Changes in physiological signal entropy in patients with obstructive sleep apnoea: a systematic review.

Background and Objective.Obstructive sleep apnoea (OSA) affects an estimated 936 million people worldwide, yet only 15% receive a definitive diagnosis. Diagnosis of OSA poses challenges due to the dynamic nature of physiological signals such as oxygen saturation (SpO2) and heart rate variability (HRV). Linear analysis methods may not fully capture the irregularities present in these signals. The application of entropy of routine physiological signals offers a promising method to better measure variabilities in dynamic biological data. This review aims to explore entropy changes in physiological signals among individuals with OSA.Approach.Keyword and title searches were performed on Medline, Embase, Scopus, and CINAHL databases. Studies had to analyse physiological signals in OSA using entropy. Quality assessment used the Newcastle-Ottawa Scale. Evidence was qualitatively synthesised, considering entropy signals, entropy type, and time-series length.Main results.Twenty-two studies were included. Multiple physiological signals related to OSA, including SpO2, HRV, and the oxygen desaturation index (ODI), have been investigated using entropy. Results revealed a significant decrease in HRV entropy in those with OSA compared to control groups. Conversely, SpO2and ODI entropy values were increased in OSA. Despite variations in entropy types, time scales, and data extraction devices, studies using receiver operating characteristic curves demonstrated a high discriminative accuracy (>80% AUC) in distinguishing OSA patients from control groups.Significance. This review highlights the potential of SpO2entropy analysis in developing new diagnostic indices for patients with OSA. Further investigation is needed before applying this technique clinically.

Low-intensity transcranial ultrasound stimulation improves memory behavior in an ADHD rat model by modulating cortical functional network connectivity.

Working memory in attention deficit hyperactivity disorder (ADHD) is closely related to cortical functional network connectivity (CFNC), such as abnormal connections between the frontal, temporal, occipital cortices and with other brain regions. Low-intensity transcranial ultrasound stimulation (TUS) has the advantages of non-invasiveness, high spatial resolution, and high penetration depth and can improve ADHD memory behavior. However, how it modulates CFNC in ADHD and the CFNC mechanism that improves working memory behavior in ADHD remain unclear. In this study, we observed working memory impairment in ADHD rats, establishing a corresponding relationship between changes in CFNCs and the behavioral state during the working memory task. Specifically, we noted abnormalities in the information transmission and processing capabilities of CFNC in ADHD rats while performing working memory tasks. These abnormalities manifested in the network integration ability of specific areas, as well as the information flow and functional differentiation of CFNC. Furthermore, our findings indicate that TUS effectively enhances the working memory ability of ADHD rats by modulating information transmission, processing, and integration capabilities, along with adjusting the information flow and functional differentiation of CFNC. Additionally, we explain the CFNC mechanism through which TUS improves working memory in ADHD. In summary, these findings suggest that CFNCs are important in working memory behaviors in ADHD.

Distribution of euptyctimous mite Phthiracarus longulus (Acari: Oribatida) under future climate change in the Palearctic.

The aim of this paper is to prepare, describe and discuss the models of the current and future distribution of Phthiracarus longulus (Koch, 1841) (Acari: Oribatida: Euptyctima), the oribatid mite species widely distributed within the Palearctic. We used the maximum entropy (MAXENT) method to predict its current and future (until the year 2100) distribution based on macroclimatic bio-variables. To our best knowledge, this is the first-ever prediction of distribution in mite species using environmental niche modelling. The main thermal variables that shape the current distribution of P. longulus are the temperature annual range, mean temperature of the coldest quarter and the annual mean temperature, while for precipitation variables the most important is precipitation of the driest quarter. Regardless of the climatic change scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) our models show generally the northward shift of species range, and in Southern Europe the loss of most habitats with parallel upslope shift. According to our current model, the most of suitable habitats for P. longulus are located in the European part of Palearctic. In general, the species range is mostly affected in Europe. The most stable areas of P. longulus distribution were the Jutland with surrounding southern coasts of Scandinavia, islands of the Danish Straits and the region of Trondheim Fjord.

Microstructure evolution and catalytic activity of AlCo1-xFeNiTiMox high entropy alloys fabricated by powder metallurgy route.

