Higher Entropy Research Articles

Spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive breast cancer

Deregulation of lipid composition in adipose tissue adjacent to breast tumour is observed in ex vivo and animal models. Novel non-invasive magnetic resonance imaging (MRI) allows rapid lipid mapping of the human whole breast. We set out to elucidate the spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive (ER +) breast cancer. Thirteen participants (mean age, 62 ± [SD] 6 years) with ER + breast cancer and 13 age-matched postmenopausal healthy controls were scanned on MRI. The number of double bonds in triglycerides was computed from MRI images to derive lipid composition maps of monounsaturated, polyunsaturated, and saturated fatty acids (MUFA, PUFA, SFA). The spatial heterogeneity measures (mean, median, skewness, entropy and kurtosis) of lipid composition in the peri-tumoural region and the whole breast of participants and in the whole breast of controls were computed. The Ki-67 proliferative activity marker and CD163 antibody on tumour-associated macrophages were assessed histologically. Mann Whitney U or Wilcoxon tests and Spearman’s coefficients were used to assess group differences and correlations, respectively. For comparison against the whole breast in participants, peri-tumoural MUFA had a lower mean (median (IQR), 0.40 (0.02), p < .001), lower median (0.42 (0.02), p < .001), a negative skewness with lower magnitude (− 1.65 (0.77), p = .001), higher entropy (4.35 (0.64), p = .007) and lower kurtosis (5.13 (3.99), p = .001). Peri-tumoural PUFA had a lower mean (p < .001), lower median (p < .001), a positive skewness with higher magnitude (p = .005) and lower entropy (p = .002). Peri-tumoural SFA had a higher mean (p < .001), higher median (p < .001), a positive skewness with lower magnitude (p < .001) and lower entropy (p = .012). For comparison against the whole breast in controls, peri-tumoural MUFA had a negative skewness with lower magnitude (p = .01) and lower kurtosis (p = .009), however there was no difference in PUFA or SFA. CD163 moderately correlated with peri-tumoural MUFA skewness (rs = − .64), PUFA entropy (rs = .63) and SFA skewness (rs = .59). There was a lower MUFA and PUFA while a higher SFA, and a higher heterogeneity of MUFA while a lower heterogeneity of PUFA and SFA, in the peri-tumoural region in comparison with the whole breast tissue. The degree of lipid deregulation was associated with inflammation as indicated by CD163 antibody on macrophages, serving as potential marker for early diagnosis and response to therapy.

Task-induced changes in brain entropy.

Entropy indicates irregularity of a dynamic system, with higher entropy indicating higher irregularity and more transit states. In the human brain, regional brain entropy (BEN) has been increasingly assessed using resting state fMRI (rs-fMRI), while changes of regional BEN during task-based fMRI have been scarcely studied. The purpose of this study is to characterize task-induced regional BEN alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only (task BEN) and then compared to BEN of rs-fMRI (resting BEN). Moreover, BEN was separately calculated from the control blocks of the task-fMRI runs (control BEN) and compared to task BEN. Finally, control BEN was compared to resting BEN to test for residual task effects in the control condition. With respect to resting state, task performance unanimously induced BEN reduction in the peripheral cortical area and BEN increase in the centric part of the sensorimotor and perception networks. Control compared to resting BEN showed similar entropy alterations, suggesting large residual task effects. Task compared to control BEN was characterized by reduced entropy in occipital, orbitofrontal, and parietal regions.

