Lower Entropy Values Research Articles

Investigation of Two‐Dimensional Ruddlesden–Popper BZA2PbX4 (X = I, Br, and Cl) and Mixed‐Halides BZA2PbBrxCl4−x Perovskites: Opto‐Electromechanical, Thermodynamic Properties, Moisture, and Strain Effects

Herein, the properties of BZA2PbX4 (X = I, Br, and Cl) and mixed halides BZA2PbBrxCl4‐x (x = 1, 2, and 3) perovskites are comprehensively investigated, utilizing density functional theory (DFT). As there has been no prior comprehensive study on the substantial characteristics of mixed halides and the distinctive features of BZA2PbI4, BZA2PbBr4, and BZA2PbCl4 using DFT, a significant gap in the existing literature is addressed. The optical and electronic properties, band structures, band edge orbitals, and effective masses with and without spin‐orbit coupling effects are analyzed. The investigation also examines the impact of strain (tensile and compressive) on the properties of these materials. It is revealed that among the investigated compounds, BZA2PbCl4 emerges as the most thermodynamic and moisture stable. This trend is corroborated by the energy fluctuation, with BZA2PbCl4 demonstrating a narrower range, indicative of superior thermal stability. Furthermore, Voigt–Ruess–Hill approximations indicate that the mechanical moduli increase as the halide changes from I to Cl. Moreover, BZA2PbI4 exhibits the highest level of disorder, whereas BZA2PbCl4 has the lowest entropy value at the same temperature. The results highlight that these materials emerge as promising candidates for applications in the active layer of perovskite light‐emitting diodes and optoelectronic devices.

Behavioral and cortical dynamics underlying superior accuracy in short-distance passes

Improved pass accuracy is a prominent determinant of success in football. It demands an effective interaction of complex behavioral and cortical dynamics. Exploring differences in the ability to sustain an accurate pass behavior in a stable setting and the associated cortical dynamics at different expertise levels may provide an insight into skilled strategies contributing to superior accuracy in football. The aim of this study is to compare trial-to-trial variability of pass biomechanics and the corresponding cortical dynamics during short-distance passes between novices and experienced football players. Thirty participants (15 novices, 15 football players) performed 90 short-distance passes. The intertrial variability of pass biomechanics (foot acceleration, range of hip flexion, knee flexion and foot rotation) was assessed by means of multiscale entropy. The task-related cortical dynamics were analyzed via source-derived event-related spectral perturbations. Experienced players demonstrated higher accuracy and overall lower entropy values across multiple time scales which was significant for hip flexion. The electroencephalography data revealed group differences in parieto-occipital alpha desynchronization and frontal theta synchronization in successive phases of passes. The current findings suggest that experienced football players may show a skilled ability to recruit and retain pass biomechanics promoting higher accuracy, whereas novices may show an explorative behavior with higher spatial variability. This difference may be associated with distinctive visuospatial and attentional strategies acquired with expertise in football. Our study provides an insight into expertise-specific behavioral and cortical dynamics of superior accuracy in football and a basis for its prospective investigation in enriched contexts.

Generalized weighted permutation entropy analysis of SPEI index in Serbia as a proxy of corn production

AbstractIn this work we analyse yearly standardized precipitation evapotranspiration index for accumulation timescale of 6 months (SPEI‐6) for August which has been identified as a strong proxy for corn production in Serbia. By applying recently proposed method generalized weighted permutation entropy (GWPE), which provides the information about predictability of large/small fluctuations in nonstationary time series, on SPEI index calculated from high‐resolution gridded dataset for the period 1951–2022, we found that the small fluctuation sequences of consecutive 4‐year SPEI values are the most predictable (indicated by lowest entropy values). The order of large fluctuation sequences is less predictable, while the order of sequences considering all magnitudes of fluctuations are the least predictable (indicated by highest entropy values). We also analysed 4‐month SPEI‐6 sequences for May, June, July and August (during the growing season of corn) along the years under study and found that they display lower GWPE values and thus are more predictable than yearly SPEI‐6 August sequences (small fluctuations displaying lower entropy values than large fluctuations). Regarding spatial distributions, in both cases SPEI‐6 sequences show similar pattern for large fluctuations: higher predictability (lower entropy values) in northern and eastern part of Serbia and lower predictability (higher entropy values) in southern and western part. For small fluctuations spatial distribution of GWPE values indicates that yearly SPEI‐6 August series are more predictable in the western part of the country while May to August monthly SPEI‐6 sequences are more predictable in the eastern part.

