Search Results (662)

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

This paper investigates the cultural, spiritual, and ecological use and value of fish by-products in the material practices of Alaska Native (Indigenous Peoples are the descendants of the populations who inhabited a geographical region at the time of colonisation and who retain some or all of their own social, economic, cultural, and political institutions. In this paper, I use the terms “Indigenous” and “Native” interchangeably. In some countries, one of these terms may be favoured over the other.) and Greenlandic Inuit women. It aims to uncover how fish remnants—skins, bones, bladders, vertebrae, and otoliths—were transformed through tanning, dyeing, and sewing into garments, containers, tools, oils, glues, and adornments, reflecting sustainable systems of knowledge production rooted in Arctic Indigenous lifeways. Drawing on interdisciplinary methods combining Indigenist research, ethnographic records, and sustainability studies, the research contextualises these practices within broader environmental, spiritual, and social frameworks. The findings demonstrate that fish-based technologies were not merely utilitarian but also carried symbolic meanings, linking wearers to ancestral spirits, animal kin, and the marine environment. These traditions persisted even after European contact and the introduction of glass trade beads, reflecting continuity and cultural adaptability. The paper contributes to academic discourse on Indigenous innovation and environmental humanities by offering a culturally grounded model of zero-waste practice and reciprocal ecology. It argues that such ancestral technologies are directly relevant to contemporary sustainability debates in fashion and material design. By documenting these underexamined histories, the study provides valuable insight into Indigenous resilience and offers a critical framework for integrating Indigenous knowledge systems into current sustainability practices. Full article

The design and planning of Western-style constructions during the early Qing Dynasty in China constituted a significant multicultural encounter that fused technological advancement with aesthetic innovation. This cultural interplay is particularly evident in the imperial garden and pavilion projects commissioned by the Qing court, which served as physical and symbolic sites of cross-cultural dialogue. Influenced by the intellectual and artistic movements of the European Renaissance, Western architectural concepts gradually found their way into the spatial and visual language of Chinese architecture, especially within the royal gardens and aristocratic buildings of the time. These structures were not simply imitative but rather represented a selective adaptation of Western ideas to suit Chinese imperial tastes and principles. This article examines the architectural language that emerged from this encounter between Chinese and European cultures, analysing symbolic motifs, spatial design, ornamental aesthetics, the application of linear perspective, and the integration of foreign architectural forms. These elements collectively functioned as tools to construct a unique visual discourse that communicated both political authority and cultural hybridity. The findings underscore that this architectural phenomenon was not merely stylistic imitation, but rather a dynamic convergence of technological knowledge and artistic vision across cultural boundaries. 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

Modeling accident investigation reports is crucial for elucidating accident causation mechanisms, analyzing risk evolution processes, and formulating effective accident prevention strategies. However, such reports are typically long, hierarchically structured, and information-dense, posing unique challenges for existing language models. To address these domain-specific characteristics, this study proposes SAFE-Transformer, a Structure-Aware and Format-Enhanced Transformer designed for long-document modeling in the emergency safety context. SAFE-Transformer adopts a dual-stream encoding architecture to separately model symbolic section features and heading text, integrates hierarchical depth and format types into positional encodings, and introduces a dynamic gating unit to adaptively fuse headings with paragraph semantics. We evaluate the model on a multi-label accident intelligence classification task using a real-world corpus of 1632 official reports from high-risk industries. Results demonstrate that SAFE-Transformer effectively captures hierarchical semantic structure and outperforms strong long-text baselines. Further analysis reveals an inverted U-shaped performance trend across varying report lengths and highlights the role of attention sparsity and label distribution in long-text modeling. This work offers a practical solution for structurally complex safety documents and provides methodological insights for downstream applications in safety supervision and risk analysis. 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

