Search Results (590)

Volatility is a core determinant of risk management and return optimization in financial investment. We develop an integrated stock-volatility prediction framework that couples multi-dimensional entropy indicators with machine learning models and links the resulting forecasts to a dynamic Barbell Strategy. The strategy controls drawdowns while retaining upside and remains feasible for individual investors. Using data for the Chinese CSI 300, CSI 500, and CSI 1000 index ETFs and a government bond ETF, we construct predictive features and estimate Yang–Zhang Volatility. The framework incorporates four entropy indicators—Shannon Entropy, Fuzzy Entropy, Permutation Entropy, and Dispersion Entropy—and evaluates model performance under 10-day, 15-day, and 20-day prediction and rebalancing frequencies. The empirical results reveal that the volatility forecasting model for the CSI 1000 has the highest R Squared. In practical trading applications, the Random Forest achieves the optimal risk-adjusted returns, and the 15-day and 20-day portfolio frequencies realize a better trade-off between return and risk control. Full article

Understanding how visual structure shapes attentional allocation is central to models of perceptual processing. Less is known about how formal properties like harmony and complexity shape exploration independent of salience or semantic content. This study examined how controlled structural variations relate to attention, facial engagement, and subjective judgment using eye-tracking and webcam-based facial coding. Participants ($N=40$) viewed stimuli derived from a common geometric base, manipulated into three conditions: high harmony (symmetrical, low complexity), high complexity (asymmetrical, disorganized), and structured complexity (high complexity with underlying order). Eye movements and facial expressions were recorded during free viewing. Metrics included time to first fixation, fixation duration, number of fixations, scanpath entropy, spatial dispersion, and facial-coding indices (neutral, happy, and surprise expression, and the ambient/focal coefficient K). None of the eye-tracking or facial-coding metrics differed significantly across conditions; given that the study was powered to detect only medium-to-large effects, this indicates no detectable difference under the present webcam-based, brief-exposure design rather than evidence that visual structure has no effect on attention. Subjective complexity ratings differed robustly across conditions, surviving correction for multiple comparisons: unexpectedly, the high-complexity condition was rated as less complex than the harmony and structured-complexity conditions, indicating the intended manipulation did not translate into perceived complexity as designed. A nominally significant difference in pleasantness ratings did not survive this correction. Using repeated-measures correlation to account for the non-independence of within-participant observations, scanpath entropy showed a nominal negative association with pleasantness that did not survive correction for multiple comparisons, and the coefficient K showed a weaker and partly inconsistent pattern of association with independent oculomotor indices than initial uncorrected analyses suggested. These findings indicate that, in the present study, formal visual structure shaped subjective complexity judgments more robustly than it shaped overt attentional or facial-affective engagement, a pattern consistent with—though not conclusive proof of—a broader dissociation between evaluative and attentional responses reported in face perception and developmental aesthetics research. Beyond this substantive finding, we report in detail how accounting for repeated-measures non-independence and applying an explicit multiplicity strategy changed our statistical conclusions, offering a worked methodological example for similarly structured webcam-based eye-tracking and facial-coding studies. Full article

To address expansive soil swelling–shrinkage distress and the limitations of single carbide slag (brittle failure), pure fiber (limited strength), and cement (high carbon emissions), this study develops a multi-indicator optimization method for carbide slag–polypropylene fiber composite improvement. Twenty-five full-factorial tests were conducted under three schemes: single slag, single fiber, and composite improvement. Six indicators (USR, LSR, VSR, cohesion, internal friction angle, UCS) were used to construct a CRITIC–entropy–TOPSIS model. The new insight brought by this research lies in coupling combination weighting with TOPSIS to eliminate the deviation of one-sided optimal formulas biased toward swelling inhibition or strength growth obtained by conventional single weighting and conventional range analysis, filling the gap of a multi-objective evaluation system balancing indicator conflict and dispersion, and realizing multi-index balanced optimization. The optimal C8P0.3 (8% slag + 0.3% fiber) has a relative closeness of 0.93. Compared to raw soil, USR drops by 77.07%, and cohesion and UCS increase by 164.30% and 71.91%; compared to C8P0, cohesion and UCS increase by 8.56% and 10.16%, relieving brittleness. XRD/SEM reveal that slag hydration forms rigid C-S-H networks while fibers create flexible 3D networks, jointly achieving rigid–flexible synergy that fills pores and bridges cracks. This work provides an optimization approach for low-carbon expansive subgrade stabilization. Full article