This investigation presents the synthesis of equiatomic and non-equiatomic AlCo1-xFeNiTiMox (x = 0, 0.1, 0.25 and 1.0) high entropy alloys fabricated by mechanical alloying. Mo partially replaced Co. Classic thermodynamic calculations, such as mixing enthalpy (ΔHmix), configurational entropy (ΔSmix), the atomic size difference (δ), entropy to enthalpy ratio (Ω), electronegativity difference (△χ), and valence electron concentration (VEC) were used. Considering δ, Ω and VEC parameters, a BCC solid solution and an intermetallic phase can be predicted due to the partial replacement of Co by Mo. X-ray and electron diffraction of equiatomic HEA without Mo content revealed that after 35 h of milling, a Fe-type BCC lattice phase was formed in the alloy and two L21 phases, in addition to a minimal amount of FCC phase. As the Mo content increased, the Fe-type BCC phase was steadily replaced by the Mo-type BCC phase and the Fe-type FCC phase, and two L21 phases were also developed. When the 5 at% Mo-containing (x = 0.25) alloy was further milled for 80 h, the amount of phases remained almost the same; only the grain size was strongly reduced. The influence of the Mo addition on the properties of studied alloys was also confirmed in the decolourisation of Rhodamine B using a modified photo-Fenton process. The decolourisation efficiency within 20 min was 72% for AlCoFeNiTi and 87% for AlCo0.75FeNiTiMo0.25 using UV light with 365 nm wavelength.

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis.

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

Analysis of Shannon's entropy to contrast between the Embodied and Neurocentrist hypothesis of conscious experience.

We usually accept that consciousness is in the brain. This statement corresponds to a Neurocentrist view. However, with all the physical and physiological data currently available, a convincing explanation of how consciousness emerges has not been given this topic is aborded by Anil Seth (2021). Because of this, a natural question arises: Is consciousness really in the brain or not? If the answer is no, this corresponds to the Embodied perspective. We cannot discriminate between these two points of view because we cannot identify how the organism processes the information. If we try to measure information processing in the brain, then the Neurocentrist view is unavoidable. For example, the information integration theory of Tononi's research group and the global work area theory developed by Dehaene and Baars, focus solely on the brain without considering aspects of Embodied vision (See Tononi, 2021; Dehaene, 2021). In this article, we propose an index based on Shannon's entropy, capable of identifying the leading processing elements acting: Are they mainly inner or external? In order to validate it, we performed simulations with networks accounting for different amounts of internal and outer layers. Since Shannon's entropy is an abstract measure of the information content, this index is not dependent on the physical network nor the proportion of different layers. Therefore, we validate the index as free of bias. This index is a way to discriminate between Embodied from Neurocentrist hypotheses.

An improved nutcracker optimization algorithm for discrete and continuous optimization problems: Design, comprehensive analysis, and engineering applications.

This study is presented to examine the performance of a newly proposed metaheuristic algorithm within discrete and continuous search spaces. Therefore, the multithresholding image segmentation problem and parameter estimation problem of both the proton exchange membrane fuel cell (PEMFC) and photovoltaic (PV) models, which have different search spaces, are used to test and verify this algorithm. The traditional techniques could not find approximate solutions for those problems in a reasonable amount of time, so researchers have used metaheuristic algorithms to overcome those shortcomings. However, the majority of metaheuristic algorithms still suffer from slow convergence speed and stagnation into local minima problems, which makes them unsuitable for tackling these optimization problems. Therefore, this study proposes an improved nutcracker optimization algorithm (INOA) for better solving those problems in an acceptable amount of time. INOA is based on improving the performance of the standard algorithm using a newly proposed convergence improvement strategy that aims to improve the convergence speed and prevent stagnation in local minima. This algorithm is first applied to estimating the unknown parameters of the single-diode, double-diode, and triple-diode models for a PV module and a solar cell. Second, four PEMFC modules are used to further observe INOA's performance for the continuous optimization challenge. Finally, the performance of INOA is investigated for solving the multi-thresholding image segmentation problem to test its effectiveness in a discrete search space. Several test images with different threshold levels were used to validate its effectiveness, stability, and scalability. Comparison to several rival optimizers using various performance indicators, such as convergence curve, standard deviation, average fitness value, and Wilcoxon rank-sum test, demonstrates that INOA is an effective alternative for solving both discrete and continuous optimization problems. Quantitively, INOA could solve those problems better than the other rival optimizers, with improvement rates for final results ranging between 0.8355 % and 3.34 % for discrete problems and 4.97 % and 99.9 % for continuous problems.

