Parameters Entropy Research Articles (Page 1)

Electrocorticography (ECoG) signals provide a valuable window into neural activity, yet their complex structure makes reliable classification challenging. This study addresses the problem by proposing a feature-selective framework that integrates multiple feature extraction techniques with statistical feature selection to improve classification performance. Power spectral density, wavelet-based features, Shannon entropy, and Hjorth parameters were extracted from ECoG signals obtained during a visual task. The most informative features were then selected using analysis of variance (ANOVA), and classification was performed with several machine learning methods, including decision trees, support vector machines, neural networks, and long short-term memory (LSTM) networks. Experimental results show that the proposed framework achieves high accuracy across individual patients as well as the combined dataset, with clear separability between classes confirmed through t-SNE visualization. In addition, analysis of selected features highlights the prominent role of electrodes located near the visual cortex, providing insights into the spatial distribution of neural activity.

Abstract In this work we apply the gravity-thermodynamics approach for the case of generalized mass-to-horizon entropy, which is a two-parameter extension of Bekenstein–Hawking entropy that arises from the extended mass-to-horizon relation, that is in turn required in order to have consistency with the Clausius relation. We extract the modified Friedmann equations and we obtain an effective dark energy sector arising from the novel terms. We derive analytical solutions for the dark energy density parameter, the dark energy equation-of-state parameter, and the deceleration parameter, and we show that the Universe exhibits the usual thermal history with the succession of matter and dark energy epochs. Additionally, depending on the value of the entropy parameters, the dark energy equation-of-state parameter can either lie in the phantom regime at high redshifts entering into the quintessence regime at small redshifts, or it can lie in the quintessence regime at high redshifts and experience the phantom-divide crossing at small redshifts, while in the far future in all cases it asymptotically obtains the cosmological constant value $$-1$$ - 1 . Finally, we perform observational confrontation with Supernova Type Ia (SNIa), Cosmic Chronometers (CC) and Baryonic Acoustic Oscillations (BAO) datasets, showing that the scenario is in agreement with observations.

Epilepsy has diverse seizure types that challenge diagnosis and treatment, requiring automated and accurate classification to improve patient outcomes. Traditional electroencephalogram (EEG)-based diagnosis relies on manual interpretation, which is subjective and inefficient, particularly for multi-class differentiation in imbalanced datasets. This study aims to develop a hybrid framework for automated multi-class seizure type classification using segment-wise EEG processing and multi-band feature engineering to enhance precision and address data challenges. EEG signals from the TUSZ dataset were segmented into 1-s windows with 0.5-s overlaps, followed by the extraction of multi-band features, including statistical measures, sample entropy, wavelet energies, Hurst exponent, and Hjorth parameters. The mutual information (MI) approach was employed to select the optimal features, and seven machine learning models (SVM, KNN, DT, RF, XGBoost, CatBoost, LightGBM) were evaluated via 10-fold stratified cross-validation with a class balancing strategy. The results showed the following: (1) XGBoost achieved the highest performance (accuracy: 0.8710, F1 score: 0.8721, AUC: 0.9797), with -band features dominating importance. (2) Confusion matrices indicated robust discrimination but noted overlaps in focal subtypes. This framework advances seizure type classification by integrating multi-band features and the MI method, which offers a scalable and interpretable tool for supporting clinical epilepsy diagnostics.

Investigation of biophysical properties of ion channels with nonlinear methods.

On 2 December 2020, an earthquake with a magnitude of Mw 4.5 occurred near the city of Thiva (Greece). The aftershock sequence, triggered by ruptures on or near the Kallithea fault, continued until January 2021. Seven months later, new seismic activity began a few kilometers west of the initial events, with the swarm displaying a general trend of spatiotemporal migration toward the east–southeast until the middle of 2022. In order to understand the physical and statistical pattern of the swarm, the seismicity was relocated using HypoDD, and the magnitude of completeness was determined using the frequency–magnitude distribution. In order to define the existence of spatiotemporal seismicity clusters in an objective way, the DBSCAN clustering algorithm was applied to the 2020–2022 Thiva earthquake sequence. The extracted clusters permit the analysis of the spatiotemporal scaling properties of the main clusters using the Non-Extensive Statistical Physics (NESP) approach, providing detailed insights into the nature of the long-term correlation of the seismic swarm. The statistical pattern observed aligns with a Q-exponential distribution, with qD values ranging from 0.7 to 0.8 and qT values from 1.44 to 1.50. Furthermore, the frequency–magnitude distributions were analyzed using the fragment–asperity model proposed within the NESP framework, providing the non-additive entropic parameter (qM). The results suggest that the statistical characteristics of earthquake clusters can be effectively interpreted using NESP, highlighting the complexity and non-additive nature of the spatiotemporal evolution of seismicity. In addition, the analysis of the properties of the seismicity clusters extracted using the DBSCAN algorithm permits the suggestion of possible physical mechanisms that drive the evolution of the two main and larger clusters. For the cluster that activated first and is located in the west–northwest part, an afterslip mechanism activated after the 2 September 2021, M 4.0 events seems to predominately control its evolution, while for the second activated cluster located in the east–southeast part, a normal diffusion mechanism is proposed to describe its migration pattern. Concluding, we can state that in the present work the application of the DBSCAN algorithm to recognize the existence of any possible spatiotemporal clustering of seismicity could be helping to provide detailed insight into the statistical and physical patterns in earthquake swarms.

