Search Results (784)

One of the most popular and innovative methods to analyse signals is by using Ordinal Patterns (OPs). The OP encoding is based on transforming a (univariate) signal into a symbolic sequence of OPs, where each OP represents the number of permutations needed to order a small subset of the signal’s magnitudes. This implies that OPs are conceptually clear, methodologically simple to implement, and robust to noise, and that they can be applied to short signals. Moreover, they simplify the statistical analyses that can be carried out on a signal, such as entropy and complexity quantifications. However, because of the relative ordering, information about the magnitude of the signal at each timestamp is lost—this being one of the major drawbacks of this method. Here, we propose a way to use the signal magnitudes discarded in the OP encoding as a complementary variable to its permutation entropy. To illustrate our approach, we analyse synthetic trajectories from logistic and Hénon maps—with and without added noise—and real-world signals, including intracranial electroencephalographic recordings from rats in different sleep-wake states and frequency fluctuations in power grids. Our results show that, when complementing the permutation entropy with the variability in the signal magnitudes, the characterisation of these signals is improved and the results remain explainable. This implies that our approach can be useful for feature engineering and improving AI classifiers, as typical machine learning algorithms need complementary signal features as inputs to improve classification accuracy. Full article

As geographic information visualization continues to gain prominence, dynamic symbols are increasingly employed in map-based applications. However, the optimal visual coding for dynamic point symbols—particularly concerning encoding type, animation rate, and modulation area—remains underexplored. This study examines how these factors influence user performance in visual search tasks through two eye-tracking experiments. Experiment 1 investigated the effects of two visual coding factors: encoding types (flashing, pulsation, and lightness modulation) and animation rates (low, medium, and high). Experiment 2 focused on the interaction between encoding types and modulation areas (fill, contour, and entire symbol) under a fixed animation rate condition. The results revealed that search performance deteriorates as the animation rate of the fastest target symbol exceeds 10 fps. Flashing and lightness modulation outperformed pulsation, and modulation areas significantly impacted efficiency and accuracy, with notable interaction effects. Based on the experimental results, three visual coding strategies are recommended for optimal performance in map-based interfaces: contour pulsation, contour flashing, and entire symbol lightness modulation. These findings provide valuable insights for optimizing the design of dynamic point symbols, contributing to improved user engagement and task performance in cartographic and geovisual applications. Full article

Background/Objectives: Cognitive impairment is one of the most common and debilitating clinical features of Multiple Sclerosis (MS). Neuropsychological assessment, however, is time-consuming and requires personal resources, so, due to limited resources in daily clinical practice, information on cognitive profiles is often lacking, despite its high prognostic relevance. Time-saving and effective tools are required to bridge this gap. This study evaluates the sensitivity and specificity of a revised version of TRACK-MS (TRACK-MS-R), a recently published screening tool to identify cognitive impairment in MS in a fast and reliable way, offering a balance between efficiency and diagnostic yield for the individual patient. Methods: In this prospective cross-sectional study, 102 MS patients and 94 age-, sex-, and education-matched healthy controls (HC) completed an extensive neuropsychological assessment, including TRACK-MS-R, to test for cognitive processing speed (Symbol Digit Modalities Test, SDMT) and verbal fluency (Regensburger Word Fluency Test, RWT). Sensitivity of TRACK-MS-R was assessed by using the BICAMS-M battery as a reference, and specificity was determined by comparing MS patients to HC. Results: TRACK-MS-R demonstrated high sensitivity (97.44%) when compared to the gold standard as represented by BICAMS-M for early and accurately detecting cognitive impairment in MS patients. Additionally, as a potential cognitive marker, TRACK-MS-R showed a specificity of 82.98% in distinguishing MS patients from healthy controls. Conclusions: TRACK-MS-R proves to be a highly sensitive and time-efficient screening tool for detecting cognitive impairment in patients with MS, while demonstrating good specificity compared to HC. Whereas high sensitivity is a prerequisite for a valid screening tool, its relatively modest specificity compared to BICAMS-M (62.9%) calls for caution in interpreting standalone results but instead indicates more extensive neuropsychological testing. Its briefness and diagnostic accuracy support its implementation in routine clinical practice, particularly in time-constrained settings. Full article

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

This paper investigates the error performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) systems employing one-bit analog-to-digital converters (ADCs) over Rayleigh fading channels. In cooperative DF MIMO systems, detection errors at the relay may propagate to the destination, thereby degrading overall detection performance. Although joint maximum likelihood detection can efficiently mitigate error propagation by leveraging probabilistic information from a source-to-relay link, its computational complexity is impractical. To address this issue, an approximate maximum likelihood (AML) detection scheme is introduced, which significantly reduces complexity while maintaining reliable performance. However, its analysis under one-bit ADCs is challenging because of its nonlinearity. The main contributions of this paper are summarized as follows: (1) a tractable upper bound on the pairwise error probability (PEP) of the AML detector is derived using Jensen’s inequality and the Chernoff bound, (2) the asymptotic behavior of the PEP is analyzed to reveal the achievable diversity gain, (3) the analysis shows that full diversity is attained only when symbol pairs in the PEP satisfy a sign-inverted condition and the relay correctly decodes the source symbol, and (4) the simulation results verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed analysis. Full article

