Search Results (100)

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

The Buades Gallery (1973–2003) was not merely a commercial space in Madrid. In the history of art in Spain, it served as a professional and political node for Spanish conceptualism, an art form which, due to its idiosyncrasies, required its own channels of distribution. This article seeks to examine the trajectory of Mercedes Buades in alignment with this movement, re-evaluating her role from a feminist perspective and highlighting the importance of certain agents who have traditionally been invisibilised. To this end, a theoretical approach is adopted, following the sociology of art and the social history of art, paying particular attention to the contributions of Enrico Castelnuovo, Pierre Bourdieu and Núria Peist. These frameworks enable an analysis of the role of the gallerist as a structuring agent within the artistic field, capable of generating symbolic capital and establishing dynamics of production, circulation and consumption in the context of post-Franco Spain, a country that lacked a consolidated museum infrastructure at the time. Even so, Mercedes Buades established a model of gallery practice that, beyond its commercial dimension, contributed decisively to the symbolic configuration of contemporary art in Spain and formed part of a network of artistic visibility that promoted experimental art. Full article

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

This study explores the evolution of China’s intangible cultural heritage (ICH) governance through the lens of discursive institutionalism, with a specific focus on how institutional discourse and arrangements shape the spatial configuration and symbolic meaning of ICH-related landscapes. By analyzing policy discourse, governance actors, resource mobilization, and regulatory mechanisms, the study traces the transition from community-led practices to increasingly formalized and spatialized systems under the influence of the 2003 UNESCO Convention. Drawing on a combination of historical policy analysis and place-specific institutional insights, the research finds that while institutional reforms have enhanced administrative coherence and international alignment, they have also at times disrupted vernacular meanings and weakened residents’ place-based cultural attachments. Conversely, localized revitalization initiatives can foster community resilience and landscape justice. These findings are derived from an interpretive synthesis of institutional trajectories and spatial governance practices. Overall, the study contributes to the theoretical integration of discursive institutionalism and cultural geography, offering new insights into heritage governance and sustainable cultural planning in rapidly urbanizing contexts. 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

Masked cold spray additive manufacturing (CSAM) is investigated for fabricating nickel-based electrodes with pyramidal pin-fins that enlarge the active area for the hydrogen-evolution reaction (HER). To bypass the high cost of purely CFD-driven optimization, we construct a two-stage machine learning (ML) framework trained on 48 high-fidelity CFD simulations. Stage 1 applies sampling and a K-nearest-neighbor kernel-density-estimation algorithm that predicts the spatial distribution of impacting particles and re-allocates weights in regions of under-estimation. Stage 2 combines sampling, interpolation and symbolic regression to extract key features, then uses a weighted random forest model to forecast particle velocity and temperature upon impact. The ML predictions closely match CFD outputs while reducing computation time by orders of magnitude, demonstrating that ML-CFD integration can accelerate CSAM process design. Although developed for a masked setup, the framework generalizes readily to unmasked cold spray configurations. Full article

This paper is devoted to the experimental study of the integral approximation method in entropy optimization problems arising from the application of the Randomized Machine Learning method. Entropy-optimal probability density functions contain normalizing integrals from multivariate exponential functions; as a result, when computing these distributions in the process of solving an optimization problem, it is necessary to ensure efficient computation of these integrals. We investigate an approach based on the approximation of integrand functions, which are applied to the solution of several configurations of problems with model and real data with linear static models using a symbolic computation mechanism. Computational studies were carried out under the same conditions, with the same initial data and values of hyperparameters of the used models. They have shown the performance and efficiency of the proposed approach in the Randomized Machine Learning problems based on linear static models. Full article

