Search Results (172)

The integration of large-scale wind power clusters significantly reduces the inertia level of the power system, increasing the risk of frequency instability. Accurately assessing the equivalent virtual inertia of wind farms is critical for grid stability. Addressing the dual bottlenecks in existing inertia assessment methods, where physics-based modeling requires full control transparency and data-driven approaches lack interpretability for inertia response analysis, thus failing to reconcile commercial confidentiality constraints with analytical needs, this paper proposes a symbolic regression framework for inertia evaluation in doubly fed wind farms with limited control information constraints. First, a dynamic model for the inertia response of DFIG wind farms is established, and a mathematical expression for the equivalent virtual inertia time constant under different control strategies is derived. Based on this, a nonlinear function library reflecting frequency-active power dynamic is constructed, and a symbolic regression model representing the system’s inertia response characteristics is established by correlating operational data. Then, sparse relaxation optimization is applied to identify unknown parameters, allowing for the quantification of the wind farm’s equivalent virtual inertia. Finally, the effectiveness of the proposed method is validated in an IEEE three-machine nine-bus system containing a doubly fed wind power cluster. Case studies show that the proposed method can fully utilize prior model knowledge and operational data to accurately assess the system’s inertia level with low computational complexity. Full article

The double wishbone suspension (DWS) system is widely used in automotive engineering because of its favorable kinematic properties, which affect vehicle dynamics, handling, and ride comfort; hence, it is important to have an accurate 3D model, simulation, and analysis of the system in order to optimize its design. This requires efficient computational tools for parametric study. The development of effective computational tools that support parametric exploration stands as an essential requirement. Our research demonstrates a complete Wolfram Mathematica system that creates parametric 3D kinematic models and conducts simulations, performs analyses, and generates interactive visualizations of DWS systems. The system uses homogeneous transformation matrices to establish the spatial relationships between components relative to a global coordinate system. The symbolic geometric parameters allow designers to perform flexible design exploration and the kinematic constraints create an algebraic equation system. The numerical solution function NSolve computes linkage positions from input data, which enables fast evaluation of different design parameters. The integrated 3D visualization module based on Mathematica’s manipulate function enables users to see immediate results of geometric configurations and parameter effects while calculating exact 3D coordinates. The resulting robust, systematic, and flexible computational environment integrates parametric 3D design, kinematic simulation, analysis, and dynamic visualization for DWS, serving as a valuable and efficient tool for engineers during the design, development, assessment, and optimization phases of these complex automotive systems. Full article

Spiritual intelligence (SI) is defined as a unique form of hermeneutic–relational intelligence that enables individuals to integrate cognitive, emotional, and symbolic dimensions to guide their thoughts and actions with reflection, aiming for existential coherence rooted in a transcendent system of meaning. It functions as a metacognitive framework that unites affective, cognitive, and symbolic levels in dialog with a sense of meaning that is considered sacred or transcendent, where “sacred,” in this context, refers inclusively to any symbolic reference or value that a person or culture perceives as inviolable, fundamental, or orienting. It can derive from religious traditions but also from ethical, philosophical, or civil visions. It functions as a horizon of meaning from which to draw coherence and guidance and which orients the understanding of oneself, the world, and action. SI appears as the ability to interpret one’s experiences through the lens of values and principles, maintaining a sense of continuity in meaning even during times of ambiguity, conflict, or discontinuity. It therefore functions as a metacognitive ability that brings together various mental functions into a cohesive view of reality, rooted in a dynamic dialog between the self and a value system seen as sacred. Full article

This article traces the historical evolution of mural painting as a medium of cultural expression from prehistoric cave art to contemporary street interventions. Adopting a diachronic and interdisciplinary approach, it investigates how muralism has developed across civilizations in relation to techniques, symbolic systems, social function, and its embeddedness in architectural and urban contexts. The analysis is structured around key historical periods using emblematic case studies to examine the interplay between materiality, iconography, and socio-political meaning. From sacred enclosures and civic monuments to post-industrial walls and digital projections, murals reflect shifting cultural paradigms and spatial dynamics. This study emphasizes how mural painting, once integrated into sacred and imperial architecture, has become a tool for public participation, protests, and urban storytelling. Particular attention is paid to the evolving relationship between wall painting and the spaces it inhabits, highlighting the transition from permanence to ephemerality and from monumentality to immediacy. This article contributes to mural studies by offering a comprehensive framework for understanding the technical and symbolic transformations of the medium while proposing new directions for research in the context of digital urbanism and cultural memory. Full article

Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symbolic regression framework is trained on simulation data from molecular dynamics and correlates the values of the self-diffusion coefficients with macroscopic properties, such as density, temperature, and the width of confinement. New expressions are derived for nine different molecular fluids, while an all-fluid universal equation is extracted to capture molecular behavior as well. In such a way, a highly computationally demanding property is predicted by easy-to-define macroscopic parameters, bypassing traditional numerical methods based on mean squared displacement and autocorrelation functions at the atomistic level. To achieve generalizability and interpretability, simple symbolic expressions are selected from a pool of genetic programming-derived equations. The obtained expressions present physical consistency, and they are discussed in terms of explainability. The accurate prediction of the self-diffusion coefficient both in bulk and confined systems is important for advancing the fundamental understanding of fluid behavior and leading the design of nanoscale confinement devices containing real molecular fluids. Full article

Against the backdrop of China’s urban transformation from incremental expansion to stock regeneration, community regeneration has emerged as a critical mechanism for enhancing urban governance efficacy. As fundamental units of urban systems, the regeneration of communities requires comprehensive approaches to address complex socio-spatial challenges, with public participation serving as the core driver for achieving sustainable renewal goals. However, significant regional disparities persist in the effectiveness of public participation across China, necessitating the systematic institutionalization of participatory practices. Guangzhou, as a pioneering city in institutional innovation and the practical exploration of urban regeneration, provides a representative case for examining the evolutionary trajectory of participatory planning. This research employs Arnstein’s Ladder of Participation theory, utilizing literature analysis and comparative case studies to investigate the evolution of participatory mechanisms in Guangzhou’s community regeneration over four decades. The study systematically examined the transformation of public engagement models across multiple dimensions, including organizational frameworks of participation, participatory effectiveness, diversified financing models, and the innovation of policy instruments. Three paradigm shifts were identified: the (1) transition of participants from “passive responders” to “active constructors”, (2) advancement of engagement phases from “fragmented intervention” to “whole-cycle empowerment”, and (3) evolution of participation methods from “unidirectional communication” to “collaborative co-governance”. It identifies four drivers of participatory effectiveness: policy frameworks, financing mechanisms, mediator cultivation, and engagement platforms. To enhance public engagement efficacy, the research proposes the following: (1) a resilient policy adaptation mechanism enabling dynamic responses to multi-stakeholder demands, (2) a diversified financing framework establishing a “government guidance + market operation + resident contribution” cost-sharing model, (3) a professional support system integrating “localization + specialization” capacities, and (4) enhanced digital empowerment and institutional innovation in participatory platform development. These mechanisms collectively form an evolutionary pathway from “symbolic participation” to “substantive co-creation” in urban regeneration governance. Full article

This paper reexamines the enduring role of esoteric traditions, as articulated by Frances Yates, in shaping the intellectual landscape of the scientific revolution and their resonance in the digital age. Challenging the linear, progress-centered narratives of traditional historiographies, it explores how esoteric principles—symbolized by transformative motifs like the Philosopher’s Stone—provided a framework for early scientific inquiry by promoting hidden knowledge, experimentation, mathematics, and interdisciplinary synthesis. This paper argues that moments of accelerated scientific and technological development magnify the visibility of esoteric structures, demonstrating how the intellectual configurations of Renaissance learned circles persist in contemporary expert domains. In particular, artificial intelligence exemplifies the revival of esoteric modes of interpretation, as AI systems—much like their Renaissance predecessors—derive authority through the identification of unseen patterns and the extrapolation of hidden truths. By bridging Renaissance esotericism with the modern information revolution, this study highlights how such traditions are not mere relics of the past but dynamic paradigms shaping the present and future, potentially culminating in new forms of digital mysticism. This study affirms that the temporal gap during periods of rapid technological change between industrial practice and formal scientific treatises reinforces esoteric knowledge structures. Full article

This article proposes an innovative methodology for data-driven modeling and simulation of transportation management through cross-sectoral collaboration in small businesses. The present research is multidisciplinary and interdisciplinary in nature. We investigate the improvements in logistics management that can be achieved through cross-sector collaboration in agriculture and forestry. A data-driven method, such as symbolic regression, is used to identify the relationships between factors in a modeled system using mathematical expressions. These expressions are directly integrated into the simulation models. Simulation spreads the modeling of transportation processes over a period of time. The system dynamics model is designed to analyze and assess the performance of a system based on its past behavior and is, therefore, deterministic. The discrete-event model enables the simulation of future scenarios and outcomes over time, given random input variables. As new data become available, relationships within the symbolic regression method are discovered more accurately, and simulations are updated accordingly. The tools offered for implementation are supplemented by a multi-user web simulation. The proposed case study is based on a real-life example. The obtained results allow small agricultural companies to use transportation and labor resources more efficiently when organizing the transportation of their agricultural and forestry products. Integrating data-driven models into simulations enables a better interpretation of data across the entire data value chain. Full article

