Research has indicated that microRNAs (miRNAs), a special class of non-coding RNAs (ncRNAs), can perform important roles in different biological and pathological processes. miRNAs’ functions are realized by regulating their respective target genes (targets). It is thus critical to identify and analyze miRNA-target interactions for a better understanding and delineation of miRNAs’ functions. However, conventional knowledge discovery and acquisition methods have many limitations. Fortunately, semantic technologies that are based on domain ontologies can render great assistance in this regard. In our (...) previous investigations, we developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), to provide the community with common data elements and data exchange standards in the miRNA research. This paper describes (1) our continuing efforts in the OMIT ontology development and (2) the application of the OMIT to enable a semantic approach for knowledge capture of miRNA-target interactions. (shrink)

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved (...) in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed. (shrink)

This study examines whether the gender of the directors on fully independent audit committees affects the ability of the committees in constraining earnings management and thus their effectiveness in overseeing the financial reporting process. Using a sample of 525 firm-year observations over the period 2003 to 2005, we are unable to identify an association between the proportion of female directors on audit committees and the extent of earnings management.

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented (...) to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method. (shrink)

This article proposes a systematic application of recent developments in the logic of preference to a number of topics in deontic logic. The key junction is the well-known Hansson conditional for dyadic obligations. These conditionals are generalized by pairing them with reasoning about syntactic priority structures. The resulting two-level approach to obligations is tested first against standard scenarios of contrary-to-duty obligations, leading also to a generalization for the Kanger-Anderson reduction of deontic logic. Next, the priority framework is applied to model (...) two intuitively different sorts of deontic dynamics of obligations, based on information changes and on genuine normative events. In this two-level setting, we also offer novel takes on vexed issues such as the Chisholm paradox and modelling strong permission. Finally, the priority framework is shown to provide a unifying setting for the study of operations on norms as such, in particular, adding or deleting individual norms, and even merging whole norm systems in different manners. (shrink)

The research expects to explore the psychological mobilization of innovative teaching methods of Music Majors under the new curriculum reform. The relevant theories of college students’ innovative teaching methods are analyzed under deep learning together with the innovation and construction of music courses. Thereupon, college students’ psychological mobilization is studied. Firstly, the relationship between innovation and entrepreneurship teaching and deep learning is obtained through a literature review. Secondly, the music classroom model is designed based on the deep learning theory, and (...) the four dimensions of the music curriculum are defined to innovate and optimize the music teaching model. Finally, the Questionnaire Survey is used to analyze the design classroom model. Only 15% of the 180 respondents understand the concept of deep learning, 32% like interactive music learning, and 36% like competitive comparative music classroom learning. And the students who study instrumental music have higher significant differences in learning motivation than those who study vocal music. In addition to classroom learning, 16% of people improve their music skills through music equipment. College students like interactive music classes and competitive comparison classes that can give more play to their subjective initiative. After the new curriculum reform, the music curriculum based on deep learning can stimulate students’ interest in learning and participate in the mobilization of students’ learning psychology. Therefore, in the future of music education and teaching, there is a need to pay more attention to students’ psychological status. The research results can provide references and practical significance for the innovative teaching activities of music classrooms after the new curriculum reform. (shrink)

According to the conflict monitoring hypothesis, conflict monitoring and inhibitory control in cognitive control mainly cause activity in the anterior cingulate cortex and control-related prefrontal cortex in many cognitive tasks. However, the role of brain regions in the default mode network in cognitive control during category induction tasks is unclear. To test the role of the ACC, PFC, and subregions of the DMN elicited by cognitive control during category induction, a modified category induction task was performed using simultaneous fMRI scanning. (...) The results showed that the left middle frontal gyrus and bilateral dorsal ACC/medial frontal gyrus were sensitive to whether conflict information appears, but not to the level of conflict. In addition, the bilateral ventral ACC, especially the right vACC, a part of the DMN, showed significant deactivation with an increase in cognitive effort depending on working memory. These findings not only offer further evidence for the important role of the dorsolateral PFC and dorsal ACC in cognitive control during categorization but also support the functional distinction of the dorsal/ventral ACC in the category induction task. (shrink)

This paper mainly introduces the application of immersive media in Chinese public cultural participation from the perspective of cultural education, as well as the important value of the application of immersive media in expanding the breadth, accuracy, and depth of education and thereby improving the quality of education. On this basis, the realistic path of the role of immersive media technology in further realizing public cultural participation is discussed. First of all, through a questionnaire survey, it analyzes the problems existing (...) in Chinese public cultural participation from the perspective of cultural education and the application of immersive media in Chinese public cultural participation. Secondly, from the aspects that the application of immersive media can extend the space of cultural participation, enrich the content of cultural participation and strengthen the value recognition of cultural participation, it demonstrates that immersive media helps to solve the dilemma of Chinese public cultural participation as a whole and thus improve the quality of education. This will bring new possibilities for expanding Chinese public cultural participation and promoting public social education and cultural quality education. Finally, it discusses the further development of immersive media technology in the field of cultural education from the aspects of optimizing the ecology of immersive media development, promoting the R&D and application of immersive media technology, and promoting the integration of immersive media with cultural participation space and cultural education content. (shrink)