Mesoporous High-Entropy Alloy Films.

High-entropy alloys (HEAs) are promising materials for electrochemical energy applications due to their excellent catalytic performance and durability. However, the controlled synthesis of HEAs with a well-defined structure and a uniform composition distribution remains a challenge. Herein, a soft template-assisted electrodeposition technique is used to fabricate a mesoporous HEA (m-HEA) film with a uniform composition distribution of Pt, Pd, Rh, Ru, and Cu, providing a suitable platform for investigating structure-performance relationships. Electrochemical deposition enables the uniform nucleation and grain growth of m-HEA, which can be deposited onto many conductive substrates. The m-HEA film exhibits an enhanced mass activity of 4.2 A mgPt-1 toward methanol oxidation reaction (MOR), which is 7.2-fold and 35-fold higher than a mesoporous Pt film and commercial Pt black, respectively. Experimental characterization indicates that structural defects and a low work function of the m-HEA film offer sufficient active sites and fast electron-transfer kinetics. Furthermore, theoretical calculations demonstrate that the variety of favorable adsorption sites on multimetallic elements of HEA reduces the barriers for dehydration pathways and *CO species removal, ensuring optimal performance for complex MOR reactions. This work provides an effective approach to designing a variety of HEA catalysts with well-controlled porous structures for targeted electrocatalytic applications.

High-Entropy Materials for Application: Electricity, Magnetism, and Optics.

High-entropy materials (HEMs) have recently emerged as a prominent research focus in materials science, gaining considerable attention because of their complex composition and exceptional properties. These materials typically comprise five or more elements mixed approximately in equal atomic ratios. The resultant high-entropy effects, lattice distortions, slow diffusion, and cocktail effects contribute to their unique physical, chemical, and optical properties. This study reviews the electrical, magnetic, and optical properties of HEMs and explores their potential applications. Additionally, it discusses the theoretical calculation methods and preparation techniques for HEMs, thereby offering insights and prospects for their future development.

Spontaneous Orientation Polarization of Anisotropic Equivalent Dipoles Harnessed by Entropy Engineering for Ultra-Thin Electromagnetic Wave Absorber.

The synthesis of carbon supporter/nanoscale high-entropy alloys (HEAs) electromagnetic response composites by carbothermal shock method has been identified as an advanced strategy for the collaborative competition engineering of conductive/dielectric genes. Electron migration modes within HEAs as manipulated by the electronegativity, valence electron configurations and molar proportions of constituent elements determine the steady state and efficiency of equivalent dipoles. Herein, enlightened by skin-like effect, a reformative carbothermal shock method using carbonized cellulose paper (CCP) as carbon supporter is used to preserve the oxygen-containing functional groups (O·) of carbonized cellulose fibers (CCF). Nucleation of HEAs and construction of emblematic shell-core CCF/HEAs heterointerfaces are inextricably linked to carbon metabolism induced by O·. Meanwhile, the electron migration mode of switchable electron-rich sites promotes the orientation polarization of anisotropic equivalent dipoles. By virtue of the reinforcement strategy, CCP/HEAs composite prepared by 35% molar ratio of Mn element (CCP/HEAs-Mn2.15) achieves efficient electromagnetic wave (EMW) absorption of - 51.35 dB at an ultra-thin thickness of 1.03 mm. The mechanisms of the resulting dielectric properties of HEAs-based EMW absorbing materials are elucidated by combining theoretical calculations with experimental characterizations, which provide theoretical bases and feasible strategies for the simulation and practical application of electromagnetic functional devices (e.g., ultra-wideband bandpass filter).

Dynamic allostery drives autocrine and paracrine TGF-ß signaling.

