H. C. B. Liu

Common spatial pattern is an effective algorithm for extracting electroencephalogram features of motor imagery ; however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels, wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II, which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.

Based on 2010 to 2019 Chinese outward foreign direct investment panel data from 39 host countries, this paper studies the relationships between host country cultural characteristics and Chinese OFDI. The OLS regression results show that the cultural tolerance and trust in the host countries are significantly positively correlated with Chinese OFDI, which are robust according to the system GMM tests. Further analysis reveals that cultural tolerance is more positively related to Chinese OFDI in host countries with higher legislation and economic freedom, while cultural trust is positively associated with Chinese OFDI in host countries with lower legislation and economic freedom. In addition, higher cultural tolerance and trust promote Chinese OFDI in countries with greater cultural distance. Unlike traditional studies based on cultural distance in international trade, using more representative cultural characteristics, this paper provides references to Chinese OFDI decision-making based on the root characteristics associated with heterogeneous cultural influences.

We consider Geanakoplos and Polemarchakis’s generalization of Aumman’s famous result on “agreeing to disagree", in the context of imprecise probability. The main purpose is to reveal a connection between the possibility of agreeing to disagree and the interesting and anomalous phenomenon known as dilation. We show that for two agents who share the same set of priors and update by conditioning on every prior, it is impossible to agree to disagree on the lower or upper probability of a hypothesis unless a certain dilation occurs. With some common topological assumptions, the result entails that it is impossible to agree not to have the same set of posterior probabilities unless dilation is present. This result may be used to generate sufficient conditions for guaranteed full agreement in the generalized Aumman-setting for some important models of imprecise priors, and we illustrate the potential with an agreement result involving the density ratio classes. We also provide a formulation of our results in terms of “dilation-averse” agents who ignore information about the value of a dilating partition but otherwise update by full Bayesian conditioning.

This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network, an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an agent, and interventional probabilities in a CBN to represent suppositional credences. We review a difficulty noted in the literature with such an interpretation, and suggest that a natural way to address the challenge is to go for a generalization of CBN that allows indeterminate credences. In the second part, we develop a decision-theoretic foundation for such indeterminate suppositional credences, by generalizing a theory of coherent choice functions to accommodate some form of act-state dependence. The upshot is a decision-theoretic framework that is not only rich enough to, so to speak, ground the probabilities in a subjectively interpreted causal network, but also interesting in its own right, in that it accommodates both act-state dependence and imprecise probabilities.

Sentence length reflects cognitive constraints and stylistic decisions about speech and text segmentation for effective communication, but whether sentence length distributions follow universal patterns across languages and genres remains unclear. This study investigates whether sentence lengths and sub‐sentence lengths—defined as the number of words between sentence‐ending punctuation marks and between adjacent punctuation marks—follow a unified probabilistic distribution across languages, whether this reflects linguistic genealogy, and whether the distribution is affected by genre. Given the links between sentence length, cognitive constraints, and stylistic decisions, we predicted that sentence and sub‐sentence lengths would follow a unified probabilistic distribution across languages, modulated by linguistic genealogy and genre. Analyzing news texts in 10 languages, we found that sentence and sub‐sentence length distributions both conform to a probabilistic model, the Extended Positive Negative Binomial distribution, which was previously shown to capture sentence length distributions in certain languages. To assess whether these differences align with linguistic typology, we performed cluster analysis based on mean length and distribution parameters, with results mirroring known linguistic genealogical relationships. To examine the genre effects, we analyzed sentence and sub‐sentence length distributions across three written genres in English and Chinese. Generalized linear models revealed systematic influences of both genre and language, but with varying results on different linguistic levels: genre accounted for more variance in sentence‐level metrics, whereas language exerted stronger effects at the sub‐sentence level. Sentence and sub‐sentence length distributions reflect a universal probabilistic pattern in punctuation‐based sentence segmentation, influenced by cognitive constraints and genre‐driven adaptability across languages.

Considering the importance of China’s digital economy, industrial Internet, and high-quality development, this study analyzed China’s urban network from the perspective of the communications technology service industry. Three sub-networks and a comprehensive network were constructed. The density, centrality, and cohesive subgroups of the above network were identified. The results show that: cohesion of urban networks in China is weak and resource sharing is low. From west to east, the urban network forms a multilevel diamond structure in the periphery, a parallelogram structure in the semiperiphery, and a triangle structure in the center. The spatial distribution of cohesive subgroups is scattered, disobeying the first law of geography. By constructing sub-networks and a comprehensive network, the subnetworks that dominate China’s urban networks were identified and their typical characteristics described. This study clarifies the technical support pattern behind China’s digital economy development and industrial internet construction and provides a basis for policy-makers to optimize the country’s high-quality development in the future.