In studies investigating the property value enhancements along metro lines, study ranges in most cases are measured in Euclidean distance. However, city roads are not always straight, and Euclidean distance sometimes does not equal to actual distance the passenger travels from the public transport station to their home or a commercial spot. To solve this problem, this study analyzed the indicators affecting commercial property value in metro station accessible area in Xi’an, using walking time consumption in measuring the study range. (...) Following a conceptual framework, considering empirical evidences in related studies, information in city structure, and metro distribution, the initial indicator set is built. Using hedonic price model, spatial autoregressive model, and error term, significant indicators affecting commercial property value are investigated. In this study, 14 significant indicators are determined and quantified. Threshold distance equals to 900 m is found to be appropriate in addressing the autocorrelation problem with the weighted decision matrix tool. Compared to HPM and SEM, SAR is found to perform the best in evaluating the significant indicators with the largest R2 and the lowest Akaike Info Criterion. Besides, it is discovered that the value of the commercial property lies in the study range of 20 min walking time and is obviously influenced by the metro station. The results discovered in this study could provide some empirical evidences to commercial property planning in Xi’an. (shrink)

It has been a growing trend in Chinese universities to shift from English for general purposes to English for specific purposes teaching. Against this background, large groups of teachers previously engaged in teaching EGP have become or are becoming ESP teachers, which means a complex process of learning for subject-specific information, transforming teaching practices and constructing new identities. Despite this, very little has been written about the ESP teacher cognition of language teaching or the factors influencing this shift in teaching. (...) This study involved English for Medical Purposes teachers in Chinese universities as participants, and a scale of EMP TC with 31 items was developed on the basis of questionnaire results. Data from exploratory factor analysis revealed six dimensions of the scale, namely, teacher attitude, teacher belief, teacher learning, teacher support, role identification, and teacher practice—that combine to constitute and influence EMP TC. While the identity factor has attracted wide attention in ESP teacher research, other factors have largely been neglected. Thus, this research highlights the importance of more factors in shaping and changing the language teaching cognition of EMP or ESP teachers in large, especially the teacher belief factor. In addition, results of independent samples t-tests indicated significant difference in EMP teacher learning in terms of gender, differences in EMP teacher attitude and teacher support in terms of EMP teaching experience. Suggestions for enhancing EMP TC are offered on the basis of the conclusions of this research. (shrink)

The present study aimed to delineate the characteristics of empathy and mental health in preschool teachers and examine the role of empathy in preschool teachers’ mental health. The sample in this study consisted of 4348 preschool teachers, who were divided into 4 groups according to their years of teaching experience (less than 2 years, 2 to 5 years, 5 to 10 years, and more than 10 years). The Chinese version of the Symptom Checklist 90 was used to measure the mental (...) health symptoms of the participants, and the Chinese version of the Interpersonal Reactivity Index was employed to assess various aspects of the participants’ empathy. The results indicated that preschool teachers scored higher than the controls for most mental symptoms, and most symptoms increased as teaching experience increased, independent of the effect of age. The study also found that the four dimensions of empathy showed different trends across the four teaching experience groups: fantasy remained stable, empathic concerns and perspective taking showed decreasing trends, and personal distress showed an increasing trend. Moreover, the present research found a relatively complex relationship between empathy and mental health in preschool teachers: while fantasy and personal distress positively predicted mental health symptoms in preschool teachers, perspective taking and empathic concern negatively correlated with most of the symptoms. It seems that empathy contains both risky and protective factors for individuals’ mental health, and these factors are affected by years of teaching experience. (shrink)

Social anxiety and stress type can influence strong conformity among adolescents; however, the interaction between them is not clear. In this study, 152 adolescents were recruited and assigned one of two conditions: an interaction and a judgment condition. In the interaction condition, adolescents with high social anxiety (HSA) were less likely to conform when completing a modified Asch task, compared to adolescents who had low social anxiety. In the judgment condition, adolescents with HSA were more likely to conform to the (...) opinions from the unanimous majority. The results suggest that adolescents with HSA may show different styles of strong conformity with the change of stress type. We believe that socially anxious adolescents avoid potential social situations with weaker conformity, while avoiding negative evaluations from others with stronger conformity. These findings contribute to a better understanding of the social dysfunctions among adolescents with HSA and provide a new direction for clinical interventions. (shrink)

Scientists normally earn less money than many other professions which require a similar amount of training and qualification. The economic theory of marginal utility and cost-benefit analysis can be applied to explain this phenomenon. Although scientists make less money than entertainment stars, the scientists do research work out of their interest and they also enjoy a much higher reputation and social status in some countries.

