Search Results (654)

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems. Full article

Background: This exploratory study investigates whether STEM (science, technology, engineering, and mathematics) students’ increasing reliance on off-campus resources (e.g., online platforms, private tutors) reflects an authentic preference for autonomous learning or a compensatory response to perceived deficiencies in on-campus instruction. Methodology: Using a mixed-methods design, data were collected from 118 engineering and science students. A model was developed to examine the relationship between the intensity of student criticism and their declared preference for off-campus learning. Findings: The model revealed a significant negative relationship between the intensity of criticism and the preference for off-campus instruction. This suggests that for highly critical students, external resources function primarily as a compensatory mechanism for “needs frustration” rather than a preferred alternative. The results imply that these students continue to value the frontal model but find its current implementation insufficient to meet their pedagogical needs. Conclusion: These findings challenge the assumption that digital trends signify a voluntary abandonment of the classroom. Instead, reliance on external resources is positioned as a reactive, compensatory strategy. Higher education institutions should prioritize revitalizing frontal instruction through enhanced clarity and focus to reduce dependency on off-campus platforms and restore the value of the campus experience. Full article

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment. Full article

The integration of digital tools into mathematics education has the potential to transform teaching and learning for students with special needs by fostering inclusion, accessibility, and engagement. Guided by the principles of Universal Design for Learning (UDL) and Vygotsky’s social constructivist theory, this study investigates how digital technologies can enhance learning experiences and promote inclusive education in mathematics classrooms. Using a mixed-method design, data were collected from 110 mathematics teachers and 210 Grade 11 students in special schools across Nigeria to assess the availability, utilization, and impact of digital tools on students’ engagement, motivation, collaboration, and problem-solving. Findings indicate that while access to digital tools remains limited, their effective use significantly improves students’ learning experiences and supports inclusive pedagogical practices. The study underscores the importance of policy alignment to ensure equitable access to digital resources for all students. Findings contribute to global discussions on inclusive digital pedagogies by providing empirical insights into how technology can mediate participation, interaction, and achievement in mathematics for students with special needs. Full article

This paper explores Indigenous Maya practices, ways of sensing, from a personal perspective to provoke discussion on ways to ground mathematics education through land and ancestors. This paper is largely based on my doctoral research work (2018–2022). I adopt a sensory ethnography approach as a viable means to explore Maya Elders’ ways of knowing. Over a period of three years, I walked alongside my Elders and journeyed into a world of mysticism and mathematical wonder. These experiences evoked the questions: “What are the challenges in engaging with this form of knowing as a learner and translator? How can these experiences help us to ground Indigenous forms of mathematical knowing? What insights can we learn via our own Indigenous mathematical heritage?” I argue that an embodied and sensory approach to mathematics through the ways of our ancestors leads to a more meaningful and purposeful mathematics. In this more-than-human context, the predominant view of mathematics as a-human, a-cultural, and a-historical is blurred to reveal mathematics as human and very much grounded in our ways of yearning to make sense of the world around us. Full article

The economic and societal advantages of Science, Technology, Engineering, Mathematics, and Medicine (STEMM) occupations are considerable; however, access to STEMM education and training opportunities is unequal, especially for youth from low-socioeconomic-status (SES) areas. Young people from low-SES areas may experience sustained structural, financial, and social barriers that limit their ability to develop identities as STEMM practitioners and to persist in pursuing these fields. This case study describes the design, implementation, and evaluation of a community-based mini-medical summer camp held in a low-SES area to support the development of STEMM identities and to increase 6th–11th-grade students’ biomedical and medical knowledge and career interests. The program utilized partnerships with local entities to provide access to biomedical and medical content. Nineteen students completed the program; fifteen consented to and assented to assessment using pre- and post-tests of STEMM-related knowledge and self-efficacy, and completed all measurements. Students’ STEMM knowledge levels increased significantly; however, their STEMM self-efficacy did not change, possibly due to high initial confidence and the short duration of participation. Students reported high engagement and increased interest in the sciences and medicine. Overall, this study suggests that community-centered outreach programs can increase STEMM engagement and learning in low-SES environments. Full article