Adenosine‐to‐inosine (A‐to‐I) RNA editing, capable of protein recoding, has evolved independently in animals and fungi. This study proposes adaptive hypotheses regarding its origins and phenotypic significance, suggesting that A‐to‐I editing enhances adaptability by alleviating genetic trade‐offs. In metazoans, its emergence may have been driven by a development‐defense trade‐off associated with transposable element activation during the evolution of multicellularity. Late Devonian cooling and End‐Permian warming are hypothesized to have driven the emergence of extensive A‐to‐I recoding in coleoid nervous systems and Sordariomycete sexual fruiting bodies, respectively. These adaptations may have influenced key evolutionary innovations, including the evolution of metazoan nervous systems, coleoid intelligence, and shell loss, and fungal sexual reproductive structures. Additionally, extensive A‐to‐I recoding is proposed to facilitate accelerated development and specific life‐history strategies in both animals and fungi. This paper provides new perspectives on the evolutionary forces shaping A‐to‐I RNA editing and its role in phenotypic diversity across taxa.

The integration of AI technologies in academic writing has become an increasingly prevalent practice. However, existing discussions have primarily focused on analyzing practitioners’ perceptions of AI use in academic writing, with limited attention given to authentic examples of AI-generated content in such practices. This study analyzes 91 instances of undeclared AI-generated content in published research articles listed on Retraction Watch, a watchdog organization that tracks retractions of scientific papers. The results indicate that undeclared uses of AI serve three primary functions: adding new content (49%), organizing existing content (33%), and performing language and formality checks (18%). These AI-generated instances are most frequently found in introduction and literature review sections (34%), followed by results and discussion sections (31%), and least in methodology sections (22%) of research articles. The study raises ethical concerns about the undeclared use of AI, underscoring its potential to compromise academic integrity and knowledge production within the academic community.

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

This paper investigates a two-player contest with a multiplicative sabotage effect, showing it can be converted into a standard Tullock contest with a nonlinear, endogenous cost function. We prove the existence and uniqueness of a pure strategy equilibrium. Our findings suggest that sabotage activities can be more pronounced when the productivity difference between players is small, and the more productive player might not necessarily undergo more attacks. Lazear and Rosen (1981) first-best outcome is attainable for symmetric players if sabotage is sufficiently ineffective or costly. When it is unattainable, optimal pay difference induces positive sabotage only if sabotage is ineffective but relatively inexpensive. Optimal pay difference decreases with effectiveness and increases with the marginal cost of destructive effort, exhibiting a non-monotonic relationship with productive-effort effectiveness. This non-monotonicity contrasts with the monotonicity of the first best pay difference when sabotage is infeasible.

There is an increasing trend in adopting English-medium instruction (EMI) as an academic lingua franca in response to higher education internationalization. Considered as an innovation in content teaching, its adoption rate at Indonesian higher education remains questionable. Using the Diffusion of Innovation (DOI) frameworks, this paper aims to explore the EMI attributes regarding its relative advantage, compatibility, complexity, trialability, and observability and to examine the relationships between the EMI perceived attributes and the attitudes and behavioral intention towards EMI among university students in the Indonesian context. This study is a mixed-method study. The quantitative data were collected using Likert-scale questionnaires from 125 students from two reputable universities, while the qualitative data were obtained through focus-group discussions. The quantitative data were statistically analyzed using SmartPLS to address the research problems. Thematic analysis was employed for the qualitative data for explaining and clarifying the quantitative findings. The findings show that EMI is considered very highly advantageous and compatible, highly observable and trialable, and surprisingly low in complexity to learn content courses. In addition, the EMI perceived relative advantages and compatibility that were predictive to the students’ attitudes toward EMI. Finally, perceived relative advantages, trialability, and observability of EMI were predictive to the students’ behavioral intention to enroll in EMI. EMI is therefore worth applying at higher education institutions in Indonesia as an approach to enhance both students’ content understanding and English competence and to support Indonesian universities’ internationalization vision.