Boosting overall saline water splitting by constructing a strain‐engineered high‐entropy electrocatalyst

AbstractHigh‐entropy materials (HEMs), which are newly manufactured compounds that contain five or more metal cations, can be a platform with desired properties, including improved electrocatalytic performance owing to the inherent complexity. Here, a strain engineering methodology is proposed to design transition‐metal‐based HEM by Li manipulation (LiTM) with tunable lattice strain, thus tailoring the electronic structure and boosting electrocatalytic performance. As confirmed by the experiments and calculation results, tensile strain in the LiTM after Li manipulation can optimize the d‐band center and increase the electrical conductivity. Accordingly, the as‐prepared LiTM‐25 demonstrates optimized oxygen evolution reaction and hydrogen evolution reaction activity in alkaline saline water, requiring ultralow overpotentials of 265 and 42 mV at 10 mA cm−2, respectively. More strikingly, LiTM‐25 retains 94.6% activity after 80 h of a durability test when assembled as an anion‐exchange membrane water electrolyzer. Finally, in order to show the general efficacy of strain engineering, we incorporate Li into electrocatalysts with higher entropies as well.

High-temperature mechanical properties and fracture mechanism of A6B2O17 (A= Hf, Zr; B= Ta, Nb) high-entropy ceramics

A6B2O17 (A= Hf, Zr; B= Ta, Nb) high-entropy ceramics are prepared by a solid reaction method, and the temperature-dependent mechanical properties are in situ determined by a high-temperature three-point bending combined with the digital image correlation method. The results show that (Hf1/2Zr1/2)6(Ta2/3Nb1/3)2O17 (H1Z1T2N1) ceramics exhibit superior high-temperature mechanical properties. As the temperature increases from 25 °C to 1200 °C, the fracture toughness and fracture strength decrease from 3.19 MPa·m1/2 to 2.37 MPa·m1/2 and from 97.38 MPa to 72.48 MPa, respectively. The H1Z1T2N1 ceramics still maintain impressive mechanical properties at high temperatures due to the moderate grain size, low porosity and intergranular fracture. Furthermore, the higher entropy (9.61 R J/mol·K) of this ceramic leads to a significant high entropy effect, and the lattice distortion effects cause high-density dislocation regions, inducing a dislocation toughening mechanism.

Computational pathology identifies immune-mediated collagen disruption to predict clinical outcomes in gynecologic malignancies

BackgroundThe role of immune cells in collagen degradation within the tumor microenvironment (TME) is unclear. Immune cells, particularly tumor-infiltrating lymphocytes (TILs), are known to alter the extracellular matrix, affecting cancer progression and patient survival. However, the quantitative evaluation of the immune modulatory impact on collagen architecture within the TME remains limited.MethodsWe introduce CollaTIL, a computational pathology method that quantitatively characterizes the immune-collagen relationship within the TME of gynecologic cancers, including high-grade serous ovarian (HGSOC), cervical squamous cell carcinoma (CSCC), and endometrial carcinomas. CollaTIL aims to investigate immune modulatory impact on collagen architecture within the TME, aiming to uncover the interplay between the immune system and tumor progression.ResultsWe observe that an increased immune infiltrate is associated with chaotic collagen architecture and higher entropy, while immune sparse TME exhibits ordered collagen and lower entropy. Importantly, CollaTIL-associated features that stratify disease risk are linked with gene signatures corresponding to TCA-Cycle in CSCC, and amino acid metabolism, and macrophages in HGSOC.ConclusionsCollaTIL uncovers a relationship between immune infiltration and collagen structure in the TME of gynecologic cancers. Integrating CollaTIL with genomic analysis offers promising opportunities for future therapeutic strategies and enhanced prognostic assessments in gynecologic oncology.

Occlusion-Aware Transformer With Second-Order Attention for Person Re-Identification.

Person re-identification (ReID) typically encounters varying degrees of occlusion in real-world scenarios. While previous methods have addressed this using handcrafted partitions or external cues, they often compromise semantic information or increase network complexity. In this paper, we propose a new method from a novel perspective, termed as OAT. Specifically, we first use a Transformer backbone with multiple class tokens for diverse pedestrian feature learning. Given that the self-attention mechanism in the Transformer solely focuses on low-level feature correlations, neglecting higher-order relations among different body parts or regions. Thus, we propose the Second-Order Attention (SOA) module to capture more comprehensive features. To address computational efficiency, we further derive approximation formulations for implementing second-order attention. Observing that the importance of semantics associated with different class tokens varies due to the uncertainty of the location and size of occlusion, we propose the Entropy Guided Fusion (EGF) module for multiple class tokens. By conducting uncertainty analysis on each class token, higher weights are assigned to those with lower information entropy, while lower weights are assigned to class tokens with higher entropy. The dynamic weight adjustment can mitigate the impact of occlusion-induced uncertainty on feature learning, thereby facilitating the acquisition of discriminative class token representations. Extensive experiments have been conducted on occluded and holistic person re-identification datasets, which demonstrate the effectiveness of our proposed method.

