Assessment of Classical and Non-Classical Quantitative Electroencephalographic Measures in Patients with Substance Use Disorders

Background: People diagnosed with substance use disorders (SUDs) are at risk for impairment of brain function and structure. However, physicians still do not have any clinical biomarker of brain impairment that helps diagnose or treat these patients when needed. The most common method to study these patients is the classical electroencephalographic (EEG) analyses of absolute and relative powers, but this has limited individual clinical applicability. Other non-classical measures such as frequency band ratios and entropy show promise in these patients. Therefore, there is a need to expand the use of quantitative (q)EEG beyond classical measures in clinical populations. Our aim is to assess a group of classical and non-classical qEEG measures in a population with SUDs. Methods: We selected 56 non-medicated and drug-free adult patients (30 males) diagnosed with SUDs and admitted to Rehabilitation Clinics. According to qualitative EEG findings, patients were divided into four groups. We estimated the absolute and relative powers and calculated the entropy, and the alpha/(delta + theta) ratio. Results: Our findings showed a significant variability of absolute and relative powers among patients with SUDs. We also observed a decrease in the EEG-based entropy index and alpha/(theta + delta) ratio, mainly in posterior regions, in the patients with abnormal qualitative EEG. Conclusions: Our findings support the view that the power spectrum is not a reliable biomarker on an individual level. Thus, we suggest shifting the approach from the power spectrum toward other potential methods and designs that may offer greater clinical possibilities.

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human-computer interface technologies.

Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging

The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.

The Impact of Activating Agents on Non-Enzymatic Nucleic Acid Extension Reactions

Non-enzymatic template-directed primer extension is increasingly being studied for the production of RNA and DNA. These reactions benefit from producing RNA or DNA in an aqueous, protecting group free system, without the need for expensive enzymes. However, these primer extension reactions suffer from a lack of fidelity, low reaction rates, low overall yields, and short primer extension lengths. This review outlines a detailed mechanistic pathway for non-enzymatic template-directed primer extension and presents a review of the thermodynamic driving forces involved in entropic templating. Through the lens of entropic templating, the rate and fidelity of a reaction are shown to be intrinsically linked to the reactivity of the activating agent used. Thus, a strategy is discussed for the optimization of non-enzymatic template-directed primer extension, providing a path towards cost-effective in vitro synthesis of RNA and DNA.

Annual Research Review: The power of predictability - patterns of signals in early life shape neurodevelopment and mental health trajectories

The global burden of early life adversity (ELA) is profound. The World Health Organization has estimated that ELA accounts for almost 30% of all psychiatric cases. Yet, our ability to identify which individuals exposed to ELA will develop mental illness remains poor and there is a critical need to identify underlying pathways and mechanisms. This review proposes unpredictability as an understudied aspect of ELA that is tractable and presents a conceptual model that includes biologically plausible mechanistic pathways by which unpredictability impacts the developing brain. The model is supported by a synthesis of published and new data illustrating the significant impacts of patterns of signals on child development. We begin with an overview of the existing unpredictability literature, which has focused primarily on longer patterns of unpredictability (e.g. years, months, and days). We then describe our work testing the impact of patterns of parental signals on a moment-to-moment timescale, providing evidence that patterns of these signals during sensitive windows of development influence neurocircuit formation across species and thus may be an evolutionarily conserved process that shapes the developing brain. Next, attention is drawn to emerging themes which provide a framework for future directions of research including the evaluation of functions, such as effortful control, that may be particularly vulnerable to unpredictability, sensitive periods, sex differences, cross-cultural investigations, addressing causality, and unpredictability as a pathway by which other forms of ELA impact development. Finally, we provide suggestions for prevention and intervention, including the introduction of a screening instrument for the identification of children exposed to unpredictable experiences.

Thermodynamic Assessment of Molten Bix-Sn1-x (x = 0.1 to 0.9) Alloys and Microstructural Characterization of Some Bi-Sn Solder Alloys

Properties such as lower melting temperature, good tensile strength, good reliability, and well creep resistance, together with low production cost, make the system Bi-Sn an ideal candidate for fine soldering in applications such as reballing or reflow. The first objective of the work was to determine the thermodynamic quantities of Bi and Sn using the electromotive force measurement method in an electrolytic cell (Gibbs' enthalpies of the mixture, integral molar entropies, and the integral molar excess entropies were determined) at temperatures of 600 K and 903 K. The second objective addressed is the comprehensive characterization of three alloy compositions that were selected and elaborated, namely Bi25Sn75, Bi50Sn50, and Bi75Sn25, and morphological and structural investigations were carried out on them. Optical microscopy and SEM-EDS characterization revealed significant changes in the structure of the elaborated alloys, with all phases being uniformly distributed in the Bi50Sn50 and Bi75Sn25 alloys. These observations were confirmed by XRD and EDP-XRFS analyses. Diffractometric analysis reveals the prevalence of metallic Bi and traces of Sn, the formation of the Sn0.3Bi0.7, Sn0.95Bi0.05 compounds, and SnO and SnO2 phases.

Structural and physical basis for the elasticity of elastin

Elastin function is to endow vertebrate tissues with elasticity so that they can adapt to local mechanical constraints. The hydrophobicity and insolubility of the mature elastin polymer have hampered studies of its molecular organisation and structure-elasticity relationships. Nevertheless, a growing number of studies from a broad range of disciplines have provided invaluable insights, and several structural models of elastin have been proposed. However, many questions remain regarding how the primary sequence of elastin (and the soluble precursor tropoelastin) governs the molecular structure, its organisation into a polymeric network, and the mechanical properties of the resulting material. The elasticity of elastin is known to be largely entropic in origin, a property that is understood to arise from both its disordered molecular structure and its hydrophobic character. Despite a high degree of hydrophobicity, elastin does not form compact, water-excluding domains and remains highly disordered. However, elastin contains both stable and labile secondary structure elements. Current models of elastin structure and function are drawn from data collected on tropoelastin and on elastin-like peptides (ELPs) but at the tissue level, elasticity is only achieved after polymerisation of the mature elastin. In tissues, the reticulation of tropoelastin chains in water defines the polymer elastin that bears elasticity. Similarly, ELPs require polymerisation to become elastic. There is considerable interest in elastin especially in the biomaterials and cosmetic fields where ELPs are widely used. This review aims to provide an up-to-date survey of/perspective on current knowledge about the interplay between elastin structure, solvation, and entropic elasticity.