TGF-ß, essential for development and immunity, is expressed as a latent complex (L-TGF-ß) non-covalently associated with its prodomain and presented on immune cell surfaces by covalent association with GARP. Binding to integrin αvß8 activates L-TGF-ß1/GARP. The dogma is that mature TGF-ß must physically dissociate from L-TGF-ß1 for signaling to occur. Our previous studies discovered that αvß8-mediated TGF-ß autocrine signaling can occur without TGF-ß1 release from its latent form. Here, we show that mice engineered to express TGF-ß1 that cannot release from L-TGF-ß1 survive without early lethal tissue inflammation, unlike those with TGF-ß1 deficiency. Combining cryogenic electron microscopy with cell-based assays, we reveal a dynamic allosteric mechanism of autocrine TGF-ß1 signaling without release where αvß8 binding redistributes the intrinsic flexibility of L-TGF-ß1 to expose TGF-ß1 to its receptors. Dynamic allostery explains the TGF-ß3 latency/activation mechanism and why TGF-ß3 functions distinctly from TGF-ß1, suggesting that it broadly applies to other flexible cell surface receptor/ligand systems.

Dynamic evolution of frontal-temporal network connectivity in temporal lobe epilepsy: A magnetoencephalography study.

Temporal lobe epilepsy (TLE) frequently involves an intricate, extensive epileptic frontal-temporal network. This study aimed to investigate the interactions between temporal and frontal regions and the dynamic patterns of the frontal-temporal network in TLE patients with different disease durations. The magnetoencephalography data of 36 postoperative seizure-free patients with long-term follow-up of at least 1 year, and 21 age- and sex-matched healthy subjects were included in this study. Patients were initially divided into LONG-TERM (n = 18, DURATION >10 years) and SHORT-TERM (n = 18, DURATION ≤10 years) groups based on 10-year disease duration. For reliability, supplementary analyses were conducted with alternative cutoffs, creating three groups: 0 < DURATION ≤7 years (n = 11), 7 < DURATION ≤14 years (n = 11), and DURATION >14 years (n = 14). This study examined the intraregional phase-amplitude coupling (PAC) between theta phase and alpha amplitude across the whole brain. The interregional directed phase transfer entropy (dPTE) between frontal and temporal regions in the alpha and theta bands, and the interregional cross-frequency directionality (CFD) between temporal and frontal regions from the theta phase to the alpha amplitude were further computed and compared among groups. Partial correlation analysis was conducted to investigate correlations between intraregional PAC, interregional dPTE connectivity, interregional CFD, and disease duration. Whole-brain intraregional PAC analyses revealed enhanced theta phase-alpha amplitude coupling within the ipsilateral temporal and frontal regions in TLE patients, and the ipsilateral temporal PAC was positively correlated with disease duration (r = 0.38, p <.05). Interregional dPTE analyses demonstrated a gradual increase in frontal-to-temporal connectivity within the alpha band, while the direction of theta-band connectivity reversed from frontal-to-temporal to temporal-to-frontal as the disease duration increased. Interregional CFD analyses revealed that the inhibitory effect of frontal regions on temporal regions gradually increased with prolonged disease duration (r = -0.36, p <.05). This study clarified the intrinsic reciprocal connectivity between temporal and frontal regions with TLE duration. We propose a dynamically reorganized triple-stage network that transitions from balanced networks to constrained networks and further develops into imbalanced networks as the disease duration increases.

Correlation between insulin-like growth factor and complexity of glucose time series index in patients with newly diagnosed acromegaly: a PILOT study.