How do individuals with trait anxiety perform in an approach-avoidance (ap-av) conflict situation? To answer this question, we employed computational models to explore the effect of trait anxiety on decisions in ap-av conflict situations and uncover the computational mechanism involved in resolving such conflicts. Sixty-seven participants with high or low trait anxiety completed the ap-av conflict task, which was analysed using the hierarchical drift-diffusion model (HDDM). Anxiety levels were assessed during both baseline and six-month follow-up sessions. Results showed that, during ap-av conflict decision-making, the reaction time of the high trait anxiety group was slower than that of the low trait anxiety group. The slower reaction time in the high trait anxiety group was due to a longer non-decision time. However, the intercept of drift rate and the effect of reward and aversion on drift rate significantly increased in the high trait anxiety group compared to the low trait anxiety group. The high trait anxiety group had a starting point closer to avoidance decisions. This study provides evidence that individuals with high and low trait anxiety manifest distinct alterations in the underlying computational decision-making processes during ap-av conflict situations.

To investigate the relationships among child psychological abuse and neglect (CPAN), children’s learning engagement, family socioeconomic status (family SES), and children’s academic achievement, 271 children (Mage = 9.41 ± 0.81 years old) and their parents participated in this study with a longitudinal design. Results revealed that learning engagement at T1 mediated the relationship between CPAN at T1 and academic achievement at T2 when gender, age, grade, and academic achievement at T1 were under control. Family SES at T1 moderated the relationship between children’s learning engagement at T1 and academic achievement at T2. The association between learning engagement and academic achievement was stronger among children from lower family SES. Our findings highlighted the negative impact of CPAN and the critical role of learning engagement in children’s academic achievement, especially for those from low SES families.

For the first time, this study uses the sports event—tennis as an intermediate carrier to build a bridge of interaction between wealth management characteristic talents and wealth management objects, so that the training of wealth management characteristic talents and employers can be seamlessly connected and integrated naturally. It has broadened the employment channels for talents with characteristics of wealth management, shortened the time process for the integration of wealth management-related college graduates into the society, and improved the recognition of wealth management-related students by employers. At the same time, this study proposes specific measures for wealth management-related employers to feed back the quality of wealth management talent training in colleges and universities.

ObjectiveThis study aimed to examine the effectiveness and safety of the Brain-computer interface in treatment of upper limb dysfunction after stroke.MethodsEnglish and Chinese electronic databases were searched up to July 2021. Randomized controlled trials were eligible. The methodological quality was assessed using Cochrane’s risk-of-bias tool. Meta-analysis was performed using RevMan 5.4.ResultsA total of 488 patients from 16 RCTs were included. The results showed that the meta-analysis of BCI-combined treatment on the improvement of the upper limb function showed statistical significance [standardized mean difference : 0.53, 95% CI: 0.26–0.80, P < 0.05]; BCI treatment can improve the abilities of daily living of patients after stroke, and the analysis results are statistically significant ; and the BCI-combined therapy was not statistically significant for the analysis of the Modified Ashworth Scale.ConclusionThe meta-analysis indicates that the BCI therapy or BCI combined with other therapies such as conventional rehabilitation training and motor imagery training can improve upper limb dysfunction after stroke and enhance the quality of daily life.

BackgroundRepetitive transcranial magnetic stimulation applied to the mylohyoid cortical region has positive clinical effects on post-stroke. Therefore, we conducted a meta-analysis to investigate the efficacy of rTMS for patients with post-stroke dysphagia.MethodsAccording to PRISMA guidelines, we searched the databases of MEDLINE, Cochrane Library, Embase, Web of Science, CNKI, Wangfang. We searched for studies of randomized controlled trials of rTMS to treat dysphagia after stroke and screened by inclusion and exclusion criteria. Features of RCTs were extracted. The heterogeneity of the trials was measured by I2 statistic.ResultsIn total, 11 RCTs with 463 dysphagia patients fulfilled our inclusion criteria. In our analysis, rTMS demonstrated a great beneficial effect for post-stroke dysphagia when combined with traditional swallowing exercises. Moreover, a greatly significant difference was noted based on stimulation frequency. Additionally, no significant difference was observed based on stimulation site.ConclusionsOverall, rTMS can effectively accelerate the improvement of swallowing function in patients with post-stroke swallowing disorders.

In the context of the COVID-19, we examined the relationship between college students’ ego depletion and their prosocial behavior. We explored the mediating role of social self-efficacy between ego depletion and prosocial behavior, we also examined the moderating role of personal belief in a just world in this relationship. 1,122 college students completed the ego depletion questionnaire, prosocial behavior questionnaire, social self-efficacy questionnaire, and personal belief in a just world questionnaire. The current findings suggested that: Social self-efficacy mediated the relationship between college students’ ego depletion and their prosocial behaviors. The ego depletion of college students could be used to predict their prosocial behavior through social self-efficacy. Personal belief in a just world moderated the relationship between social self-efficacy and prosocial behavior.