This study takes the Hezhen Yimakan as a case and develops a research framework encompassing requirement identification, dimensional synthesis, multidimensional requirement evaluation, design translation, and experimental validation. User requirements were first elicited through field investigation and semi-structured interviews and then organized into three dimensions—interactive experience, emotional-aesthetic experience, and cognitive understanding—through exploratory dimensional analysis. The entropy weight method was subsequently used to examine the dispersion and information-discrimination capacity of individual requirement indicators, while direct importance ratings measured users’ subjective judgments of the importance of each requirement. Design response priorities were determined by integrating qualitative evidence, entropy-weight analysis, and direct importance ratings, and a digital experience prototype was developed accordingly. An intergroup comparative experiment showed that the optimized prototype outperformed the existing interface in overall user experience, willingness to sustain cultural interest, and task-completion efficiency. The findings demonstrate that entropy-based information weights and users’ subjective importance judgments should be clearly distinguished; combining both with qualitative evidence can provide more cautious and transparent decision support for the design of digital experiences for oral-tradition intangible cultural heritage. Full article

The European Union’s common climate and energy framework—the Renewable Energy Directives and the European Green Deal—seeks to align national energy systems, yet whether electricity-generation structures converge remains underexplored. Using Eurostat data (SIEC classification), we examine the generation mix of the 27 EU member states over 1990–2024, measured as unweighted country-level source shares, with generation-weighted EU aggregates reported as a complement. We assess diversification (normalized Shannon entropy, the Herfindahl–Hirschman index and the effective number of sources) and convergence (σ- and β-convergence and source-by-source Phillips–Sul log-t club tests computed on source shares, with log-ratio balances used as a robustness check). Mix diversification rises (mean normalized entropy from 0.41 to 0.62) and, as measured by entropy, its levels converge to a single club. Convergence across sources is selective: the shares of the contracting carriers—coal, oil, nuclear and hydro—display σ-convergence, whereas wind, solar and bioenergy form single all-country convergence clubs in relative terms, even as their cross-country dispersion widens. The fossil–nuclear base remains structurally divided: coal fragments into multiple clubs with a divergent group that includes Poland, and nuclear separates member states into nuclear and non-nuclear groups. These patterns emerged during the development of the common EU policy framework; identifying the causal contribution of specific instruments is left for future work. The findings carry implications for security of supply, as exposed during the 2022 energy crisis. Full article