The effective design of green financial policies is crucial for balancing the operational pressures of heavily polluting enterprises with the goal of sustained carbon emission reduction. This study investigates the impact of the Green Finance Reform and Innovation Pilot Zone (GFRIPZ) policy by employing a multi-period difference-in-differences (DID) model based on firm-level panel data from 2012 to 2021, covering A-share listed enterprises in Shanghai and Shenzhen. The results show that GFRIPZs significantly reduced carbon emissions in pilot regions, with heterogeneous effects observed across enterprise types—particularly among large enterprises, state-owned enterprises, and those located in financially developed areas. To uncover the underlying mechanisms, we compare two behavioral responses: green innovation, marked by long-term investment in green technologies, and expedient compliance, involving short-term, strategic compliance behaviors. Our findings indicate that GFRIPZs did not effectively promote green innovation. Instead, it has encouraged a shift from productive capital investment toward un-productive, symbolic actions aimed at fulfilling policy requirements. These responses risk undermining the long-term objective of green transformation and may contribute to a broader shift from real economic activity toward speculative or less productive investments, raising concerns about the quality and sustainability of the low-carbon transition. Full article

In this work, we investigate a new design strategy for the implementation of a deep neural network (DNN)-based limited-feedback relay system by using conventional filters to acquire training data in order to jointly solve the issues of quantization and feedback. We aim to maximize the effective channel gain to reduce the symbol error rate (SER). By harnessing binary feedback information from the implemented DNNs together with efficient beamforming vectors, a novel approach to the resulting problem is presented. We compare our proposed system to a Grassmannian codebook system to show that our system outperforms its benchmark in terms of SER. Full article

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

Forecasting multivariate time series is a pivotal task in controlling multi-sensor systems. The joint forecasting of all channels may be too complex, whereas forecasting the channels independently may cause important spatial inter-dependencies to be overlooked. In this paper, we improve the performance of single-channel forecasting algorithms by designing an interpretable front-end that extracts the spatial–temporal components from the input multivariate time series. Specifically, the multivariate samples are first segmented into equal-sized matrix symbols. The symbols are decomposed into the frequency-separated Intrinsic Mode Functions (IMFs) using a 2D Empirical-Mode Decomposition (EMD). The IMF components in each channel are then forecasted independently using relatively simple univariate predictors (UPs) such as DLinear, FITS, and TCN. The symbol size is determined to maximize the temporal stationarity of the EMD residual trend using Bayesian optimization. In addition, since the overall performance is usually dominated by a few of the weakest predictors, it is shown that the forecasting accuracy can be further improved by reordering the corresponding channels to make more correlated channels more adjacent. However, channel reordering requires retraining the affected predictors. The main advantage of the proposed forecasting framework for multivariate time series is that it retains the interpretability and simplicity of single-channel forecasting methods while improving their accuracy by capturing information about the spatial-channel dependencies. This has been demonstrated numerically assuming a 64-channel EEG dataset. Full article

This paper develops the argument that, in the application of AI to improve the system of governance for higher education, machine learning will be more effective in some areas than others. To make that assertion more systematic, a classificatory taxonomy of types of decisions is necessary. This paper draws upon the classification of decision processes as either symbolic or sub-symbolic. Symbolic approaches focus on whole system design and emphasise logical coherence across sub-systems, while sub-symbolic approaches emphasise localised decision making with distributed engagement, at the expense of overall coherence. AI, especially generative AI, is argued to be best suited to working at the sub-symbolic level, although there are exceptions when discriminative AI systems are designed symbolically. The paper then uses Beer’s Viable System Model to identify whether the decisions necessary for viability are best approached symbolically or sub-symbolically. The need for leadership to recognise when a sub-symbolic system is failing and requires symbolic intervention is a specific case where human intervention may be necessary to override the conclusions of an AI system. The paper presents an initial analysis of which types of AI would support which functions of governance best, and explains why ultimate control must always rest with human leaders. Full article