Green leadership is often praised for promoting sustainability, while hospitals in reactive or resource-constrained contexts lack the infrastructure to support leadership-led environmental change, indicating that leadership without operational capacity offers little impact. Moreover, the inconsistencies between green human resource practices and environmental performance suggest that green leadership might lead to symbolic gestures rather than real improvements without a robust environmental culture or internal accountability systems. Amid intensifying environmental regulations and sustainability mandates in healthcare, this study investigates how green transformational leadership addresses the contradiction between hospitals’ resource-intensive operations and environmental accountability. Drawing on Dynamic Capabilities Theory (DCT), the research highlights policy-driven imperatives for hospitals to build adaptive leadership models that meet sustainability goals. Using data from 312 junior doctors and nurses in private hospitals, analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), the study identifies green attitude, green empowerment, and green self-efficacy as key mediators in enhancing environmental performance. Contributions of this study include (1) applying DCT to healthcare sustainability, (2) integrating psychological drivers into leadership–performance models, and (3) emphasizing nurses’ pivotal roles. The results of the study indicate that leaders who prioritize sustainability inspire staff to adopt eco-friendly practices, aligning with SDG 3, i.e., good health and well-being; SDG 12, i.e., responsible consumption and production; and SDG 7, i.e., affordable and clean energy. The findings provide actionable insights for hospital administrators and policymakers striving for environmentally accountable healthcare delivery. Full article

This study addresses the persistent challenge of balancing interpretability and robustness in black-box deep learning models for automatic modulation recognition (AMR), a critical task in wireless communication systems. To bridge this gap, we propose a novel explainable AI (XAI) framework that integrates symbolic feature interaction concepts into communication signal analysis for the first time. The framework combines a modulation primitive decomposition architecture, which unifies Shapley interaction entropy with signal physics principles, and a dual-branch XAI mechanism (feature extraction + interaction analysis) validated on ResNet-based models. This approach explicitly maps signal periodicity to modulation order in high-dimensional feature spaces while mitigating feature coupling artifacts. Quantitative responsibility attribution metrics are introduced to evaluate component contributions through modular adversarial verification, establishing a certified benchmark for AMR systems. The experimental validation of the RML 2016.10a dataset has demonstrated the effectiveness of the framework. Under the dynamic signal-to-noise ratio condition of the benchmark ResNet with an accuracy of 94.88%, its occlusion sensitivity increased by 30% and stability decreased by 22% compared to the SHAP baseline. The work advances AMR research by systematically resolving the transparency–reliability trade-off, offering both theoretical and practical tools for deploying trustworthy AI in real-world wireless scenarios. Full article

The rapid progress of generative AI has enabled remarkable creative capabilities, yet it also raises urgent concerns regarding the safety of AI-generated visual content in real-world applications such as content moderation, platform governance, and digital media regulation. This includes unsafe material such as sexually explicit images, violent scenes, hate symbols, propaganda, and unauthorized imitations of copyrighted artworks. Existing image safety systems often rely on rigid category filters and produce binary outputs, lacking the capacity to interpret context or reason about nuanced, adversarially induced forms of harm. In addition, standard evaluation metrics (e.g., attack success rate) fail to capture the semantic severity and dynamic progression of toxicity. To address these limitations, we propose Perception–Retrieval–Judgement (PRJ), a cognitively inspired framework that models toxicity detection as a structured reasoning process. PRJ follows a three-stage design: it first transforms an image into descriptive language (perception), then retrieves external knowledge related to harm categories and traits (retrieval), and finally evaluates toxicity based on legal or normative rules (judgement). This language-centric structure enables the system to detect both explicit and implicit harms with improved interpretability and categorical granularity. In addition, we introduce a dynamic scoring mechanism based on a contextual toxicity risk matrix to quantify harmfulness across different semantic dimensions. Experiments show that PRJ surpasses existing safety checkers in detection accuracy and robustness while uniquely supporting structured category-level toxicity interpretation. Full article