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an (...) eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks. (shrink)

Purpose: Short track speed skating is a racing sport with racing tactics are equally crucial to speed and technical skating skills. Therefore, to investigate the relationship between starting and finishing positions for elite skaters and subsequently, explore pacing patterns for champions are necessary.Methods: To investigate a pattern of effective tactical positioning strategy, Kendall’s tau-b correlations between starting and finishing position were calculated, with 500 m races having the most positive correlation.Results: Furthermore, starting position distributions of winners in each round, as (...) well as the fluctuations in champion starting positions across rounds were analyzed. Our findings showed that skaters on the first track were inclined to win the rounds in 500, 1,000, and 1,500 m, and the differences between starting and finishing positions for champions were minimized in semi-finals. Meanwhile, the pacing patterns were gaining more fluctuations by the increase of race distances for champions, as the average standard deviation of lap rankings equaled 0.90, 1.15, and 2.21 for 500, 1,000, and 1,500 m races, respectively.Conclusion: In conclusion, elite skaters should adopt flexible tactics at the lowest cost of energy consumption. The overall variability of lap ranking in long-distance races were distinctly higher than it in short distance. (shrink)

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to (...) that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity. (shrink)

Managing the interplay of private information within families creates challenges, especially when the information involves member identity, a complex and emotionally charged issue. Ravelingien and...

Despite numerous published writings on China's regional role, the world still knows very little about Beijing's perception and strategy. This article seeks to make an intellectual contribution in understanding China's foreign policy and its efforts to participate in East Asian integration. This article argues that under the rhetoric of peaceful development and community building, China's foreign policy is pragmatic and changes with the tide of events in international relations. China's participation in regional integration serves as a good case for examining (...) changes in Beijing's strategy. In the past two decades, China has moved from a reluctant participant of regional affairs to an active participant and potential future leader. China's adjusting role is a consequence of Beijing's pragmatism in policy and its growing economic confidence. Pragmatism has led China to and wait for the proper timing to step onto the world stage. (shrink)

As a way of human-computer interaction, game-based assessment is more suitable for young children because it is situational, interesting, and effective. National identity is an important factor affecting the overall development of young children and the future development of a country, which has attracted extensive attention from researchers. Nevertheless, the assessment of young children's national identity is still based on traditional evaluation, including questionnaires and interviews, which have the limitations of being inaccurate, dull, and time-consuming. To understand the characteristics of (...) children's national identity, it is necessary to use scientific and interactive assessment methods. The present study investigated whether the game-based assessment we developed specifically would be an appropriate tool to measure young children's national identity. The results show that the game-based assessment had good item discrimination. Exploratory factor analysis demonstrated the game covered three aspects: national cognition mastery, national emotion engagement, and national behavior tendency. The confirmatory factor analysis suggested that the model with three factors fit the data well. The internal consistency, the split-half reliability, and the test-retest reliability meet standards. Overall, the results indicated that this game can be successfully used to assess young children's national identity with acceptable validity and reliability. Our study provides strong evidence for the use of human-computer interaction in child measurement. These findings are the first to demonstrate the promise of game-based assessment in assessing children's national identity reliably and effectively. (shrink)

BackgroundPatient-centred care and patient autonomy is one of the key factors to better quality of service provision, hence patient outcomes. It enables the development of patients’ trusts which is an important element to a better doctor-patient relationship. Given the increasing number of patient disputes and conflicts between patients and doctors in Chinese public hospital, it is timely to ensure patient-centred care is fully and successfully implemented. However, limited studies have examined the views and practice in different aspects of patient-centred care (...) among doctors in the Chinese public hospitals.MethodsA quantitative approach was adopted by distributing paper-based questionnaires to doctors and patients in two hospitals (Level III and Level II) in Jinan, Shandong province, China.ResultsIn total, 614 doctors from the surgical and internal medicine units of the two hospitals participated in the survey yielding 90% response rates. The study confirmed the inconsistent views among doctors in terms of their perception and practice in various aspects patient-centred care and patient autonomy regardless of the hospital where they work (category II or category III), their unit speciality (surgical or non-surgical), their gender or seniority. The high proportion of doctors (more than 20%) who did not perceive the importance of patient consultation prior to determining diagnostic and treatment procedure is alarming. This in in part due to the belief held by more than half of the doctors that patients were unable to make rational decisions and their involvement in treatment planning process did not necessarily lead to better treatment outcomes.ConclusionThe study calls for the development of system level policy and organisation wide strategies in encouraging and enabling the practice of patient-centred care and patient autonomy with the purposes of improving the quality of the service provided to patients by Chinese hospitals. (shrink)