Electroencephalography (EEG) provides a non-invasive and cost-effective tool for supporting the assessment of attention-deficit/hyperactivity disorder (ADHD). However, the nonstationary nature of EEG produces substantial variability among signal windows recorded from the same participant, which can obscure diagnostic structure and lead to inconsistent predictions. To address this problem, we propose the Contextualized Transfer Entropy Network (CTE-Net), an end-to-end deep-learning architecture that combines global content-based contextualization with nonlinear and directed EEG connectivity estimation. CTE-Net first employs a Transformer encoder to contextualize the multichannel representations within each EEG window. The resulting signals are processed using channel-wise nonlinear temporal filters and Takens delay-coordinate embeddings. A differentiable matrix-based Transfer Entropy module, formulated using Rényi’s $\alpha$-entropy and a rational quadratic kernel, then estimates directed predictive information dependencies between all ordered electrode pairs. The resulting connectivity coefficients are used for ADHD-versus-control classification. The model was evaluated on a publicly available pediatric EEG dataset comprising 120 participants, equally divided between ADHD and control groups, using five fixed subject-wise folds and ten random training repetitions. At the window level, CTE-Net achieved an accuracy of $80.9\pm 1.7\%$, precision of $82.7\pm 2.1\%$, and sensitivity of $84.2\pm 2.3\%$. At the participant level, it achieved an accuracy of $83.4\%$ (95% CI: $78.2$–$88.2$) and an ROC-AUC of $90.2\%$ (95% CI: $85.1$–$94.6$), demonstrating competitive and comparatively balanced classification performance. Beyond classification performance, the directed Transfer Entropy representation exhibited the lowest within-subject dispersion among the analyzed representation stages, with a median reduction of $38.35\%$ relative to raw EEG. This reduction remained consistent across different PCA dimensionalities and distance definitions. These single-dataset findings support CTE-Net as a compact and interpretable methodological framework for representing directed EEG interactions while attenuating window-specific variability within individual participants. Full article

Lumpy skin disease (LSD) is a rapidly expanding transboundary vector-borne disease threatening livestock systems across Asia, with increasing risk of introduction into previously unaffected regions such as South Korea. This study developed an integrated, spatially explicit framework to assess the plausibility, timing and source-region structure of LSD virus (LSDV) introduction via windborne dispersal of infected arthropod vectors. Maximum Entropy models based on climatic, environmental, and topographic variables were used to determine habitat suitability for four major vector families (Culex spp., Musca domestica, Stomoxys calcitrans, and Culicoides spp.). All four were run via the transport simulation; S. calcitrans had the best experimental transmission efficiency, whereas Culicoides spp. provides the strongest evidence for passive long-distance transport. Livestock density and vector suitability were used to calculate the risk of LSD incidence in the adjacent source regions. A fourth-order Runge–Kutta trajectory model driven by ERA5 reanalysis winds at 850 hPa (about 1458 m) was used to predict windborne dissemination. This model included degradation of mechanically transmitted virus as well as survival limitations based on temperature, humidity, and wind. The simulated trajectories arrived in South Korea within the retention period of mechanically transported LSDV, with a median transit time of 18 h (interquartile range 10.0–30.3 h), 61.4% arriving within 24 h, and 87.1% within 48 h. North Korea accounted for 76.5% of weighted arrivals, with a median transit time of 11 h. Transit durations varied significantly by source location. For all four vector species, the predicted infectious arrival mass peaked in September and decreased thirteen-fold by November. Infected farms were found in regions of greater anticipated exposure than non-infected controls when compared to the October 2023 Korean epidemic (58.1% vs. 34.3% in the two highest risk groups; median exposure 0.214 versus 0.045; p < 0.001), although randomly relocating the dispersal surface reproduced comparable agreement in 23.6% of permutations, indicating that outbreak locations provide only limited validation of a pathway-specific introduction model. Random permutation of the dispersion surface produced a comparable result in 23.6% of permutations, indicating limited robustness of the observed spatial relationship to spatial randomization. With pairwise geographical and temporal distances significantly associated (Mantel r = +0.136, p = 0.013), spatiotemporal analysis revealed that the 74 reported outbreaks originated from a maximum of 25 introduction events over a 13-day period, consistent with significant farm-to-farm dissemination after introduction. The seasonal window, source-region structure, and transit timeframes of windborne LSDV introduction into South Korea are described in this paper. Reported epidemic sites only partially validate a pathway-specific introduction model since they reflect both the point of introduction and subsequent local transmission. This evaluation is pathway-specific and does not evaluate overall incursion risk; windborne transport is one of numerous possible introduction paths to consider. Full article

This review synthesizes 50 years of thermodynamic research in supramolecular chemistry, focusing on the interactions of diverse receptors with ionic and neutral species across various media. By evaluating a selection of foundational publications, this work highlights the critical role of host–guest selectivity. Crucially, we emphasize that a deep understanding of fundamental thermodynamics, including binding constants, enthalpy, and entropy changes, is essential to rationally guide and optimize practical applications. In doing so, we used nuclear magnetic resonance (NMR), ultraviolet-visible (UV-Vis) spectroscopy, conductometry, potentiometry and titration microcalorimetry to characterize solution processes. We consider the scope and limitations of these techniques as well as the inherent limitations of the reaction media. We analyze how solution studies correlate with solid state profiles derived from X-ray diffraction (XRD) scanning electron microscopy (SEM), energy dispersive atomic X-ray spectroscopy (EDAX), thermogravimetric analysis and infrared (IR) spectroscopy. This foundational knowledge bridges theory and practice, driving applications in environmental remediation, such as targeted pollutant removal, and the development of highly sensitive, on-site sensing devices for real-time water monitoring. Finally, based on the historical trends and current gaps identified in the selected literature, we offer strategic suggestions for further research in this area. Full article

Weighted directed networks can exhibit stronger aggregate interactions without becoming more integrated or topologically richer. Using daily prices for 32 stock indices over 2006–2026, we construct effective transfer entropy (ETE) networks from range-based variance states and recompute GLMY path homology over a directed edge-density filtration. In 88 overlapping 120-common-date windows, total ETE is associated with a higher density required for global weak connectivity and with smaller integrated first- and second-dimensional Betti curves. These are dependence-aware descriptive associations: reduced-overlap estimates are imprecise, and controls for edge concentration and mean variance reduce the connectivity association to approximately zero. Direct coverage measures show that high-intensity windows can distribute weight broadly while their strongest edges reach nodes unevenly. Exact circular-shift tests yield empty 5% BH-FDR backbones, so individual channels are not treated as established. Null-model, rank-stability, coefficient-field, and discretization checks delimit the fixed-density result. A common absolute-weight contrast yields positive rather than negative TETE–Betti associations, showing that the negative baseline relation is specific to the fixed-density filtration. The results distinguish interaction intensity, dispersion, strong-edge coverage, and directed path-homology organization as separate network dimensions. Full article

The continuous static contraction of the upper limb during overhead work often leads to progressive muscle fatigue, which is the main cause of work-related musculoskeletal disorders (WMSDs) and reduced operational safety. Traditional fatigue detection methods cannot capture subtle early fatigue characteristics under dynamic working conditions, and sensors are difficult to conveniently deploy in harsh environments. This study constructs an upper limb fatigue detection model based on wearable interface pressure signals and proposes a spatiotemporal dual-entropy fusion strategy. Sample entropy evaluates temporal muscle movement regularity, with a 10% incremental rate-of-change threshold for significant fatigue identification, superior fatigue sensitivity, and anti-interference capability. Information entropy reflects the spatial dispersion of pressure amplitude, adopting a 5% rate-of-change threshold for early fatigue warning. Combined with sEMG comparative verification and nonlinear fitting analysis, a hierarchical monitoring framework is established: single-index abnormality indicates slight fatigue, while dual-entropy synchronous elevation represents severe neuromuscular fatigue. The proposed method overcomes the limitations of single-index evaluation, accurately identifies multiple fatigue levels, and reliably monitors fatigue in real time, providing effective technical support for ergonomic monitoring and occupational safety protection in overhead operations. Full article

Thermodynamic concepts are increasingly used in nonlinear photonics to describe Rayleigh–Jeans thermalization, optical wave turbulence, condensation, negative-temperature states, and statistical mode locking. We ask how far this reasoning can be extended to localized structures maintained far from equilibrium by gain, loss, dispersion, and nonlinearity, using strongly chirped dissipative solitons (DSs) of the complex cubic–quintic Ginzburg–Landau equation as a model system. Their internal energy flows and separation of correlation scales connect coherent solitary waves with semi-incoherent wave kinetics, driven-open systems, and Bose–Einstein-condensation analogies. We review thermodynamic-like indicators based on spectral entropy, internal energy, effective temperature, and spectral condensation, and we relate them to dissipative-soliton resonance (DSR), stochastic mode-locking self-start, and redistribution between single- and multipulse attractors. Normal and anomalous group-delay dispersion provide complementary cases. In normal dispersion, DSR is accompanied by spectral localization, increasing scale separation, and growing multipulse accessibility; the statistical degree-count interpretation becomes meaningful only after the two scales separate and still requires ensemble calibration. In anomalous dispersion, the spectrum has extended wings, and noisy calculations reveal finite robust regions inside a larger existence domain, without an analogous thermodynamic turnover along the continuation tested. Thus, the unification is strongest at the level of the adiabatic solution and state-selection diagnostics, not an equilibrium thermodynamics. DSs thereby provide a photonic platform linking nonequilibrium thermodynamics, wave turbulence, driven condensates, and statistical phase-transition concepts. Full article

Traditional evaluation of Networks-on-Chip is based on aggregate metrics. Most of these metrics (latency, throughput, hop count, packet loss, energy consumption, buffer occupancy, thermal or reliability summaries) compress detailed execution traces into a few scalar values. This dimensionality reduction hides spatial, temporal, and resource-level differences between simulations. This paper introduces an Entropic Structural Observability framework for routing-independent post-simulation analysis of NoC traces. The main idea of the framework is to convert typed packet-level events into probability distributions over used resources, including routers, directed links, time windows, and critical resources. As a final report, the method builds a structural signature. This signature includes normalized entropy, effective support, concentration, spatiotemporal mutual information, distribution drift, topology-aware spatial statistics, buffer-pressure measures, and classical imbalance indicators. The analysis does not modify simulation traces; it operates as a diagnostic layer. It reveals activity distribution, temporal dependence, spatial evolution, resource pressure, and structural imbalance. The framework is evaluated on 256-router 2D and 3D mesh topologies using deterministic and adaptive routing policies under diverse traffic patterns and injection rates, enabling its structural signatures to be examined across different network dimensionalities. The results reveal distinct structural regimes: deterministic dimension-order routing shows broad spatial and temporal dispersion, DyAD exhibits stronger time-space coupling and drift, and Fully-Adaptive presents an intermediate profile with high dispersion but moderate temporal variation. Full article

Conventional statistical process monitoring approaches primarily focus on changes in location, dispersion, or distributional characteristics. However, process deterioration may also emerge through variations in uncertainty and information structure. Motivated by this limitation, this study proposes an Entropy–EWMA monitoring framework for progressively Type-II censored lifetime data. Developed under the Exponentiated Weibull distribution, the framework incorporates a censoring-adjusted entropy estimator into an adaptive EWMA structure to account for information loss and monitor changes in process uncertainty. The statistical performance of the proposed approach is evaluated through Monte Carlo simulations under different entropy degradation levels, censoring structures, observed sample sizes, and auxiliary shift-estimation smoothing parameter values. Control limits are calibrated to achieve an in-control Average Run Length close to the nominal target of $AR{L}_0=370$. The results show satisfactory in-control performance and substantially decreasing out-of-control Average Run Length as entropy degradation increases. Comparative simulations further demonstrate shorter run lengths for the Entropy–EWMA scheme under moderate and severe entropy degradation relative to the Classical EWMA. The practical applicability of the method is illustrated using Wind Turbine SCADA data under Uniform and Late Progressive Type-II censoring schemes. Neither method produces false alarms during the in-control phase, while both detect changes during the out-of-control phase. The Classical EWMA provides earlier and more persistent signals associated with location changes, whereas the Entropy–EWMA responds to changes in process uncertainty. These findings demonstrate that the two approaches capture different aspects of process behavior and can be interpreted as complementary monitoring tools. Full article

Objective: We aimed to investigate whether ultrasound-based texture analysis can differentiate liver metastases according to the histopathological origin of the primary tumor and to evaluate the quantitative texture characteristics of metastases originating from colorectal, pancreatic, and breast cancer. Materials and Methods: This prospective study included 75 patients with biopsy-proven liver metastases, comprising 25 colorectal adenocarcinoma, 25 pancreatic ductal adenocarcinoma, and 25 invasive ductal breast carcinoma metastases. Conventional B-mode ultrasound images were obtained prior to treatment. The largest metastatic lesion in each patient was manually segmented using a whole-lesion two-dimensional region of interest (ROI). Histogram-based texture analysis was performed using an in-house MATLAB-based software package (version R2021a; MathWorks, Natick, MA, USA). Extracted parameters included intensity-based metrics, dispersion measures, entropy, uniformity, and percentile values. Texture features were compared among the three groups using appropriate statistical tests. Results: Significant differences were observed among metastatic lesions according to their primary tumor origin. Significant differences were observed in the mean, median, minimum, maximum, most frequent gray-level values, root-mean-square level, root-sum-of-squares level, entropy, and all evaluated percentile parameters among groups (all p < 0.05). Pancreatic cancer metastases consistently demonstrated the highest intensity-related histogram values and percentiles, whereas breast cancer metastases exhibited the lowest values. Colorectal metastases were generally of intermediate intensity. Entropy values were significantly higher in colorectal and pancreatic metastases than in breast cancer metastases (p < 0.05), suggesting greater structural heterogeneity. No significant differences were observed for kurtosis, skewness, uniformity, or size distribution parameters (all p > 0.05). Conclusions: Ultrasound-based tissue analysis revealed distinct quantitative features among liver metastases originating from colorectal, pancreatic, and breast cancer. Density-related parameters, percentiles, and entropy show the potential to differentiate metastatic lesions based on their primary tumor origin, thus serving as a non-invasive biomarker. Full article

The King Abdulaziz Royal Reserve (KARR), one of Saudi Arabia’s eight royal reserves, displays strong seasonal vegetation turnover typical of arid-zone ecosystems, yet its environmental drivers remain largely unquantified. This study aimed to establish a seasonally resolved, statistically validated baseline of plant community composition in KARR and to quantify the soil and topographic variables associated with this compositional variation. To this end, we surveyed a defined section of KARR across all four seasons of 2023 (763 plots: 189 winter, 213 spring, 188 summer, 173 autumn) using a systematic 10 km sampling grid. A total of 289 vascular plant species from 47 families were recorded, and species richness, Shannon and Simpson diversity, and functional dispersion were quantified per community; the flora was dominated by therophytes (48.4%) and chamaephytes (21.5%) and by Saharo-Arabian phytogeographic affinities. For each season, Ward’s hierarchical clustering (k = 4) identified four plant communities, validated by PERMANOVA (p = 0.001 in every season) and distinct characteristic species. Soils across the study area were predominantly sandy loam, with texture, pH, electrical conductivity, organic matter, and macronutrients quantified per community. Canonical Correspondence Analysis showed that elevation and soil properties (clay, sand, potassium) significantly explained community composition each season (p = 0.001), though explained variance was modest (4–8%); variance partitioning showed a substantial spatial fraction exceeding the pure environmental fraction in every season. Functional dispersion was consistently lowest in one community each season; characteristic-species composition resolved this into two recurring functional types—a woody, disturbance-associated assemblage (indicated by Rhazya stricta) in winter and summer, and a psammophytic, dune-associated assemblage (indicated by Artemisia monosperma and Moltkiopsis ciliata) in spring and autumn—both reflected in Rao’s quadratic entropy (a closely related metric, r = 0.95). These results provide a seasonally resolved, statistically validated baseline of plant community composition and its environmental correlates for KARR, supporting future vegetation monitoring and habitat-based management, and contribute a data point from an under-studied hyper-arid region to the global understanding of dryland vegetation–environment relationships and grazing-driven degradation. Full article