Search Results (132)

Hate speech on social media reproduces norms of inequality and gender stereotypes, disproportionately affecting women. This study proposes a hybrid approach that integrates emotional tone classification with explicit hostility detection to strengthen preventive moderation. We constructed a corpus from three open data sets (1,236,371 records; 1,003,991 after ETL) and represented the text using TF-IDF and contextual RoBERTa embeddings. We trained individual models (RoBERTa fine-tuned, Random Forest, and XGBoost) and a stacking metamodel (Gradient Boosting) that combines their probabilities. On the test set, the ensemble outperformed the base classifiers, achieving accuracy of 0.93 in hate detection and 0.90 in emotion classification, with an AUC of 0.98 for emotion classification. We implemented a RESTful API and a web client to validate the moderation flow before publication, along with an administration panel for auditing. Performance tests in a prototype deployment (Google Colab exposed through an Ngrok tunnel) provided proof-of-concept validation, revealing concurrency limitations from around 300 users due to infrastructure constraints. In general, the results indicate that incorporating emotional tone analysis improves the model’s ability to identify implicit hostility and offers a practical way to promote safer digital environments. The probabilistic outputs produced by the ensemble model were subsequently analyzed using the Bayesian Calibration and Optimal Design under Asymmetric Risk (BACON-AR) framework, which serves as a mathematical post hoc decision layer for evaluating classification behaviour under unequal error costs. Rather than modifying the trained architecture or improving its predictive performance, the framework identifies a cost-sensitive operating threshold that minimizes the total expected risk under the selected asymmetric cost configuration. The experiments were conducted using an English-language data set; therefore, the findings of this study are limited to hate speech detection in English. Full article

In Vietnamese primary mathematics education, geometry instruction often emphasizes rote calculation and formula memorization rather than meaningful contextualization, leaving students disconnected from abstract concepts and lacking opportunities to connect learning with cultural identity. This quasi-experimental study investigates how integrating generative AI tools (ChatGPT, DALL·E, Canva) with the culturally grounded Vietnamese folktale Bánh Chưng—Bánh Giầy can support Grade 5 students’ understanding of circle geometry. Employing a mixed-methods design with 30 students divided into experimental (AI + storytelling) and control (traditional instruction) groups, the study measured cognitive and affective learning outcomes through pre/post-tests, a validated 25-item questionnaire, interviews, and classroom observations. Quantitative results revealed significant improvements in the experimental group across all measured dimensions, learning interest, attentional focus, conceptual understanding, mathematics passion, and cultural preservation awareness, with large effect sizes. Qualitative findings confirmed enhanced engagement, multimodal conceptual clarity, and cultural affective resonance. The study demonstrates that low-cost, teacher-mediated generative AI can effectively support learning in resource-constrained primary settings when anchored in local narratives. Implications for ethical AI integration and teacher professional development in Vietnamese contexts are discussed. Full article

Consciousness presents a structural puzzle: a unified, context-sensitive, globally integrated mode of experience emerging from distributed neural dynamics. While classical neuroscience has mapped synaptic, oscillatory, and network-level mechanisms with increasing precision, debate persists as to whether classical formalisms fully capture the integrative and contextual features of conscious processing. This review examines whether quantum principles offer explanatory leverage in two distinct senses: as formal mathematical frameworks for modeling contextual cognition, and as mechanistic hypotheses proposing biologically instantiated non-classical states. We surveyed empirical and theoretical developments spanning zero-quantum-coherence in MRI signals, entanglement-structured learning paradigms, quantum-inspired computational models, and proposed neural substrates, including microtubules, nuclear spins, and photonic architectures. Although certain findings have been interpreted as consistent with a non-classical structure, no study to date has demonstrated entanglement, long-lived coherence, or collapse dynamics in neural tissue under operational criteria comparable to those used in controlled quantum systems. Replication remains limited, biological entanglement witnesses are not yet established, and nonlinear classical dynamics can reproduce many putative quantum signatures. Accordingly, the decisive question is not whether the brain is quantum, but whether its dynamics exceed the explanatory reach of rigorously defined classical models. Progress hinges on replication, adversarial scrutiny, and operational criteria precise enough to discriminate genuine non-classical correlations from classical complexity. Whether quantum mechanisms ultimately prove necessary or refined classical models remain sufficient, this inquiry compels a deeper understanding of integration, contextuality, and the physical constraints shaping conscious experience. Full article