The future of innovation and economic growth depends on our ability to nurture the next generation of scientists. The global shortage of qualified STEM (Science, Technology, engineering, Mathematics) teachers has led many countries to expedite the transition of subject-matter experts from industry and academia into teaching roles. These second-career science student teachers typically participate in accelerated training programs designed to address urgent shortages. This study addresses a gap in the literature regarding effective pedagogical interventions for career-changing professionals in STEM fields, focusing on the experience and transformation of second-career science student teachers. This qualitative case study explores how a Challenge-Based Learning (CBL) course fosters the development of pedagogical competences via developing an instructional unit collaboratively, among five second-career science student teachers enrolled in an accelerated teacher education program. Drawing on data collected through instructors’ field notes, iterative work-in-progress lesson drafts, and reflective final papers, the study employs qualitative content analysis to trace changes in participants’ instructional approaches and professional identity. Findings reveal that engagement with the CBL framework promoted a significant shift from teacher-centered to learner-centered instruction, as participants increasingly integrated collaborative learning, inquiry-based activities, and reflective practices into their lesson planning and classroom teaching. The iterative nature of CBL, which emphasizes real-world problem-solving and structured opportunities for reflection and peer feedback, was instrumental in supporting participants’ adaptive expertise and confidence as novice teachers. Moreover, the course experience contributed to the emergence of a professional teaching identity, with participants reporting greater self-efficacy, a stronger sense of belonging to the teaching community, and increased motivation to persist in the profession. The results underscore the potential of integrating CBL and learning sciences principles into accelerated teacher preparation programs to enhance both cognitive and affective dimensions of teacher development. Full article

Hyperspectral unmixing (HU) is fundamental for conducting quantitative analyses in remote sensing, yet existing methods face a persistent tradeoff between model performance and physical interpretability. Although deep learning models achieve superior performance, even “gray-box” models that incorporate physical constraints still suffer from an intrinsically opaque decision-making process, which hinders their trustworthiness in critical applications. To address this challenge, this paper introduces a theory-guided unmixing framework aimed at enhancing mechanistic interpretability called the sparse and subspace-attentive transformer unmixing network (SSTU-Net). Unlike heuristic architectures, SSTU-Net is rigorously derived from the first principles of sparse rate reduction (SRR) theory. Its core modules—the multi-head subspace self-attention (MSSA) and the iterative shrinkage-thresholding algorithm (ISTA)—directly implement the essential mathematical steps of information compression and sparsification within the SRR theory, respectively. Extensive experiments on both synthetic and real hyperspectral datasets demonstrate that SSTU-Net achieves competitive performance compared to representative state-of-the-art methods—including advanced autoencoder-based networks (e.g., CyCU-Net and DAAN) and recent transformer-based unmixing architectures (e.g., DeepTrans and MAT-Net)—while strictly adhering to theoretically predicted evolutionary trajectories. More importantly, a series of specifically designed structural interpretability validation experiments mechanistically confirm the theoretically predicted behaviors, such as layer-wise information compression, feature sparsification, and subspace orthogonalization. These results reveal the internal working mechanisms of SSTU-Net, validating the feasibility and significant potential of our principled theory-guided framework for developing high-performance and trustworthy intelligent models in remote sensing. Full article

This article examines the impact of students’ prior experience with English on their academic success in a university English course. The study is based on a survey conducted among students majoring in Computer Science, Business Information Technology (BIT), and Software Technology and Design (STD) at the Faculty of Mathematics and Informatics (FMI), University of Plovdiv, at the beginning of their general English language course. We focus on students’ self-assessed language competence at the start of the course and examine how these self-assessments correspond to their actual test results. Using high-performance machine learning methods, we identify background factors that influence academic achievement, including the number of years spent learning English, the type of high school attended, and informal exposure to English. The findings aim to support more effective and tailored approaches to teaching English in technical and scientific disciplines. Full article

In intermittent convective cooling environments created by split air conditioners, the dynamic nature of the environment poses challenges to traditional steady-state thermal comfort models in predicting human thermal comfort. Therefore, this study proposes an alliesthesia-informed machine learning framework that encodes alliesthesia theory into explicit mathematical features for predicting dynamic overall thermal comfort. Data were obtained through controlled experiments under intermittent cooling conditions, and a theory-driven feature set incorporating dynamic set points and physio-psycho gap was constructed. The results demonstrate that the gradient boosting model achieved optimal performance under rigorous subject-level cross-validation (test set R² = 0.71). Interpretability analysis confirmed that model decisions are highly dependent on exposure time and alliesthesia features, whose importance far exceeds that of conventional environmental parameters, revealing that the core of thermal comfort perception lies in the dynamic interplay between physiological states and psychological expectations. Furthermore, the proposed few-shot personalized calibration strategy can effectively accommodate individual differences with minimal user data. This study demonstrates that the framework not only enhances prediction accuracy but also improves model interpretability and generalizability by incorporating alliesthesia-inspired feature representations, offering a new perspective for developing next-generation human-centric intelligent environmental control systems. Full article