This study examines the psychological mechanisms through which BTS’s perceived star attributes—expertise, authenticity, likability, and similarity—influence fan loyalty within fandom-driven tourism. Anchored in activity theory and content theory of motivation, the proposed model identifies psychological need fulfillment and emotional satisfaction as sequential mediators linking celebrity perception to loyalty behaviors. Data were obtained from 916 BTS fans across six English-speaking countries via a structured online survey. To test the hypothesized relationships, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed. Results demonstrate that perceived star attributes significantly enhance psychological need fulfillment, which subsequently predicts emotional satisfaction and loyalty. Additionally, the attributes exert direct effects on emotional satisfaction, supporting both reflective and intuitive engagement pathways. All hypothesized paths were statistically significant, and the model exhibited strong overall fit (SRMR = 0.039; NFI = 0.875). Theoretically, this study advances loyalty research by foregrounding the roles of symbolic consumption, emotional resonance, and identity-based alignment in global fandom contexts. Practically, the findings offer insights for tourism marketers, destination planners, and entertainment brands seeking to design emotionally immersive, narrative-rich tourism experiences. Recommendations are provided for developing BTS-themed content aligned with fans’ identity motivations to foster sustained emotional engagement and destination loyalty. Full article

Readability formulas are mathematical functions that assess the ‘difficulty’ level of a given text. They play a crucial role in aligning educational texts with student reading abilities; however, existing models are often not tailored to specific linguistic or regional contexts. This study aims to develop and evaluate two novel readability formulas specifically designed for the Mexican Spanish language, targeting elementary education levels. The formulas were trained on a corpus of 540 texts drawn from official elementary-level textbooks issued by the Mexican public education system. The first formula was constructed using multiple linear regression, emulating the structure of traditional readability models. The second was derived through genetic programming (GP), a machine learning technique that evolves symbolic expressions based on training data. Both approaches prioritize interpretability and use standard textual features, such as sentence length, word length, and lexical and syntactic complexity. Experimental results show that the proposed formulas outperform several well-established Spanish and non-Spanish readability formulas in distinguishing between grade levels, particularly for early and intermediate stages of elementary education. The GP-based formula achieved the highest alignment with target grade levels while maintaining a clear analytical form. These findings underscore the potential of combining machine learning with interpretable modeling techniques and highlight the importance of linguistic and curricular adaptation in readability assessment tools. Full article

Amid the accelerating processes of modernization and commercialization, traditional rural public spaces are increasingly losing their cultural value and social functions. This study investigates the transformative role of art intervention in enhancing the quality and cultural significance of rural public spaces, with a focus on Machang Village in Tengchong, China. The study first develops a conceptual model to explore the causal relationships and pathways between these influencing factors. Drawing on this framework, the research then uses Structural Equation Modeling (SEM) to empirically test a multi-dimensional resident satisfaction model that incorporates spatial aesthetics, functional suitability, historical-cultural identity, and emotional cognition. Through field surveys and data collected from 224 residents, the study reveals that cultural emotions and functional completeness are the most influential factors in driving overall satisfaction. Artistic innovation and aesthetics contribute moderately, indicating that visual creativity alone is insufficient without deeper cultural integration and functional coherence. The findings suggest a dual-pathway satisfaction mechanism, where both symbolic emotional resonance and practical usability shape residents’ perceptions of public space quality. The study offers theoretical and practical insights into optimizing rural public space design, advocating for art-led, community-engaged, and culturally embedded approaches to rural revitalization. Full article

Blue-and-white porcelain patterns embody rich symbolic meanings and play a pivotal role in the transmission of Chinese intangible cultural heritage. However, their contemporary application often faces challenges due to complex visual forms and contextual interpretations. This study adopts a semiotic perspective to reinterpret blue-and-white porcelain motifs as cultural heritage symbols, aiming to assess their potential for sustainable preservation and modern revitalization. A hybrid evaluation framework is proposed, combining Grey System Theory and the Fuzzy Evaluation Method to quantitatively analyze 40 representative patterns across five key dimensions: cultural symbolism, esthetic value, communicative potential, modern applicability, and sustainability. Data were collected from expert panels, public surveys, and market performance, with the Analytic Hierarchy Process (AHP) employed to determine the relative importance of each dimension. The results reveal that plant and geometric patterns exhibit high adaptability and symbolic clarity, making them ideal for reinterpretation in modern design. Conversely, complex narrative and animal-based motifs demonstrate weaker performance in communicative efficiency and sustainability, indicating the need for visual simplification and semantic transformation. This study provides a theoretical and methodological foundation for the revitalization of traditional porcelain heritage in contemporary design practice, contributing to the global dissemination and sustainable development of cultural heritage symbols. Full article