Потоки інформації в мережі з обмеженнями кількості потоків, дозволених на кожному вузлі

This paper explores a classic problem in transport network research: the analysis of networks with specified total traffic values for each node. We employ linear algebraic methods to derive a comprehensive set of solutions, ensuring statistical reliability and enabling robust analysis of the results. A mathematical model is presented for determining solution sets in fully connected, loop-free networks with three and four nodes. Based on this model, we developed software to calculate the statistical distribution of entropy values within the network. Furthermore, we investigate the statistical properties of information flow entropy for networks with and without constraints that permit uniform flow distribution. This characteristic holds practical significance for analyzing network dynamics and predicting flow redistribution processes from initial unbalanced states, which inherently proceed towards higher entropy. The findings presented in this paper hold additional practical implications for network imbalance detection and adaptability to diverse network topologies. The results can serve as a foundation for algorithms designed to quantify the degree of network imbalance induced by substantial external influences that do not significantly alter the overall network load. This capability proves particularly valuable in identifying covert DDoS (Distributed Denial of Service) attacks that aim to reduce network bandwidth by supplanting legitimate traffic. While the proposed method has been demonstrated on fully connected networks, it demonstrates potential for adaptation to networks with a wide range of topological structures. This includes networks with partial connectivity or loosely connected networks, which constitute a significant proportion of real-world networks. The significance of the method is further amplified by advancements in cloud computing technologies, which offer substantial computational power and enable the accumulation of extensive statistics regarding information flow distributions across networks of diverse purposes. Such advancements create opportunities for the integration of the developed network analysis technique with machine learning and artificial intelligence technologies, fostering enhanced automation, scalability, and adaptability.

Synthesis and properties of a ternary transition metal compound as positive electrode for high-performance supercapacitors

A nanostructured NixFeyCuz(CO3)(OH)2 electrode with a large surface area was deposited on a Ni-foam substrate using a facile hydrothermal method. The well-organized microscopic and free-standing nano-sized ternary metal compounds exhibited high electrical conductivity and competent ion transport ability. This composite is particularly attractive for high-performance power-storage systems owing to its binder-free nature and exceptional value as a current-carrying electrode. Ternary electrodes containing three transition metals have a higher entropy than binary electrodes, which reduces the movement distance of ions on the electrode surface and enhances key electrochemical advantages, thereby providing a high specific capacitance with durability and cycling stability when coated with a highly conductive electrochemical pattern. The NixFeyCuz(CO3)(OH)2 compound demonstrated remarkable specific surface area of 106 m2 g−1 and high specific capacities of 271.8 and 75.2 mAh g−1 at current densities of 3 and 15 A g−1. In addition, an asymmetric supercapacitor fabricated with the NixFeyCuz(CO3)(OH)2 compound as the positive electrode and graphene as the negative electrode exhibited a high energy density of 55.1 W h kg−1 at power and current densities of 398.5 W kg−1 and 2 A g−1, respectively, as well as a remarkable cycling stability of 84.3 %, which was maintained following 10,000 long cycles.