This article offers a critical reading of the fishing neighbourhood of Paramos, located on the northern coast of Portugal, through a methodological approach that combines documentary photography and cognitive cartography. The study investigates the relationships between identity, landscape, and power within a territory marked by spatial fragmentation, symbolic exclusion, and functional indeterminacy. By means of a structured visual essay supported by field observation and interpretive maps, Paramos is examined as a liminal urban enclave whose ambiguities reveal tensions between memory, informal appropriation, and control devices. Drawing on authors such as Lefebvre, Augé, Hayden, Domingues, Foucault, and Latour, the article argues that the photographic image, used as a critical tool, can unveil hidden territorial logics and contribute to a more inclusive and situated spatial discourse. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

We present a pipeline for synthetic simplification of text in French that combines large language models with structured semantic guidance. Our approach enhances data generation by integrating contextual knowledge from Wikipedia and Vikidia articles and injecting symbolic control through lightweight knowledge graphs. To construct document-level representations, we implement a progressive summarization process that incrementally builds running summaries and extracts key ideas. Simplifications are generated iteratively and assessed using semantic comparisons between input and output graphs, enabling targeted regeneration when critical information is lost. Our system is implemented using LangChain’s orchestration framework, allowing modular and extensible coordination of LLM components. Evaluation shows that context-aware prompting and semantic feedback improve simplification quality across successive iterations. 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

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

Ancient temple complexes in China’s mountainous landscapes exemplify a profound synthesis of environmental adaptation and cultural expression. This research investigates the spatial logic underlying the Wudang Mountain temple complex—a UNESCO World Heritage site—through integrated geospatial analysis of environmental factors. Using GIS-based modeling, GeoDetector, and regression analysis, we systematically assess how terrain, hydrology, climate, vegetation, and soil conditions collectively influenced site selection. The results reveal a clear hierarchical clustering pattern, with dense temple cores in the southwestern highlands, ridge-aligned belts, and a dominant southwest–northeast orientation that reflects intentional alignment with mountain ridgelines. Temples consistently occupy zones with moderate thermal, hydrological, and vegetative stability while avoiding geotechnical extremes such as lowland humidity or unstable slopes. Regression analysis confirms that site preferences vary across temple types, with soil pH, porosity, and bulk density emerging as significant influencing factors, particularly for cliffside temples. These findings suggest that ancient temple planning was not merely a passive response to sacred geography but a deliberate process that actively considered terrain, climate, soil, and other environmental factors. While environmental constraints strongly shaped spatial decisions, cultural and symbolic considerations also played an important role. This research deepens our understanding of how environmental factors influenced the formation of historical landscapes and offers theoretical insights and ecologically informed guidance for the conservation of mountain cultural heritage sites. 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

The Amazon rainforest hosts a diverse array of forest types, including those where Brazil nut (Bertholletia excelsa) occurs, which plays a crucial ecological and economic role. The Brazil nut is the second most important non-timber forest product in the Amazon, a symbol of development and sustainable use in the region, promoting the conservation of the standing forest. Understanding the vertical structure of these forests is essential to assess their ecological complexity and inform sustainable management strategies. We used terrestrial laser scanning (TLS) to assess the vertical structure of Amazonian forests with the occurrence of Brazil nut (Bertholletia excelsa) at regional (Amazonas, Mato Grosso, Pará, and Amapá) and local scales (forest typologies in Amapá). TLS allowed high-resolution three-dimensional characterization of canopy layers, enabling the extraction of structural metrics such as canopy height, rugosity, and leaf area index (LAI). These metrics were analyzed to quantify the forest vertical complexity and compare structural variability across spatial scales. These findings demonstrate the utility of TLS as a precise tool for quantifying forest structure and highlight the importance of integrating structural data in conservation planning and forest monitoring initiatives involving B. excelsa. 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

The aim of this research is to thoroughly investigate the influence of the 2025 Donald Trump Presidential Inauguration on informational efficiency of the U.S. exchange-traded fund market in the context of political risk. The data set includes daily observations for twenty U.S. Exchange-Traded Funds (ETFs). The whole sample comprises the period from 20 October 2024 to 20 April 2025. Since the Presidential Inauguration of Donald Trump took place on 20 January 2025, two sub-samples of an equal length are analyzed: (1) the period before the 2025 U.S. Presidential Inauguration from 20 October 2024 to 19 January 2025 and (2) the period after the 2025 U.S. Presidential Inauguration from 20 January 2025 to 20 April 2025. Since the whole sample period is not long (six months), to estimate market efficiency, modified Shannon entropy based on symbolic encoding with two thresholds is used. The empirical findings are visualized by symbol-sequence histograms. The proposed research hypothesis states that the U.S. ETF market’s informational efficiency, as measured by entropy, substantially decreased during the turbulent period after the Donald Trump Presidential Inauguration compared to the period before the Inauguration. The results unambiguously confirm the research hypothesis and indicate that political risk could affect the informational efficiency of markets. To the best of the author’s knowledge, this is the first study exploring the influence of the Donald Trump Presidential Inauguration on the informational efficiency of the U.S. ETF market. Full article