In his seminars, Deleuze claims that Spinoza is ‘an absolute geometrist’. This article contextualizes, explains and substantiates this aspect of Deleuze’s interpretation of Spinoza. I position Deleuze’s reading within both the long-running scholarly debate on Spinoza’s relationship to mathematics and within the evolution of Deleuze’s own relation to Spinoza. Deleuze’s idea that Spinoza is a geometrist is shown to consist of three elements. First, according to Spinoza, geometry is more fundamental than arithmetic. Second, Spinoza frees geometry from the realm of fiction and abstract and develops, as Deleuze says, a ‘mathematics of the real’. Third, Spinoza finds in geometry a language of univocity, by which he can avoid the equivocity and hierarchy of the Aristotelian worldview. Full article

The evolution of future Internet and sixth-generation (6G) networks is driving a paradigm shift from classical bit-centric communication toward meaning-aware and task-oriented communication models. Traditional information theory, while fundamental for ensuring reliable symbol transmission, does not account for semantic relevance or task effectiveness, which are critical for emerging applications such as autonomous systems, immersive services, and ultra-low-latency communications. This article presents a comprehensive review of Semantic Communications (SemCom) from a future Internet perspective. The review systematically analyses representative extensions of classical information theory aimed at quantifying semantic information, including semantic information measures, semantic channel capacity, and semantic rate–distortion formulations. In addition, the main mathematical and computational frameworks enabling practical semantic communication systems are examined, including the Information Bottleneck principle, learning-based end-to-end communication architectures, and reinforcement learning approaches for task-oriented optimization under network constraints. The review further discusses the role of semantic metrics, contextual modelling, and task-driven performance evaluation in the design of semantic-aware communication systems. The analysis identifies key open challenges, particularly the lack of a unified theoretical framework, the need for robust and context-aware semantic performance metrics, and the integration of semantic awareness into network-level design. Overall, this review highlights Semantic Communications as a promising paradigm for future Internet and 6G networks, where communication efficiency is increasingly determined by semantic relevance and task effectiveness rather than bit-level fidelity alone. Full article

This paper develops a rigorous mathematical and computational framework for four-dimensional chess defined on the discrete hypercubic lattice $\{1,\dots , 8{\}}^4$. We formalize piece movement using displacement sets in ${\mathbb{Z}}^4$, define adjacency via the Chebyshev metric, and analyze the resulting move graphs for rooks, bishops, knights, queens, and kings. We establish exact mobility formulas, parity invariants, and connectivity properties, consolidating known product-graph results for rooks and kings while introducing a boundary-sensitive analysis of the four-dimensional knight verified by exhaustive enumeration. The mathematical framework is complemented by a fully implemented 4D chess engine and interactive visualization environment rendering all 64 (z,w)-slices of the hypercube simultaneously. The system supports full move legality, generalized special rules, multi-king checkmate detection, and reproducible state enumeration. Performance measurements and exploratory branching-factor estimates are obtained through reproducible random playouts using the publicly available implementation. We contextualize this ruleset within existing work on move graphs on ${\mathbb{Z}}_n^m$, higher-dimensional leapers, spectral properties of grid graphs, toroidal analogs, and multidimensional visualization. Exploratory qualitative feedback (N = 18) is included to examine whether the visualization design is interpretable and navigable in practice, providing feasibility-oriented observations on how slice-based 4D projection and layered board rendering are perceived by non-expert users in an exploratory context. Together, the mathematical results, implemented engine, and visualization form a coherent foundation for the study of strategy, complexity, and human interaction in four-dimensional game systems. The framework provides a basis for future investigations into spectral analysis of move graphs, symmetry-aware search, hierarchical planning, and educational applications in high-dimensional geometry. Full article

This study aimed to evaluate the application of mathematical modeling in sustainable community-based disaster risk management (CBDRM), paying particular attention to the incorporation of financial risk mitigation mechanisms such as insurance and community-based risk pooling. A structured literature search was conducted in the Scopus and ScienceDirect databases, followed by bibliometric and qualitative analysis of relevant studies in mathematics, economics, and disaster management. During the analysis, 17 peer-reviewed journal articles met the inclusion criteria and were examined based on publication trends, geographical distribution, modeling methods, and the extent to which financial protection mechanisms were incorporated into quantitative frameworks. The findings indicated growing academic interest in recent years and showed considerable methodological diversity, including stochastic optimization, vulnerability indices, agent-based simulations, and econometric models. Despite these advancements, major financial risk mitigation elements, such as premium design, fund management, and payout procedures, remained inadequately incorporated into existing modeling structures and were frequently addressed as separate analytical components. The focus on studies in high-income countries raised concerns about contextual applicability in climate-vulnerable and low-income regions. The review showed the need for more operationally incorporated modeling frameworks that connect quantitative risk assessment with community-level financial resilience strategies to support sustainable CBDRM. Full article