Effect of zero-point motion on properties of quantum particles adsorbed on a substrate

We qualitatively investigate the effect of zero-point motion (ZPM) on the structure and properties of a film composed of quantum particles adsorbed on a graphite substrate. The amplitude of ZPM is controlled by a change of the particle mass while keeping the interactions fixed. In that sense it is assumed that the interactions can be controlled by future doping methods. The worm-algorithm path integral Monte Carlo (WAPIMC) method is applied to simulate this system in the grand-canonical ensemble, where particles can be exchanged with the external particle reservoir. Another method, namely the multiconfigurational time-dependent Hartree method for bosons is additionally applied to verify some of the WAPIMC results and to provide further information on the entropy and the condensate fraction. Several important findings are reported. It is found that ZPM plays an important role in defining order and disorder in the crystalline structure of the adsorbed film. The total energy of the film drops with a reduction in the amplitude of ZPM, that is, it becomes more negative which is an indication to stronger adsorption. For a few particle numbers, a significant condensate fraction is detected that however drops sharply at critical values of the ZPM amplitude. Most importantly, a connection is established between chaos, in coordinate as well as momentum space, and the Heisenberg uncertainty principle. The importance of the present study lies in the fact that adsorbed two-dimensional films serve as an excellent experimental testbed for demonstrating low-dimensional quantum phenomena in the ground state. The present examination contributes also to a further understanding of the properties of heavy quantum particles adsorbed on substrates.

Irregularity of instantaneous gamma frequency in the motor control network characterize visuomotor and proprioceptive information processing

Objective.The study aims to characterize movements with different sensory goals, by contrasting the neural activity involved in processing proprioceptive and visuo-motor information. To accomplish this, we have developed a new methodology that utilizes the irregularity of the instantaneous gamma frequency parameter for characterization.Approach.In this study, eight essential tremor patients undergoing an awake deep brain stimulation implantation surgery repetitively touched the clinician's finger (forward visually-guided/FV movement) and then one's own chin (backward proprioceptively-guided/BP movement). Neural electrocorticographic recordings from the motor (M1), somatosensory (S1), and posterior parietal cortex (PPC) were obtained and band-pass filtered in the gamma range (30-80 Hz). The irregularity of the inter-event intervals (IEI; inverse of instantaneous gamma frequency) were examined as: (1) auto-information of the IEI time series and (2) correlation between the amplitude and its proceeding IEI. We further explored the network connectivity after segmenting the FV and BP movements by periods of accelerating and decelerating forces, and applying the IEI parameter to transfer entropy methods.Main results.Conceptualizing that the irregularity in IEI reflects active new information processing, we found the highest irregularity in M1 during BP movement, highest in PPC during FV movement, and the lowest during rest at all sites. Also, connectivity was the strongest from S1 to M1 and from S1 to PPC during FV movement with accelerating force and weakest during rest.Significance. We introduce a novel methodology that utilize the instantaneous gamma frequency (i.e. IEI) parameter in characterizing goal-oriented movements with different sensory goals, and demonstrate its use to inform the directional connectivity within the motor cortical network. This method successfully characterizes different movement types, while providing interpretations to the sensory-motor integration processes.

Screening of Oral Squamous Cell Carcinoma Through Color Intensity-Based Textural Features

Background Early screening and diagnosis of oral squamous cell carcinoma (OSCC) has always been a major challenge for pathologists. Artificial intelligence (AI)-assisted screening tools can serve as an adjunct for the objective interpretation of Papanicolaou (PAP)-stained oral smears. Aim This study aimed to develop a handy and sensitive computer-assisted AI tool based on color-intensity textural features to be applied to cytologic images for screening and diagnosis of OSCC. Methodology The study included two groups consisting of 80 OSCC subjects and 80 control groups. PAP-stained smears were collected from both groups. The smears were analyzed in Matlab software computed data and color intensity-based textural features such as entropy, contrast, energy, homogeneity, and correlation, were quantitatively extracted. Results In this study, a statistically significant difference was noted for entropy, energy, correlation, contrast, and homogeneity. It was found that entropy and contrast were found to be higher with a decrease in homogeneity, correlation, and energy in OSCC when compared to the control group. Receiver operating characteristic curve analysis was done and accuracy, sensitivity, and specificity were found to be 88%, 91%, and 81%, respectively. Conclusion The gray-level co-occurrence matrix (GLCM) color intensity-based textural features play a significant role in differentiating dysplastic and normal cells in the diagnosis of OSCC. Computer-aided textural analysis has the potential to aid in the early detection of oral cancer, which can lead to improved clinical outcomes.

Effects of Sensory Input Interactions on Components of Nonlinear Dynamics of Postural Sway in Aging

Postural control involves complex nonlinear dynamics influenced by the interaction and adaptation of different sensory inputs. However, it is not how these inputs interact with one another due to the complex complications associated with aging, particularly concerning the nonlinear dynamics of postural sway. This study aimed to examine how different sensory inputs, surface conditions, and aging factors to influence postural control mechanisms between young and older by investigating the nonlinear dynamics of postural control using the stabilogram diffusion analysis (SDA) and entropy methods. SDA parameters were much greater on foam surfaces than on firm surfaces for both groups in eyes-open and eyes-closed conditions (p ≤ 0.05). For older subjects, there were significant differences in entropy values between firm and foam surfaces (p ≤ 0.05) but no significant difference between eyes conditions (p > 0.05). For both SDA and entropy parameters, surface and age interaction potentially revealed significant differences between young and older subjects (p ≤ 0.05) than eyes and age interaction. The present study provided insight into uncovering the complex relationships between sensory inputs, surface conditions, age, and their potential interaction effects on postural control mechanisms that could mitigate falls and alleviate the fear of falling, particularly in older populations.

Thermodynamic Analysis to Evaluate the Effect of Diet on Brain Glucose Metabolism: The Case of Fish Oil

Inefficient glucose metabolism and decreased ATP production in the brain are linked to ageing, cognitive decline, and neurodegenerative diseases (NDDs). This study employed thermodynamic analysis to assess the effect of fish oil supplementation on glucose metabolism in ageing brains. Data from previous studies on glucose metabolism in the aged human brain and grey mouse lemur brains were examined. The results demonstrated that Omega-3 fish oil supplementation in grey mouse lemurs increased entropy generation and decreased Gibbs free energy across all brain regions. Specifically, there was a 47.4% increase in entropy generation and a 47.4 decrease in Gibbs free energy in the whole brain, indicating improved metabolic efficiency. In the human model, looking at the specific brain regions, supplementation with Omega-3 polyunsaturated fatty acids (n-3 PUFAs) reduced the entropy generation difference between elderly and young individuals in the cerebellum and particular parts of the brain cortex, namely the anterior cingulate and occipital lobe, with 100%, 14.29%, and 20% reductions, respectively. The Gibbs free energy difference was reduced only in the anterior cingulate by 60.64%. This research underscores that the application of thermodynamics is a comparable and powerful tool in comprehending the dynamics and metabolic intricacies within the brain.