Optical communication is a critical technology for future deep space exploration, offering substantial advantages in transmission capacity and spectrum utilization. This paper establishes a comprehensive theoretical framework for avalanche photodiode (APD)-based deep space optical uplink communication under combined channel impairments, including atmospheric and coronal turbulence induced beam scintillation, pointing errors, angle-of-arrival (AOA) fluctuations, link attenuation, and background noise. A closed-form analytical channel model unifying these effects is derived and validated through Monte Carlo simulations. Webb and Gaussian approximations are employed to characterize APD output statistics, with theoretical symbol error rate (SER) expressions for pulse position modulation (PPM) derived under diverse impairment scenarios. Numerical results demonstrate that the Webb model achieves higher accuracy by capturing APD gain dynamics, while the Gaussian approximation remains viable when APD gain exceeds a channel fading-dependent gain threshold. Key system parameters such as APD gain and field-of-view (FOV) angle are analyzed. The optimal APD gain significantly influences the achievement of optimal SER performance, and angle of FOV design balances AOA fluctuations tolerance against noise suppression. These findings enable hardware optimization under size, weight, power, and cost (SWaP-C) constraints without compromising performance. Our work provides critical guidelines for designing robust APD-based deep space optical uplink communication systems. Full article

Complexity of non-orthogonal multiple access (NOMA) digital signal processing schemes is particularly relevant in mobile environments because of the varying channel conditions of every single user. In contrast to legacy modulation and coding schemes (MCSs), NOMA MCSs typically have irregular symbol constellations with asymmetric symbol decision regions affecting synchronization at the receiver. Research papers investigating signal processing in this emerging field usually lack sufficient details for facilitating software-defined radio (SDR) implementation. This work presents a new symbolic framework approach for simulating signal processing functions in SDR transmit–receive paths in a dynamic NOMA downlink use case. The proposed framework facilitates simple and intuitive implementation and testing of NOMA schemes and can be easily expanded and implemented on commercially available SDR hardware. We explicitly address several important design and measurement parameters and their relationship to different tasks, including variable constellation processing, carrier and symbol synchronization, and pulse shaping, focusing on quadrature amplitude modulation (QAM). The advantages of the proposed approach include intuitive symbolic modeling in a dynamic framework for NOMA signals; efficient, more accurate, and less time-consuming design flow; and generation of synthetic training data for machine-learning models that could be used for system optimization in real-world use cases. Full article

This paper examines the philosophy of “Dao follows nature” (道法自然) and investigates how Daoist clothing transforms abstract cosmological concepts into a “wearable interface for spiritual practice” through the use of materials, colors, and patterns. By integrating symbol system analysis, material culture theory, and the philosophy of body practice, this study uncovers three layers of symbolic mechanisms inherent in Daoist attire. First, the materials embody the tension between “nature and humanity”, with the intentional imperfections in craftsmanship serving as a critique of technological alienation. Second, the color coding disrupts the static structure of the Five Elements system by dynamically shifting between sacred and taboo properties during rituals while simultaneously reconstructing symbolic meanings through negotiation with secular power. Third, the patterns (such as star constellations and Bagua) employ directional arrangements to transform the human body into a miniature cosmos, with dynamic designs offering a visual path for spiritual practice. This paper introduces the concept of a “dynamic practice interface”, emphasizing that the meaning of Daoist clothing is generated through the interaction of historical power, individual experience, and cosmological imagination. This research fills a critical gap in the symbolic system of Daoist art and provides a new paradigm for sustainable design and body aesthetics, framed from the perspective of “reaching the Dao through objects”. Full article

The pursuit of Artificial General Intelligence (AGI) demands AI systems that not only perceive but also reason in a human-like manner. While symbolic systems pioneered early breakthroughs in logic-based reasoning, such as MYCIN and DENDRAL, they suffered from brittleness and poor scalability. Conversely, modern deep learning architectures have achieved remarkable success in perception tasks, yet continue to fall short in interpretable and structured reasoning. This dichotomy has motivated growing interest in Neural–Symbolic AI, a paradigm that integrates symbolic logic with neural computation to unify reasoning and learning. This survey provides a comprehensive and technically grounded overview of AI reasoning in the deep learning era, with a particular focus on Neural–Symbolic AI. Beyond a historical narrative, we introduce a formal definition of AI reasoning and propose a novel three-dimensional taxonomy that organizes reasoning paradigms by representation form, task structure, and application context. We then systematically review recent advances—including Differentiable Logic Programming, abductive learning, program induction, logic-aware Transformers, and LLM-based symbolic planning—highlighting their technical mechanisms, capabilities, and limitations. In contrast to prior surveys, this work bridges symbolic logic, neural computation, and emergent generative reasoning, offering a unified framework to understand and compare diverse approaches. We conclude by identifying key open challenges such as symbolic–continuous alignment, dynamic rule learning, and unified architectures, and we aim to provide a conceptual foundation for future developments in general-purpose reasoning systems. Full article