Calculation Of Entropy Research Articles

In silico evaluation of bisphosphonates identifies leading candidates for SARS-CoV-2 RdRp inhibition.

The novel coronavirus disease (COVID-19) pandemic has resulted in 777 million confirmed cases and over 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Post Acute Sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this work and our recent paper that identified alendronate as a promising candidate. In this study, we have investigated all bisphosphonates (BPs) on the ChEMBL database which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211), two of which closely resemble the approved drugs minodronate and zoledronate. This work and our recent paper together provide an in silico mechanistic explanation for alendronate and zoledronate users having dramatically reduced odds of SARS-CoV-2 testing, COVID-19 diagnosis, and COVID-19-related hospitalizations, and indicate that similar observational studies in Japan with minodronate could be valuable.

The time-course changes in postural control variability between neck pain and asymptomatic dental students

Changes in postural control associated with clinical practice or specific conditions such as the presence of neck pain remain unexplored in dental students. Therefore, this study aimed to explore the time-course changes in postural control complexity among dental students enrolled in clinical practice, comparing those with and without neck pain. We used an online Nordic Musculoskeletal Questionnaire for group allocation and center of pressure (CoP) oscillations with a tri-axial Bertec force plate. Baseline data were acquired from dental students with neck pain (NP) (n = 21) and asymptomatic in a control group (n = 23), before starting their clinical practice, and assessments were repeated after their first semester. CoP fluctuations were determined through the calculation of sample entropy. Both groups had similar postural control at baseline, but asymptomatic students exhibited more irregular CoP AP (p = 0.013) and ML (p = 0.015) oscillations, while students with neck pain showed a more rigid pattern (p = 0.004) in the AP direction at the endpoint. Our results showed that dental students’ postural control complexity decreased during the first semester of clinical practice. Over time, asymptomatic students exhibited more random postural control patterns, while students with neck pain demonstrated more rigid postural control during upright stance, indicating that postural control complexity differs between students with and without neck pain when exposed to clinical training.

Orientational Disorder and Molecular Correlations in Hybrid Organic-Inorganic Perovskites: From Fundamental Insights to Technological Applications.

Hybrid organic-inorganic perovskites (HOIP) have emerged in recent years as highly promising semiconducting materials for a wide range of optoelectronic and energy applications. Nevertheless, the rotational dynamics of the organic components and many-molecule interdependencies, which may strongly impact the functional properties of HOIP, are not yet fully understood. In this study, we quantitatively analyze the orientational disorder and molecular correlations in archetypal perovskite CH3NH3PbI3 (MAPI) by performing comprehensive molecular dynamics simulations and entropy calculations. We found that, in addition to the usual vibrational and orientational contributions, rigid molecular rotations around the C-N axis and correlations between neighboring molecules noticeably contribute to the entropy increment associated with the temperature-induced order-disorder phase transition, ΔSt. Molecular conformational changes are equally infrequent in the low-T ordered and high-T disordered phases and have a null effect on ΔSt. Conversely, the couplings between the angular and vibrational degrees of freedom are substantially reinforced in the high-T disordered phase and significantly counteract the phase-transition entropy increase resulting from other factors. Furthermore, the tendency for neighboring molecules to be orientationally ordered is markedly local, consequently inhibiting the formation of extensive polar nanodomains at both low and high temperatures. This theoretical investigation not only advances the fundamental knowledge of HOIP but also establishes physically insightful connections with contemporary technological applications like photovoltaics, solid-state cooling, and energy storage.

Analysis of the Tensile Properties of Composite Material Added Carbonisate Based on the Change of Strain Dynamics.

