The authors find East Asians to be holistic, attending to the entire field and assigning causality to it, making relatively little use of categories and formal logic, and relying on "dialectical" reasoning, whereas Westerners are more analytic, paying attention primarily to the object and the categories to which it belongs and using rules, including formal logic, to understand its behavior. The 2 types of cognitive processes are embedded in different naive metaphysical systems and tacit epistemologies. The authors speculate that the origin of these differences is traceable to markedly different social systems. The theory and the evidence presented call into question long-held assumptions about basic cognitive processes and even about the appropriateness of the process-content distinction.
Classical earth pressure theories often struggle to account for the complex coupling effects of wall displacement and spatial non-uniformity under non-limit states. This study presents an interpretable machine learning framework designed to extract universal mechanical laws from heterogeneous experimental datasets. Using a multi-source database of rigid retaining walls with sandy backfill, a three-stage feature refinement strategy is proposed that incorporates Recursive Feature Elimination, Collinearity Analysis, and Interpretability Comparison to identify a parsimonious set of five fundamental physical parameters. A SHapley Additive exPlanations-Categorical Boosting (CatBoost-SHAP) framework is established to predict the active earth pressure coefficient (<i>K</i>) and interpret the underlying mechanisms across various movement modes (RB, RT, and T). Results demonstrate that the model effectively captures the progressive evolution of shear bands and the soil arching effect. Specifically, a critical displacement threshold of Δ/H ≈ 0.006 is identified, marking the transition from mode-dominated stress non-uniformity to magnitude-driven limit states. Leave-One-Dataset-Out Cross-Validation (LODOCV) confirms the model’s ability to maintain physical consistency over purely statistical fitting despite significant inter-literature heterogeneity. Finally, a Graphical User Interface (GUI) is developed to facilitate rapid, displacement-based design in engineering practice. This research bridges the gap between empirical laboratory observations and generalized mechanical logic, providing a data-driven foundation for refined geotechnical design.
This paper critically looks at the representation of femininity in the poetry of Akka Mahadevi, a twelfth-century Indian saint-poet, through a hermeneutic textual analysis of her select poems. In sharp contrast to the discussion of the feminine within the framework of gender politics in contemporary literary theory, this study argues that Akka Mahadevi’s poetry redefines femininity as a spiritual force, not as a site of subjugation – a means of divine communion rather than just a mode of resistance against patriarchal structures. The study also seeks to pitch her radical conception of femininity rooted in devotion, intuition, and transcendence against the transactional logic of modernity. Through a contextual interpretation of themes such as renunciation, devotion, and feminine spirituality within the broader framework of the Bhakti tradition and mystical hermeneutics, this study highlights the dialectics of devotion and gender identity in her poetry.
Abstract The intersection of digital technology and the knowledge economy has led to the rapid development of the online knowledge payment (OKP) industry, which has attracted increasing attention from scholars across various disciplines. However, research in this field remains relatively fragmented and lacks a coherent framework for understanding the evolutionary trajectory and mechanisms of OKP platforms. This study addresses this gap by conducting a bibliometric review of 226 core papers retrieved from Scopus and Web of Science databases and applying the Elaboration Likelihood Model (ELM) to interpret the underlying business logic of OKP models. Through ELM-guided classification, this paper distinguishes between central route mechanisms (such as knowledge quality and credibility in paid Q&A) and peripheral route mechanisms (such as emotional appeal and interactivity in live broadcast formats). The bibliometric analysis reveals emerging research trends focused on hybrid platform strategies, artificial intelligence-driven personalization, and blockchain-enabled trust systems, indicating a shift from static content monetization to dynamic, user-centered knowledge experiences. By integrating the ELM with quantitative mapping of the OKP research landscape, this study constructs a dual-perspective framework that links user cognitive processing with evolving platform affordances. The findings theoretically illustrate how persuasion and participation coexist in knowledge-driven digital environments, offering practical guidance for platform designers, knowledge creators, and policymakers seeking to promote innovation and user engagement in the OKP field.
History of scholarship and learning. The humanities, Social Sciences
With the rapid development of machine learning, challenging question and answer datasets have also emerged, and the machine reading comprehension technology has emerged. Traditional machine reading comprehension methods mostly focus on the understanding word level semantics, with the weak ability to extract logical relationships from text, resulting in the lower ability of logical reasoning. In order to strengthen the ability of machine reading comprehension method to extract the logical relationship of text and the ability of logical reasoning, a neural symbol model based on logical reasoning was proposed, and the logical expressions captured by the neural symbol model were converted into text input and trained in a mixed reasoning reading comprehension model based on symbolic logic. The mixed reasoning reading comprehension model based on symbolic logic is different from the traditional machine reading comprehension model. It uses symbolic definition and logical capture to extract logical symbols and generate logical expressions. The research results show that the accuracy and F-measure values of the neural symbol model based on the logical reasoning are 70.08% and 70.05%, respectively, when the training set sample size is 4000. The accuracy of the mixed reasoning reading comprehension model based on symbolic logic in the logical reasoning data set of the standard postgraduate entrance examination is 88.31%, which is higher than the 58.74% of the language perception map network model. The accuracy rate in the four-choice and one-choice question-and-answer data set is 40.92%, which is 1.58% higher than that of the language awareness graph network model. In summary, the neural symbol model and hybrid inference reading comprehension model proposed in the study have superior performance, which can capture the logical relationship of text in data sets well, improve the model feature abstraction and reasoning ability, effectively shorten the training time and improve the model efficiency.