Traditional talent identification systems often rely on static assessments and overlook the dynamic nature of long-term development. To address this limitation, this study proposes a biomimetic predictive framework inspired by crayfish behavioral ecology. The Crayfish Optimization Algorithm (COA), derived from adaptive foraging and competition mechanisms observed in crayfish, is enhanced through a Thinking Innovation Strategy (TIS) to form TISCOA for hyperparameter optimization of a Gradient Boosting Decision Tree model. Using a five-year longitudinal dataset of 160 elite mathematical students, the framework models Professional Achievement in Mathematics (PAM) from multidimensional baseline indicators. Comparative experiments with multiple metaheuristic optimizers show that the proposed approach achieves stable generalization performance within the examined cohort. Feature attribution analysis indicates that non-cognitive factors, particularly Emotion Regulation, contribute substantially to long-term outcomes, while temporal variables such as the Latency Period further shape developmental trajectories. Residual analysis highlights heterogeneous patterns that may reflect unobserved contextual influences. Overall, the study demonstrates how a biologically inspired optimization mechanism can support interpretable and stability-oriented longitudinal prediction in small-sample educational settings. Full article

This study addresses the growing vulnerability of societies to extreme weather events intensified by climate change and explores how Secondary Education can foster sustainability competences aligned with the European GreenComp framework. A mixed-methods design was used, combining a content analysis of 279 curricular units from educational legislation and STEM subjects in Compulsory Secondary Education and Baccalaureate, a questionnaire administered to 190 students, and the design and classroom implementation of a GreenComp-based teaching intervention. The curricular analysis revealed uneven integration of sustainability competences across STEM disciplines, with stronger presence in Biology, Geology and Technology, and limited representation in Mathematics and Physics and Chemistry. Student perceptions showed fragmented understandings of extreme weather events, their causes and consequences, and limited awareness of global frameworks such as the SDGs and COP meetings. The implemented teaching sequence improved students’ knowledge of extreme events, strengthened their recognition of links with climate change, and increased awareness of mitigation, adaptation, and the role of education and political action. Overall, the findings highlight both opportunities and gaps in current curricula and demonstrate the potential of contextualized, inquiry-based STEM approaches to develop sustainability competences and better prepare students to face extreme weather events. Full article

Most pre-trained language representation models are designed to encode contextualized semantic information for general language processing tasks. However, they are insufficient for math word problem (MWP) solving, which requires not only linguistic syntax and semantic understanding but also numerical reasoning. In this work, we introduce SSN4Solver, a deep neural solver that improves MWP-solving performance by symmetrically fusing syntax, semantics, and numeracy representations within its contextual encoder. Our approach jointly captures syntactic structures from dependency trees, semantic features from part-of-speech tags, and the attributes and relations of numerical entities. By treating these heterogeneous information sources in a balanced and aligned manner, SSN4Solver constructs a rich, multi-faceted representation for MWP solving without introducing substantial computational overhead, empowering human–computer interaction (HCI) applications such as adaptive educational interfaces and intelligent tutoring systems. Extensive experiments demonstrate that SSN4Solver outperforms existing baseline models. In addition, a visualization scheme is designed to elucidate how the three types of representations contribute to the solving process. SSN4Solver thus offers a scalable solution, contributing to the development of HCI systems that are both intelligent and mathematically effective. Full article

Large Language Models (LLMs) generate fluent text but often struggle with reliable multi-step reasoning, factual grounding, and stable use of long context, especially when inputs are incomplete, inconsistent, or imprecise. To address these challenges, we propose a Creative AI framework that integrates DIKWP-TRIZ with a semantic-mathematical constraint layer. DIKWP-TRIZ extends TRIZ by embedding a DIKWP (Data–Information–Knowledge–Wisdom–Purpose) network, enabling purposeful, value-aware transformations and explicit repair operations under 3-No conditions. The semantic layer introduces three context-indexed constraints over concept–expression mappings (Existence, Contextual Uniqueness, and Transitivity), making ambiguities and contradictions explicit and checkable during inference and generation. We enumerate the DIKWP × DIKWP transformation type space (25 ordered pairs over {D,I,K,W,P}) and provide candidate TRIZ inventive principles for each type as design-time guidance. A global Purpose controller steers transformation selection and enforces goal alignment and ethical constraints. We present a reference architecture and qualitative case analyses against a standard LLM, illustrating how the framework structures intermediate steps, surfaces assumptions, and supports traceable explanations. Quantitative benchmarking remains for future work. Full article