This paper presents the application of Kolmogorov-Sinai (EK-S) metric entropy calculations performed on experimental data sets (relative elongations ε) recorded during static tensile testing of a composite material with carbonisate. The EK-S calculation method makes it possible to represent the dynamics of strain change occurring during the endurance test. The depiction of the change in the dynamics of elongation compared to the course of the tensile curve makes it possible to analyse the strength properties of the tested specimens. The material used for the study is a layered epoxy composite with the addition of carbonisate obtained by pyrolysis from organic waste from MDF (Medium Density Fibreboard) furniture boards. For the tested material, two variants were prepared without the addition of carbonisate (samples designated as I and III) and two variants differing in the percentage of carbonisate: 5% by mass (sample IV) and 7.5% by mass (sample II), with a constant fraction of 0.5 mm. Analyses showed a slight deterioration in the tensile properties of composites containing carbonisate. SEM (Scanning Electron Microscope) studies of the carbonisate samples revealed the presence of cracks, pores and local delamination, which correlates with a reduction in strength parameters. For sample II, the tensile strength (Rm) was 9.032% lower compared to the base sample I and the tensile strain decreased by 0.098%. For sample IV, a decrease in parameters was also observed compared to base sample III-the strength decreased by 13.29%, and the tensile strain decreased by 10.64%. The results obtained in this study were additionally decided to be analysed using metric entropy calculations, which makes it possible to capture significant qualitative changes occurring in the structure of the tested samples not depending on the results of the static tensile test. In the context of epoxy composites with the addition of carbonisate, this analysis can contribute to a better understanding of the influence of the carbonisate obtained in the pyrolysis process on the structure of the composite and its performance properties.

Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy.

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants. The analysis revealed significant differences in fractal features across five emotional states (neutral, amused, bored, relaxed, and scared), particularly those derived from wavelet entropy. A cross-correlation analysis showed robust correlations between fractal features and both the arousal and valence dimensions of emotion, challenging the conventional view of EDA as a predominantly arousal-indicating measure. The application of machine learning for emotion classification using fractal features achieved a leave-one-subject-out accuracy of 84.3% and an F1 score of 0.802, surpassing the performance of previous methods on the same dataset. This study demonstrates the potential of fractal analysis in capturing the intricate, multi-scale dynamics of EDA signals for emotion recognition, opening new avenues for advancing emotion-aware systems and affective computing applications.

Data science shows that entropy correlates with accelerated zeolite crystallization in Monte Carlo simulations.

We have performed a data science study of Monte Carlo (MC) simulation trajectories to understand factors that can accelerate the formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, MC simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates the crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [Bores et al., Phys. Chem. Chem. Phys. 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in MC simulations going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the smooth overlap of atomic positions approach, which represents all two- and three-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all three-body correlations and as high as 0.94 with only two-body correlations. SVM decision functions reveal relatively broad/narrow histograms for 1OSDA/2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous MC simulations. We conclude that altering synthesis conditions in ways that substantially change the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

Improving Multiscale Fuzzy Entropy Robustness in EEG-Based Alzheimer's Disease Detection via Amplitude Transformation.

This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer's disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as well as the selection of key parameters such as embedding dimension (m) and similarity criterion (r), which often result in inconsistent outcomes when applied to multivariate data, such as electroencephalography signals. To address these challenges and to generalize the possibility of adopting Multiscale Fuzzy Entropy as a diagnostic tool for Alzheimer's disease, this research explores amplitude transformation preprocessing on electroencephalography signals in Multiscale Fuzzy Entropy calculation across varying parameters. The statistical analysis of the obtained results demonstrates that amplitude transformation preprocessing significantly enhances Multiscale Fuzzy Entropy's ability to detect Alzheimer's disease, achieving higher and more consistent significant comparison percentages, with an average of 73.2% across all parameter combinations, compared with only one raw data combination exceeding 65%. Clustering analysis corroborates these findings, showing that amplitude transformation improves the differentiation between Alzheimer's disease patients and healthy subjects. These results highlight the potential of amplitude transformation to stabilize Multiscale Fuzzy Entropy performance, making it a more reliable tool for early Alzheimer's disease detection.

Heterotic strings and quantum entanglement

We construct ℤN orbifolds of the ten-dimensional heterotic string theories appropriate for implementing the stringy replica method for the calculation of quantum entanglement entropy. A novel feature for the heterotic string is that the gauge symmetry must be broken by a Wilson line to ensure modular invariance. We completely classify the patterns of symmetry breaking. We show that the tachyonic contributions in all cases can be analytically continued, with a finite answer in the domain 0 < N ≤ 1, relevant for calculating entanglement entropy across the Rindler horizon. We discuss the physical implications of our results.