Analysis of fetal heart rate fluctuations in women diagnosed with preeclampsia during the latent phase of labor

IntroductionFetal heart rate variability (fHRV) is a tool used to investigate the functioning of the fetal autonomic nervous system. Despite the significance of preeclampsia, fHRV during the latent phase of labor has not been extensively studied. This study aimed to evaluate fetal cardiac autonomic activity by using linear and nonlinear indices of fHRV analysis in women diagnosed with preeclampsia without hypertensive treatment during gestation, compared to normotensive women during the latent phase of labor.MethodsA cross-sectional and exploratory study was conducted among pregnant women in the latent phase of labor, forming three study groups: normotensive or control (C, 38.8 ± 1.3 weeks of pregnancy, n = 22), preeclampsia with moderate features (P, 37.6 ± 1.4 weeks of pregnancy n = 10), and preeclampsia with severe features (SP, 36.9 ± 1.2 weeks of pregnancy, n = 12). None of the participants received anti-hypertensive treatment during their pregnancy. Linear and nonlinear features of beat-to-beat fHRV, including temporal, frequency, symbolic dynamics, and entropy measures, were analyzed to compare normotensive and preeclamptic groups.ResultsSignificantly lower values of multiscale entropy (MSE) and short-term complexity index (Ci) were observed in the preeclamptic groups compared to the C group (p < 0.05). Additionally, higher values of SDNN (standard deviation of R-R intervals) and higher values of low-frequency power (LF) were found in the P group compared to the C group.ConclusionOur findings indicate that changes in the complexity of fetal heart rate fluctuations may indicate possible disruptions in the autonomic nervous system of fetuses in groups affected by undiagnosed preeclampsia during pregnancy. Reduced complexity and shifts in fetal autonomic cardiac activity could be associated with preeclampsia’s pathophysiological mechanisms during the latent phase of labor.

Cleaner reactive dyeing with the recycled dyeing wastewater

In order to recycle water and inorganic salts, sodium persulphate (SPS) was used to degrade the C.I. Reactive Red 195 (RR195) dyeing wastewater. The thermodynamic and kinetic relationships of RR195 dyeing in cleaner and conventional dyeing processes were analyzed and compared. The feasibility of the one-bath cyclic dyeing in the recycled dyeing wastewater was confirmed through the properties of dye utilization, color parameters and energy conservation. The appropriate conditions were 0.3 g/L of SPS and treatment at 95 °C for 30 min, which resulted in a decolorization rate of 99.7% for the dyeing wastewater, mainly due to the action of radical sulfate. When cotton fabric was dyed with the cleaner dyeing, the kinetic was pseudo-second-order and the thermodynamic was Freundlich adsorption. There was a 47.5% reduction in half dyeing time, a greater diffusion coefficient and standard affinity, and lower entropy and enthalpy values when dyeing in reused wastewater. Cotton fabrics were dyed with the proposed cleaner dyeing process, which reduced the amount of water and inorganic salt by 95.8% and 89.8%, respectively. The total organic carbon (TOC), biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) were 53.6%, 69.8% and 42.6% lower than the conventional dyeing process, respectively. The recommended technology not only reduces the amount of dyeing wastewater but also enables the recycling of inorganic salts and water, which makes great progress in the textile industry.

Unveiling realistic mobility patterns with home–origin–destination data aggregation

The availability of increasingly abundant mobility data in recent years has opened up new avenues for researchers to unravel human mobility patterns. Data aggregation methods have been introduced to gain a quantitative understanding of collective individual movements using these data. Nevertheless, the widely adopted origin–destination (OD) aggregation method for human mobility data lacks an essential piece of information: home location, which plays a vital role in characterizing individual movement patterns. In this study, we propose a novel data aggregation approach called home–origin–destination (HOD) with the aim of improving the accuracy of human mobility estimation. We compare the performance of various data aggregation methods for estimating population distribution. Our experimental results reveal more realistic mobility patterns when incorporating estimated home information, where individuals move out in the morning and return home before midnight. To further evaluate the effectiveness of the HOD approach, we conduct an entropy analysis to measure the unpredictability of human mobility. The HOD results exhibit lower entropy values than those in the other two cases, OD and home–destination (HD). These findings underscore the importance of incorporating home information in understanding and modeling human mobility. By leveraging the HOD data aggregation method, we can achieve more accurate population distribution estimates and capture the inherent dynamics of human movement