How photoplethysmography can be used to detect major depressive disorder among patients with obstructive sleep apnea during sleep.

Shannon entropy, a cornerstone of information theory, statistical physics, and inference methods, is uniquely identified by the Shannon-Khinchin or Shore-Johnson axioms. Generalizations of Shannon entropy, motivated by the study of nonextensive or nonergodic systems, relax some of these axioms and lead to entropy families indexed by certain entropic parameters. In general, the selection of these parameters requires preknowledge of the system or encounters inconsistencies. Here we introduce a simple axiom for any entropy family: namely, that no entropic parameter can be inferred from a completely uninformative (uniform) probability distribution. When applied to the Uffink-Jizba-Korbel and Hanel-Thurner entropy families, the axiom selects only Rényi entropy as viable. It also extends consistency with the maximum likelihood principle, which can then be generalized to estimate the entropic parameter purely from data, as we confirm numerically. Remarkably, in a generalized maximum entropy framework the axiom implies that the maximized log-likelihood always equals minus Shannon entropy, even if the inferred probability distribution maximizes a generalized entropy and not Shannon's, solving a series of problems encountered in previous approaches.

ABSTRACTThe dihydroxyacetophenone (DHAP) ligand was anchored on Fe3O4 NPs, and its vanadyl(IV) complex was synthesised. This catalyst was investigated by different techniques of FT‐IR, XRD, XPS, TEM, FESEM, TGA, ICP‐OES, EDX, DLS, BET and TGA. Benzoxazoles were synthesised in high yields using 3,5‐di‐tert‐butyl catechol (0.5 mmol), ammonium acetate (0.5 mmol), aldehydes (0.5 mmol), solvent (ethanol 5 mL) and heterogeneous homogeneous catalyst (0.18 mol%). Benzoxazoles were synthesised in mild conditions of ambient temperature in an ethanol solvent under air oxygen for 3 h. This efficient oxidative cyclisation reaction suggests that the catalyst is bi‐active system due to the presence of two active centres of redox‐active vanadyl(IV) group for oxidative transformation and uncoordinated free hydroxyl groups on the DHAP ligand. These last groups can affect the dispersibility of the catalyst in solvent via hydrogen bonding. Moreover, they gather catechol, aldehyde and acetate substrate on catalyst surface and help vanadyl(IV) centre in substrate activation via proton shuttling to faciliate reaction. The catalyst was reused for six consecutive cycles with no significant loss in performance. The product yield in the sixth cycle was 87%. A hot filtration test confirmed the catalyst's stability and heterogeneous nature. The kinetic analysis of benzoxazole production was conducted using the spectrofluorimetric technique. This catalysis in molecular liquid provides insight into molecular interactions at the solution/catalyst interphase. The activation parameters of enthalpy, entropy and rate constants at different temperatures were obtained. The activation parameters of enthalpy ∆H≠ = 1.67 × 104 kJ mol−1, entropy ∆S≠ = −193.01 kJ mol−1 and rate constant of 0.7140 k (mol3 L−3 min−1) at 30°C were also obtained.