Thermodynamics of solids including anharmonicity through quasiparticle theory

The quasiharmonic approximation (QHA) in combination with density-functional theory is the main computational method used to calculate thermodynamic properties under arbitrary temperature and pressure conditions. QHA can predict thermodynamic phase diagrams, elastic properties and temperature- and pressure-dependent equilibrium geometries, all of which are important in various fields of knowledge. The main drawbacks of QHA are that it makes spurious predictions for the volume and other properties in the high temperature limit due to its approximate treatment of anharmonicity, and that it is unable to model dynamically stabilized structures. In this work, we propose an extension to QHA that fixes these problems. Our approach is based on four ingredients: (i) the calculation of the n-th order force constants using randomly displaced configurations and regularized regression, (ii) the calculation of temperature-dependent effective harmonic frequencies ω(V, T) within the self-consistent harmonic approximation (SCHA), (iii) Allen’s quasiparticle (QP) theory, which allows the calculation of the anharmonic entropy from the effective frequencies, and (iv) a simple Debye-like numerical model that enables the calculation of all other thermodynamic properties from the QP entropies. The proposed method is conceptually simple, with a computational complexity similar to QHA but requiring more supercell calculations. It allows incorporating anharmonic effects to any order. The predictions of the new method coincide with QHA in the low-temperature limit and eliminate the QHA blowout at high temperature, recovering the experimentally observed behavior of all thermodynamic properties tested. The performance of the new method is demonstrated by calculating the thermodynamic properties of geologically relevant minerals MgO and CaO. In addition, using cubic SrTiO3 as an example, we show that, unlike QHA, our method can also predict thermodynamic properties of dynamically stabilized phases. We expect this new method to be an important tool in geochemistry and materials discovery.

Density Functional Theory Study on Formation Energy, Formation Entropy, and Thermal Equilibrium Concentration of Tin and Vacancy in Germanium Layer

Germanium tin (Ge1-x Sn x )-related semiconductors are attractive for electronic and optoelectronic devices. One of the main problems with Ge1-x Sn x layer growth is Sn precipitation since the thermal equilibrium concentration of Sn in the Ge matrix is as low as 1 at.% below 500oC [1]. There have been many experimental challenges to doping higher concentrations of Sn in Ge [2]. The purpose of this study is to derive the thermal equilibrium concentration of substitutional Sn (Sns) and interstitial Sn (Sni) in the Ge layer through theoretical calculations. The impact of the incorporation of vacancy (V) by Sn doping is another research topic in this study.We carried out density functional theory calculations within the generalized gradient approximation (GGA) and sx-LDA [3] hybrid functional for electron exchange and correlation, as implemented in the CASTEP code. Two types of calculation models; two bulk cubic models and one (001) layer model, were prepared. For the bulk models, 64 and 512 Ge-atom supercells were prepared to calculate the formation energy (Ef ) of Sns, Sni at <110> dumbbell (D)-site, at tetrahedral (T)-site, at hexagonal (H)-site, V, and Sns V in Ge crystals. The degeneracy number (w) of each defect was investigated and obtained as w = 1 (Sns), 12 (Sni at D-site), 1 (Sni at T-site), 3 (Sni at H-site), 3 (V), and 12 (V at nearest of Sns). The 64-atom model was also used for the formation (vibration) entropy (Sf ) calculations with the linear-response method [4]. For a layer model, 16 Ge atoms are included in each of the 32 layers. The depth dependence of Ef of Sns, Sni, V, and SnV was obtained.Table 1 summarizes the calculated Ef and Sf of each defect in bulk Ge crystal. It is clear that the Ef of Sns from GGA is close to that from sx-LDA, while the Ef of the other defects from GGA is far smaller than that from sx-LDA. This is due to the prediction of Ge as metallic from GGA. The defect levels of Sni, V, and SnV that should be formed in the bandgap (Eg ) are mixed with conduction band. By using the Ef of Sns obtained from GGA (512 atoms) and the other defects from sx-LDA, the thermal equilibrium concentration Ceq = w×4.42×1022 exp(Sf/k)exp(-Ef/kT) (cm-3) was determined. For Sns, Ceq (Sns) = 4.42×1022 exp(1.80) exp(-0.28 eV/kT) cm-3 and Ceq (Sns) = 3.71×1021 cm-3 at 500 oC, which is about 8.4 at.% of Ge concentration. This value is comparable to the experimental value of 1 at.% below 500oC [1]. The Ceq (Sni) is negligible in comparison with the Ceq (Sns) as the Ef of Sni is larger than 4 eV. This result indicates that Sni atoms are not incorporated into the Ge matrix during layer growth.Figure 1 (a) shows the arrangement of Sns atoms in the (001) layer model. The Ef for Sns and Sni obtained from GGA are summarized in Table 2. The Ef at the surface (1st layer) decreases dramatically to about 0 eV for Sns, and to 0.51 eV for Sni at T-site, as shown in Fig. 1 (b). By ignoring the difference in the number of sites and formation entropy of Sns and Sni on the surface, the ratio W of Sn present on the surface being distributed to interstitial and substitutional positions was obtained: W = Ceq (Sni)/Ceq (Sns) = exp(-0.51 eV/kT)/exp(0) = 0.05% at 500oC. Since Sni atoms are not incorporated into the Ge bulk, they will precipitate on the surface of the Ge layer.Finally, Fig. 2 shows the charge-state dependence of the Ef of V in Ge determined from sx-LDA. These results agree with the previous study using the HSE06 hybrid functional [5]. When the Fermi level is near the center of Eg , there are three charge states of V: V 0, V -1, and V -2. The Ceq (V) at the melting point is 2.9×1014 cm-3, which is one order smaller than the experimental value (2.9-3.9×1015 cm-3) [6]. It was also confirmed that the binding energy of Sn and V 0 is 0.54 eV, and Sn enters first then takes V from the surface.AcknowledgementThis work was supported by JST, CREST Grant Number JPMJCR21C2, Japan.References F. A. Trumbore, J. Electrochem. Soc. 103 (1956) 597.S. Zaima et al., Sci. Technol. Adv. Mater. 16 (2015) 043502.D. M. Bylander and L. Kleinman, Phys. Rev. B 41 (1990) 7868.S. Baroni et al., Rev. Mod. Phys. 73 (2001) 515.K. Sueoka et al., ECS JSSST. 5 (2016) P3176.A. G. Tweet, Phys. Rev. 106 (1957) 221. Figure 1