Synthesizing Historical Research Leads to a Simple, Compatible, and Extensible Big History Framework and Periodization

Many questions remain unanswered regarding how Big History events should be organized and interpreted. For example, should Big History be divided into periods or processes, when it is clearly a complex process of simultaneous interacting processes? In such an uncertain environment, how are models constructed and evaluated? When we look at Big History, at what level of abstraction do we start? This paper gleans important insights from a number of historical papers to develop a consistent view of Big History. It is important to consider many aspects of an evolving system. For example, how does the evolving system 1) learn from its experiences in the environment; 2) extract energy and resources to combat the trend towards chaos and higher entropy; 3) organize itself at multiple levels to meet new challenges? Various traditional scholarly disciplines contribute to various aspects of Big History, including astronomy, geology, evolutionary biology, evolutionary anthropology, and the history of civilizations. This suggests a base framework of cosmic and terrestrial phases. The terrestrial phase saw the sequential evolution of life, humans, and civilization. Based on this framework, the various disciplines’ timelines are consolidated along with their dynamic systems models. This base framework is then expanded to include more details. This simple approach meets many criteria for an effective framework: it integrates with knowledge from other disciplines; it provides a simple, understandable model; it can be extended in detail with nested transitions; and it immediately expresses the acceleration of evolving complex adaptive systems (CAS). Similarly, proposed frameworks offer slightly different perspectives.

Brain Function Entropy Analysis during Dual‐Tasking: Application of Functional Near Infrared Spectroscopy to Screen Alzheimer’s Disease

AbstractBackgroundDespite a high prevalence, over half of patients with dementia never receive an evaluation, indicating a need for a quick and objective routine test for screening cognitive decline in older adults. Due to neural death and disconnections, Alzheimer’s disease (AD) is associated with a low value of nonlinear complexity of brain function, which can be quantified using multiscale entropy analysis.MethodOur proposed solution is an upper extremity function dual‐task screening method, during which, brain function is measured by functional near infrared spectroscopy (fNIRS). The aim of this study was to investigate association between fNIRS entropy during upper‐extremity dual‐tasking and cognitive status in older adults. The sample population for this data includes 40 older adults with ages ranging from 65‐96 years. Participants were categorized into cognitive normal (CN), mild cognitively impaired (MCI), and AD based on MoCA test results. The fNIRS device took measurements over the frontal (right and left) and parietal (right and left) brain regions. Participants were first instructed to rest for three minutes, during which they were asked to relax and not think about any specific topic. Participants were then asked to flex and extend their right arm consistently for three minutes, while counting backwards by intervals of three.ResultWe used multivariable repeated measures analysis using mixed effects modeling to assess the effect of cognitive status on multiscale entropy, with task condition (rest and dual‐task) as within‐subject parameter and sex as covariate. We observed a significant effect of cognitive status on multiscale entropy of left frontal (p = 0.0196) and right frontal brain regions (p = 0.0448), with 0.198, 0.267, and 0.301 entropy values on average across AD, MCI, and CN, respectively. There was also a significant effect of task on multiscale entropy of right frontal (p = 0.0165), left frontal (p = 0.0209) and left parietal regions (p = 0.0011), with 39.79% higher entropy during dual‐task compared to rest trials.ConclusionThe results of this study indicate potential for the use of fNIRS multiscale entropy analysis as a screening tool for cognitive impairment. More research is necessary to better understand other potential confounding factors as well as validate this approach in a larger sample size.

Super Resolution of Satellite-Derived Sea Surface Temperature Using a Transformer-Based Model

Sea surface temperature (SST) is one of the most important factors related to the ocean and the climate. In studying the domains of eddies, fronts, and current systems, high-resolution SST data are required. However, the passive microwave radiometer achieves a higher spatial coverage but lower resolution, while the thermal infrared radiometer has a lower spatial coverage but higher resolution. In this paper, in order to improve the performance of the super-resolution SST images derived from microwave SST data, we propose a transformer-based SST reconstruction model comprising the transformer block and the residual block, rather than purely convolutional approaches. The outputs of the transformer model are then compared with those of the other three deep learning super-resolution models, and the transformer model obtains lower root-mean-squared error (RMSE), mean bias (Bias), and robust standard deviation (RSD) values than the other three models, as well as higher entropy and definition, making it the better performing model of all those compared.