The correlation between aerodynamic data obtained from ground and flight tests is crucial in developing aerospace vehicles. This paper proposes methods for modelling this correlation that combine feature extraction and symbolic regression. The neighborhood component analysis (NCA) method is utilized to extract features from the high-dimensional state space and then symbolic regression (SR) is applied to find the concise optimal expression. First, a simulation example of the NASA Twin Otter aircraft is used to validate the NCA and the SR tool developed by the research team in modeling the aerodynamic coefficient deviation between ground and flight due to an unpredictable inflight icing failure. Then, the method and tool are applied to real flight tests of two types of aerospace vehicles with different configurations. The final optimized mathematical models show that the two vehicles’ pitching moment coefficient deviations are related to the angle of attack (AOA) only. The mathematical model built using NCA and the SR tool demonstrates higher fitting accuracy and better generalization performance for flight test data than other typical data-driven methods. The mathematical model delivers a multi-fold enhancement in fitting accuracy over data-driven methods for all fight cases. For UAV flight test data, the average root mean square error (RMSE) of the mathematical model demonstrates a maximum improvement of 37% in accuracy compared to three data-driven methods. For XRLV flight test data, the prediction accuracy of the mathematical model shows an enhancement exceeding 80% relative to Gaussian kernel SVM and Gaussian process data-driven models. The research verifies the feasibility and effectiveness of the data feature extraction combined with the symbolic regression method in mining the correlation law between ground and flight deviations of aerodynamic characteristics. This study provides valuable insight for modeling problems with finite data samples and explicit physical meanings. Full article

In this paper, we present a practical partitioned intelligent-reflecting-surface-aided transmit spatial modulation (PIRS-TSM) scheme, where spatial modulation is implemented at the transmitter and partitioning is conducted on the IRS to enhance the spectral efficiency (SE) and reliability for multiple-input single-output (MISO) systems. The theoretical analysis of average bit error rate (ABER) based on maximum likelihood (ML) detection and the computational complexity analysis are provided. Experimental simulations demonstrate that the PIRS-TSM scheme obtains a significant ABER enhancement under the same SE compared to the existing partitioned IRS-aided transmit space shift keying or generalized space shift keying schemes by additionally carrying modulated symbols. Moreover, the system performances with different configurations of antenna numbers and symbol modulation orders under the same SE are investigated as a practical application reference. Full article

Modern 5G network slicing centers on the precise design of virtual, independent networks operating over a shared physical infrastructure, each configured to meet specific service requirements. This approach plays a vital role in enabling highly customized and flexible service delivery within the 5G ecosystem. In this study, we present the application of a genetic programming symbolic classifier to a dedicated network slicing dataset, resulting in the generation of accurate symbolic expressions for classifying different network slice types. To address the issue of class imbalance, we employ oversampling strategies that produce balanced variations of the dataset. Furthermore, a random search strategy is used to explore the hyperparameter space comprehensively in pursuit of optimal classification performance. The derived symbolic models, refined through threshold tuning based on prediction correctness, are subsequently evaluated on the original imbalanced dataset. The proposed method demonstrates outstanding performance, achieving a perfect classification accuracy of 1.0. Full article

In the dialogue between “wind” and “earth”, terroir-built heritage and the natural environment together construct the cultural landscape of agrarian civilization. Understanding historical heritage within the broader landscape system and recognizing the cultural connotations and collective spatial memory embedded in this dialogue are crucial for identifying the value of heritage, excavating urban history, and promoting high-quality development. This article examines the Water-distributing Shrine landscape structure (WSLS)—a Japanese model comprising four spatial elements: focus, boundary, direction, and domain—and explores its relevance for interpreting the spatial logic of Chinese historical cities. The study adopts a visual-analytical method combining literature review, historical document analysis, field observation, and diagrammatic interpretation. Through a case study of Taiyuan, a city shaped by the Fen River and surrounding mountain systems, this study analyzes the historical characteristics of WSLS elements, reconstructs Taiyuan’s cultural landscape structure, and proposes integrated heritage conservation strategies. Rather than treating cultural relics as isolated objects, the approach emphasizes structural relationships between nature and culture, revealing how spatial configuration encodes collective values. This study aims to preserve the spatial logic and symbolic landscape system of agrarian civilizations and offers a reference for other Chinese cities seeking to rediscover and protect their historical landscape heritage. Full article

My essay aims at considering the mythological figure of Melusina and her literary development, starting from the Middle Ages up to the Romantic period. The main purpose is to determine how this fictional entity, originally regarded as the symbol of nature and its fecundity, has changed over the time in relation to the historical and cultural complex and how this has reverberated in terms of interpretation of the identity of the literary character. I will consider the medieval versions of Jean D’Arras (1392), with some consequent references to Coudrette (1401–1405) and von Ringoltingen (1456), and the German romantic fairytale rewriting of Ludwig Tieck (1800). If the thematic nucleus remains the same, the configuration of the female character changes by reflecting the new Romantic poetics in terms of interest towards femininity, subjectivity and the study of the morphology of the Earth. In particular, Melusina is no longer seen as a mere and passive object, but as a subject who for the first time, hiding in an emblematic cave, reveals to the reader her own interiority and her own truth, totally assimilating herself to the external environment. The conclusion will show how the cultural subtext modifies the interpretation of this atavistic character. Full article