Monitoring autonomic responses in Parkinson’s disease individuals: non-linear and chaotic global metrics of heart rate variability

Aim To examine and compare the autonomic responses, as assessed through the non-linear and chaotic global metrics of heart rate variability in two groups: the Parkinson’s Disease Group (PDG) and the Control Group (CG), both at rest and during an active tilt test. Methods The study encompassed 46 participants (PDG: n = 23; 73.73 ± 7.28 years old; CG: n = 23; 70.17 ± 8.20 years old). Initial data collection involved the acquisition of participant’s characteristics. The autonomic modulation was estimated both at rest and during the active tilt test. For this assessment, we computed non-linear indices derived from five entropies (Approximate, Sample, Shannon, Renyi, Tsallis), Detrended Fluctuation Analysis and the seven chaotic global metrics (hsCFP1-hsCFP7). Results At rest, the PDG exhibited lower values of hsCFP3 (0.818 ± 0.116 vs. 0.904 ± 0.065; p < 0.05) and Sample Entropy (0.720 ± 0.149 vs. 0.799 ± 0.171; p < 0.05). During the test, the PDG demonstrated lower values of ApEn, while the CG presented lower values of SampEn, hsCFP1, hsCFP3, hsCFP7, and higher values of hsCFP5. An interaction was observed, indicating that hsCFP1 and hsCFP3 exhibit differential behavior for the CG and PDG in response to the test. Conclusion subjects with PD exhibited reduced complexity of the RR interval series at rest, and a diminished autonomic response to the active tilt test when compared with the CG. The test, together with non-linear indices, may serve for assessing the Autonomic Nervous System in individuals with PD in a clinical setting. The interpretation of these data should be approached with caution, given the possible influences of pharmacotherapies and the inclusion of diabetic participants.

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran).

Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and - 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to - 344, - 332.8, and - 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.

Generalized weighted permutation entropy analysis of satellite hot-pixel time series in Brazilian biomes

In this work we analyze hot-pixel satellite data, proxy for natural and anthropogenic vegetation and forest fires, using recently proposed method Generalized weighted permutation entropy (GWPE). The current analysis of data from 2002 to 2022 for six Brazilian biomes sheds new light on differences among the biomes in terms of ordinal pattern entropy, for both small and large fluctuations. It is found that for all biomes large fluctuations in fire temporal series show higher complexity level than small fluctuations, small fluctuations being more predictable as indicated by lower entropy values. Moreover, for fire sensitive biomes Amazon and Atlantic Forest the three dimensional complexity entropy curves in the complexity-entropy-scale causality box (CESCB) are rather similar (indicating similar dynamics for both small and large fluctuations), as well as for fire dependent biomes Pantanal and Pampa, while fire independent Caatinga biome shows different, distinct behavior. Finally, the similarity in lower parts of the CESCB curves (corresponding to small fluctuations) between Amazon and Cerrado reflects the changes of fire dynamics due to human activities.

Variance of entropy for testing time-varying regimes with an application to meme stocks

Shannon entropy is the most common metric for assessing the degree of randomness of time series in many fields, ranging from physics and finance to medicine and biology. Real-world systems are typically non-stationary, leading to entropy values fluctuating over time. This paper proposes a hypothesis testing procedure to test the null hypothesis of constant Shannon entropy in time series data. The alternative hypothesis is a significant variation in entropy between successive periods. To this end, we derive an unbiased sample entropy variance, accurate up to the order O(n-4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^{-4})$$\\end{document} with n the sample size. To characterize the variance of the sample entropy, we first provide explicit formulas for the central moments of both binomial and multinomial distributions describing the distribution of the sample entropy. Second, we identify the optimal rolling window length to estimate time-varying Shannon entropy. We optimize this choice using a novel self-consistent criterion based on counting significant entropy variations over time. We corroborate our findings using the novel methodology to assess time-varying regimes of entropy for stock price dynamics by presenting a comparative analysis between meme and IT stocks in 2020 and 2021. We show that low entropy values correspond to periods when profitable trading strategies can be devised starting from the symbolic dynamics used for entropy computation, namely periods of market inefficiency.