BACKGROUND: Acromegaly has a high risk of abnormal glucose metabolism. The complexity of the glucose time series index (CGI) is calculated from refined composite multi-scale entropy analysis of the continuous glucose monitoring (CGM) data. CGI is a new indicator of glucose imbalance based on ambulatory glucose monitoring technology, which allows for earlier response to glucose metabolism imbalance and correlates with patient prognosis. OBJECTIVE: To compare the differences in glucose metabolic profile and CGI between acromegaly with normal glucose tolerance (NGT) and healthy subjects. METHODS: Eight newly diagnosed patients with acromegaly (GH group) and eight age- and gender-matched healthy subjects (Control group) were included in this study. All participants underwent oral glucose tolerance test (OGTT) and 72-h CGM. A refined composite multi-scale entropy analysis was performed on the CGM data to calculate the CGI and we compare the differences in glycemic profiles and CGI between the two groups. RESULTS: After OGTT, compared with the control group, patients in the GH group had higher 2 h blood glucose (BG) (mmol/L) [GH vs control, 6.7 (6.1, 7.0) vs 5.2 (3.8, 6.3), P = 0.012], 3 h BG [5.1 (3.8, 6.5) vs 4.0 (3.4, 4.2), P = 0.046], mean BG [6.3 (6.1, 6.5) vs 5.5 (5.1, 5.9), P = 0.002], 2 h insulin (mU/L) [112.9 (46.8, 175.5) vs 34.1 (17.1, 55.6), P = 0.009], and 3 h insulin [26.8 (17.1, 55.4) vs 10.4 (4.2, 17.8), P = 0.016]. CGI was lower in the GH group [2.77 (1.92, 3.15) vs 4.2 (3.3, 4.8), P = 0.008]. Spearman's correlation analysis showed insulin-like growth factor (IGF) (r = -0.897, P < 0.001) and mean glucose (r = -0.717, P = 0.003) were significantly negatively correlated with CGI. Multiple linear stepwise regression showed that IGF-1 (r = -0.652, P = 0.028) was independent factor associated with CGI in acromegaly. CONCLUSION: IGF-1 was significantly associated with CGI, and CGI may serve as a novel marker to evaluate glucose homeostasis in acromegaly with normal glucose tolerance.

Effect of confinement on water properties in super-hydrophilic pores using MD simulations with the mW model.

CONTEXT: We explore the influence of strongly hydrophilic confinement on various properties of water, such as density, enthalpy, potential energy, radial distribution function, entropy, specific heat capacity, structural dynamics, and transition temperatures (freezing and melting temperatures), using monatomic water (mW) model. The properties of water are found to be dependent on confinement and the wall-fluid surface interaction. Hysteresis loops are observed for density, enthalpy, potential energy, and entropy around the transition temperatures, while the size of hysteresis loops varies with confinement and surface interaction. In smaller pore sizes (H ≤ 20), the solid phase displays a higher density compared to the liquid phase, which is unconventional behavior compared to bulk water systems due to the pronounced hydrophilic properties of the confinement surface. Specific heat capacity exhibits more oscillations in the confined system compared to bulk water, stemming from uneven enthalpy differences across equal temperature intervals. During phase transformation in both heating and quenching processes, there is an abrupt change observed in specific heat capacity. Confinement exerts a notable impact on entropy in the solid phase, but its influence is negligible in the liquid phase. At lower pore sizes (H < 25 Å), there is more fluctuation in freezing temperature for all wall-fluid interactions, which diminishes beyond pore sizes of H > 25 Å. Similarly, more oscillatory behavior is observed in melting temperatures at lower pore sizes (H < 40 Å), which diminishes at higher pore sizes (H > 40 Å). During the quenching process, a sudden jump in the in-plane orientational and tetrahedral order parameters indicates the formation of an ordered phase, specifically a diamond crystalline structure. The percentages of different crystalline structures (cubic diamond, hexagonal diamond, and 2D hexagonal) vary with both the confinement size and the wall-fluid interaction strength. METHODS: Cooling and heating simulations are conducted with the mW water model using LAMMPS for different nanoscale confinement separation sizes ranging from 8.5 to 70 Å within the temperature range of 100-350 K. The water is modeled using two-body and three-body interaction potential (Stillinger-Weber potential) and the confinement is introduced using LJ 9-3 water-wall interaction potential. Entropy is calculated using RDF data obtained from the simulation experiments for each temperature point with increments or decrements of 2.5 K. The transition temperatures are estimated using the specific heat capacity analysis.

Thermodynamic origin of the affinity, selectivity and domain specificity of metallothionein for essential and toxic metal ions.

