Twenty Years of Entropy Research: A Bibliometric Overview

Analyzing entropy features in time-series data for pattern recognition in neurological conditions

In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties. To address these challenges, we introduce an information theory-based pipeline that leverages specialized features to identify patterns in neurological time-series data while minimizing privacy risks. We incorporate various entropy methods based on the characteristics of different scenarios and entropy. For stochastic state transition applications, we incorporate Shannon's entropy, entropy rates, entropy production, and the von Neumann entropy of Markov chains. When state modeling is impractical, we select and employ approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The pipeline's effectiveness and scalability are demonstrated through pattern analysis in a dementia care dataset and also an epileptic and a myocardial infarction dataset. The results indicate that our information theory-based pipeline can achieve average performance improvements across various models on the recall rate, F1 score, and accuracy by up to 13.08 percentage points, while enhancing inference efficiency by reducing the number of model parameters by an average of 3.10 times. Thus, our approach opens a promising avenue for improved, efficient, and critical statistical information-considered pattern recognition in medical time-series data.

A review of the nutritional value and biological activities of sturgeon processed byproducts

Sturgeons are a type of subcold water fish distributed in eastern Europe, on both sides of the North Pacific, in eastern Asia, in western North America, and on the east coast of North America. Its production capacity is strong, and it is easy to breed. However, the sturgeon industry has the problems of a single product structure, a short industrial chain and poor market sales. In this context, developing the sturgeon industry is crucial to research the nutritional value of sturgeon processing byproducts and developing diversified products. Therefore, this paper summarizes the research on the nutritional value of sturgeon processing byproducts and the current situation of processing and utilization over the past 10 years. First, CiteSpace visual analysis software and the bibliometric analysis platform were used to analyze the status of sturgeon research. The Web of Science (WOS) database was used as the literature source to fit the keywords of sturgeon literature in the past ten years. After excluding the two keywords sturgeon and sturgeon meat, the relevant literature is analyzed and sorted, focusing on the literature in the last five years. Second, a comprehensive and in-depth review (sturgeon, processing, byproducts as the keywords to search Google Scholar and Web of Science) was conducted on the research of the nutritional components contained in sturgeon and the processing of nutritional components in byproducts to provide a reliable reference for the research and processing of the sturgeon industry.

Entropy and Semi-Entropies of Regular Symmetrical Triangular Interval Type-2 Fuzzy Variables

Fuzzy entropy has a wide range of applications in uncertainty problems. Due to the dual-complexity of its characteristics and calculation, the study on type-2 fuzzy entropy is rare, let alone the semi-ones. Given this, the paper takes the lead in proposing the credibility-based type-2 entropy and semi-entropies delivered around a specific symmetric type-2 fuzzy variable. After presenting the relevant theorems and definitions, we give the corresponding examples of linear entropy and semi-entropies to illustrate and verify the favorable property of our study. This series of formulas on type-2 entropy proposed has a strong advantage in reducing computational complexity. It can be commonly applied to measure fuzziness and solve return-oriented and cost-oriented problems in the business field. A sequence of measures on type-2 fuzzy entropy developed in this paper delivers fresh insights into this field. It also provides a new reasonable tool for the decision-making on cost and investment control in companies.

Future research avenues at the nexus of circular economy and digitalization

Purpose

The authors posit that one of the key enablers of the circular economy will be the digital transformation – in other words, “digitalization.” In this study, the authors examine and visualize the interaction of the circular economy and digitalization by using scientific publications. They explore possible synergies and future research avenues at this junction.

Design/methodology/approach

The authors first apply bibliometrics to explore and visualize the relationships between the circular economy and digitalization in the academic literature. Following the clustering of topics, they define key emerging factors for each cluster. Based on this analysis, they suggest future research avenues.

Findings

The authors find that there are four main clusters at the junction of circular economy and digitalization, including (1) sharing economy, (2) additive manufacturing, (3) business models and (4) industrial ecology and remanufacturing. They then dig deeper into these topics to better understand what factors would shape the future of the clusters. They conclude that sharing economy perspective and additive manufacturing may be enhanced by regulation-based and behavioral change-based approaches. Circular business models should be developed to maintain circularity in industry. Finally, digital manufacturing should be implemented within the framework of industrial ecology and remanufacturing principles to increase efficiency, productivity and traceability in the circular economy.

Originality/value

Digitalization offers significant potentials toward breakthrough sustainability by creating a circular economy. Hence, understanding the relationship between circular economy and digitalization is important to achieve sustainable development goals.

Effects of Bullying Forms on Adolescent Mental Health and Protective Factors: A Global Cross-Regional Research Based on 65 Countries

Adolescent bullying is a public health issue of great global concern. Given the serious negative effect of bullying on adolescent mental health, it is critical to seek protective factors to protect adolescent mental health. From a global cross-regional perspective, the study aims to explore the relationship between forms of bullying and adolescent mental health and the role of parental support as a protective factor. Data were drawn from adolescents aged 12–17 years in 65 countries from the Global School-based Student Health Survey between 2003 and 2015. After controlling the state-fixed effects, individual adolescent behavior, and family factors, the ordinary least squares model was used to analyze the influence of bullying frequency and forms of bullying on adolescent mental health. The results found that the prevalence of bullying in the sample of 167,286 adolescents was 32.03%, with the highest prevalence of bullying in the sample countries in Africa. Verbal bullying had the highest prevalence and the most significant negative effect on adolescent mental health. The study also discussed the differences in bullying among adolescents by gender, age, and region. “Parental supervision”, “parental connectedness” and “parental bonding” played a positive and protective role in the mental health of adolescents who experienced bullying.

EntropyHub: An open-source toolkit for entropic time series analysis

An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website- www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

BACKGROUND

We developed CEPS as an open access MATLAB^® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements.

METHODS

Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system.

RESULTS

The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing.

CONCLUSIONS

We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

Constructal Design of Elliptical Cylinders with Heat Generating for Entropy Generation Minimization

A heat dissipation model of discrete elliptical cylinders with heat generation on a thermal conduction pedestal cooled by forced convection is established. Constructal design is conducted numerically by taking the distributions of thermal conductivity and heat generating intensity as design variables, the dimensionless entropy generation rate (DEGR) as performance indicator. The optimal designs for discrete elliptical cylinders with heat generating are obtained respectively, i.e., there are optimal distributions of heat generating intensity with its fixed total amount of heat sources, and there are optimal distributions of thermal conductivity with its fixed total amount of heat sources. These optimums for minimum DEGRs are different at different Reynolds numbers of airflow. The heat generating intensity can be decreased one by one appropriately in the fluid flow direction to achieve the best effect. When the Reynolds number of airflow is smaller, the thermal conductivity of heat source can be increased one by one appropriately in the fluid flow direction to achieve the best effect; when the Reynolds number of airflow is larger, the thermal conductivity of each heat source should be equalized to achieve the best effect. The results can give thermal design guidelines for the practical heat generating devices with different materials and heat generating intensities.