Entropy Sharing in Ransomware: Bypassing Entropy-Based Detection of Cryptographic Operations

This study presents a groundbreaking approach to the ever-evolving challenge of ransomware detection. A lot of detection methods predominantly rely on pinpointing high-entropy blocks, which is a hallmark of the encryption techniques commonly employed in ransomware. These blocks, typically difficult to recover, serve as key indicators of malicious activity. So far, many neutralization techniques have been introduced so that ransomware utilizing standard encryption can effectively bypass these entropy-based detection systems. However, these have limited capabilities or require relatively high computational costs. To address these problems, we introduce a new concept entropy sharing. This method can be seamlessly integrated with every type of cryptographic algorithm and is also composed of lightweight operations, masking the high-entropy blocks undetectable. In addition, the proposed method cannot be easily nullified, contrary to simple encoding methods, without knowing the order of shares. Our findings demonstrate that entropy sharing can effectively bypass entropy-based detection systems. Ransomware utilizing such attack methods can cause significant damage, as they are difficult to detect through conventional detection methods.

Indications of flat bands driving the δ to α volume collapse of plutonium

On cooling from the melt, plutonium (Pu) undergoes a series of structural transformations accompanied by a ≈ 28% reduction in volume from its δ phase to its α phase at low temperatures. While Pu's partially filled 5f-electron shells are known to be involved, their precise role in the transformations has remained unclear. By using calorimetry measurements on α-Pu and gallium-stabilized δ-Pu combined with resonant ultrasound and X-ray scattering data to account for the anomalously large softening of the lattice with temperature, we show here that the difference in electronic entropy between the α and δ phases dominates over the difference in phonon entropy. Rather than finding an electronic specific heat characteristic of broad f-electron bands in α-Pu, as might be expected to occur within a Kondo collapsed phase in analogy with cerium, we find it to be indicative of flatter subbands. An important role played by Pu's 5f electrons in the formation of its larger unit cell α phase comprising inequivalent lattice sites and varying bond lengths is therefore suggested.

Entropy driven incommensurate structures in the frustrated kagome staircase Co3V2O8

Co3V2O8features spin-3/2 moments arrayed on a kagome staircase lattice. A spin density wave with a continuously evolving propagation vector ofk⃗=(0,δ,0), showing both incommensurate states and multiple commensurate lock-ins, is observed at temperatures above the ferromagnetic ground state. Previous work has suggested that this changing propagation vector could be driven by changes in exchange interactions due to Co atom displacements. We present a straightforward model showing that a Hamiltonian with competing (but temperature independent) interactions can semi-quantitatively reproduce this behavior using a mean field approximation. The simulated spin density wave magnetic structures feature buckled kagome planes that are either ferromagnetically or antiferromagnetically ordered. Propagation vectors that differ fromδ=1/2will have multiple different ways of arranging these ferromagnetic layers that have very similar energies. This classical stacking entropy appears to be crucial in stabilizing the temperature-dependent propagation vector.

The Conformational Contribution to Molecular Complexity and Its Implications for Information Processing in Living Beings and Chemical Artificial Intelligence

This work highlights the relevant contribution of conformational stereoisomers to the complexity and functions of any molecular compound. Conformers have the same molecular and structural formulas but different orientations of the atoms in the three-dimensional space. Moving from one conformer to another is possible without breaking covalent bonds. The interconversion is usually feasible through the thermal energy available in ordinary conditions. The behavior of most biopolymers, such as enzymes, antibodies, RNA, and DNA, is understandable if we consider that each exists as an ensemble of conformers. Each conformational collection confers multi-functionality and adaptability to the single biopolymers. The conformational distribution of any biopolymer has the features of a fuzzy set. Hence, every compound that exists as an ensemble of conformers allows the molecular implementation of a fuzzy set. Since proteins, DNA, and RNA work as fuzzy sets, it is fair to say that life's logic is fuzzy. The power of processing fuzzy logic makes living beings capable of swift decisions in environments dominated by uncertainty and vagueness. These performances can be implemented in chemical robots, which are confined molecular assemblies mimicking unicellular organisms: they are supposed to help humans "colonise" the molecular world to defeat diseases in living beings and fight pollution in the environment.

Research on Classification Algorithm of Silicon Single-Crystal Growth Temperature Gradient Trend Based on Multi-Level Feature Fusion

In the process of silicon single-crystal preparation, the timely identification and adjustment of abnormal conditions are crucial. Failure to promptly detect and resolve issues may result in a substandard silicon crystal product quality or even crystal pulling failure. Therefore, the early identification of abnormal furnace conditions is essential for ensuring the preparation of perfect silicon single crystals. Additionally, since the thermal field is the fundamental driving force for stable crystal growth and the primary assurance of crystal quality, this paper proposes a silicon single-crystal growth temperature gradient trend classification algorithm based on multi-level feature fusion. The aim is to accurately identify temperature gradient changes during silicon crystal growth, in order to promptly react to early growth failures and ensure the stable growth of high-quality silicon single crystals to meet industrial production requirements. The algorithm first divides the temperature gradient trend into reasonable categories based on expert knowledge and qualitative analysis methods. Then, it fuses the original features of actual production data, shallow features extracted based on statistical information, and deep features extracted through deep learning. During the fusion process, the algorithm considers the impact of different features on the target variable and calculates mutual information based on the difference between information entropy and conditional entropy, ultimately using mutual information for feature weighting. Subsequently, the fused multi-level feature vectors and their corresponding trend labels are input into a Deep Belief Network (DBN) model to capture process dynamics and classify trend changes. Finally, the experimental results demonstrate that the proposed algorithm can effectively predict the changing trend of thermal field temperature gradients. The introduction of this algorithm will help improve the accuracy of fault trend prediction in silicon single-crystal preparation, thereby minimizing product quality issues and production interruptions caused by abnormal conditions.

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus-subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric's performance in important node identification is greatly improved, and it demonstrates good network resilience.