HPMCAS-Based Amorphous Solid Dispersions in Clinic: A Review on Manufacturing Techniques (Hot Melt Extrusion and Spray Drying), Marketed Products and Patents.

Today, therapeutic candidates with low solubility have become increasingly common in pharmaceutical research pipelines. Several techniques such as hot melt extrusion, spray drying, supercritical fluid technology, electrospinning, KinetiSol, etc., have been devised to improve either or both the solubility and dissolution to enhance the bioavailability of these active substances belonging to BCS Class II and IV. The principle involved in all these preparation techniques is similar, where the crystal lattice of the drug is disrupted by either the application of heat or dissolving it in a solvent and the movement of the fine drug particles is arrested with the help of a polymer by either cooling or drying to remove the solvent. The dispersed drug particles in the polymer matrix have higher entropy and enthalpy and, thereby, higher free energy in comparison to the crystalline drug. Povidone, polymethaacrylate derivatives, hydroxypropyl methyl cellulose (HPMC) and hydroxypropyl methylcellulose acetate succinate derivatives are commonly used as polymers in the preparation of ASDs. Specifically, hydroxypropylmethylcellulose acetate succinate (HPMCAS)-based ASDs have become well established in commercially available products and are widely explored to improve the solubility of poorly soluble drugs. This article provides an analysis of two widely used manufacturing techniques for HPMCAS ASDs, namely, hot melt extrusion and spray drying. Additionally, details of HPMCAS-based ASD marketed products and patents have been discussed to emphasize the commercial aspect.

Efficient perturbation techniques for preserving privacy of multivariate sensitive data

Cloud data is increasing significantly recently because of the advancement of technology which can contain individuals’ sensitive information, such as medical diagnostics reports. While deriving knowledge from such sensitive data, different third party can get their hands on this sensitive information. Therefore, privacy preservation of such sensitive data has become a vital issue. Data perturbation is one of the most often used data mining approaches for safeguarding privacy. A significant challenge in data perturbation is balancing the privacy and utility of data. Securing an individual’s privacy often entails the forfeiture of the data utility, and the contrary is true. Though there exist several approaches to deal with the trade-off between privacy and utility, researchers are always looking for new approaches. In order to address this critical issue, this paper proposes two data perturbation approaches namely NOS2R and NOS2R2. The proposed perturbation techniques are experimented with over ten benchmark UCI data set for analyzing privacy protection, information entropy, attack resistance, data utility, and classification error. The proposed approaches are compared with two existing approaches 3DRT and NRoReM. The thorough experimental analysis exhibits that the best-performing approach NOS2R2 offers 15.48% higher entropy and 15.53% more resistance against ICA attack compared to the best existing approach NRoReM. Furthermore, in terms of utility, the accuracy, f1-score, precision and recall of NOS2R2 perturbed data are 42.32%, 31.22%, 30.77% and 16.15% more close to the original data respectively than the NRoReM perturbed data.

Late gadolinium enhancement entropy as a new measure of myocardial tissue heterogeneity for prediction of adverse cardiac events in patients with hypertrophic cardiomyopathy

