Modeling and Validation of Key Performance Indicators for Business: An Entropy-Based Approach | Chapter 8 | Leading the Charge: A Guide to Management, Entrepreneurship and Technology in the Dynamic Business Landscape Edition 1

A company management system based on key performance indicators (KPIs) allows optimizing the company’s ecosystem by helping managers effectively manage available resources in key functions based on information about the company’s economic and operational activities.

The article focuses on developing KPIs in a company and understanding the necessary criteria to be taken into account to improve the efficiency of the new system implementation at the micro and macro levels. This will help not only to achieve short-term goals but also in the strategic aspect will allow us to remain at the appropriate level of competitiveness, adapt to changes, and develop despite external challenges.

The article actualizes that the effectiveness of KPIs directly depends on how well the indicators are structured, and how correctly they are interpreted and updated.

In this study, we propose utilizing information theory tools to develop complexity-informed decision-making systems through precursor indicators. Specifically, classical information, approximation, fuzzy, permutation, and distribution entropies was examined. To test the effectiveness of these indicators, stock returns of the four most and least developed and globalized companies were analyzed. The findings reveal that the determinism or chaotic behavior of these companies is variable, as indicated by entropy metrics calculated using a sliding window algorithm. The performance of companies fluctuates with market conditions, showing increased efficiency and demand, reflected by rising trends and minimal entropy during certain periods, and maximum entropy during periods of disturbance. Empirical results demonstrate that approximation-based entropies are particularly resilient across varying market conditions and effectively distinguish between developed and chaotic companies.

 

Author (s) Details

 

Serhii Hushko
State University of Economics and Technology, Liberation Square 2, Kryvyi Rih, Ukraine.

 

Victoria Solovieva
State University of Economics and Technology, Liberation Square 2, Kryvyi Rih, Ukraine.

 

Bielinskyi Andrii
State University of Economics and Technology, Liberation Square 2, Kryvyi Rih, Ukraine.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-98-3/CH8

Unlocking the Complexity of the Mind: A Comprehensive Statistical Exploration of Network Neuroscience and Brain Connectivity | Book Publisher International

Network neuroscience is an interdisciplinary field that combines concepts and methods from neuroscience, network science, and complex systems theory to study the organization and function of the brain as a complex network. It focuses on understanding how the brain's structural and functional connectivity patterns give rise to various cognitive processes and behaviors. In network neuroscience, the brain is represented as a network, where individual brain regions or nodes are connected by edges representing structural or functional connections between them. These connections can be studied using various imaging techniques such as diffusion tensor imaging (DTI) for structural connectivity and functional magnetic resonance imaging (fMRI) for functional connectivity. Researchers in network neuroscience analyze brain networks at different levels of organization, from microscopic to macroscopic scales, and use network science tools to study their topology, dynamics, and information-processing properties. By investigating the brain as a complex network, network neuroscience aims to uncover fundamental principles underlying brain function and dysfunction, with potential implications for understanding neurological and psychiatric disorders, as well as for developing new therapeutic interventions. One key aspect of network neuroscience is its focus on identifying network-level principles that govern the brain's organization and function. By studying the brain as a complex network, researchers can uncover principles of network architecture, such as small-worldness, modularity, and hierarchical organization, which are thought to play crucial roles in information processing and integration in the brain. Understanding these principles not only sheds light on normal brain function but also provides insights into how disruptions in these networks may contribute to neurological and psychiatric disorders. Another important area of research within network neuroscience is the investigation of dynamic interactions within brain networks. The brain is not a static entity but rather a dynamic system that continuously undergoes changes in its activity and connectivity patterns. Network neuroscience seeks to understand the dynamic nature of brain networks, including how network topology evolves over time, how information is transmitted and integrated across the network, and how the brain adapts to different cognitive demands and environmental stimuli. Moreover, network neuroscience has practical applications in fields such as neuroimaging analysis, brain-computer interfaces, and personalized medicine. For example, researchers use network-based approaches to develop novel algorithms for analyzing neuroimaging data, which can improve the accuracy of brain mapping and biomarker discovery. Additionally, network neuroscience provides insights into individual differences in brain connectivity patterns, which can be leveraged to develop personalized interventions for neurological and psychiatric disorders. Network neuroscience offers a powerful framework for understanding the complex interplay between brain structure, function, and behavior. Network neuroscience integrates neuroscience, network science, and complex systems theory to study how the brain's connectivity shapes cognition and behavior. By analyzing brain networks, researchers uncover principles of brain organization and function, advancing our understanding of neurological and psychiatric disorders. This interdisciplinary approach also enhances neuroimaging analysis and facilitates the development of personalized interventions. In sum, network neuroscience provides valuable insights into the intricate relationship between brain structure, function, and behavior.