The small Cys-rich protein metallothionein (MT) binds several metal ions in clusters within its two domains. While the affinity of MT for both toxic and essential metals has been well studied, the thermodynamics of this binding has not. We have used isothermal titration calorimetry measurements to quantify the change in enthalpy (ΔH) and change in entropy (ΔS) when metal ions bind to the two ubiquitous isoforms of MT. The seven Zn2+ that bind sequentially at pH 7.4 do so in two populations with different coordination thermodynamics, an initial four that bind randomly with individual tetra-thiolate coordination and a subsequent three that bind with bridging thiolate coordination to assemble the metal clusters. The high affinity of MT for both populations is due to a very favourable binding entropy that far outweighs an unfavourable binding enthalpy. This originates from a net enthalpic penalty for Zn2+ displacement of protons from the Cys thiols and a favourable entropic contribution from the displaced protons. The thermodynamics of other metal ions binding to MT were determined by their displacement of Zn2+ from Zn7MT and subtraction of the Zn2+-binding thermodynamics. Toxic Cd2+, Pb2+ and Ag+, and essential Cu+, also bind to MT with a very favourable binding entropy but a net binding enthalpy that becomes increasingly favourable as the metal ion becomes a softer Lewis acid. These thermodynamics are the origin of the high affinity, selectivity and domain specificity of MT for these metal ions and the molecular basis for their in vivo binding competition.

Validation of Linear and Nonlinear Gait Variability Measures Derived From a Smartphone System Compared to a Gold-Standard Footswitch System During Overground Walking.

Smartphones, with embedded accelerometers, may be a viable method to monitor gait variability in the free-living environment. However, measurements estimated using smartphones must first be compared to known quantities to ensure validity. This study assessed the validity and reliability of smartphone-derived gait measures compared to a gold-standard footswitch system during overground walking. Seventeen adults completed three 8-minute overground walking trials during 3 separate visits. The stride time series was calculated as the time difference between consecutive right heel contact events within the footswitch and smartphone-accelerometry signals. Linear (average stride time, stride time standard deviation, and stride time coefficient of variation) and nonlinear (fractal scaling index, approximate entropy, and sample entropy) measures were calculated for each stride time series. Bland-Altman plots with 95% limits of agreement assessed agreement between systems. Intraclass correlation coefficients assessed reliability across visits. Bland-Altman plots revealed acceptable limits of agreement for all measures. Intraclass correlation coefficients revealed good-to-excellent reliability for both systems, except for fractal scaling index, which was moderate. The smartphone system is a valid method and performs similarly to gold-standard research equipment. These findings suggest the development and implementation of an inexpensive, easy-to-use, and ubiquitous telehealth instrument that may replace traditional laboratory equipment for use in the free-living environment.

Assessment of entropy accumulation in human subjects when exposed to low energy availability.

Background & aims: Adequate energy availability is essential for the body to maintain its physiological functions and achieve optimal health, especially among athletes. Unfortunately, low energy availability (LEA) is common among athletes, and it has been associated with impairments in health and performance. In contrast, an energy-restricted diet has been linked to longevity, but it is unclear how LEA affects athletes' lifespans. The goal of the present study was to assess the impact of LEA thermodynamically on the lifespan of athletes. Methods: Data from seven healthy young endurance-trained athletes (24 ± 4 years) who underwent short-term low energy availability (125 (kJ/day) per kg FFM) once with low protein content (LEA-LP; 0.8 g/kg) and with high protein content (LEA-HP; 125 (kJ/day) per kg FFM, 1.7 g/kg), as well as a control diet (CON; 230 (kJ/day) per kg FFM, 1.7 g/kg), were used in the calculations. The athletes followed each diet for five days and expended 67.5 (kJ/day) per kg FFM. entropy generation-based thermodynamic calculations were performed based on the metabolic activity of the athletes, which was determined from oxygen consumption and carbon dioxide production rates. Results: Low energy availability was successfully induced during LEA-LP (62 ± 8 (kJ/day) per kg FFM; 95%Cl: 53-70) and LEA-HP (64 ± 8 (kJ/day) per kg FFM; 95%Cl: 56-71) diets. Despite of achieving energy deficit of -6658 ± 2110 kJ/day (95%Cl: 8609-(-) 4707) (LEA-LP), -5781 ± 623 (95%Cl: 26591-(-)4707) (LEA-HP) and excessive energy of 772 ± 1915 (95%Cl: 845-2388) (CON) statistical analyses revealed no significant differences in lifespan estimations among diets (CON: 72 ± 8 years (95%Cl: 65-79), LEA-LP: 74 ± 7 years (95%Cl: 68-80), and LEA-HP: 73 ± 11 (95%Cl: 62-83)). Conclusions: This study suggests valuable insights into the intricate relationship between energy availability, entropy generation, and lifespan among athletes. Whereas entropy generation levels and the lifespan of athletes remained stable depending on diets, the distinguished differences in energy deficiency and energy availability underline the need for a profounder investigation of underlying physiological mechanisms to improve the health and performance of athletes.