ObjectivesEntropy is a new late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR)–derived parameter that is independent of signal intensity thresholds. Entropy can be used to measure myocardial tissue heterogeneity by comparing full pixel points of tissue images. This study investigated the incremental prognostic value of left ventricular (LV) entropy in patients with hypertrophic cardiomyopathy (HCM).MethodsThis study enrolled 337 participants with HCM who underwent 3.0-T CMR. The LV entropy was obtained by calculating the probability distribution of the LV myocardial pixel signal intensities of the LGE sequence. Patients who underwent CMR imaging were followed up for endpoints. The primary endpoint was defined as readmission to the hospital owing to heart failure. The secondary endpoint was the composite of the primary endpoint, sudden cardiac death and non-cardiovascular death.ResultsDuring the median follow-up of 24 months ± 13 (standard deviation), 43 patients who reached the primary and secondary endpoints had a higher entropy (6.20 ± 0.45, p < 0.001). The patients with increased entropy (≥ 5.587) had a higher risk of the primary and secondary endpoints, compared with HCM patients with low entropy (p < 0.001 for both). In addition, Cox analysis showed that LV entropy provided significant prognostic value for predicting both primary and secondary endpoints (HR: 1.291 and 1.273, all p < 0.001). Addition of LV entropy to the multivariable model improved model performance and risk reclassification (p < 0.05).ConclusionLV entropy assessed by CMR was an independent predictor of primary and secondary endpoints. LV entropy assessment contributes to improved risk stratification in patients with HCM.Critical relevance statementMyocardial heterogeneity reflected by entropy the derived parameter of LGE has prognostic value for adverse events in HCM. The measurement of LV entropy helped to identify patients with HCM who were at risk for heart failure and sudden cardiac death.Key points• Left ventricular entropy can reflect myocardial heterogeneity in HCM patients.• Left ventricular entropy was significantly higher in HCM patients who reached endpoint events.• Left ventricular entropy helps to predict the occurrence of heart failure and death in HCM patients.Graphical

Electro-activation to achieve entropy increase of organic cathode for potassium-ion batteries

Developing highly stable cathode materials is the key to achieving long-life K-ion batteries (KIBs). Considering that cathode with higher entropy can promote the ion adsorption process, we developed a method with heat treatment and synergistic electro-activation of 3,4,9,10-perylene-tetracarboxylic dianhydride (EA-PTCDA) to realize entropy increase in PTCDA, which achieves a stable K-ion storage. From our characterization results, the molecular stacking structure of EA-PTCDA and the C–O and C–H bending vibration at the edge position of PTCDA molecules decreased after electro-activation, indicating the achievement of entropy increase in the EA-PTCDA cathode. After treatment, our EA-PTCDA exhibits a high discharge capacity of 92 mA h g−1 after 100 cycles at 20 mA g−1 for KIBs. Even at a high current density of 200 mA g−1, our EA-PTCDA also maintains a discharge specific capacity of 66 mA h g−1 after 1000 cycles, showing shallow capacity decay. We believe that our method of achieving entropy increase in cathode materials based on electro-activation provides a reference for achieving high-performance KIBs.

Temporary disruption in language processing reflected as multiscale temporal discoordination in a recurrent network

By juxtaposing time series analyses of activity measured from a fully recurrent network undergoing disrupted processing and of activity measured from a continuous meta-cognitive report of disruption in real-time language comprehension, we present an opportunity to compare the temporal statistics of the state-space trajectories inherent to both systems. Both the recurrent network and the human language comprehension process appear to exhibit long-range temporal correlations and low entropy when processing is undisrupted and coordinated. However, when processing is disrupted and discoordinated, they both exhibit more short-range temporal correlations and higher entropy. We conclude that by measuring human language comprehension in a dense-sampling manner similar to how we analyze the networks, and analyzing the resulting data stream with nonlinear time series analysis techniques, we can obtain more insight into the temporal character of these discoordination phases than by simply marking the points in time at which they peak.

Temporary disruption in language processing reflected as multiscale temporal discoordination in a recurrent network

By juxtaposing time series analyses of activity measured from a fully recurrent network undergoing disrupted processing and of activity measured from a continuous meta-cognitive report of disruption in real-time language comprehension, we present an opportunity to compare the temporal statistics of the state-space trajectories inherent to both systems. Both the recurrent network and the human language comprehension process appear to exhibit long-range temporal correlations and low entropy when processing is undisrupted and coordinated. However, when processing is disrupted and discoordinated, they both exhibit more short-range temporal correlations and higher entropy. We conclude that by measuring human language comprehension in a dense-sampling manner similar to how we analyze the networks, and analyzing the resulting data stream with nonlinear time series analysis techniques, we can obtain more insight into the temporal character of these discoordination phases than by simply marking the points in time at which they peak.