 

Author (s) Details

 

Tahmineh Azizi

Faculty of Biostatistics, University of Missouri St. Louis, United States and Faculty of Mathematics and Statistics, Grand Canyon University, United States.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-81-975317-4-3

The Statistical Physics Concepts of Microbes | Chapter 10 | New Frontiers in Physical Science Research Vol. 3

 Microbe attitude can be characterized utilizing the framework of non-evenness statistical mechanics because intermittent activity is dependent on encircling dynamics, which are a function of interplays with the surroundings. Microbial motion is typically thought expected Brownian, however when perturbations in the environment change the fluctuations in the randomness of motion, the approximate situation resolves to the instance of Multi-Fractal Brownian motion. Bacterial cells dwell in an surroundings with a low Reynolds number, that makes inertia in their motion inconsequential. The use of the Fokker-Planck and Lotka-Volterra expressions yields two together qualitative and quantitative judgments. In this chapter, we have taken highest in rank possible measures to define a difficult biological order using a physicsbased methodology.

Author(s) Details:

Preet Sharma,
Non-Linear Sciences Research Group, Department of Chemistry and Physics, Midwestern State University, Texas, USA.

Please see the link here: https://stm.bookpi.org/NFPSR-V3/article/view/8596

Data Visualization and Mental Models in the Understanding and Use of Complex Systems | Chapter 7 | Research Developments in Science and Technology Vol. 8

In order to comprehend and make use of complicated data patterns, we now frequently rely on visual elements. Metaphors, or mental models, are also tools we use to manipulate information. Two sections make up this chapter. The first section covers data visualisation (the use of visual objects to help understand and use complex sets of data). I start by giving a quick overview of data visualization's past. I next go through some current developments. Finally, I discuss a recent research that contrasts a more conventional textual presentation (a spreadsheet) with a visual representation of the data (a bar chart), demonstrating that the visual representation is superior in a number of respects. The concept of mental models is discussed in Chapter 2's second section (metaphors held in the mind). I go through the theory behind mental models and give several illustrations of these models. I then go on to discuss a research that represents a Navy personnel planning procedure using a mental model (a system of water tanks). The research demonstrates that the water tank metaphor is a plausible paradigm, but it is not successful in improving participants' comprehension or application of the planning process. According to the research, certain models should use animation-related elements. In general, it is considered that mental models and evidence-based visualisations are helpful tools for comprehending data utilisation and presentation.

Author(s) Details:

B. Charles Tatum,

National University, San Diego, CA, USA.

Please see the link here: https://stm.bookpi.org/RDST-V8/article/view/7400

Establishment of the New Solution for Complex Systems in Multidisciplinary Science Proven by System Analysis Theory and Simulator

This paper establishes a very important scientific solution to science of complexity for physicists, and presents a multidisciplinary involved physics and engineering. The innovative solution for complex systems presented here is verified on the basis of principles in engineering such as feedback-system analysis using the classical control theory. This paper proposes that a complex system is a closedloop system with a negative feedback element and is a solvable problem. A complex system can be analyzed using the system analysis theory in control engineering, and its behavior can be realized using a specially designed simulator.

Author(s) Details

Deok-Soo Cha
Eho Technology Co., Busan, South Korea.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/210

An Overview: The Theory of the Organization and the New Paradigms | Chapter 9 | Current Strategies in Economics and Management Vol.5

This paper proposes to tackle a subject that many authors have warned should not be taken lightly: “the new paradigms of Science” and the theory of management. The paper begins with a brief explanation about the changing environment which began during the 1970s. This section contains background and relevant criticism made by several authors, and sets out the argument for the need to change to a new paradigm. This is followed by an exploration of new concepts and ideas that have emerged in New Science that have direct relevance to developing new organizational models. Finally, a way to envision and conceptualize the organization as a living entity and to undertake the construction of a new paradigm is suggested. 

Author(s) Details

Aquiles Limone
Escuela de Ingeniería Comercial, Universidad de Valparaíso, Viña del Mar, Chile.

View Book :-  http://bp.bookpi.org/index.php/bpi/catalog/book/199