Melting simulations of high-entropy carbonitrides by deep learning potentials

The melting temperature is a crucial property of materials that determines their potential applications in different industrial fields. In this study, we used a deep neural network potential to describe the structure of high-entropy (TiZrTaHfNb)CxN1−x carbonitrides (HECN) in both solid and liquid states. This approach allows us to predict heating and cooling temperatures depending on the nitrogen content to determine the melting temperature and analyze structure changes from atomistic point of view. A steady increase in nitrogen content leads to increasing melting temperature, with a maximum approaching for 25% of nitrogen in the HECN. A careful analysis of pair correlations, together with calculations of entropy in the considered liquid phases of HECNs allows us to explain the origin of the nonlinear enhancement of the melting temperature with increasing nitrogen content. The maximum melting temperature of 3580 ± 30 K belongs to (TiZrTaHfNb)C0.75N0.25 composition. The improved melting behavior of high-entropy compounds by the addition of nitrogen provides a promising way towards modification of thermal properties of functional and constructional materials.

Modified multiscale Renyi distribution entropy for short-term heart rate variability analysis.

Multiscale sample entropy (MSE) is a prevalent complexity metric to characterize a time series and has been extensively applied to the physiological signal analysis. However, for a short-term time series, the likelihood of identifying comparable subsequences decreases, leading to higher variability in the Sample Entropy (SampEn) calculation. Additionally, as the scale factor increases in the MSE calculation, the coarse-graining process further shortens the time series. Consequently, each newly generated time series at a larger scale consists of fewer data points, potentially resulting in unreliable or undefined entropy values, particularly at higher scales. To overcome the shortcoming, a modified multiscale Renyi distribution entropy (MMRDis) was proposed in our present work. The MMRDis method uses a moving-averaging procedure to acquire a family of time series, each of which quantify the dynamic behaviors of the short-term time series over the multiple temporal scales. Then, MMRDis is constructed for the original and the coarse-grained time series. The MMRDis method demonstrated superior computational stability on simulated Gaussian white and 1/f noise time series, effectively avoiding undefined measurements in short-term time series. Analysis of short-term heart rate variability (HRV) signals from healthy elderly individuals, healthy young people, and subjects with congestive heart failure and atrial fibrillation revealed that MMRDis complexity measurement values decreased with aging and disease. Additionally, MMRDis exhibited better distinction capability for short-term HRV physiological/pathological signals compared to several recently proposed complexity metrics. MMRDis was a promising measurement for screening cardiovascular condition within a short time.