Kinetics and mechanistic investigation of the substitution from dinuclear Pt(II) complexes bridged by N,N’-bis(2-pyridylcarboxamide)phenylenediamines

The rates of substitution of the chloride ligand in dinuclear complexes, [Pt2(N,N'-bis(2-pyridylcarboxamide)-1,3-phenylenediamine)Cl4] (1), [Pt2(N,N'-bis(2-pyridylcarboxamide)-1,4-phenylenediamine)Cl4] (2), [Pt2(N,N'-bis(3-isoquinolylcarboxamide)-1,3-phenylenediamine)Cl4] (3) and [Pt2(N,N'-bis(3-isoquinolylcarboxamide)-1,4-phenylenediamine)Cl4] (4), by three bio-relevant nucleophiles, thiourea (TU), N,N-dimethylthiourea (DMTU) and N,N,N’,N’-tetramethylthiourea (TMTU), were investigated. The mononuclear analogue [Pt(N-phenylpyridine-2-carboxamide)Cl2] (5) was included to compare the results. The kinetics of the reactions were studied under pseudo first-order conditions in a methanol solution (I = 0.1 M LiCl) as a function of concentration and temperature using the stopped-flow spectrophotometer. The observed pseudo first-order rate constants for the substitution reactions of all the complexes were calculated from two well-separated steps and obey the rate law k obs (1 st /2 nd ) = k 2 (1 st /2 nd )[Nu]. The reactivity of the complexes decreases in the order 2 > 1 > 4 > 3 > 5. The conformational symmetry of the complexes as controlled by the phenylenediamine bridges, steric hindrance due to the bridges as well as the σ-donor capacity of the coordinated groups around the metal centers influence the reactivity of the dinuclear complexes. The low enthalpy (ΔH #) and negative intrinsic entropy (ΔS #) values support an associative mechanism of substitution. The kinetic data are supported by DFT calculations.

Bradycardia May Decrease Cardiorespiratory Coupling in Preterm Infants.

Bradycardia, frequently observed in preterm infants, presents significant risks due to the immaturity of their autonomic nervous system (ANS) and respiratory systems. These infants may face cardiorespiratory events, leading to severe complications like hypoxemia and neurodevelopmental disorders. Although neonatal care has advanced, the influence of bradycardia on cardiorespiratory coupling (CRC) remains elusive. This exploratory study delves into CRC in preterm infants, emphasizing disparities between events with and without bradycardia. Using the Preterm Infant Cardio-Respiratory Signals (PICS) database, we analyzed interbeat (R-R) and inter-breath intervals (IBI) from 10 preterm infants. The time series were segmented into bradycardic (B) and non-bradycardic (NB) segments. Employing information theory measures, we quantified the irregularity of cardiac and respiratory time series. Notably, B segments had significantly lower entropy values for R-R and IBI than NB segments, while mutual information was higher in NB segments. This could imply a reduction in the complexity of respiratory and cardiac dynamics during bradycardic events, potentially indicating weaker CRC. Building on these insights, this research highlights the distinctive physiological characteristics of preterm infants and underscores the potential of emerging non-invasive diagnostic tools.

Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery

The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows.

Research on point cloud simplification algorithm for ring forgings based on joint entropy evaluation

There are numerous redundant points in the point cloud model of ring forgings obtained by 3D laser scanner. How to remove the redundant points while keeping the model characteristics unchanged is a critical issue. This paper proposes a point cloud simplification algorithm based on the joint entropy evaluation theory. Firstly, the K-D tree is used to search for the K-neighbors of the sampled points. Secondly, a surface is fitted to the spatial neighborhood of the sampled points using the least squares method. The curvature operator of the sampled points is derived on the fitted surface using Riemannian geometry theory. After that, an energy operator is defined by using the normal vectors and distances of the sampled points and their neighborhood points. The joint entropy values of all points in the model are determined based on the probability distributions of these two operators in the local neighborhood. Finally, the data points are sorted by entropy value. Data points with high entropy values are put into the data set U1. Data points with low entropy values are clustered through the K-Means algorithm of swarm optimization. The redundant points outside the cluster centers are removed, and the cluster centers are put into the data set U2. The final simplification results are obtained by integrating data sets U1 and U2. The experimental results show that the point cloud simplification algorithm proposed in this paper is effective and feasible.