A Hybrid Cryptosystem Incorporating a New Algorithm for Improved Entropy

Today, safeguarding sensitive content through encryption is crucial. This work presents a hybrid cryptosystem for images that employs both asymmetric and symmetric encryption. The asymmetric component involves applying the Diffie-Hellman protocol and the ElGamal cryptosystem to securely transmit two constants. These constants are necessary for the symmetrical aspect to generate dynamic permutations, substitution boxes, and round keys. Following an encryption process with fourteen rounds, the encrypted images are processed by an algorithm proposed to enhance entropy, a critical metric for assessing encryption quality. It increases the frequencies of the basic colors to achieve a histogram closely resembling a uniform distribution, but it increases the image size by approximately 8%. This improves the entropy values achieved by the hybrid cryptosystem, bringing them remarkably close to the ideal value of 8.0. In specific instances, the entropy values were elevated from 7.99926 to 8.0. The proposed method exhibits resilience against various attacks, including differential, linear, brute force, and algebraic attacks, as evaluated through the entropy, correlation, goodness of fit, Discrete Fourier Transform (DFT), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Avalanche Criteria (AC), contrast, energy, and homogeneity. Further, encrypted images are subjected to noise attacks ranging from 20% to 50% noise, including additive, multiplicative, occlusion noise, as well as the newly introduced χ2 noise. The noise damage is quantified using the proposed Similarity Parameter (SP), and a 3 × 3 median filter is employed to enhance the visual quality.

An entropy trap model of thermodynamic anomalies for dual-amorphous water undergoing liquid-liquid phase transition

Water displays numerous anomalously thermodynamic behaviors. However, the working principles behind these anomalies are not well understood, and the liquid-liquid phase transition (LLPT) is often regarded as the potential reason. In this study, we developed an entropy trap model to characterize the thermodynamic LLPT in dual-amorphous water, i.e. having both low-density and high-density liquid water. From the Adam-Gibbs model and free-volume theory, thermodynamic behaviors of water have been described using the proposed model, in which the constitutive relationships among density, heat capacity, thermal expansivity and glass transition temperature have been formulated. Moreover, the glass transition and its connection to thermodynamic behaviors were also investigated for dual-amorphous water. Finally, experimental data reported in the literature were used to verify effectiveness of the proposed model. This study is expected to provide a physical insight into the anomalous thermodynamics of dual-amorphous water undergoing the LLPT.

Thermodynamics, Environmental and Sustainability Impacts of a Turbofan Engine Under Different Design Conditions Considering Variable Needs in the Aviation Industry

In this study, thermodynamic analysis is implemented to the kerosene-fuelled high by-pass turbofan (HBP-TF) engine to assess entropy, exergy, environmental, and sustainability metrics for different design variables such as pressure ratio of high-pressure compressor (HPC-PR) ranging from 7.5 to 8.5 and turbine inlet temperature (TIT) varying from 1400 to 1525 K considering variable needs in the aviation industry. As a novelty, entropic improvement potential (EIP) index for turbomachinery components and specific irreversibility production for the whole engine are calculated. Sustainability-based parameters for different cases are compared with the baseline values of the HBP-TF engine. The combustor has the highest entropy production of 44.4425 kW K-1 at the baseline. The higher TIT increases the entropy production of the combustor by 16.56%, whereas the higher HPC-PR decreases it by 5.83%. The higher TIT and HPC-PR favorably affect the sustainable efficiency factor of the engine, which is observed as 1.5482 at baseline and increases by 4.5% and 0.058% with the increment of TIT and HPC-PR, respectively. The higher TIT and higher HPC-PR results in lowering sustainability of the engine. The specific irreversibility production of the engine decreases by 3.78% and 0.1171% respectively, as TIT and HPC-PR reach the highest point considered in the study.

Neutrinos to Astrovirology: Signatures

In the 20th century, the concept of terrestrial life's unity was solidified, and the 21st century saw the emergence and establishment of astrovirology. To date, life originating beyond Earth has not been identified. The singular instance where NASA investigated potential microfossils in Martian ejecta found on Earth has since been refuted. This report suggests that a more comprehensive discussion and analysis of life's biosignatures and communication methods are essential. Such approaches are crucial not only to avoid overlooking the possible existence of extra-terrestrial intelligence (ETI) but also to prevent potential human infections that could arise from extra-terrestrial contact. In addition terrestrial infections by microorganism that originally derived from Earth and were returned, require investigation due to potential mutations and subsequent increased pathogenicity.

The coherence between PSMC6 and α-ring in the 26S proteasome is associated with Alzheimer's disease

Alzheimer's disease (AD) is a heterogeneous age-dependent neurodegenerative disorder. Its hallmarks involve abnormal proteostasis, which triggers proteotoxicity and induces neuronal dysfunction. The 26S proteasome is an ATP-dependent proteolytic nanomachine of the ubiquitin-proteasome system (UPS) and contributes to eliminating these abnormal proteins. This study focused on the relationship between proteasome and AD, the hub genes of proteasome, PSMC6, and 7 genes of α-ring, are selected as targets to study. The following three characteristics were observed: 1. The total number of proteasomes decreased with AD progression because the proteotoxicity damaged the expression of proteasome proteins, as evidenced by the downregulation of hub genes. 2. The existing proteasomes exhibit increased activity and efficiency to counterbalance the decline in total proteasome numbers, as evidenced by enhanced global coordination and reduced systemic disorder of proteasomal subunits as AD advances. 3. The synergy of PSMC6 and α-ring subunits is associated with AD. Synergistic downregulation of PSMC6 and α-ring subunits reflects a high probability of AD risk. Regarding the above discovery, the following hypothesis is proposed: The aggregation of pathogenic proteins intensifies with AD progression, then proteasome becomes more active and facilitates the UPS selectively targets the degradation of abnormal proteins to maintain CNS proteostasis. In this paper, bioinformatics and support vector machine learning methods are applied and combined with multivariate statistical analysis of microarray data. Additionally, the concept of entropy was used to detect the disorder of proteasome system, it was discovered that entropy is down-regulated continually with AD progression against system chaos caused by AD. Another conception of the matrix determinant was used to detect the global coordination of proteasome, it was discovered that the coordination is enhanced to maintain the efficiency of degradation. The features of entropy and determinant suggest that active proteasomes resist the attack caused by AD like defenders, on the one hand, to protect themselves (entropy reduces), and on the other hand, to fight the enemy (determinant reduces). It is noted that these are results from biocomputing and need to be supported by further biological experiments.

Open Problems within Nonextensive Statistical Mechanics

Nonextensive statistical mechanics has developed into an important framework for modeling the thermodynamics of complex systems and the information of complex signals. To mark the 80th birthday of the field's founder, Constantino Tsallis, a review of open problems that can stimulate future research is provided. Over the thirty-year development of NSM, a variety of criticisms have been published ranging from questions about the justification for generalizing the entropy function to the interpretation of the generalizing parameter q. While these criticisms have been addressed in the past and the breadth of applications has demonstrated the utility of the NSM methodologies, this review provides insights into how the field can continue to improve the understanding and application of complex system models. The review starts by grounding q-statistics within scale-shape distributions and then frames a series of open problems for investigation. The open problems include using the degrees of freedom to quantify the difference between entropy and its generalization, clarifying the physical interpretation of the parameter q, improving the definition of the generalized product using multidimensional analysis, defining a generalized Fourier transform applicable to signal processing applications, and re-examining the normalization of nonextensive entropy. This review concludes with a proposal that the shape parameter is a candidate for defining the statistical complexity of a system.

Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer

Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.

Continual Reinforcement Learning for Quadruped Robot Locomotion

The ability to learn continuously is crucial for a robot to achieve a high level of intelligence and autonomy. In this paper, we consider continual reinforcement learning (RL) for quadruped robots, which includes the ability to continuously learn sub-sequential tasks (plasticity) and maintain performance on previous tasks (stability). The policy obtained by the proposed method enables robots to learn multiple tasks sequentially, while overcoming both catastrophic forgetting and loss of plasticity. At the same time, it achieves the above goals with as little modification to the original RL learning process as possible. The proposed method uses the Piggyback algorithm to select protected parameters for each task, and reinitializes the unused parameters to increase plasticity. Meanwhile, we encourage the policy network exploring by encouraging the entropy of the soft network of the policy network. Our experiments show that traditional continual learning algorithms cannot perform well on robot locomotion problems, and our algorithm is more stable and less disruptive to the RL training progress. Several robot locomotion experiments validate the effectiveness of our method.

Ion entropy and accurate entropy-based FDR estimation in metabolomics

Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

Heat Capacity of Indium or Gallium Sesqui-Chalcogenides

The chalcogenides of p-block elements constitute a significant category of materials with substantial potential for advancing the field of electronic and optoelectronic devices. This is attributed to their exceptional characteristics, including elevated carrier mobility and the ability to fine-tune band gaps through solid solution formation. These compounds exhibit diverse structures, encompassing both three-dimensional and two-dimensional configurations, the latter exemplified by the compound In2Se3. Sesqui-chalcogenides were synthesized through the direct reaction of highly pure elements within a quartz ampoule. Their single-phase composition was confirmed using X-ray diffraction, and the morphology and chemical composition were characterized using scanning electron microscopy. The compositions of all six materials were also confirmed using X-ray photoelectron spectroscopy and Raman spectroscopy. This investigation delves into the thermodynamic properties of indium and gallium sesqui-chalcogenides. It involves low-temperature heat capacity measurements to evaluate standard entropies and Tian-Calvet calorimetry to elucidate the temperature dependence of heat capacity beyond the reference temperature of 298.15 K, as well as the enthalpy of formation assessed from DFT calculations.

Entropy-Driven Strongly Confined Low-Toxicity Pure-Red Perovskite Quantum Dots for Spectrally Stable Light-Emitting Diodes

Spectrally stable pure-red perovskite quantum dots (QDs) with low lead content are essential for high-definition displays but are difficult to synthesize due to QD self-purification. Here, we make use of entropy-driven quantum-confined pure-red perovskite QDs to fabricate light-emitting diodes (LEDs) that have low toxicity and are efficient and spectrum-stable. Based on experimental data and first-principles calculations, multiple element alloying results in a 60% reduction in lead content while improving QD entropy to promote crystal stability. Entropy-driven QDs exhibit photoluminescence with 100% quantum yields and single-exponential decay lifetimes without alteration of their morphology or crystal structure. The pure-red LEDs utilizing entropy-driven QDs have spectrally stable electroluminescence, achieving a brightness of 4932 cd/m2, a maximum external quantum efficiency of over 20%, and a 15-fold longer operational lifetime than the CsPbI3 QD-based LEDs. These achievements demonstrate that entropy-driven QDs can mitigate local compositional heterogeneity and ion migration.

Heteronanostructured Field-Effect Transistors for Enhancing Entropy and Parameter Space in Electrical Unclonable Primitives

Hardware security is not a new problem but is ever-growing in consumer and medical domains owing to hyperconnectivity. A physical unclonable function (PUF) offers a promising hardware security solution for cryptographic key generation, identification, and authentication. However, electrical PUFs using nanomaterials or two-dimensional (2D) transition metal dichalcogenides (TMDCs) often have limited entropy and parameter space sources, both of which increase the vulnerability to attacks and act as bottlenecks for practical applications. We report an electrical PUF with enhanced entropy as well as parameter space by incorporating 2D TMDC heteronanostructures into field-effect transistors (FETs). Lateral heteronanostructures of 2D molybdenum disulfide and tungsten disulfide serve as a potent entropy source. The variable feature of FETs is further leveraged to enhance the parameter space that provides multiple challenge-response pairs, which are essential for PUFs. This combination results in stably repeatable yet highly variable FET characteristics as alternative electrical PUFs. Comprehensive PUF performance analyses validate the bit uniformity, reproducibility, uniqueness, randomness, false rates, and encoding capacity. The 2D material heteronanostructure-driven electrical PUFs with strong FET-to-FET variability can potentially be augmented as an immediately deployable and scalable security solution for various hardware devices.

An Investigation into the Stability Source of Collagen Fiber Modified Using Cr(III): An Adsorption Isotherm Study

The enhanced hydrothermal stability of leather, imparted by little Cr(III), has traditionally been ascribed to strong coordinate bonds. However, this explanation falls short when considering that the heat-induced shrinking of collagen fiber is predominantly driven by rupturing weak H-bonds. This study explored the stability source via adsorption thermodynamics using collagen fiber as an adsorbent. Eleven isotherm models were fitted with the equilibrium dataset. Nine of these models aptly described Cr(III) adsorption based on the physical interpretations of model parameters and error functions. The adsorption equilibrium constants from six models could be transformed into dimensionless thermodynamic equilibrium constants. Based on the higher R2 of the van't Hoff equation, thermodynamic parameters (∆G°, ∆H°, ∆S°) from the Fritz-Shluender isotherm model revealed that the adsorption process typifies endothermic and spontaneous chemisorption, emphasizing entropy increase as the primary driver of Cr(III) bonding with collagen. Thus, the release of bound H2O from collagen is identified as the stability source of collagen fiber modified by Cr(III). This research not only clarifies the selection and applicability of the isotherm model in a specific aqueous system but also identifies entropy, rather than enthalpy, as the principal stability source of Cr-leather. These insights facilitate the development of novel methods to obtain stable collagen-based material.

Nonlinear Analysis of the Hand and Foot Force-Time Profiles in the Four Competitive Swimming Strokes

Human locomotion on water depends on the force produced by the swimmer to propel the body forward. Performance of highly complex motor tasks like swimming can yield minor variations that only nonlinear analysis can be sensitive enough to detect. The purpose of the present study was to examine the nonlinear properties of the hand/feet forces and describe their variations across the four competitive swimming strokes performing segmental and full-body swimming. Swimmers performed all-out bouts of 25 m in the four swimming strokes, swimming the full-body stroke, with the arm-pull only and with the leg kicking only. Hand/foot force and swimming velocity were measured. The Higuchi's fractal dimension (HFD) and sample entropy (SampEn) were used for the nonlinear analysis of force and velocity. Both the arm-pull and leg kicking alone were found to produce similar peak and mean hand/foot forces as swimming the full-body stroke. Hand force was more complex in breaststroke and butterfly stroke; conversely, kicking conditions were more complex in front crawl and backstroke. Moreover, the arm-pull and kicking alone tended to be more complex (higher HFD) but more predictable (lower SampEn) than while swimming the full-body stroke. There was no loss of force production from segmental swimming to the full-body counterpart. In conclusion, the number of segments in action influences the nonlinear behavior of the force produced and, when combining the four limbs, the complexity of the hand/foot force tends to decrease.

A Robust Machine Learning Model for Diabetic Retinopathy Classification

Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usually used for data classification. AI has a large connection with image processing and feature classification, and it can also be successfully applied to analyzing fundus eye images. Diabetic retinopathy (DR) is a disease that can cause vision loss and blindness, which, from an imaging point of view, can be shown when screening the eyes. Image processing tools can analyze and extract the features from fundus eye images, and these corroborate with ML classifiers that can perform their classification among different disease classes. The outcomes integrated into automated diagnostic systems can be a real success for physicians and patients. In this study, in the form image processing area, the manipulation of the contrast with the gamma correction parameter was applied because DR affects the blood vessels, and the structure of the eyes becomes disorderly. Therefore, the analysis of the texture with two types of entropies was necessary. Shannon and fuzzy entropies and contrast manipulation led to ten original features used in the classification process. The machine learning library PyCaret performs complex tasks, and the empirical process shows that of the fifteen classifiers, the gradient boosting classifier (GBC) provides the best results. Indeed, the proposed model can classify the DR degrees as normal or severe, achieving an accuracy of 0.929, an F1 score of 0.902, and an area under the curve (AUC) of 0.941. The validation of the selected model with a bootstrap statistical technique was performed. The novelty of the study consists of the extraction of features from preprocessed fundus eye images, their classification, and the manipulation of the contrast in a controlled way.

Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox-Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox-Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox-Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes.

Thermodynamics of Formation and Liquid-Vapor Phase Transitions of Antimony Alloys with Selenium and Sulfur

The authors conducted liquid solution studies of antimony with selenium and sulfur in order to provide information on the thermodynamic functions of the formation of these alloys. The studies are based on the vapor pressure values of the components, comprising the double partial systems of antimony with antimony chalcogenides (Sb2Se3 and Sb2S3) and antimony chalcogenides with chalcogens (Se and S). We calculated the thermodynamic functions of mixing (graphical dependencies) and evaporation (tabular data) based on the partial vapor pressure values of components, which are represented by temperature-concentration dependencies. Based on the partial pressure values of melt components, we calculated the boundaries of liquid and vapor coexistence fields at atmospheric pressure (101.3 kPa) and in a vacuum (0.9 kPa). We established the absence of the stratification region on the Sb2S3-S diagram due to the fact that, on state diagrams, the stratification region is indicated at temperatures above 530 °C, while the boiling point of liquid sulfur at an atmospheric pressure corresponds to 429 °C. Based on the position of the field boundaries (L + V) on the state diagrams, the separation of antimony alloys with selenium and sulfur via distillation into elements at atmospheric pressure is difficult due to the high boiling points of antimony-based alloys in a vacuum: Sb2Se3-Se melts require some number of condensate re-evaporation cycles.

Quantifying the Resilience of a Healthcare System: Entropy and Network Science Perspectives

In this study, we consider the human body and the healthcare system as two complex networks and use theories regarding entropy, requisite variety, and network centrality metrics with resilience to assess and quantify the strengths and weaknesses of healthcare systems. Entropy is used to quantify the uncertainty and variety regarding a patient's health state. The extent of the entropy defines the requisite variety a healthcare system should contain to be able to treat a patient safely and correctly. We use network centrality metrics to visualize and quantify the healthcare system as a network and assign the strengths and weaknesses of the network and of individual agents in the network. We apply organization design theories to formulate improvements and explain how a healthcare system should adjust to create a more robust and resilient healthcare system that is able to continuously deal with variations and uncertainties regarding a patient's health, despite possible stressors and disturbances at the healthcare system. In this article, these concepts and theories are explained and applied to a fictive and a real-life example. We conclude that entropy and network science can be used as tools to quantify the resilience of healthcare systems.

Ideal Agent System with Triplet States: Model Parameter Identification of Agent-Field Interaction

On the capital market, price movements of stock corporations can be observed independent of overall market developments as a result of company-specific news, which suggests the occurrence of a sudden risk event. In recent years, numerous concepts from statistical physics have been transferred to econometrics to model these effects and other issues, e.g., in socioeconomics. Like other studies, we extend the approaches based on the "buy" and "sell" positions of agents (investors' stance) with a third "hold" position. We develop the corresponding theory within the framework of the microcanonical and canonical ensembles for an ideal agent system and apply it to a capital market example. We thereby design a procedure to estimate the required model parameters from time series on the capital market. The aim is the appropriate modeling and the one-step-ahead assessment of the effect of a sudden risk event. From a one-step-ahead performance comparison with selected benchmark approaches, we infer that the model is well-specified and the model parameters are well determined.

From Black Holes Entropy to Consciousness: The Dimensions of the Brain Connectome

It has been shown that the theory of relativity can be applied physically to the functioning brain, so that the brain connectome should be considered as a four-dimensional spacetime entity curved by brain activity, just as gravity curves the four-dimensional spacetime of the physical world. Following the most recent developments in modern theoretical physics (black hole entropy, holographic principle, AdS/CFT duality), we conjecture that consciousness can naturally emerge from this four-dimensional brain connectome when a fifth dimension is considered, in the same way that gravity emerges from a 'flat' four-dimensional quantum world, without gravitation, present at the boundaries of a five-dimensional spacetime. This vision makes it possible to envisage quantitative signatures of consciousness based on the entropy of the connectome and the curvature of spacetime estimated from data obtained by fMRI in the resting state (nodal activity and functional connectivity) and constrained by the anatomical connectivity derived from diffusion tensor imaging.

Strain Energy and Entropy Based Scaling of Buckling Modes

A new utilization of entropy in the context of buckling is presented. The novel concept of connecting the strain energy and entropy for a pin-ended strut is derived. The entropy of the buckling mode is extracted through a surrogate model by decomposing the strain energy into entropy and virtual temperature. This concept rationalizes the ranking of buckling modes based on their strain energy under the assumption of given entropy. By assigning identical entropy to all buckling modes, they can be ranked according to their deformation energy. Conversely, with identical strain energy assigned to all the modes, ranking according to entropy is possible. Decreasing entropy was found to represent the scaling factors of the buckling modes that coincide with the measurement of the initial out-of-straightness imperfections in IPE160 beams. Applied to steel plane frames, scaled buckling modes can be used to model initial imperfections. It is demonstrated that the entropy (scale factor) for a given energy roughly decreases with the inverse square of the mode index. For practical engineering, this study presents the possibility of using scaled buckling modes of steel plane frames to model initial geometric imperfections. Entropy proves to be a valuable complement to strain energy in structural mechanics.

The Impact of COVID-19 on Weak-Form Efficiency in Cryptocurrency and Forex Markets

The COVID-19 pandemic has had an unprecedented impact on the global economy and financial markets. In this article, we explore the impact of the pandemic on the weak-form efficiency of the cryptocurrency and forex markets by conducting a comprehensive comparative analysis of the two markets. To estimate the weak-form of market efficiency, we utilize the asymmetric market deficiency measure (MDM) derived using the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach, along with fuzzy entropy, Tsallis entropy, and Fisher information. Initially, we analyze the temporal evolution of these four measures using overlapping sliding windows. Subsequently, we assess both the mean value and variance of the distribution for each measure and currency in two distinct time periods: before and during the pandemic. Our findings reveal distinct shifts in efficiency before and during the COVID-19 pandemic. Specifically, there was a clear increase in the weak-form inefficiency of traditional currencies during the pandemic. Among cryptocurrencies, BTC stands out for its behavior, which resembles that of traditional currencies. Moreover, our results underscore the significant impact of COVID-19 on weak-form market efficiency during both upward and downward market movements. These findings could be useful for investors, portfolio managers, and policy makers.

Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, it was suggested that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories-fluctuations, rapid environmental responses, and long-term changes-converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems.

A quantitative information measure applied to texture perception attributes during mastication

We have calculated an entropy or information measure of previously reported experimentally determined temporal dominance of sensations (TDS) data of texture attributes for two sets of emulsion filled gels throughout the mastication cycle. The samples were emulsion filled gels and two-layered emulsion filled gels. We find that the entropy measure follows an average curve, which is different for each set. The specifics of the entropy curve may serve as a fingerprint for the perception of a specific food sample.

Subjective time dilation as a result of entropy

Subjective time dilation is an effect discovered using the oddball paradigm, where expanding visual stimuli, but not shrinking ones, induce a prolongation of subjective temporal perception compared to static stimuli. This disparity is often seen as another demonstration of humans' evolved reaction to approaching threats, since visual expansion resembles approaching potential threats and warrants extra attention. In this study, we show that by manipulating the relative sizes of stimuli, both expanding and shrinking stimuli can induce prolongation of subjective time in an oddball paradigm. We propose an alternative explanation based on information theory, linking subjective temporal perception to entropy, an objective property of stimulus that is information uncertainty. Temporal function as a logistic function thus serves as the framework inside which content functions like information processing are able to operate coherently.

On network construction and module detection for molecular graph of titanium dioxide

Titanium dioxide is the most common and valuable oxide among four types of oxides of titanium. Its physicochemical properties make it a very valuable compound. The main objective of this article is to initially detect the modules based on highly connected links of the network of the degree-based topological indices. This information is lately integrated with different physical properties of the chemical compound of titanium dioxide to develop different mathematical frameworks based on master regulatory indices of the modules. This connection can be helpful in studying the physical measures at a deeper level in the form of different degree based topological indices.Communicated by Ramaswamy H. Sarma.

Designing a Novel Approach Using a Greedy and Information-Theoretic Clustering-Based Algorithm for Anonymizing Microdata Sets

Data anonymization is a technique that safeguards individuals' privacy by modifying attribute values in published data. However, increased modifications enhance privacy but diminish the utility of published data, necessitating a balance between privacy and utility levels. K-Anonymity is a crucial anonymization technique that generates k-anonymous clusters, where the probability of disclosing a record is 1/k. However, k-anonymity fails to protect against attribute disclosure when the diversity of sensitive values within the anonymous cluster is insufficient. Several techniques have been proposed to address this issue, among which t-closeness is considered one of the most robust privacy techniques. In this paper, we propose a novel approach employing a greedy and information-theoretic clustering-based algorithm to achieve strict privacy protection. The proposed anonymization algorithm commences by clustering the data based on both the similarity of quasi-identifier values and the diversity of sensitive attribute values. In the subsequent adjustment phase, the algorithm splits and merges the clusters to ensure that they each possess at least k members and adhere to the t-closeness requirements. Finally, the algorithm replaces the quasi-identifier values of the records in each cluster with the values of the cluster center to attain k-anonymity and t-closeness. Experimental results on three microdata sets from Facebook, Twitter, and Google+ demonstrate the proposed algorithm's ability to preserve the utility of released data by minimizing the modifications of attribute values while satisfying the k-anonymity and t-closeness constraints.

Small Stochastic Data Compactification Concept Justified in the Entropy Basis

Measurement is a typical way of gathering information about an investigated object, generalized by a finite set of characteristic parameters. The result of each iteration of the measurement is an instance of the class of the investigated object in the form of a set of values of characteristic parameters. An ordered set of instances forms a collection whose dimensionality for a real object is a factor that cannot be ignored. Managing the dimensionality of data collections, as well as classification, regression, and clustering, are fundamental problems for machine learning. Compactification is the approximation of the original data collection by an equivalent collection (with a reduced dimension of characteristic parameters) with the control of accompanying information capacity losses. Related to compactification is the data completeness verifying procedure, which is characteristic of the data reliability assessment. If there are stochastic parameters among the initial data collection characteristic parameters, the compactification procedure becomes more complicated. To take this into account, this study proposes a model of a structured collection of stochastic data defined in terms of relative entropy. The compactification of such a data model is formalized by an iterative procedure aimed at maximizing the relative entropy of sequential implementation of direct and reverse projections of data collections, taking into account the estimates of the probability distribution densities of their attributes. The procedure for approximating the relative entropy function of compactification to reduce the computational complexity of the latter is proposed. To qualitatively assess compactification this study undertakes a formal analysis that uses data collection information capacity and the absolute and relative share of information losses due to compaction as its metrics. Taking into account the semantic connection of compactification and completeness, the proposed metric is also relevant for the task of assessing data reliability. Testing the proposed compactification procedure proved both its stability and efficiency in comparison with previously used analogues, such as the principal component analysis method and the random projection method.

The Intransitive Logic of Directed Cycles and Flipons Enhances the Evolution of Molecular Computers by Augmenting the Kolmogorov Complexity of Genomes

Cell responses are usually viewed as transitive events with fixed inputs and outputs that are regulated by feedback loops. In contrast, directed cycles (DCs) have all nodes connected, and the flow is in a single direction. Consequently, DCs can regenerate themselves and implement intransitive logic. DCs are able to couple unrelated chemical reactions to each edge. The output depends upon which node is used as input. DCs can also undergo selection to minimize the loss of thermodynamic entropy while maximizing the gain of information entropy. The intransitive logic underlying DCs enhances their programmability and impacts their evolution. The natural selection of DCs favors the persistence, adaptability, and self-awareness of living organisms and does not depend solely on changes to coding sequences. Rather, the process can be RNA-directed. I use flipons, nucleic acid sequences that change conformation under physiological conditions, as a simple example and then describe more complex DCs. Flipons are often encoded by repeats and greatly increase the Kolmogorov complexity of genomes by adopting alternative structures. Other DCs allow cells to regenerate, recalibrate, reset, repair, and rewrite themselves, going far beyond the capabilities of current computational devices. Unlike Turing machines, cells are not designed to halt but rather to regenerate.

A Catastrophic Biodiversity Loss in the Environment Is Being Replicated on the Skin Microbiome: Is This a Major Contributor to the Chronic Disease Epidemic?

There has been a catastrophic loss of biodiversity in ecosystems across the world. A similar crisis has been observed in the human gut microbiome, which has been linked to "all human diseases affecting westernized countries". This is of great importance because chronic diseases are the leading cause of death worldwide and make up 90% of America's healthcare costs. Disease development is complex and multifactorial, but there is one part of the body's interlinked ecosystem that is often overlooked in discussions about whole-body health, and that is the skin microbiome. This is despite it being a crucial part of the immune, endocrine, and nervous systems and being continuously exposed to environmental stressors. Here we show that a parallel biodiversity loss of 30-84% has occurred on the skin of people in the developed world compared to our ancestors. Research has shown that dysbiosis of the skin microbiome has been linked to many common skin diseases and, more recently, that it could even play an active role in the development of a growing number of whole-body health problems, such as food allergies, asthma, cardiovascular diseases, and Parkinson's, traditionally thought unrelated to the skin. Damaged skin is now known to induce systemic inflammation, which is involved in many chronic diseases. We highlight that biodiversity loss is not only a common finding in dysbiotic ecosystems but also a type of dysbiosis. As a result, we make the case that biodiversity loss in the skin microbiome is a major contributor to the chronic disease epidemic. The link between biodiversity loss and dysbiosis forms the basis of this paper's focus on the subject. The key to understanding why biodiversity loss creates an unhealthy system could be highlighted by complex physics. We introduce entropy to help understand why biodiversity has been linked with ecosystem health and stability. Meanwhile, we also introduce ecosystems as being governed by "non-linear physics" principles-including chaos theory-which suggests that every individual part of any system is intrinsically linked and implies any disruption to a small part of the system (skin) could have a significant and unknown effect on overall system health (whole-body health). Recognizing the link between ecosystem health and human health allows us to understand how crucial it could be to maintain biodiversity across systems everywhere, from the macro-environment we inhabit right down to our body's microbiome. Further, in-depth research is needed so we can aid in the treatment of chronic diseases and potentially change how we think about our health. With millions of people currently suffering, research to help mitigate the crisis is of vital importance.

Characterization of the normal fetal circulatory system of the ductus venosus using sound complexity parameters

The aim of this study was to characterize the normality of the fetal circulatory system through the time between ventricular systoles of the ductus venosus in the three gestational trimesters in healthy fetuses using nonlinear methods of the complexity of the signal. A prospective cohort study was conducted at the Instituto de Medicina Integral Prof. Fernando Figueira (IMIP) from December 2019 to May 2020. Pregnant women between 11 and 14 weeks, with intrauterine pregnancy and healthy fetus were included. Patients with multiple gestation, positive screening for congenital malformation, including heart disease, and under 18 years of age were excluded. Doppler velocimetry ultrasonography of the ductus venosus was performed between the 11th and 14th weeks, 20th and 24th weeks, and 28th and 32nd weeks of gestation, and then the sound signal was extracted and segmented from the videos. To compare the means between the gestational trimesters of the approximate entropy (ApEn) and Lempel-Ziv complexity (CLZ) of the time between ventricular systoles, the Friedman test was used, with a significance level of 5%. No statistically significant difference was found between the 1st, 2nd, and 3rd trimesters regarding the mean ApEn (P=0.281) and CLZ (P=0.595) of the time between ventricular systoles of the ductus venosus. Ductus venosus systolic time was not sensitive to differentiate fetal cardiovascular dynamics between gestational trimesters. This study pioneered the characterization of cardiovascular normality by nonlinear parameters of the fetal ductus venosus in all three trimesters.