This paper conducts an in-depth study on the outage probability performance of relay-based semantic communication systems and proposes a multi-mode intelligent relay design framework to address complex scenarios such as background knowledge differences, channel quality fluctuations, and computational limitations at the destination node. Based on a three-node two-hop communication model (source node–relay node–destination node) and integrating the DeepSC model, the study achieves cross-layer collaboration between semantic encoding/decoding and channel encoding/decoding. The proposed relay node operates in four innovative modes: semantic cooperative decode-and-forward, semantic adaptive forwarding, semantic-enhanced forwarding, and semantic-bit hybrid forwarding, each tailored to different levels of background knowledge matching, channel conditions, and computational constraints at the destination node. Through theoretical derivations, this paper presents the first closed-form expressions for the outage probability of the four relay modes, systematically quantifying the coupling effects of semantic symbol redundancy, background knowledge differences, and computational conversion efficiency on system reliability. The results show that semantic adaptive forwarding significantly reduces outage probability when background knowledge differences are minimal. When the destination node has limited computational power, the semantic-bit hybrid mode enhances communication reliability by flexibly adjusting the transmission strategy. Moreover, proper configuration of semantic symbol redundancy plays a crucial role in maintaining semantic information integrity and resisting channel interference. Monte Carlo simulations validate the theoretical analysis, demonstrating that the dynamic switching mechanism of the multi-mode relay outperforms single-mode strategies. This research provides theoretical support for reliable transmission and resource optimization in 6G semantic communication systems, uncovering the potential of joint optimization between semantic parameters and dynamic channel conditions. It holds significant implications for advancing future intelligent communication systems. Full article

Photonic millimeter-wave communication systems are promising for high-capacity, high-speed wireless networks, and their production is driven by the growing demand from data-intensive applications. However, challenges such as inter-symbol interferences (ISIs), inter-band interferences (IBIs), symbol timing offsets (STOs), and nonlinearity impairments exist, especially in non-orthogonal multiband configurations. This paper proposes and demonstrates the neural network-based waveform-to-symbol converter (NNWSC) for a coordinated multi-input and single-output (MISO) photonic millimeter-wave system with multiband multiplexing. The NNWSC replaces conventional matched filtering, down-sampling, and equalization, simplifying the receiver and enhancing interference resilience. Additionally, it reduces computational complexity, improving operational feasibility. As a proof of concept, experiments are conducted in a 16QAM non-orthogonal multiband carrierless amplitude and phase (NM-CAP) modulation system with coordinated MISO configurations in a scenario where two base stations have 5 km and 10 km fiber links, respectively. Data were collected across various roll-off factors, sub-band spacings, and received optical power (ROP) levels. Based on the proposed method, a coordinated MISO photonic millimeter-wave (mmWave) communication system at 91.9 GHz is demonstrated at a transmission speed of 30 Gbps. The results show that the NNWSC-based receiver achieves significant bit error rate (BER) reductions compared to conventional receivers across all configurations. The tolerances to the STO of NNWSC are also studied. These findings highlight NNWSC integration as a promising solution for high-frequency, interference-prone environments, with potential improvements for low-SNR and dynamic STO scenarios. Full article

The integration of Deep Learning and Symbolic Artificial Intelligence (AI) offers a promising hybrid framework for enhancing diagnostic accuracy and explainability in critical applications such as COVID-19 detection using computerized tomography (CT) scans. This study proposes a novel hybrid AI model that leverages the strengths of both approaches: the automated feature extraction and classification capabilities of Deep Learning and the logical reasoning and interpretability of Symbolic AI. Key components of the model include the adaptive deformable module, which improves spatial feature extraction by addressing variations in lung anatomy, and the attention-based encoder, which enhances feature saliency by focusing on critical regions within CT scans. Experimental validation using performance metrics such as F1-score, accuracy, precision, and recall demonstrates the model’s significant improvement over baseline configurations, achieving near-perfect accuracy (99.16%) and F1-score (0.9916). This hybrid AI framework not only achieves state-of-the-art diagnostic performance but also ensures interpretability through its symbolic reasoning layer, facilitating its adoption in healthcare settings. The findings underscore the potential of combining advanced machine learning techniques with symbolic approaches to create robust and transparent AI systems for critical medical applications. Full article