X-Ray Cavity Dynamics and Their Role in the Gas Precipitation in Planck Sunyaev–Zeldovich (SZ) Selected Clusters

We study active galactic nucleus (AGN) feedback in nearby (z < 0.35) galaxy clusters from the Planck Sunyaev–Zeldovich sample using Chandra observations. This nearly unbiased mass-selected sample includes both relaxed and disturbed clusters and may reflect the entire AGN feedback cycle. We find that relaxed clusters better follow the one-to-one relation of cavity power versus cooling luminosity, while disturbed clusters display higher cavity power for a given cooling luminosity, likely reflecting a difference in cooling and feedback efficiency. Disturbed clusters are also found to contain asymmetric cavities when compared to relaxed clusters, hinting toward the influence of the intracluster medium (ICM) “weather” on the distribution and morphology of the cavities. Disturbed clusters do not have fewer cavities than relaxed clusters, suggesting that cavities are difficult to disrupt. Thus, multiple cavities are a natural outcome of recurrent AGN outbursts. As in previous studies, we confirm that clusters with short central cooling times, t cool, and low central entropy values, K 0, contain warm ionized (10,000 K) or cold molecular (<100 K) gas, consistent with ICM cooling and a precipitation/chaotic cold accretion scenario. We analyzed archival Multi-Unit Spectroscopic Explorer observations that are available for 18 clusters. In 11/18 of the cases, the projected optical line emission filaments appear to be located beneath or around the cavity rims, indicating that AGN feedback plays an important role in forming the warm filaments by likely enhancing turbulence or uplift. In the remaining cases (7/18), the clusters either lack cavities or their association of filaments with cavities is vague, suggesting alternative turbulence-driven mechanisms (sloshing/mergers) or physical time delays are involved.

Deep insights into kinetics, optimization and thermodynamic estimates of methylene blue adsorption from aqueous solution onto coffee husk (Coffee arabica) activated carbon

In the current study, an attempt was made to synthesize coffee husk (CH) activated carbon by chemical modification approach (sulphuric acid-activated CH (SACH) activated carbon) and was used as a valuable and economical sorbent for plausible remediation of Methylene blue (MB) dye. Batch mode trials were carried out by carefully varying the batch experimental variables: SACH activated carbon (SACH AC) dosage, pH, initial dye concentration, temperature, and contact time. The optimum equilibrium time for adsorption by SACH activated carbon was obtained as 60 min, and the maximum adsorption took place at 30 °C. Morphological and elemental composition, crystallinity behaviour, functional groups, and thermal stability were examined using SEM with EDX, XRD, FTIR, BET, TGA, and DTA and these tests showed successful production of activated carbon. The outcomes showed that chemical activation enhanced the number of pores and roughness which possibly maximized the adsorptive potential of coffee husk. The Box-Benken design (BBD) was used to optimize the MB dye adsorption studies and 99.48% MB dye removed at SACH AC dosage of 4.83 g/L at 30 °C for 60 min and pH 8.12, and the maximum adsorption was yielded for sulphuric acid-activated coffee husk carbon carbon with 88.1 mg/g maximum MB adsorption capacity. Langmuir- Freundlich model deliberately provided a better fit to the equilibrium data. The SACH AC-MB dye system kinetics showed a high goodness-of-fit with pseudo second order model, compared to other studied models. Change in Gibbs's free energy (ΔGo) of the system indicated spontaneity whereas low entropy value (ΔSo) suggested that the removal of MB dye on the SACH activated carbon was an enthalpy-driven process. The exothermic nature of the sorption cycle was affirmed by the negative enthalpy value (ΔHo). The adsorptive-desorptive studies reveal that SACH AC could be restored with the maximum adsorption efficiency being conserved after the fifth cycles. Overall, the outcomes revealed that sulphuric acid-activated coffee husk activated carbon (SACH AC) can be used as prompt alternative for low-cost sorbent for treating dye-laden synthetic wastewaters.