This study investigates the relationships among students’ socio-contextual ecosystems including interactions with parents, teachers, and society (i.e., participation in cocurricular activities), their dual-pathway self-regulatory strategies—emotional self-regulation (SR) and motivated learning strategies (MLS)—and their mathematics achievement emotions. Drawing on data from a sample of 1269 Chinese secondary school students, the findings indicate that all dimensions of parent–teacher–society interactions significantly predict students’ mathematics achievement emotions (i.e., enjoyment and anger) through the mediation of both emotional self-regulation strategies (i.e., positive reappraisal and rumination) and motivated learning strategies. Results in this study revealed a clear differential mediation pattern: teacher–student interactions exerted significant direct effects on students’ emotions alongside indirect effects through their self-regulation (partial mediation). In contrast, the impact of social cocurricular activities was fully mediated by students’ self-regulatory processes. Notably, parent–child interactions directly influenced enjoyment but only affected anger indirectly through self-regulation. These results unpack the “black box” of how socio-contextual ecosystems shape student well-being, highlighting the critical and distinct roles of dual self-regulation pathways. The study provides a novel theoretical framework for understanding achievement emotions and offers actionable insights for building supportive, sustainable learning environments that foster both emotional and academic resilience. Full article

This study presents the implementation of a project-based learning (PjBL) methodology in Science, Technology, Engineering, and Mathematics (STEM) disciplines to enhance experiential and collaborative learning within an introductory engineering course. The primary objective was to deepen students’ understanding of electrical energy storage principles, with a particular focus on charging processes and energy conservation, through the emulation of an electrochemical supercapacitor. Engineering students at Tecnológico de Monterrey designed, modeled, and analyzed a double-loop RC equivalent circuit comprising two capacitors, utilizing both computer simulations and laboratory experiments. The PjBL methodology was structured into three phases: engagement, which contextualizes the problem and emphasizes the significance of supercapacitors; research, which encompasses system design, mathematical modeling, and simulation; and action, which entails circuit assembly and the resolution of differential equations using Kirchhoff’s laws. The results indicate that this approach effectively integrates theoretical and practical knowledge, develops technical skills, and promotes collaboration. Furthermore, it aligns learning outcomes with explicit assessment criteria, illustrating compatibility with outcome-based pedagogical frameworks such as Outcome-Based Education (OBE). Full article

Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper presents an architectural approach and implementation of a semantic search module integrated into an existing cloud-based modeling and simulation system. The proposed method employs a strategy for serializing graph structures into textual descriptions, followed by the generation of vector embeddings via local ONNX inference and indexing within a vector database (Qdrant). Experimental validation performed on a diverse corpus of complex dynamic models, compares the proposed approach against traditional information retrieval methods (Full-Text Search, Keyword Search in PostgreSQL, and Apache Lucene with Standard and BM25 scoring). The results demonstrate the distinct advantage of semantic search, achieving high precision (over 90%) within the scope of the evaluated corpus and effectively eliminating information noise. In comparison, keyword search exhibited only 24.8% precision with a significant rate of false positives, while standard full-text analysis failed to identify relevant models for complex conceptual queries (0 results). Despite a recorded increase in latency (~2 s), the study proves that the vector-based approach is a significantly more robust solution for detecting hidden semantic connections in mathematical model databases, providing a foundation for future developments toward multi-vector indexing strategies. Full article

Tertiary mathematics teaching is predominantly face-to-face, yet large, diverse cohorts and limited contact hours constrain opportunities for individually paced practice and timely feedback. We developed three bespoke, self-paced online numeracy modules, each targeting a specific mathematical concept and disciplinary context. Module design was informed by learning theory (constructivist, active learning, Universal Design for Learning, inclusive learning practices). We ran a qualitative pilot study to gain insight into user perceptions of modules in terms of engagement and perceived learning support, conducting semi-structured interviews with undergraduate science students (n = 11) and educators (n = 7). We applied thematic analysis to interview data, which generated the following insights. Students—many reporting high mathematics anxiety—responded positively, valuing low-stakes iterative practice, clear stepwise scaffolding, multimodal presentation, contextualized examples aligned to their course, and a supportive instructor voice. These features were described as reducing anxiety, reframing errors as part of learning, and supporting inclusion, despite prevalent math avoidance in the cohort. Staff feedback was more cautious, recognizing similar strengths but focusing on areas for improvement. We argue that bespoke, contextualized modules can augment face-to-face instruction by delivering individualized pacing and immediate feedback at scale, while contributing to the creation of an accessible, inclusive, supportive learning environment. Future work should quantify learning outcomes, track affective changes longitudinally, and isolate contributions of specific design features across diverse cohorts and disciplines. Full article