Entropy Considerations in Stochastic Electrodynamics

The use of entropy concepts in the field of stochastic electrodynamics is briefly reviewed here. Entropy calculations that have been fully carried out to date are discussed in two main cases: first, where electric dipole oscillators interact with zero-point, or zero-point plus Planckian, or Rayleigh–Jeans radiation; and second, where only these radiation fields exist within a cavity. The emphasis here is on the first, more complicated, case, where both charged particles and radiation fields are present and interacting. Unlike the usual exposition on entropy in classical statistical mechanics, involving probabilistic notions of phase-space occupation, the calculations to date for both particles and fields, or for fields alone, follow the caloric entropy method, where the notions of heat flow, adiabatic surfaces, and isothermal conditions are utilized. Probability notions certainly still enter into the calculations, as the fields and charged particles interact stochastically together, following Maxwellian electrodynamics. Examples of phase-space calculations for harmonic oscillators and classical hydrogen atoms are carried out, emphasizing how much farther caloric entropy calculations have successfully gone.

Entanglement in ( 1+1 )-dimensional free scalar field theory: Tiptoeing between continuum and discrete formulations

We review some classic works on ground state entanglement entropy in (1+1)-dimensional free scalar field theory. We point out identifications between the methods for the calculation of entanglement entropy and we show how the formalism developed for the discretized theory can be utilized in order to obtain results in the continuous theory. We specify the entanglement spectrum and we calculate the entanglement entropy for the theory defined on an interval of finite length L. Finally, we derive the modular Hamiltonian directly, without using the modular flow, via the continuum limit of the expressions obtained in the discretized theory. In a specific coordinate system, the modular Hamiltonian assumes the form of a free field Hamiltonian on the Rindler wedge. Published by the American Physical Society 2024

A dissipative structure theory based emotion updating method applied to multi-stage emergency decision making

Emotions of decision makers (DMs) play an important role in decision-making and have been widely concerned by scholars, but few of them have focused on the dynamic changes in emotions caused by emergency risk situations and their impacts on emergency decision making (EDM). Therefore, a dissipative structure theory (DST) based emotion updating method of DMs is proposed and applied to multi-stage emergency decision making (MEDM). First, an analysis of the openness, nonlinear, and dynamic evolution of emergency risk systems demonstrates their suitability for application within the DST framework, based on which the research problem and its solution procedure are then described. Second, a DST-based model is developed for judging the emergency risk situation at different time, utilizing calculation of fuzzy entropy of decision information and probabilities of scenarios being controlled by alternatives. Then a probability distribution for emotion updating is defined, with accompany propositions demonstrating its capacity to accurately capture the characteristics of emotion evolving with the risk situation. Finally, the proposed emotion updating method for DMs is applied to MEDM, with a case study and the comparison with other approaches carried out to demonstrate the efficacy of the proposed method.

Application of Kolmogorov-Sinai's Metric Entropy for the Analysis of Mechanical Properties in the Bending Test of Epoxy-Rubber-Glass Composites.

The article presents an analysis of the results obtained during the three-point bending test for seven variants of epoxy rubber-glass composites manufactured according to innovative technology. Different contents of rubber recyclate (3, 5, and 7%) and different methods of distribution of the recyclate in the composite structure (1, 2, and 3 layers with a constant share of 5% of the recyclate) were used in the tested materials. To determine the stress values at which critical failures of the tested materials are initiated in the bending test, an analysis was carried out using the Kolmogorov-Sinai (EK-S) metric entropy calculations. The analysis results showed that for each of the above-mentioned variants of the tested epoxy-glass composites, the onset of critical changes occurring in the material structure occurs below the recorded values of the flexural strength Rmg. The decrease in the RmgK-S value in relation to Rmg is different for different material variants and depends mainly on the % content of rubber recyclate and the amount and method of decomposition of rubber recyclate in the layers of the analyzed materials. The research showed that the introduction of rubber recyclate into the composition of composites has a positive effect on their strength properties. This process allows for the efficient use of hard to degrade waste and opens up the possibility of using the newly developed materials in many industrial sectors.