Fluorine-lodged high-valent high-entropy layered double hydroxide for efficient, long-lasting zinc-air batteries.

Efficient and stable bifunctional oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) catalysts are urgently needed to unlock the full potential of zinc-air batteries (ZABs). High-valence oxides (HVOs) and high entropy oxides (HEOs) are suitable candidates for their optimal electronic structures and stability but suffer from demanding synthesis. Here, a low-cost fluorine-lodged high-valent high-entropy layered double hydroxide (HV-HE-LDH) (FeCoNi2F4(OH)4) is conveniently prepared through multi-ions co-precipitation, where F- are firmly embedded into the individual hydroxide layers. Spectroscopic detections and theoretical simulations reveal high valent metal cations are obtained in FeCoNi2F4(OH)4, which enlarge the energy band overlap between metal 3d and O 2p, enhancing the electronic conductivity and charge transfer, thus affording high intrinsic OER catalytic activity. More importantly, the strengthened metal-oxygen (M-O) bonds and stable octahedral geometry (M-O(F)6) in FeCoNi2F4(OH)4 prevent structural reorganization, rendering long-term catalytic stability. Furthermore, an efficient three-phase reaction interface with fast oxygen transportation was constructed, significantly improving the ORR activity. ZABs assembled with FeCoNi2F4(OH)4@HCC (hydrophobic carbon cloth) cathodes deliver a top performance with high round-trip energy efficiency (60.6% at 10 mA cm-2) and long-term stability (efficiency remains at 58.8% after 1050 charge-discharge cycles).

Entropy-extreme model for predicting the development of cyber epidemics at early stages.

The approaches used in biomedicine to analyze epidemics take into account features such as exponential growth in the early stages, slowdown in dynamics upon saturation, time delays in spread, segmented spread, evolutionary adaptations of the pathogen, and preventive measures based on universal communication protocols. All these characteristics are also present in modern cyber epidemics. Therefore, adapting effective biomedical approaches to epidemic analysis for the investigation of the development of cyber epidemics is a promising scientific research task. The article is dedicated to researching the problem of predicting the development of cyber epidemics at early stages. In such conditions, the available data is scarce, incomplete, and distorted. This situation makes it impossible to use artificial intelligence models for prediction. Therefore, the authors propose an entropy-extreme model, defined within the machine learning paradigm, to address this problem. The model is based on estimating the probability distributions of its controllable parameters from input data, taking into account the variability characteristic of the last ones. The entropy-extreme instance, identified from a set of such distributions, indicates the most uncertain (most negative) trajectory of the investigated process. Numerical methods are used to analyze the generated set of investigated process development trajectories based on the assessments of probability distributions of the controllable parameters and the variability characteristic. The result of the analysis includes characteristic predictive trajectories such as the average and median trajectories from the set, as well as the trajectory corresponding to the standard deviation area of the parameters' values. Experiments with real data on the infection of Windows-operated devices by various categories of malware showed that the proposed model outperforms the classical competitor (least squares method) in predicting the development of cyber epidemics near the extremum of the time series representing the deployment of such a process over time. Moreover, the proposed model can be applied without any prior hypotheses regarding the probabilistic properties of the available data.

Nature heals: An informational entropy account of self-organization and change in field psychotherapy.

This paper reviews biophysical models of psychotherapeutic change based on synergetics and the free energy principle. These models suggest that introducing sensory surprise into the patient-therapist system can lead to self-organization and the formation of new attractor states, disrupting entrenched patterns of thoughts, emotions, and behaviours. We propose that the therapist can facilitate this process by cultivating epistemic trust and modulating embodied attention to allow surprising affective states to enter shared awareness. Transient increases in free energy enable the update of generative models, expanding the range of experiences available within the patient-therapist phenomenal field. We hypothesize that patterns of disorganization at behavioural and physiological levels, indexed by increased entropy, complexity, and lower determinism, are key markers and predictors of psychotherapeutic gains. Future research should investigate how the therapist's openness to novelty shapes therapeutic outcomes.