Confinement is known to significantly influence the properties of water, yet the precise mechanisms, particularly within nanotubes of varying diameters, remain under investigation. This study examines how confinement impacts the structural and thermodynamic behavior of water molecules inside carbon nanotubes (CNTs) with diameters ranging from 0.8 nm to 3.0 nm, across temperatures from 230 K to 420 K. We present findings on entropy, radial density, self-diffusion coefficients, and orientational order parameters derived from molecular dynamics simulations. Our results reveal that in ultra-narrow 0.8 nm CNTs, water molecules forms a single-file arrangement with some offset molecules, leading in two distinct peaks in the density profile, which differs from earlier reports suggesting a single peak for single-file water. In 0.8 nm CNTs, water demonstrates subdiffusive behavior, whereas larger CNTs show Fickian diffusion. Further analysis reveals a shift from non-Arrhenius to Arrhenius thermodynamic behavior along the CNT axis with increasing temperature. Additionally, a freezing transition of water is observed in 1.0 nm CNTs. Entropy analysis suggests that confined water is more stable than bulk water, though this stability is influenced by the degree of confinement. This study provides deeper insights into the structure and dynamics of water under confinement.

We employed an optimization method for the approximate entropy (ApEn) parameters ( , ) to evaluate the influence of changes in contraction intensity, visual feedback conditions, and joint angles in force ApEn during an isometric force-matching task. Seventeen participants performed an index finger abduction isometric force task in six contraction intensities (5-75% of maximum voluntary contraction, MVC), with and without visual feedback of the force, and in three different metacarpophalangeal (MCP) joint angles. Force variability, complexity (ApEn), and power spectrum density (PSD) were assessed, and a correlation analysis was performed between these variables. The best ApEn ( , ) pair for muscle force analysis was and force standard deviation (SD). Visual feedback influenced the ApEn; however, the comparison between experimental conditions (force intensity and joint angle) was similar. Both the force ApEn and the coefficient of variation (CoV) were reduced as a function of contraction intensity and without visual feedback. Conversely, the force SD and the PSD in the low-frequency band increased with contraction intensity and the absence of visual feedback. The changes in the MCP joint angle affected the MVC values and force CoV, with no significant effect on the force ApEn. The PSD in the low-frequency band (< 5Hz) showed a strong negative correlation with force ApEn in both visual feedback conditions. ApEn is influenced by force level and visual feedback, and it is strongly correlated with low-frequency force oscillations, which are related to the muscle's common drive.

This study aims to explore whether Guzheng playing training has a positive impact on the brain functional state of children with Autism Spectrum Disorder (ASD) based on power spectral and sample entropy analyses. Eight ASD participants were selected to undergo four months of Guzheng playing training, with one month as a training cycle. Electroencephalogram (EEG) signals and behavioral data were collected for comparative analysis. The results showed that after Guzheng playing training, the relative power of the alpha band in the occipital lobe of ASD children increased, and the relative power of the theta band in the parietal lobe decreased. The differences compared with typically developing (TD) children were narrowed. Moreover, some channels exhibited a gradual increase or decrease in power with the extended training period. Meanwhile, the sample entropy parameter also showed a similar upward trend, which was consistent with the behavioral data representation. The study shows that Guzheng training can enhance the brain function of ASD patients, with better effects from longer training. Guzheng playing training could be used as a daily intervention for autism.

Purpose. The study aims to develop and characterize a new nanostructured FeCo0,854Nb0,146NiB0,7Si0,3 high-entropy metallic glass with enhanced soft magnetic and mechanical properties. The research seeks to explore the interplay between the alloy’s amorphous structure and its functional properties to advance the understanding of high-entropy metallic glasses. Design / Method / Approach. The amorphous films of the FeCo0,854Nb0,146NiB0,7Si0,3 alloy was synthesized using splat-quenching technique. The cooling rate, estimated based on the film thickness, was ~106 K/s. Structural properties were analyzed via X-ray diffraction (XRD), differential thermal analysis (DTA), and electrical resistivity measurements. Magnetic properties were assessed using a B–H curve tracer and vibrating sample magnetometer, while microhardness was measured with a PMT-3 tester. Findings. The alloy exhibits a fully glassy structure with a crystallite size of ~3 nm, low coercivity (40 A/m), high saturation magnetization (74 A·m2/kg), and microhardness ≥ 8000 MPa, indicating decent soft magnetic and mechanical properties. Theoretical Implications. The research provides significant insights into the role of atomic-size differences, configurational entropy, and thermodynamic parameters in stabilizing the glassy phase in high-entropy alloys. It advances the theoretical framework for designing high-entropy amorphous materials. Practical Implications. The characteristics of the material make it promising for use in electronic devices and mechanical engineering parts. Originality / Value. This study provides a comprehensive analysis of the high-entropy metallic glass FeCo0,854Nb0,146NiB0,7Si0,3, offering new insights of its magnetic and mechanical properties through advanced characterization techniques. Research Limitations / Future Research. Further studies are needed to investigate the long-term stability of the fabricated amorphous alloy. Article Type. Applied Research. PURL: https://purl.org/cims/4.284

Bursty bulk flows (BBFs) play a crucial role in transporting energy, mass, and magnetic flux from the Earth’s magnetotail to the near-Earth region. However, their impulsive nature and small spatial scale pose significant difficulties for in-situ observations, given that only a handful number of spacecraft operate within the vast expanse of the magnetotail. Consequently, accurately predicting their behavior remains a challenging goal. In this study, we employ the XGBoost machine learning algotithm to predict the variation range of several essential BBF properties, including duration, magnetic field, plasma moments, and specific entropy parameters. The observed characteristics of a BBF are shaped by its formation in the downstream tail and its journey until it reaches the spacecraft. Therefore, we use both the background properties of the plasma sheet prior to the arrival of the BBF and the attributes of indirectly related variables during the BBF interval as inputs. Trained on 17 years of THEMIS data, we explore different input configurations. One approach involves incorporating optimal parameter combinations, utilizing as many input parameters as possible to predict upper and lower bounds of a target variable. Within this framework, we further apply the leave-one-feature-out method to quantitatively assess the contribution of each input, identifying the most dominant factor influencing BBFs in a statistical sense. Another approach involves cross-instrument prediction, leveraging measurements from a different payload. Our findings reveal that including observed background values enhances prediction accuracy by 10–20 percentage points. This study offers data-driven insights to improve BBF predictability, providing valuable guidance for future space weather monitoring and theoretical research.

Abstract The keyframe image character of 3D animation refers to the image state of the character or scene and other objects presented at specific keyframes in the animation timeline. The character texture will be blurred, distorted and discontinuous in the animation sequence due to motion, perspective change or lighting difference. Due to the lack of optical flow information to guide the current 3D animation keyframe image enhancement process, it is difficult to effectively distinguish the foreground character from the background elements, which affects the algorithm’s ability of recognizing the animation character’s movement patterns. To this end, a character texture enhancement method based on the SIFT algorithm and LK (Lucas-Kanade) optical flow is investigated for 3D animation keyframe images. The SIFT algorithm is used to extract the feature points of the character in each frame of 3D animation image as the initial tracking template, the LK optical flow method is used to calculate the optical flow vector of the feature points in the new frame, and the weighted directional histogram is used to estimate the direction of the character’s movement, and then the feature points in the tracking template are optimized through feature matching and weight updating to complete the tracking of the character’s target in each frame of the image. According to the target tracking results, a fractional-order differential mask is constructed in the character target region, and an adaptive function is constructed by combining the modes of the local gradient, information entropy and variance parameters of the image, so as to realize the enhancement of the character texture of the key frame images of the 3D animation by dynamically adjusting the order of the fractional-order differential. The experimental results show that the method can realize the dynamic tracking of 3D animation image character, and the extracted 3D animation image character feature points show a balanced distribution, and the distribution ratio is reasonable. It can realize the texture enhancement of 3D animation keyframe images, and the enhanced animation character clothing, facial details and other information are clearly recognizable, so as to improve the overall quality and visual impact of 3D animation.

Texture analysis (TA) is a powerful tool for extracting quantitative information, assessing myocardial heterogeneity, evaluating therapeutic efficacy, and predicting outcomes in heart disease. This study investigated whether TA based on gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) can enhance the prediction of response to cardiac resynchronization therapy (CRT). A total of 165 patients who underwent gated SPECT MPI and received CRT were enrolled in the study. Quantitative analysis of SPECT imaging generated 1225TA features. Phase analysis of resting gated short-axis SPECT myocardial perfusion images was utilized to assess left ventricular (LV) systolic and diastolic mechanical dyssynchrony (LVMD), including phase standard deviation (PSD), phase bandwidth (PBW), and entropy. Patients were categorized into CRT response and non-response groups based on a ≥5% improvement in LV ejection fraction (LVEF) measured by echocardiography at the 6-month follow-up. Variables with a p-value <0.05 in the univariate logistic regression analysis were incorporated into a backward stepwise multivariate logistic regression model for further analysis. During follow-up, 60.0% (99 of 165 patients) demonstrated a response to CRT. Univariate logistic regression analysis revealed that CRT response was significantly associated with N-terminal pro-brain natriuretic peptide (NT-proBNP), non-sustained ventricular tachycardia (NS-VT), LV end-diastolic diameter (LVEDD), LV end-systolic diameter (LVESD), scar burden, systolic and diastolic PSD, PBW, entropy, and 51TA parameters. In the backward stepwise multivariate regression analysis, inverse difference moment normalized (IDMN), NS-VT, NT-proBNP, diastolic PSD, and LVEDD emerged as independent predictors of CRT response. TA based on gated SPECT MPI provides independent prognostic predictor for CRT response in medically treated Heart failure patients.

Images serve as significant conduits of information and are extensively utilized in several facets of life. As chaotic encryption evolves, current chaotic key generators have grown increasingly prevalent and susceptible to compromise. We present an advanced chaos architecture that integrates numerous nonlinear functions and incorporates common chaotic maps as perturbation factors. The produced two-dimensional QWT chaotic map exhibits a more stable chaotic state and a broader chaotic range in comparison to existing maps. Simultaneously, we developed a novel roulette scrambling technique that shifts the conventional in-plane scrambling to cross-plane scrambling. Upon evaluation, the encrypted image demonstrates commendable performance regarding information entropy, correlation, and other parameters, while its encryption algorithm exhibits robust security.

ABSTRACT Background This study investigated the acoustic characteristics of forced expiratory lung sounds during bronchial provocation tests and their predictive value for airway hyperresponsiveness (AHR). Research design and methods Participants underwent a bronchial provocation test with incremental methacholine doses (0.072–1.25 mg). Forced expiratory volume in the first second (FEV1) was measured using spirometry, with pre-saline FEV1 as the baseline. AHR was defined as a ≥ 20% decline in FEV1%Ref (FEV1 relative to baseline). Simultaneously, lung sounds were recorded from the right lower lung field. Thirty-five acoustic features were extracted from the first-second forced expiratory lung sound, including 24 spectral parameters, 5 mel-frequency cepstral coefficients (MFCCs), and 6 entropy parameters. Correlation analysis, group comparisons, and logistic regression were conducted to assess the relationship between acoustic features and AHR. Results Seventeen patients tested positive for AHR. AHR was associated with decreased spectral parameters (A1–A3, MFCC2–MFCC5) and increased spectral entropy (p < 0.05). Logistic regression identified effective power (PT) and MFCC5 as independent predictors, yielding an AUC of 0.856 (95% CI: 0.769–0.944). Conclusions Acoustic features of breath sounds can predict AHR, offering a potential noninvasive alternative to bronchial challenge tests.

Evaluation of spectral entropy monitor with different concentrations of isoflurane in Horses.

The asymptotic correspondence between the probability mass function of the q-deformed multinomial distribution and the q-generalised Kullback–Leibler divergence, also known as Tsallis relative entropy, is established. The probability mass function is generalised using the q-deformed algebra developed within the framework of nonextensive statistics, leading to the emergence of a family of divergence measures in the asymptotic limit as the system size increases. The coefficients in the asymptotic expansion yield Tsallis relative entropy as the leading-order term when q is interpreted as an entropic parameter. Furthermore, higher-order expansion coefficients naturally introduce new divergence measures, extending Tsallis relative entropy through a one-parameter generalisation. Some fundamental properties of these extended divergences are also explored.

The observed scaling properties in the three aftershock sequences of the recent strong earthquakes of magnitudes Mw 6.1, Mw 6.4 and Mw 6.7, which occurred in the Ionian island region on the 26 January 2014 (onshore Cephalonia Island), 17 November 2015 (Lefkada Island) and 25 October 2018 (offshore Zakynthos Island), respectively, are presented. In the analysis, the frequency–magnitude distributions in terms of the Gutenberg–Richter scaling relationship are studied, along with the temporal evolution of the aftershock sequences, as described by the Omori–Utsu formula. The processing of interevent times distribution, based on non-extensive statistical physics, indicates a system in an anomalous equilibrium with long-range interactions and a cross over behavior from anomalous to normal statistical mechanics for greater interevent times. A discussion of this cross over behavior is given for all aftershock sequences in terms of superstatistics. Moreover, the common value of the Tsallis entropic parameter that was obtained suggests that aftershock sequences are systems with very low degrees of freedom. Finally, a scaling of the migration of the aftershock zones as a function of the logarithm of time is discussed regarding the rate strengthening rheology that governs the evolution of the afterslip process. Our results contribute to the understanding of the spatiotemporal evolution of aftershocks using a first principles approach based on non extensive statistical physics suggesting that this view could describe the process within a universal view.