% to 6.12%) of the total GDP of Wuhan in 2017. Therefore, in the process of formulating a regional air quality improvement scheme, apart from establishing hierarchical emission-reduction standards and policies, policy makers should give integrated consideration to the relationship between regional economic development, environmental protection and residents’ health benefits. Furthermore, for improving air quality, air quality compensation mechanisms can be established on the basis of the status quo and trends of air quality, population distribution, and economic development factors. (shrink)

In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then extracted from each channel. (...) Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods. (shrink)

Linear regression and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative ε dragging linear regression method for robust classification on noised and nonlinear (...) data. First, a technique called negative ε dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision. (shrink)

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait. However, the bionic prosthesis needs to know the next movement at the beginning of a new gait, especially in complex operation environments. In this paper, an upcoming locomotion prediction scheme via multilevel classifier fusion was proposed for the complex operation. At first, two motion states, including steady state and transient state, were defined. Steady-state recognition was backtracking of a completed (...) gait, which would be used as prior knowledge of motion prediction. In steady-state recognition, surface electromyographic and inertial sensors were fused to improve recognition accuracy; five typical locomotion modes were recognized by random forest classifier with over 97.8% accuracy. The transient state was defined as an observation period at the initial stage of upcoming movement, in which only the sEMG signal was recorded due to the limitation of sliding window length. LightGBM classifier was validated to outperform other methods in the accuracy and prediction time of transient-state recognition. Finally, a simplified HMM model based on prior knowledge and observation result was constructed to predict upcoming locomotion. The results indicated that the locomotion prediction was over 91% accuracy. The proposed scheme implements the locomotion prediction at the initial stage of each gait and provides critical information for the gait control of lower limb prosthesis. (shrink)