Kerr/CFT correspondence for the extremal accelerating Kerr–Taub–NUT black hole

In this paper, we explore the application of Kerr/CFT correspondence to the accelerating Kerr–Taub–NUT spacetime. We demonstrate that the correspondence can be maintained if the near-horizon angular coordinate is appropriately rescaled. This rescaling allows the Cardy formula to accurately reproduce the extremal Bekenstein–Hawking entropy, a departure from the typical direct recovery of extremal Bekenstein–Hawking temperature in Kerr/CFT literature. This discrepancy is attributed to the presence of multiple sources of conic singularity in the spacetime, specifically the acceleration and NUT parameters. To support the Kerr/CFT holography, we investigate the hidden conformal symmetry in the near-horizon region of the extremal black hole geometry. Our findings confirm that the angular coordinate rescaling does not introduce inconsistencies within the Kerr/CFT framework, as evidenced by the preserved hidden conformal symmetry and consistent entropy matching calculations in the near-horizon geometry of the extremal accelerating Kerr–Taub–NUT black hole.

Imprints of Barrow–Tsallis cosmology in primordial gravitational waves

Both the Barrow and Tsallis δ entropies are one-parameter generalizations of the black-hole entropy, with the same microcanonical functional form. The ensuing deformation is quantified by a dimensionless parameter Δ, which in the case of Barrow entropy represents the anomalous dimension caused by quantum fluctuations over the horizon surface, while in Tsallis’ case, it describes the deviation of the holographic scaling from extensivity. Here, we utilize the gravity-thermodynamics conjecture with the Barrow–Tsallis entropy to investigate the implications of the related modified Friedmann equations on the spectrum of primordial gravitational waves. We show that, with the experimental sensitivity of the next generation of gravitational wave detectors, such as the Big Bang Observer, it will be possible to discriminate deviations from the ΛCDM model up to Δ≲O(10-3). In this limit, Barrow–Tsallis entropy reduces to a logarithmic correction to holographic scaling, which is nearly universally predicted by both entanglement entropy calculations in the UV regime and by several candidate theories of quantum gravity. Hence, our considerations and results are expected to have general validity in the quantum gravity framework.

The Relationship Between Misinformation And Social Noise And Their Impact On The Information Ecosystem

Social noise and social entropy are new concepts modeled after Shannon’s information and communication theory, in which the interference of noise between the sender and receiver is measured using entropy. Social noise in the context of social media plays a vital role in magnifying and spreading misinformation, which in turn impacts the overall information ecosystem. Ecosystems are made of interconnected and integrated parts that rely on one another to maintain balance and survive. Studies related to social noise and misinformation have shown that social noise can contribute significantly to spreading misinformation and potentially alter the original intended message (Alsaid &amp; Pampapura, 2022); Alsaid et al. (2024). paper investigates methods of quantifying social noise using entropy to minimize the spread of misinformation on social media, particularly X. Using a combination of Uncertainty Reduction Theory (URT) and Social Entropy, data analysis was performed using one million tweets harvested from #Ukraine. Data analysis involved several methods: sentiment analyses, term association, network maps, and entropy computation. Results have shown a direct relationship between social noise and social entropy as a measure of uncertainty. Also, social noise and uncertainty decrease with the use of URLs and rich content. It is evident from the results that the entropy value is influenced by the accuracy of keywords identified using topic modeling as descriptive of social noise constructs. Semantic analysis of tweets can help improve the definition of social noise constructs, leading to enhanced and more accurate entropy calculation. Future studies may consider advanced machine learning and AI

Cross-cultural adaptation of the Czech version of the core outcome measures index for low back and neck pain.

The Core Outcome Measures Index (COMI) is a short, multidimensional instrument translated into several languages that covers five domains recommended in the assessment of outcome in patients with low-back and neck pain. The purpose of this study was to cross-culturally adapt the COMI from English to Czech language and to test the face and construct validity and reproducibility of its results in patients with low-back and neck pain. Participants (n = 125) were included from primary and secondary care. The participants reported moderate pain and disability levels. All participants filled in the COMI forms before and after surgery. Descriptive statistics, Wilcoxon paired test, Crombach's alpha, principal component analysis and information entropy calculation were used. The instrument was successfully forward and back-translated. It can be seen that the questionnaire applied as part of our intervention study produces answers with a sufficient degree of variability and with a satisfactory degree of representation of extreme values. It can be also seen that the questionnaire can diagnose an objectively occurring change associated with the surgeon within the intervention procedure. Our other findings support the idea of ​​a possible reduction in the number of questions that measure the same latent variable. Our investigations also showed that it is possible to reduce the range of the point scale of the perception of pain to 5 degrees of intensity and thus unify the range with the other questions. The Czech COMI shows acceptable properties and is thus suitable to use as a short instrument for measuring important domains in patients with low-back and neck pain.