Educational assessments tests are often constructed using testlets because of the flexibility to test various aspects of the cognitive activities and broad content sampling. However, the violation of the local item independence assumption is inevitable when tests are built using testlet items. In this study, simulations are conducted to evaluate the performance of item response theory models and testlet response theory models for both the dichotomous and polytomous items in the context of equating tests composed of testlets. We also examine (...) the impact of testlet effect, length of testlet items, and sample size on estimating item and person parameters. The results show that more accurate performance of testlet response theory models over item response theory models was consistently observed across the studies, which supports the benefits of using the testlet response theory models in equating for tests composed of testlets. Further, results of the study indicate that when sample size is large, item response theory models performed similarly to testlet response theory models across all studies. (shrink)

Multi-level teaching has been proven to be more effective than a one-size-fits-all learning approach. This study aimed to develop and implement a multi-level remedial teaching scheme in various high school classes containing students of a wide range of learning levels and to determine its effect of their learning. The deterministic inputs noisy and gate model of cognitive diagnosis theory was used to classify students at multiple levels according to their knowledge and desired learning outcomes. A total of 680 senior high (...) school students from central provinces in China participated in the initial cognitive diagnostic test, and 1,615 high school sophomores from seven high schools in China participated in a formal cognitive diagnosis test. Thirty-six high school students from Southwestern China participated in the think-aloud protocols, and 258 seniors from three high schools in southwest China participated in the remedial teaching experiment. Through an analysis of students’ think-aloud protocols, cognitive errors of students at all levels were determined, and multi-level remedial teaching programs were designed to address these common cognitive errors. The remedial teaching programs were then implemented in three schools and compared with a control group. The results indicated that the students in the experimental group showed a more significant improvement. In this study, the steps of designing multi-level remedial teaching include assessment, classification, and preparing a teaching scheme, which are feasible and can have remarkable teaching effects. This process can be used for reference by teachers of various subjects. (shrink)

With the rapid development of science and technology, UAVs have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects (...) of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm, Gradient Boosting Decision Tree algorithm, K Nearest Neighbor algorithm, and Decision Tree algorithm. Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield. (shrink)

ObjectiveThis study aimed to develop a diagnostic model of multi-kinematic parameters for patients with amnestic mild cognitive impairment (aMCI).MethodIn this cross-sectional study, 94 older adults were included (33 cognitively normal, CN; and 61 aMCI). We conducted neuropsychological battery tests, such as global cognition and cognitive domains, and collected gait parameters by an inertial-sensor gait analysis system. Multivariable regression models were used to identify the potential diagnostic variables for aMCI. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic accuracy (...) of kinematic parameters in discriminating aMCI from healthy subjects.ResultsMultivariable regression showed that multi-kinematic parameters were the potential diagnostic variables for aMCI. The multi-kinematic parameter model, developed using Timed Up and Go (TUG) time, stride length, toe-off/heel stride angles, one-leg standing (OLS) time, and braking force, showed areas under ROC (AUC), 0.96 [95% confidence interval (CI), 0.905–0.857]; sensitivity, 0.90; and specificity, 0.91. In contrast, a single kinematic parameter’s sensitivity was 0.26–0.95 and specificity was 0.21–0.90. Notably, the separating capacity of multi-kinematic parameters was highly similar to Montreal Cognitive Assessment (MoCA; AUC: 0.96 vs. 0.95). Compared to cognitive domain tests, the separating ability was comparable to Auditory Verbal Learning Test (AVLT) and Boston Naming Test (BNT; AUC: 0.96 vs. 0.97; AUC: 0.96 vs. 0.94).ConclusionWe developed one diagnostic model of multi-kinematic parameters for patients with aMCI in Foshan. (shrink)

The pattern, process, and mechanism of residential heterogeneity vary significantly with different geographical scales. However, most traditional methods ignore the checkboard and modifiable areal unit problem, which may cover up the complexity and hierarchy of social space. Taking Hangzhou city as an example, a multiscalar method was proposed based on the information entropy theory to estimate residential heterogeneity and its scale sensitivity. Based on the sixth population census of Hangzhou and the housing price database of 6,536 residential districts from 2008 (...) to 2018, we explore the scale effect and dynamic characteristics of residential heterogeneity. The results of spatial simulation and geostatistical analysis based on Python Spatial Analysis Library module show that the multiscalar algorithm better presents the real segregation pattern than traditional method, which is one of the new models and technologies in urban geography complex system. Exploring residential heterogeneity through multiscalar lens provides an important basis for the gradual and refined urban renewal. (shrink)