An Empirical Study on Vagueness Detection in Privacy Policy Texts

Website privacy policies outline how a website handles personal information. Many such policies are written vaguely, making it difficult for users to comprehend what data is collected and how it’s used, posing a threat to privacy and security. The empirical study in this paper aims to address this issue by applying transformer models to automatically detect the level of vagueness in privacy policy texts. We calculate the vagueness scores based on annotations obtained through arithmetic mean, geometric mean, and harmonic mean of the individual annotations and discretize the policy text into four distinct vagueness categories. Our detailed numerical study shows that transformer models perform well for the vagueness classification tasks and they can be effective in enhancing the clarity and transparency of privacy policies. Our experiments also highlight the importance of reliable annotations and show that the transformer model performance can be enhanced with more reliable annotations with lower standard deviation and entropy values.

0130 Reduced Entropy in EEG During Rapid Eye Movement Sleep in Men with Depression

Abstract Introduction Depression is an increasingly prevalent mental health condition. Patients with depression have disrupted rapid eye movement (REM) sleep as characterized by shortened REM latency, increased REM sleep duration, and increased REM density. However, it is unknown whether the dynamic patterns of EEG within REM episodes are altered in these patients. Here we investigated the complexity of EEG during REM sleep using the analysis of irregularity on multiple temporal time scales and compared the difference in such a nonlinear property of EEG between patients with depression and controls. Methods We analyzed the overnight EEG recordings (256 Hz) collected from the left central channel in 19 older men with depression and 19 age-matched otherwise healthy control men in the Osteoporotic Fractures in Men Study (MrOS) obtained from the National Sleep Research Resource (NSRR). To quantify the irregularity/complexity in EEG fluctuations during REM, a Multiscale Entropy (MSE) analysis was performed on each 30-second epoch to calculate the entropy at different time scales between ~0.004 and ~0.078 seconds (i.e., 1/256, 2/256, …, 20/256 seconds). Results As compared to control men, men with depression had significantly lower entropy values at multiple time scales between 0.023-0.031 seconds (time scale =6/256 second: 0.363□0.008 [SE] for depression, 0.408□0.009 for controls; time scale =7/256 second: 0.295□0.009 for depression, 0.342□0.008 for controls; time scale =8/256 second: 0.225□0.009 for depression, 0.269□0.008 for controls; all FDR-adjusted P-values&amp;lt; 0.001), indicating reduced temporal irregularity/complexity in EEG fluctuations. The differences were not statistically significant at smaller or larger time scales. Conclusion EEG complexity/irregularity within REM sleep were altered in patients with depression. Our results indicate the capability of MSE analysis in capturing important complexity features of EEG fluctuations, which might be missed using those entropy algorithms focused on EEG fluctuations at a single time scale. Support (if any) RF1AG059867, RF1AG064312. The National Heart, Lung, and Blood Institute provided funding for the ancillary MrOS Sleep Study, "Outcomes of Sleep Disorders in Older Men," under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. The NSRR was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002).

Modulation of center-of-pressure signal in children on the autism spectrum: A case-control study

BackgroundProper postural and motor control plays a fundamental role in the child’s ontogenetic development. So far, the postural control in children on the autism spectrum has mainly been assessed with standard posturographic measurements of center of pressure (COP) displacements. Research questionWhat are the differences in postural control between autistic and typically developing children? MethodsThe study group comprised 16 autistic children aged 6–10 years, identified by a psychiatrist. The control group consisted of 16 typically developing children aged 6–10 years with no posture deformities, no pervasive developmental disorder and no history of postural control or movement deficits.The data were collected during quiet standing with eyes open using a force plate. To gain a better insight into the postural control processes, the rambling-trembling and sample entropy analyses were used in COP data processing. ResultsCompared to typically developing children, those with autism spectrum had significantly higher values of COP and rambling trajectory parameters in the antero-posterior direction during quiet standing. The variables of the trembling trajectory did not differ significantly between the groups. The autistic children had significantly lower values of sample entropy in the antero-posterior direction compared to typically developing children. SignificanceMore advanced measures of COP displacements including the rambling-trembling method and sample entropy revealed differences in postural control between autistic and typically developing children. These methods may therefore contribute to functional assessment of postural control deficits in children on the autism spectrum.

Selecting features by utilizing intuitionistic fuzzy Entropy method

Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods.