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Background: A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information. Results: BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification. Conclusions: An information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants. [ABSTRACT FROM AUTHOR]

Anurans have several characteristics in their acoustic communication that distinguish them. These characteristics are influenced by body-related, environmental and social variables. However, the complexity of anuran repertoire and the effect of such variables on it are still understudied. Dendropsophus minutus is a Neotropical treefrog that has a wide repertoire composed of three notes, grouped in at least 71 unique combinations. To quantify complexity, we used the entropy measurement, since it measures the randomness of a sequence. Therefore, we could test if repertoire complexity in D. minutus is related with body condition, social context, and environmental variables. We recorded vocalisations of 80 males in three sites and found 44 unique note combinations not described before, increasing the species' repertoire to 115 combinations. We observed that the species has near maximum entropy for note frequency, but a low entropy for note sequencies, showing a clear a bias for some note combinations. We found a positive relationship between repertoire complexity and air temperature. Thus, based on our results, the complexity of this species' repertoire is an additional trait influenced by climate, and should be considered in future studies. [ABSTRACT FROM AUTHOR]

The storage, transmission, and processing of data become significant problems when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here, we propose a novel method that allows for the scalability of digital assets, including blockchain databases in the download, validation, and confidentiality processes, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method by which to generate an information-entropic numerical barcode representation of a digital asset. Using this technique, a 1–2 Mb block of digital data can be represented by a few bytes, significantly reducing the size of a blockchain. The entropic barcode file can be utilized on its own or as an optically machine-readable entropic barcode for secure data transmission, processing, labeling, identification, and one-way encryption, as well as for compression, validation, and digital tamper-proof checks. The mathematics of this process and all the steps involved in its implementation are discussed in detail in this article. [ABSTRACT FROM AUTHOR]

This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens through which to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5 grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian mixture model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices. [ABSTRACT FROM AUTHOR]

In network services, there are often conflicts among privacy protection, trust, and service quality, which greatly reduces the regulatory effectiveness of networks. Using the basic method of information theory, this paper considers the optimization mechanism of trust and the balance between privacy and service utility from the perspective of quantifying information flow. First, we build a trust model based on multiple factors, such as direct trust, service trust, and trust cloud network. The weight of trust factors is determined by category diversity and information entropy theory. Second, based on statistical knowledge and information entropy, we propose a new privacy measurement model with hierarchical weights. Third, we establish a trust privacy service relationship model to realize the trade-off between privacy, service, and trust in networks. The demander can choose to establish trust, protect privacy, or obtain high-quality services according to personal preferences and needs. Fourth, we propose a privacy trust mapping relationship model and a privacy disclosure selection model. Finally, we design different experimental schemes. These simulation results show that our research can not only provide better quality of service but also better protect privacy and help interactive parties in networks establish a strong trust relationship. This research will improve the effectiveness of networks and can be applied to class II vaccine multi-agent collaborative supervision based on technology. [ABSTRACT FROM AUTHOR]

In most silent speech research, continuously observing tongue movements is crucial, thus requiring the use of ultrasound to extract tongue contours. Precisely and in real-time extracting ultrasonic tongue contours presents a major challenge. To tackle this challenge, the novel end-to-end lightweight network DAFT-Net is introduced for ultrasonic tongue contour extraction. Integrating the Convolutional Block Attention Module (CBAM) and Attention Gate (AG) module with entropy-based optimization strategies, DAFT-Net establishes a comprehensive attention mechanism with dual functionality. This innovative approach enhances feature representation by replacing traditional skip connection architecture, thus leveraging entropy and information-theoretic measures to ensure efficient and precise feature selection. Additionally, the U-Net's encoder and decoder layers have been streamlined to reduce computational demands. This process is further supported by information theory, thus guiding the reduction without compromising the network's ability to capture and utilize critical information. Ablation studies confirm the efficacy of the integrated attention module and its components. The comparative analysis of the NS, TGU, and TIMIT datasets shows that DAFT-Net efficiently extracts relevant features, and it significantly reduces extraction time. These findings demonstrate the practical advantages of applying entropy and information theory principles. This approach improves the performance of medical image segmentation networks, thus paving the way for real-world applications. [ABSTRACT FROM AUTHOR]

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network's operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model's correlation. [ABSTRACT FROM AUTHOR]

The concept of entropy and information gain of BE-algebras in scientific disciplines such as information theory, data science, supply chain and machine learning assists us to calculate the uncertanity of the scientific processes of phenomena. In this respect the notion of filter entropy for a transitive BE-algebra is introduced and its properties are investigated. The notion of a dynamical system on a transitive BE-algebra is introduced. The concept of the entropy for a transitive BE-algebra dynamical system is developed and, its characteristics are considered. The notion of equivalent transitive BE-algebra dynamical systems is defined, and it is proved the fact that two equivalent BE-algebra dynamical systems have the same entropy. Theorems to help calculate the entropy are given. Specifically, a new version of Kolmogorov– Sinai Theorem has been proved. The study introduces the concept of information gain of a transitive BE-algebra with respect to its filters and investigates its properties. This study proposes the use of filter entropy to approximate the level of risk introduced by a BE-algebra dynamical system. This aim is reached by defining the information gain with respect to the filters of a BE-algebra. This methodology is well developed for use in engineering, especially in industrial networks. This paper proposes a novel approach to assess the quantity of uncertainty, and the impact of information gain of a BE-algebra dynamical system. [ABSTRACT FROM AUTHOR]

In the theory of special relativity, energy can be found in two forms: kinetic energy and rest mass. The potential energy of a body is actually stored in the form of rest mass, the interaction energy too, but temperature is not. Information acquired about a dynamical system can be potentially used to extract useful work from it. Hence, the "mass–energy–information equivalence principle" that has been recently proposed. In this paper, it is first recalled that for a thermodynamic system made of non-interacting entities at constant temperature, the internal energy is also constant. So, the energy involved in a variation in entropy ( T Δ S ) differs from a change in the potential energy stored or released and cannot be associated to a corresponding variation in mass of the system, even if it is expressed in terms of the quantity of information. This debate gives us the opportunity to deepen the notion of entropy seen as a quantity of information, to highlight the difference between logical irreversibility (a state-dependent property) and thermodynamical irreversibility (a path-dependent property), and to return to the nature of the link between energy and information that is dynamical. [ABSTRACT FROM AUTHOR]

In the domain of modern manufacturing, computer numerical control (CNC) milling machines have emerged as instrumental assets. However, the data they generate is of vast amount, but usually contains redundancies and displays consistent patterns, making it inefficient for deep learning training. This paper proposes a novel sampling algorithm tailored for CNC milling machine data, emphasizing both diversity and efficiency. The proposed method leverages the entropy concept from the information‐theoretic perspective to evaluate and enhance data diversity, aiming to achieve efficient learning with high accuracy. This in turn enables to not only facilitates a deeper understanding of CNC data characteristics but also contributes significantly to the optimization of deep learning training processes in the context of CNC milling data. [ABSTRACT FROM AUTHOR]

Knowledge of mixing phenomena is of great value in the mineral and other chemical and biochemical industries. This work aims to analyze the quality of mixedness (QM), the intrinsic mass transfer (MT) number, and the MT efficiency based on information entropy theory in the counter-current microstructured slurry bubble column. A thorough analysis is conducted to assess the effects of particle loading, gas and slurry velocity, and axial variation on the QM. The range of gas velocity, slurry velocity, particle size, and particle loading was 0.011–0.075 m/s, 0.018–0.058 m/s, 242.72–408.31 μm, and 15.54–88.94 kg/m3, respectively. QM is a time-dependent parameter, and the concept of contact time has been used for scale-up purposes. The maximum QM was achieved at dimensionless times of 0.40 × 10−3, 0.15 × 10−3, and 0.85 × 10−3 for the maximum superficial gas velocity, particle loading, and axial height, respectively. The gas velocity positively influenced both the intrinsic MT number and its efficiency. In contrast, the slurry velocity and particle loading had a negative effect. The present theoretical analysis will pave the path for industrial process intensification in counter-current flow systems. [ABSTRACT FROM AUTHOR]

One of the fundamental inequalities which is used in many inequities is Jensen inequality. In fact, it is a base of some inequality such as the arithmetic mean, harmonic mean inequality also in inequality with respect to entropies and information theory. The purpose of this research paper is to give a new interesting refinement of Jensen inequality for two particular finite sequences by using uniformly convex function. Also, we give some applications of this in information theory. [ABSTRACT FROM AUTHOR]

The main application scenario for wearable sensors involves the generation of data and monitoring metrics. fNIRS (functional near-infrared spectroscopy) allows the nonintrusive monitoring of human visual perception. The quantification of visual perception by fNIRS facilitates applications in engineering-related fields. This study designed a set of experimental procedures to effectively induce visible alterations and to quantify visual perception in conjunction with the acquisition of Hbt (total hemoglobin), Hb (hemoglobin), and HbO2 (oxygenated hemoglobin) data obtained from HfNIRS (high-density functional near-infrared spectroscopy). Volunteers completed the visual task separately in response to different visible changes in the simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visual difference, and the time point of the task change. This study consisted of one simulated scene, two visual variations, and four visual tasks. The simulation scene featured a car driving location. The visible change suggested that the brightness and saturation of the car operator interface would change. The visual task represented the completion of the layout, color, design, and information questions answered in response to the visible change. This study collected data from 29 volunteers. The volunteers completed the visual task separately in response to different visual changes in the same simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visible difference, and the time point of the task change. The data analysis methods in this study comprised a combination of channel dimensionality reduction, feature extraction, task classification, and score correlation. Channel downscaling: This study used the data of 15 channels in HfNIRS to calculate the mutual information between different channels to set a threshold, and to retain the data of the channels that were higher than those of the mutual information. Feature extraction: The statistics derived from the visual task, including time, mean, median, variance, extreme variance, kurtosis, bias, information entropy, and approximate entropy were computed. Task classification: This study used the KNN (K-Nearest Neighbors) algorithm to classify different visual tasks and to calculate the accuracy, precision, recall, and F1 scores. Scoring correlation: This study matched the visual task scores with the fluctuations of Hbt, Hb, and HbO2 and observed the changes in Hbt, Hb, and HbO2 under different scoring levels. Mutual information was used to downscale the channels, and seven channels were retained for analysis under each visual task. The average accuracy was 96.3% ± 1.99%; the samples that correctly classified the visual task accounted for 96.3% of the total; and the classification accuracy was high. By analyzing the correlation between the scores on different visual tasks and the fluctuations of Hbt, Hb, and HbO2, it was found that the higher the score, the more obvious, significant, and higher the fluctuations of Hbt, Hb, and HbO2. Experiments found that changes in visual perception triggered changes in Hbt, Hb, and HbO2. HfNIRS combined with Hbt, Hb, and HbO2 recorded by machine learning algorithms can effectively quantify visual perception. However, the related research in this paper still needs to be further refined, and the mathematical relationship between HfNIRS and visual perception needs to be further explored to realize the quantitative study of subjective and objective visual perception supported by the mathematical relationship. [ABSTRACT FROM AUTHOR]

In the race for quantum computing supremacy, the key factor lies in maximizing the number of stable qubits by far, as each additional qubit doubles the computing power. Namely, it makes sense various ecosystems of organizations and developers gravitate toward these extraordinarily expensive supercomputers. Concurrently, the drive to democratize quantum computing has given rise to cloud-based operating systems built upon classical models. However, a growing demand forecast underscores the need for executing an infinite stream of near-real-time quantum tasks accessible via the cloud. This vacancy represents a potential boundary between quantum and classical operating systems. To address this, a refinement method called XIRAC-Q is introduced, which harnesses the principles of information theory for optimization. By maximizing the entropy toleration of the system, our approach enhances overall performance, particularly as the number of processes and tasks approaches infinity. Unlike the limited literature that has explored information theory principles solely for task priority alignment in classical computers, yielding limited advantage, our work integrates information theory and entropy in the design cycle of quantum operating system infrastructure. This paper highlights the novel advantages offered by the proposed paradigm, encompassing improved performance, scalability, and adaptability, which are thoroughly explained and explored. [ABSTRACT FROM AUTHOR]

The article intertwines the study of wave packet dynamics with information-theoretic measurements in one dimensional (D = 1) system. A localized wave packet at time has been considered here and its change at later instant of time is calculated for the position space wave function. The momentum space wave function is obtained by taking the Fourier transform of the position space wave function at time . These wave functions are then employed to construct the wave packet's respective probability densities in position and momentum space, and later have been used to compute the corresponding position and momentum space Shannon (S) and Fisher information (I) entropies. It has been observed that although the Shannon and Fisher information entropies explicitly depend on the standard deviation, neither the Shannon entropy sum nor the product of Fisher information entropies is. Moreover, for dimensional systems, the computed values of the Shannon and Fisher entropies are found to satisfy the lower bounds of the Bialynicki-Birula and Myceilski (BBM) inequality relation and the Stam-Cramer-Rao inequalities better known as the Fisher based uncertainty relation. Thus, our theoretical study explores the validation of information-theoretic measurements for wave packet dynamics using the basic formulations of information theory. [ABSTRACT FROM AUTHOR]

In this paper, we provide geometric insights with visualization into the multivariate Gaussian distribution and its entropy and mutual information. In order to develop the multivariate Gaussian distribution with entropy and mutual information, several significant methodologies are presented through the discussion, supported by illustrations, both technically and statistically. The paper examines broad measurements of structure for the Gaussian distributions, which show that they can be described in terms of the information theory between the given covariance matrix and correlated random variables (in terms of relative entropy). The content obtained allows readers to better perceive concepts, comprehend techniques, and properly execute software programs for future study on the topic's science and implementations. It also helps readers grasp the themes' fundamental concepts to study the application of multivariate sets of data in Gaussian distribution. The simulation results also convey the behavior of different elliptical interpretations based on the multivariate Gaussian distribution with entropy for real-world applications in our daily lives, including information coding, nonlinear signal detection, etc. Involving the relative entropy and mutual information as well as the potential correlated covariance analysis, a wide range of information is addressed, including basic application concerns as well as clinical diagnostics to detect the multi-disease effects. [ABSTRACT FROM AUTHOR]

In modern realities, an important place for the effective management of the cattle breeding industry is a properly implemented selection and breeding process with the sampling of cows that are marked by the highest indicators of a set of productive qualities. In this case, the application of entropy and information analysis is one of the most expedient, as it makes it possible to assess the economic and useful qualities of animals as fully as possible. The purpose of the study is to evaluate and analyze the degree of organization of the biological system of dairy cattle productive traits under the influence of hereditary factors such as the age and origin of cows. During the research, methods generally accepted in zootechnics and methods using information and entropy analysis, which are adapted and modified in animal husbandry, were used. The data obtained from the entropy and information analysis show that for cattle of the Black-and-White breed of different lineal affiliations, the level of organization of systems varies - R from 0.009 to 1.341 bits. Moreover, the most stable trait from the point of view of variability was the fat content in milk, the level of unconditional entropy in the section of lactations was equal to 3.333-4.550 bits, which indicates a smaller influence of disorganized factors on the level of manifestation of this trait, and a greater dependence on hereditary factors, i.e. origin. Although in general, a reliable influence of the lineal affiliation of cows on indicators of unconditional entropy and organization of the system was not established, the influence of the age factor on indicators of entropy was observed. Thus, the researched livestock is not characterized by uniformity and consolidation in terms of the main selection characteristics, except for the fat content in milk, which indicates a wide range of variability and serves as a flexible material both for selection and breeding work and for increasing the level of milk productivity. Therefore, the use of empirical data of information theory can be a kind of marker when predicting hereditary traits of a particular productivity, since entropy and information analysis provides wider and deeper values of trait variability. [ABSTRACT FROM AUTHOR]

Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework for comparing competing models through the use of Bayesian model evidence (BME). However, a BME‐based analysis has two main limitations: (a) it does not account for a model's posterior predictive performance after using the data for calibration and (b) it leads to biased results when comparing models that use different subsets of the observations for calibration. To address these limitations, we propose augmenting BMS and BMJ analyses with additional information‐theoretic measures: expected log‐predictive density (ELPD), relative entropy (RE) and information entropy (IE). Exploring the connection between Bayesian inference and information theory, we explicitly link BME and ELPD together with RE and IE to highlight the information flow in BMS and BMJ analyses. We show how to compute and interpret these scores alongside BME, and apply the framework to a controlled 2D groundwater setup featuring five models, one of which uses a subset of the data for calibration. Our results show how the information‐theoretic scores complement BME by providing a more complete picture concerning the Bayesian updating process. Additionally, we demonstrate how both RE and IE can be used to objectively compare models that feature different data sets for calibration. Overall, the introduced Bayesian information‐theoretic framework can lead to a better‐informed decision by incorporating a model's post‐calibration predictive performance, by allowing to work with different subsets of the data and by considering the usefulness of the data in the Bayesian updating process. Key Points: We complement Bayesian model evidence (BME) with information‐theoretic scores for Bayesian model selection (BMS) and similarity analysisWe demonstrate that, unlike BME, relative entropy is suited to compare models that use different subsets of the data for calibrationWe provide a BMS methodology based on Bayesian and information‐theoretic scores, including approaches that help to interpret the results [ABSTRACT FROM AUTHOR]

Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novelmethod of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in difierent frequency bands originating from intracranial electroencephalography recordings of humans andmarmosetmonkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition efiects are present. [ABSTRACT FROM AUTHOR]

This article is devoted to the study of the entropic orderliness of road safety systems of various dimensionalities. The author's methodology for quantitative assessment of the quality of the road safety systems is based on the use of information entropy analysis, the essence of which is to assess the significance (or "weights") of various information-technological stages of the road traffic accident rate formation process. The main emphasis in this paper is on the philosophical interpretation of the results of entropic evaluation of the orderliness of urban road safety systems. The article aimed to philosophically understand the reasons for the diversity in the results of assessing the entropy of road safety (RS) in Russian cities. Within the framework of this goal, the results of the analysis of the state of the issue, ideological approaches and methods for assessing the relative entropy of urban road safety systems were presented. The study was based on analyzing statistics that characterize the processes of the formation of road traffic accidents in Russian cities classified into three groups based on population size. The experimental results obtained were explained from the point of view of human psychology. Rather, results were explained from the perspective of human psychology. The final results of the study once again illustrated the objectivity of Hegel's dialectical laws and, perhaps, once again shattered illusions about the possibility of achieving high levels of road safety in cities by building rigid systems to regulate the actions of traffic participants. In the author's opinion, the results of the presented philosophical analysis will be useful to managers specializing in the management of complex systems (not only transport but also other fields) to comprehend the contradictions of the complex nature of humans and the paradoxes of their behavior when their freedom of action is restricted through external control. [ABSTRACT FROM AUTHOR]

The optimization objective function of sensor management for target identification is commonly established based on information theory indicators such as information gain, discrimination, discrimination gain, and quadratic entropy, which can control the sensors to reduce the overall uncertainty of all targets to be identified but ignores the speed of target being confirmed as identified. Therefore, inspired by the maximum posterior criterion of target identification and the target identification confirmation mechanism, we study a sensor management method that preferentially allocates resources to identifiable targets. Firstly, in the distributed target identification framework based on Bayesian theory, an improved identification probability prediction method that provides feedback the global identification results to local classifiers is proposed, which can improve the accuracy of identification probability prediction. Secondly, an effective sensor management function based on information entropy and expected confidence level is proposed to optimize the identification uncertainty itself rather than its variation, which can increase the priority of targets that satisfy the desired confidence level. In the end, the sensor management for target identification is modeled as a sensor allocation problem, and the optimization objective function based on the effective function is constructed, which can improve the target identification speed. The experimental results show that the correct identification rate of the proposed method is comparable to the methods based on information gain, discrimination, discrimination gain, and quadratic entropy in different scenarios, but the average time to confirm the identification is the shortest. [ABSTRACT FROM AUTHOR]

The reliability of the machining process is a vital indicator in the manufacturing process of mechanical parts, and directly related to the machining quality of mechanical products. In order to effectively utilize machining process information and machining quality inspection data to evaluate the reliability of the machining process, this paper extends the relevant concepts of information theory to the field of mechanical manufacturing error analysis and establishes a reliability analysis model of the machining process. The model introduces information entropy into the analysis of machining process reliability by defining vertex entropy to measure the uncertainty of the machining quality of each process, and thus realizing the determination of critical processes using a fully quantitative method. Meanwhile, the transfer coefficient defined by mutual information is used to measure the transfer of machining error between two processes. Through combining vertex entropy and the transfer coefficient, the quantitative analysis of machining process reliability is achieved. The model proposed in this paper addresses the issue that the subjective factors of researchers in other related studies have an impact on the analysis results. Finally, the correctness of the proposed model is verified using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The projection pursuit model is an important tool for processing high-dimensional nonnormal and nonlinear data, and it has a wide range of applications. In this paper, clustering analysis and information entropy theory are simultaneously introduced into the projection pursuit model. The K-means clustering method is used to cluster high-dimensional data, and information entropy is used to measure the overall dispersion and local aggregation of projection data. A projection pursuit model based on clustering and information entropy is proposed. The new model has both the classification advantages of clustering analysis and the evaluation advantages of the projection pursuit model. In the case test, different cases are tested by using the Shapiro-Wilk test, Kolmogorov-Smirnov test, Epps-Pulley test, etc. The results show that in most cases, the new model is better than the original model, and the advantage is clearer when the data dimension is higher. Finally, the new model is applied to the agricultural drought risk assessment of Qiqihar, a major grain-producing area in China that is prone to drought. The regional agricultural drought risk shows a downward trend over time. Spatially, the risk of the central and southern regions is low, while the risk of the northern and western regions is high. Regional ability to resist disasters is the main reason for the spatial and temporal differences. This paper extends projection pursuit model theory, analyzes the characteristics of the spatiotemporal variation in regional agricultural drought risk, and provides a reference for identifying the hidden dangers of drought disasters and disaster reduction. [ABSTRACT FROM AUTHOR]

A theoretical approach for creating power law behaviour in broadband communication networks with GeoM or SGeoM specifications utilizing the Rényian maximum entropy functional formalism is undertaken. When specifying the SGeoM, the special case of M/M/1 queue's equilibrium distribution emerges. The suggested method simplifies closed-form equations for a queueing distribution, and the service time density and cumulative distribution functions reduce to the appropriate Rényian, Tsallisian, and Shannonian situations. The concept works well in power law activated - communication networks. Some influential applications of GeoM to WSNs are provided. [ABSTRACT FROM AUTHOR]

This research attempts to construct a second order observation model in understanding the significance of Artificial intelligence (AI) in changing the global power structure. Because of the inevitable ubiquity of AI in the world societies' near future, it impacts all the sections of society triggering socio-technical iterative developments. Its horizontal impact and states' race to become leader in the AI world asks for a vivid understanding of its impact on the international system. To understand the latter, Triple Helix (TH) model along with Shannon's information entropy has been used to operationalize system's theory. This model uses Shannon's information theory to calculate the uncertainty generated from the interactions between the sub-systems considered. Data for the latter has been taken from Sanford Artificial Intelligence Laboratory 2019 report. It is found that European countries are the most effected in the AI era, with probability of losing their global influence and thus creating power void. Emerging powers such as India, Canada, South Africa and Brazil have better chances to fill the void and emerge as global influences. [ABSTRACT FROM AUTHOR]

Understanding the protein-RNA interaction mechanism can help us to further explore various biological processes. The experimental techniques still have some limitations, such as the high cost of economy and time. Predicting protein-RNA-binding sites by using computational methods is an excellent research tool. Here, we developed a universal method for predicting protein-specific RNA-binding sites, so one general model for a given protein was constructed on a fixed dataset by fusing the data of different experimental techniques. At the same time, information theory was employed to characterize the sequence conservation of RNA-binding segments. Conversation difference profiles between binding and nonbinding segments were constructed by information entropy (IE), which indicates a significant difference. Finally, the 19 proteins-specific models based on random forest (RF) were built based on IE encoding. The performance on the independent datasets demonstrates that our method can obtain competitive results when compared with the current best prediction model. [ABSTRACT FROM AUTHOR]

Nevertheless, since both entropies are used outside of pure mathematics to talk about things in the world I as entropic i , I propose that a conceptual differentiation is needed between thermodynamic entropy and informational entropy.[2] I take it to be, therefore, the task of philosophy to I conceptually i differentiate between thermodynamic entropy and informational entropy. Furthermore, as above, information entropy (and therefore intropy) measurably increases in relation to the amount of information and the rate of change of that information since both quantity and speed affect complexity. 2 Bernard Stiegler's attempt at such a task, that informational entropy equates to the becoming-obsolescent of information in its distinction to "knowledge" is inadequate in so far as there is little to no engagement with the actual differences between thermodynamics and information theory. The task of this article is to transduce - transduction being a mutually constitutive analogical deduction and justification of concepts I across i disciplines - Claude Shannon's concept of informational entropy, as used in data science and information theory to quantify statistical certainty, into a working philosophical concept: I intropy i . [Extracted from the article]

The performance of the neighborhood rough set model in feature selection is limited by nonobjective parameter selection method, the uncertainty measures considered only from a single view, and high time cost caused by processing high‐dimensional data. To solve the above problems, this study first defines the interclass boundary to granulate the samples in different classes, and three types of neighborhood concepts—negative perspective, neutral perspective, and positive perspective—are put forward based on different cognitive perspectives. Then, the concept of the multiperspective rough set model is developed. The most prominent feature of this model is the discovery of differences between classes from the given data, without any parameters. Second, by integrating the information theory and algebraic views under the multiperspective rough set model, multiview entropy measures are proposed to effectively measure the uncertainty in data. Moreover, a nonmonotonic feature selection algorithm based on the mutual information in the multiview entropy measures under the neutral perspective as the evaluation function of feature importance is designed to resolve the disadvantages of the algorithms based on the monotone evaluation function. Finally, Information Gain is introduced to preliminarily decrease the dimension of high‐dimensional data sets to promote classification accuracy and reduce time consumption. The experimental results confirm that the proposed algorithm is efficient in eliminating noise and increasing classification accuracy. [ABSTRACT FROM AUTHOR]

Modeling and inference are central tomost areas of science and especially to evolving and complex systems. Critically, the information we have is often uncertain and insufficient, resulting in an underdetermined inference problem; multiple inferences, models, and theories are consistent with available information. Information theory (in particular, the maximum information entropy formalism) provides a way to deal with such complexity. It has been applied to numerous problems, within and across many disciplines, over the last few decades. In this perspective, we review the historical development of this procedure, provide an overview of the many applications of maximum entropy and its extensions to complex systems, and discuss in more detail some recent advances in constructing comprehensive theory based on this inference procedure. We also discuss efforts at the frontier of information-theoretic inference: application to complex dynamic systems with time-varying constraints, such as highly disturbed ecosystems or rapidly changing economies. [ABSTRACT FROM AUTHOR]

Featured Application: The results observed here have applications in all branches of medical sciences and genetics research, including evolutionary biology, pandemic monitoring, genetic therapies, pharmaceuticals, and bioinformatics. The current scientific consensus is that genetic mutations are random processes. According to the Darwinian theory of evolution, only natural selection determines which mutations are beneficial in the course of evolution, and there is no deterministic correlation between any parameter and the probability that these mutations will occur. Here, we investigate RNA genetic sequences of the SARS-CoV-2 virus using Shannon's information theory, and we report a previously unobserved relationship between the information entropy of genomes and their mutation dynamics. Based on the analysis presented here, we are able to formulate a governing law of genetic mutations, stating that genomes undergo genetic mutations over time driven by a tendency to reduce their overall information entropy, challenging the existing Darwinian paradigm. [ABSTRACT FROM AUTHOR]

In this article the problem of data‐driven structural damage detection is considered exploiting historical data collected from a structure. First, a novel technique based on Kalman filtering and on a combination of regression trees theory from machine learning and auto‐regressive system identification from control theory is derived to build switching models that can be used to detect structural damages. A technique is also proposed leveraging principal component analysis together with the poly‐exponential approach to create nonlinear models to be used for structural damage detection. Finally, a novel sensors selection algorithm based on the notions of entropy and information gain from information theory is developed to reduce the number of sensors without affecting or even improving, as it happens in our experimental setup, the model accuracy. The presented techniques are validated on three independent experimental datasets, showing that the proposed algorithms outperform previous and classical approaches, improving the prediction accuracy and the damage detection sensitivity while reducing the number of sensors. [ABSTRACT FROM AUTHOR]

The purpose of this study is to clearly understand the self-organized critical phenomenon of concrete in the process of uniaxial compression. During the compression tests, acoustic emission (AE) waveform and its characteristic parameters were collected synchronously. The AE characteristic parameters information entropy was defined by combining information entropy theory with AE characteristic parameters in the time domain and frequency domain. Moreover, the evolution law of the AE characteristic parameters weighted information entropy composed of typical characteristic parameters in the time domain and the AE peak frequency information entropy in the frequency domain are obtained during the loading process. The results reveal that, as the stress grows, the AE characteristic parameters weighted information entropy of concrete shows an evolution characteristic of "descending, stabilizing and rising," while the AE peak frequency information entropy shows the evolution characteristic of "ascending and declining." Meanwhile, as the AE characteristic parameters weighted information entropy reaches the minimum value (e≈0.9 peak stress), the critical state of concrete ends. After that, the information entropy value increases rapidly, and the penetrating cracks initiate. When the specimen is destroyed, the information entropy value reaches the maximum. The evolution process of cracks in concrete, switching between a chaotic state that develops in random and an organized state that develops in order, follows certain laws (disordered–ordered–disordered), which corresponds well to the AE characteristic parameters weighted information entropy. The research results provide a new method for concrete damage monitoring based on AE. [ABSTRACT FROM AUTHOR]

When processing signals, information entropy theory and data fusion theory have their own advantages. The former can improve the sensitivity of signals, while the latter can superimpose multisource information to correct system deviations and obtain the best identification results. Therefore, we introduce two theories into structural damage identification to improve the reliability of damage identification. First, based on the modal strain energy damage identification index, combined with information entropy and data fusion theory, a fusion entropy index (FE) and an entropy weight fusion index (EWF) are constructed. Then, the simply supported beam and truss structure model are established for damage simulation, which verified that the FE index and EWF index can accurately locate the damage. The polynomial fitting method is used to identify the damage degree of the structure, and the identification results obtained are more accurate. Finally, a simple-supported steel beam model is established in the laboratory for verification and analysis. The results show that the proposed FE index and EWF index have high damage sensitivity, noise resistance, and robustness, and relatively speaking, EWF index damage recognition ability is better. The method proposed in this paper provides an empirical method for practical engineering application. [ABSTRACT FROM AUTHOR]

One of the most powerful laws in physics is the second law of thermodynamics, which states that the entropy of any system remains constant or increases over time. In fact, the second law is applicable to the evolution of the entire universe and Clausius stated, "The entropy of the universe tends to a maximum." Here, we examine the time evolution of information systems, defined as physical systems containing information states within Shannon's information theory framework. Our observations allow the introduction of the second law of information dynamics (infodynamics). Using two different information systems, digital data storage and a biological RNA genome, we demonstrate that the second law of infodynamics requires the information entropy to remain constant or to decrease over time. This is exactly the opposite to the evolution of the physical entropy, as dictated by the second law of thermodynamics. The surprising result obtained here has massive implications for future developments in genomic research, evolutionary biology, computing, big data, physics, and cosmology. [ABSTRACT FROM AUTHOR]

This article investigates a form of rank deficiency in phenotypic covariance matrices derived from geometric morphometric data, and its impact on measures of phenotypic integration. We first define a type of rank deficiency based on information theory then demonstrate that this deficiency impairs the performance of phenotypic integration metrics in a model system. Lastly, we propose methods to treat for this information rank deficiency. Our first goal is to establish how the rank of a typical geometric morphometric covariance matrix relates to the information entropy of its eigenvalue spectrum. This requires clear definitions of matrix rank, of which we define three: the full matrix rank (equal to the number of input variables), the mathematical rank (the number of nonzero eigenvalues), and the information rank or "effective rank" (equal to the number of nonredundant eigenvalues). We demonstrate that effective rank deficiency arises from a combination of methodological factors—Generalized Procrustes analysis, use of the correlation matrix, and insufficient sample size—as well as phenotypic covariance. Secondly, we use dire wolf jaws to document how differences in effective rank deficiency bias two metrics used to measure phenotypic integration. The eigenvalue variance characterizes the integration change incorrectly, and the standardized generalized variance lacks the sensitivity needed to detect subtle changes in integration. Both metrics are impacted by the inclusion of many small, but nonzero, eigenvalues arising from a lack of information in the covariance matrix, a problem that usually becomes more pronounced as the number of landmarks increases. We propose a new metric for phenotypic integration that combines the standardized generalized variance with information entropy. This metric is equivalent to the standardized generalized variance but calculated only from those eigenvalues that carry nonredundant information. It is the standardized generalized variance scaled to the effective rank of the eigenvalue spectrum. We demonstrate that this metric successfully detects the shift of integration in our dire wolf sample. Our third goal is to generalize the new metric to compare data sets with different sample sizes and numbers of variables. We develop a standardization for matrix information based on data permutation then demonstrate that Smilodon jaws are more integrated than dire wolf jaws. Finally, we describe how our information entropy-based measure allows phenotypic integration to be compared in dense semilandmark data sets without bias, allowing characterization of the information content of any given shape, a quantity we term "latent dispersion". [ Canis dirus ; Dire wolf; effective dispersion; effective rank; geometric morphometrics; information entropy; latent dispersion; modularity and integration; phenotypic integration; relative dispersion.] [ABSTRACT FROM AUTHOR]

Quantification of the dependence characteristics in hydrological time series is essential for understanding hydrological variability and for managing water resources. However, how determining a suitable model for describing the dependent components is still a challenge. In this article, we proposed a correlation coefficient‐based information criterion (CCIC) to determine the optimal model order of regression‐based models for quantifying the dependence characteristics in hydrological time series. CCIC was developed by combining the index of correlation coefficient and the information entropy index. The former was used to reflect the fitting error of the model and the latter was used as a penalty term to reflect the model uncertainty. They have similar roles as the two terms of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) used commonly in hydrology. Results of Monte‐Carlo experiments verified higher and more stable accuracy of determining the true model order by CCIC than by AIC and BIC. Moreover, results indicated that the mean value of a time series had a big impact on the accuracy of CCIC. If the mean value of a time series was far from its initial value, the estimation of CCIC would have a big bias, causing low accuracy in the determination of suitable model order. The application of CCIC to annual precipitation from 520 stations in China further confirmed its advantages over AIC and BIC and indicated the significant short‐term dependence characteristics of annual precipitation in the Yangtze River basin. The proposed CCIC approach has potential for wider use in hydrometeorology. Key Points: Correlation coefficient‐based information criterion (CCIC) was proposed to determine the optimal order of regression models for quantifying dependence characteristics in hydrologic time seriesMC experiments verified higher and more stable accuracy of determining the true model order by CCIC than by Akaike Information Criterion and Bayesian Information Criterion used commonlyMean value of a time series had a big impact on the accuracy of CCIC for determining optimal order for regression models [ABSTRACT FROM AUTHOR]

• Notation of region of interest with critical contribution to failure probability is proposed. • A new active-learning function is devised based on the theory of information entropy. • Sufficient PC-Kriging accuracy in the range of the region of interest can be secured. • Computational burden can be significantly alleviated by the new active-learning function. • The proposed approach outperforms other adaptive surrogate-assisted simulation methods. A new active-learning function is developed to integrate Polynomial-Chaos Kriging with probability density evolution method. First, the relative importance of each representative point to the probability of failure is separately measured, thereby the region of interest is defined for the probability density evolution method, which covers the representative points exerting vital contributions to the probability of failure. Then, a new learning function called the probability density evolution method-oriented information entropy is readily devised based on the information theory, and the stopping condition is defined by specifying a threshold for the learning function values. Two examples are studied to show the efficacy of the adaptive Polynomial-Chaos Kriging probability density evolution method, and the recommended values of two key parameters associated with this new learning function are provided. Moreover, comprehensive comparisons are conducted against several existing reliability methods. The results highlight the advantage of the proposed active-learning function for structural reliability analysis in terms of both computational accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Fitts studied the problem of information capacity and transfer in the speed–accuracy motor paradigm using a theoretical approach developed from Shannon and Weaver's information theory. The information processing (bit/s) estimated in Fitts' study is calculated from the movement time required to achieve the required task index of difficulty but is essentially different from Shannon's information entropy. Thus, we estimated the information entropy of multiple human movement trajectories and the mutual information among trajectories for the continuous aiming task in Fitts' paradigm. Further, we estimated the information processing moment by moment. Two methods were considered: (1) encoded values encompassing the coordinates of the three dimensions and (2) coordinate values associated with each direction in the three dimensions. Information entropy indicates the magnitude of variation at each time point, and the structure of this variation varies with the index of difficulty. The ratio of entropy to mutual information was examined, and it was found that information was processed from the first half of the trajectory in difficult tasks. In addition, since these values calculated from the encoded method were higher than those from the conventional method, this method may be able to estimate these values successfully. [ABSTRACT FROM AUTHOR]

The Unruh effect can be considered a source of particle production. The idea has been widely employed in order to explain multiparticle production in hadronic and heavy-ion collisions at ultrarelativistic energies. The attractive feature of the application of the Unruh effect as a possible mechanism of the multiparticle production is the thermalized spectra of newly produced particles. In the present paper, the total entropy generated by the Unruh effect is calculated within the framework of information theory. In contrast to previous studies, here the calculations are conducted for the finite time of existence of the non-inertial reference frame. In this case, only a finite number of particles are produced. The dependence on the mass of the emitted particles is taken into account. Analytic expression for the entropy of radiated boson and fermion spectra is derived. We study also its asymptotics corresponding to low- and high-acceleration limiting cases. The obtained results can be further generalized to other intrinsic degrees of freedom of the emitted particles, such as spin and electric charge. [ABSTRACT FROM AUTHOR]

Geological entropy is based on Shannon information entropy and measures order in the structure of a spatial random variable. Metrics have been defined to quantify geological entropy in multidimensional (2D and 3D) heterogeneous systems, for instance, porous and fractured geological media. This study introduces GEOENT, a toolbox that can efficiently be used to calculate geological entropy metrics for any kind of input-gridded field. Additionally, the definition of geological entropy metrics is updated to consider anisotropy in the structure of the heterogeneous system. Directional entrograms provide more accurate descriptions of spatial order over different Cartesian directions. This study presents the development of the geological entropy metrics, a description of the toolbox, and examples of its applications in different datasets, including 2D and 3D gridded fields, representing a variety of heterogeneous environments at different scales, from pore-scale microtomography (μCT) images to aquifer analogues. [ABSTRACT FROM AUTHOR]

In this paper, we present several new theoretical measures based on information entropy that can be used to analyze the information content of a chiral molecule. Starting from a differentiation between "chiral" and "achiral" portions in a chiral molecule, we define a new concept that allows us to quantify the complexity of chiral constitutional 2D‐isomers of C10 to C20 alkanes. Various new chiral and achiral information measures founded on joint entropy, mutual information, and conditional entropy are presented providing an access to a set of regression equations. Then, introducing a case‐based measure of entropy, we demonstrate that the distribution of the chiral complexity in these molecules is mostly skewed‐right: 60% of the chiral isomers follow a 60/40 distribution rule, which indicates a concentration of chiral complexity in a small number of topological features. Furthermore, by replacing 2D topological distances by 3D distances, the application of these new information measures goes from conformational to racemization and deracemization studies. Interestingly, when the geometrical distances between atoms and the chiral center(s) are taken into account when determining the chiral information entropy, one can observe a significative Pearson correlation coefficient (R = 0.70) between the chiral entropy of 3D molecules and the continuous chirality measure. Finally, we show that our approach is applicable to almost any type of chiral organic chemical structures if in the entropy equation, atoms are represented by their electrotopological state (E‐state) index instead of connectivity. [ABSTRACT FROM AUTHOR]

Uncertainty is a central concept to library and information science. Its physical manifestation is defined as the amount of entropy or noise in information—an idea whose origins lie in the information theory movement of the post–World War II era. Borrowing from the tenets of information theory, uncertainty is also defined as a cognitive state of uneasiness that is often described in negative terms by information behaviorists. Hence, the reduction of uncertainty is a major goal of information seeking. However, little attention is paid to positive uncertainty in information behavior research, and it may be argued that there are mechanisms or traits that can motivate information seekers to embrace uncertainty and find joy in it. In the age of a global pandemic that has been defined by uncertainty, the call for a positive take on uncertainty is more important than ever. This essay explores the mechanisms of joy in information uncertainty. [ABSTRACT FROM AUTHOR]

Predicting the water-conducting fractured zone (WCFZ) height is the key to roof water disaster prevention and environmental protection. Field WCFZ data were collected from 24 working faces of Shendong, Shenfu, Yushen, and Yuheng mining areas in the northern Ordos Basin. A fitting formula was obtained by a multiple regression model and the weight of each factor was calculated by information entropy theory, allowing a prediction of the WCFZ height in the area. The formula was improved by applying a safety factor of 1.33, which met the 95.8% reliability requirements of the samples. Finally, the 31,114 working face of the Jinjie coal mine was selected as a case study. The spatial characteristics of the WCFZ in the study area were analyzed using a geographic information system (GIS) and the WCFZ height was measured using borehole TV. The predicted value was 49.6 m and the field measured value exceeded 42.4 m, making the model-predicted value much closer that predictions calculated with other empirical formulas. The results indicate that this method quantitatively predicted the WCFZ height in the study area and enabled the WCFZ height in the study area to be visualized with GIS technology. It is an effective tool for analyzing and predicting the height of the WCFZ and for preventing mine water inrush. [ABSTRACT FROM AUTHOR]

In several applications, the assumption of normality is often violated in data with some level of skewness, so skewness affects the mean's estimation. The class of skew–normal distributions is considered, given their flexibility for modeling data with asymmetry parameter. In this paper, we considered two location parameter (μ) estimation methods in the skew–normal setting, where the coefficient of variation and the skewness parameter are known. Specifically, the least square estimator (LSE) and the best unbiased estimator (BUE) for μ are considered. The properties for BUE (which dominates LSE) using classic theorems of information theory are explored, which provides a way to measure the uncertainty of location parameter estimations. Specifically, inequalities based on convexity property enable obtaining lower and upper bounds for differential entropy and Fisher information. Some simulations illustrate the behavior of differential entropy and Fisher information bounds. [ABSTRACT FROM AUTHOR]