We present Hyperbolic Symmetric Hypermodular Neural Operators (ONHSH), a novel operator learning framework for solving partial differential equations (PDEs) in curved, anisotropic, and modularly structured domains. The architecture integrates three components: hyperbolic-symmetric activation kernels that adapt to non-Euclidean geometries, modular spectral smoothing informed by arithmetic regularity, and curvature-sensitive kernels based on anisotropic Besov theory. In its theoretical foundation, the Ramanujan–Santos–Sales Hypermodular Operator Theorem establishes minimax-optimal approximation rates and provides a spectral-topological interpretation through noncommutative Chern characters. These contributions unify harmonic analysis, approximation theory, and arithmetic topology into a single operator learning paradigm. In addition to theoretical advances, ONHSH achieves robust empirical results. Numerical experiments on thermal diffusion problems demonstrate superior accuracy and stability compared to Fourier Neural Operators and Geo-FNO. The method consistently resolves high-frequency modes, preserves geometric fidelity in curved domains, and maintains robust convergence in anisotropic regimes. Error decay rates closely match theoretical minimax predictions, while Voronovskaya-type expansions capture the tradeoffs between bias and spectral variance observed in practice. Notably, ONHSH kernels preserve Lorentz invariance, enabling accurate modeling of relativistic PDE dynamics. Overall, ONHSH combines rigorous theoretical guarantees with practical performance improvements, making it a versatile and geometry-adaptable framework for operator learning. By connecting harmonic analysis, spectral geometry, and machine learning, this work advances both the mathematical foundations and the empirical scope of PDE-based modeling in structured, curved, and arithmetically. Full article

Agent-based modeling (ABM) is a powerful tool for simulating building users’ dynamic behavior in demand response (DR) programs. However, ABM faces several challenges, particularly in encoding building users’ natural language features and common sense into rules or mathematical equations. To overcome these limitations, this paper proposes an agent framework based on large language models (LLMs) to simulate building users’ air-conditioning setpoint adjustment behavior under DR. This framework leverages LLMs’ natural language processing capabilities to replicate building users’ reasoning and decision-making processes. It consists of five modules: persona, perception, decision, reflection, and memory. Agents are assigned diverse personas through natural language descriptions based on empirical survey data. LLMs drive agents to reason and make decisions based on incentive prices and historical experiences. The results show that the LLM-based agent has common sense derived from natural language-defined personas and exhibits human-like irrational characteristics. This demonstrates the feasibility of replacing rules with natural language in ABM. The LLM-based agent can more effectively model hard-to-parameterize human features and provide decision explanations through LLM outputs. The results show that the inclusion of reflection and memory modules enables the agent to learn from previous decisions and reduce unreasonable choices. Full article

Static honeypot deployment and one-shot attack-path analysis often become ineffective against adaptive adversaries because fixed decoy layouts are easy to fingerprint and risk estimates quickly go stale. This paper presents a unified, mathematically grounded TrapManager framework that couples graph representation learning with budget-constrained combinatorial optimization for dynamic cyber deception. We model attacker progression on vulnerability-based attack graphs and learn context-aware node embeddings using a Graph Attention Network (GAT) that fuses vulnerability-driven risk signals (e.g., CVSS-derived node scores) with structural features. The learned representations are used to estimate edge plausibility and rank candidate source–target routes at the path level. Given limited resources, we formulate pointTrap placement as a Mixed-Integer Programming (MIP) problem that maximizes the expected interception of high-risk paths while penalizing deployment cost under explicit budget constraints, including mandatory coverage of the top-ranked critical paths. To enable online adaptiveness, a pointTrap-triggered, event-driven feedback mechanism locally amplifies risk around alerted regions, updates path weights without retraining the GAT, and re-solves the MIP for rapid redeployment. Experiments on MulVAL-generated benchmark attack graphs and cross-domain transfer settings demonstrate fast convergence, strong discrimination between attack and non-attack edges, and early interception within a small number of hops even with minimal decoy budgets. Overall, the proposed framework provides a scalable and resource-efficient approach to closed-loop attack-path defense by integrating attention-based learning and integer optimization. Full article

Additive manufacturing (AM) has reshaped production methodologies by enabling the fabrication of complex geometries for high-performance applications. As a leading AM technique, Fused Deposition Modeling (FDM) is widely used for its versatility. However, the structural reliability of FDM-printed parts is fundamentally dictated by their mechanical performance, where impact toughness functions as a critical benchmark across demanding industrial environments. Polylactic acid (PLA) has distinguished itself as a premier biodegradable polymer, favored for its superior stiffness and processability. Nevertheless, the inherent brittleness and anisotropic behavior of FDM-printed PLA pose significant challenges, necessitating investigation of their fracture mechanics. This study firstly evaluates the impact toughness of FDM-processed PLA Izod specimens using impact tests, structured within a Taguchi design of experiments (DoE) methodology. An L27 orthogonal array was employed to investigate the influence of manufacturing parameters on impact behavior and fracture energy. Then, to achieve high-fidelity predictions from experimental data, the parametric effects were systematically investigated through an advanced machine learning framework. In the first stage, optimal prediction models were identified by evaluating five mathematical formulations hybridized with five nature-inspired optimization algorithms (GWO, SMA, GSA, FPA, and KH) across nine dataset combinations. In the second stage, these best-performing models were integrated into a metaheuristic ensemble using the GWO to perform a weighted aggregation. This hybrid ensemble methodology significantly enhanced predictive accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 5.0847%, which represents a 37.3% relative improvement over the best individual base model. 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