Parliamentary roll-call voting as a complex dynamical system: The case of Chile.

A method is proposed to study the temporal variability of legislative roll-call votes in a parliament from the perspective of complex dynamical systems. We studied the Chilean Chamber of Deputies' by analyzing the agreement ratio and the voting outcome of each vote over the last 19 years with a Recurrence Quantification Analysis and an entropy analysis (Sample Entropy). Two significant changes in the temporal variability were found: one in 2014, where the voting outcome became more recurrent and with less entropy, and another in 2018, where the agreement ratio became less recurrent and with higher entropy. These changes may be directly related to major changes in the Chilean electoral system and the composition of the Chamber of Deputies, given that these changes occurred just after the first parliamentary elections with non-compulsory voting (2013 elections) and the first elections with a proportional system in conjunction with an increase in the number of deputies (2017 elections) were held.

The interacting partner as the immediate environment: Personality, interpersonal dynamics, and bodily synchronization.

In social interactions, humans tend to naturally synchronize their body movements. We investigated interpersonal synchronization in conversations and examined its relationship with personality differences and post-interaction appraisals. In a 15-minute semi-structured conversation, 56 previously-unfamiliar dyads introduced themselves, followed by self-disclosing and argumentative conversations. Their bodily movements were video-recorded in a standardized room (112 young adults, aged 18-33, mean=20.54, SD=2.74; 58% Dutch, 31% German, 11% other). Interpersonal bodily synchronization was estimated as (a) synchronization strength using Windowed Lagged Cross-Correlations and (b) Dynamic Organization (Determinism/Entropy/Laminarity/Mean Line) using Cross-Recurrence Quantification Analysis. Bodily synchronization was associated with differences in Agreeableness and Extraversion (IPIP-NEO-120) and post-conversational appraisals (affect/closeness/enjoyment) in mixed-effect models. Agreeable participants exhibited higher complexity in bodily synchronization dynamics (higher Entropy) than disagreeable individuals, who also reported more negative affect afterward. Interpersonal synchronization was stronger among extroverts than among introverts and extroverts appraised conversations as more positive and enjoyable. Bodily synchronization strength and dynamic organization were related to the type of conversation (self-disclosing/argumentative). Interpersonal dynamics were intimately connected to differences in Agreeableness and Extraversion, varied across situations, and these parameters affected how pleasant, close, and enjoyable each conversation felt.

First-Principles Study of the Structural, Mechanical and Thermodynamic Properties of Al11RE3 in Aluminum Alloys

The stability and mechanical and thermodynamic properties of Al11RE3 intermetallics (RE = Sc, Y and lanthanide La-Lu) have been investigated by combining first-principles and Debye model calculations. It was found that the formation enthalpies of the Al11RE3 intermetallics are all negative, indicating that they are stable; moreover, the experimental values of Al11La3 and Al11Ce3 are in good agreement with the predicted values, which are −0.40 kJ/mol and −0.38 kJ/mol, respectively. The calculated results of the mechanical properties reveal that the Young’s modulus E and shear modulus G of Al11RE3 (RE = La, Ce, Pr, Nd and Sm) intermetallics are obviously greater than that of Al, implying that the stiffness, toughness, and tensile strength of them are significantly greater than those of aluminum, and that they, as strengthen phases, can effectively improve the mechanical property of aluminum alloys. The Poisson’s ratio v of Al11Sc3 (0.37) is the largest, and the heterogeneity is obvious. All the Al11RE3 intermetallics can enhance the thermostability of the aluminum because of their lower Gibbs free energy F in the range of −5.002~−4.137 eV/atom and thermal expansion coefficient α of Al in the range of 2.34~2.89 × 10−5/K at 300K, as well as higher entropy and constant volume-specific heat than aluminum at finite temperatures. With an increase in the atomic number, different change trends were observed for the formation enthalpy ΔHf, bulk modulus B, Young’s modulus E, and shear modulus G. This paper can provide ideas and help for designing a high-performance, heat-resistant aluminum alloy.