*Edited by Viduranga Waisundara*

Diabetes is a global pandemic where many remedies have been recommended as means of combating the prevalence of this disease. However, dietary control appears to be more effective than others. This book focuses on interventions concerning glycemic control, the oxidative stress-based occurrence of the disease and its prevention, as well as novel remedies. While many books have been published recently on this aspect, the book aims to serve as an update to the scientific community, as well as to those who have been adversely affected by the disease. There are many unexplored territories when it comes to diabetes, and it is hoped that this publication will open up new avenues of successfully curbing its occurrence.

Published in London, UK © 2018 IntechOpen © maakenzi / iStock

Diabetes Food Plan

Diabetes Food Plan


**1. Introduction**

There are several representations of industrial processes: linear, nonlinear or other technique using fuzzy logic and/or neural networks. Nonlinear models are found in a large part and are used to properly represent the dynamics of real processes. However, their comlexcity proves a real obstacle for control or when designing an observer or in a diagnostic strategy. So, the multimodel approach is a powerful approach developed in the aim to overcome problems related to modeling and control of industrial processes which are often complex, nonlinear and/or nonstationary.

The multimodel approach supposes the representation of the nonlinear model by a set of linear models (as designed in future by submodels or model's base) thus

forming what is called a model library or model's base. The interaction of each models of this model's base through a certain normalized validity calculation forming the global nonlinear system in its all area of operating.

The different models of the base could be of different structures and orders and no model can represent the system in its whole operating domain. Therefore, the multimodel approach aims at lowering the system complexity by studying its behavior under specific condition. **Figure 1** illustrates the concept of a process formed by a multimodel approach. This mechanism evaluates the contribution of each model in the description system's behavior.

The decision unit estimates by means of the numerical validity of each model the selection of the most relevant models at each time. The contribution of the different model's is made by a decision output unit that compute the multimodel output.

Several researchers have been interested in multimodel analysis and control and many applications have been proposed in different contexts. The multimodel approach has knowledgeable a sure interest since the publication of the work of [1]. The idea of the multimodel approach is to describe the complex nonlinear systems by a set of local models (linear or affine) characterizing the operation of the system in different areas of operation. In spite of its success in many fields, the multi-model approach remains confronted with several difficulties and some design problems such as the determination of the models' base and the adequate validity computation. Several works have treated these two points in the literature. We can refer to the works presented in [2–8]. Indeed once the model library is built it will remain intact. The number of model found is, first responsible to adequately represent the nonlinear model of the system and second, it will remain static when it is used in control or diagnosis. In this context there are very few works that have tried to reduce the models' base once it is built and this opens up a new axis of research added to the design of the multimodel approach in the representation of nonlinear systems. We can cite in this context the works of Gasso in [9–11], where the reduction of the models' base is based on an iterative procedure which include tree operation that is: elimination of less important local submodel, merging of the neighbouringsubmodel that describe the same behavior of the nonlinear system and finally a parameters optimization of the resultant structure is made on. In [12–15], The nonlinear system is modeled by a set of linear models and with the of a Gap metric in the model based control technique we can decide how many models are sufficient for control. In other context, when the process can be represented by a fuzzy model, the complexity of rule base can be minimized through two procedure: illumination of the less important rule and merging of neighboring rules that can

**Figure 1.** *Multimodel representation.*

#### *An Optimization Procedure of Model's Base Construction in Multimodel Representation… DOI: http://dx.doi.org/10.5772/intechopen.96458*

describe the same behavior of the nonlinear system [16]. The Chiu's classification method [17] is used in order to obtain the optimal systematic determination of model's base [18]. The latest work is a new approach permit to optimize the number of submodels with respect to the submodel complexity is presented. In [19] the optimal number of submodels is done based on both a reinforced combinatorial particle swarm optimization and a hybrid K-means.

In this chapter book we study a very relevant modeling approach especially where do not have an adequate model of the process. We will use the data set relative to the identification of the process in its area of operating. On the other hand the input and output measurement. The multimodel approach in this case becomes a very efficient method to overcome the problem of modeling.

The outline of this work is as follows: in Section 2, we will present the procedure used in the multimodel representation of complex nonlinear systems. Indeed, the number of models is determined using frequency-sensitive competitive learning algorithm and the operating clusters are identified using Fuzzy K- means algorithm. The structure of each local model and the validity computation are also presented to each submodel of the model's base (number of model built). Section 3 presents the recommended approach to reduce the number of submodels. Then, focusing on the use of both two type of validity calculation for each submodel and an SVD technique is used to evaluate the adequate number that can be retained in the model's base.


**7.1 MR-proADM—mid-regional proadrenomedullin**

ADM is a multipotent regulatory peptide with several biological activities including vasodilator, positive inotropic, diuretic, natriuretic, and bronchodilator. It is widely expressed throughout the body, including bone, adrenal cortex, kidney, lung, blood vessels, and heart. ADM is even present in pulmonary pneumocytes type 2, smooth muscle cells, neurons, and immune cells. It is upregulated by hypoxia, inflammatory cytokines, bacterial products, and shear stress. As ADM measurement is complicated, mid-regional proadrenomedullin (MR-proADM) is being considered as an estimate of ADM [8–10]. High levels of MR-proADM are reported in septic patients. These have been shown to be particularly specific in prognostic value, not only for early diagnosis in the context of patients initially presenting to the Emergency Department (ED) but also for risk stratification and prognosis in critically ill patients in Intensive Care Units (ICU). A study from Italy in 2020 aimed to describe the utility of MR-proADM as a prognostic biomarker in severe COVID-19 infection. Fifty seven patients who were admitted to the ICU with COVID-19 infection were studied. Multivariate logistic regression models demonstrated that MR-proADM was an independent predictor of mortality [11].


**Victor Chaban, Ph.D., MS**

**Chapter 1**

**Abstract**

home visiting nursing

**1. Introduction**

A Home-Based Heart Failure

Self-Management and Lives

heart failure and intervention strategies in the home care setting.

*Motohiro Sano and Tomoko Majima*

Nursing Intervention Supporting

In the era of a super-aged society, along with the rapid development of medical techniques to treat cardiovascular disease, there are an increasing number of aged patients with heart failure (HF). To cope with this near pandemic, coordinated-HF management combining hospital-based optimal therapy and home-based care is required. Accordingly, the concept of "living with HF" is advocated and is widely accepted as a key to improve patients' quality of life as well as prognosis. To achieve it, home visiting nursing services are essential. But these services have some difficulties. This chapter shows the importance of promoting self-management for patients with

**Keywords:** heart failure, self-management, re-hospitalization, home-based intervention,

HF is the quintessential cardiovascular syndrome of aging that results from age-related cardiovascular conditions and age-associated changes in cardiovascular structure and function. The prevalence of HF among approximately 1–2% of the adult population in developed countries, rising to ≧10% among those over 70 years of age and the most common reason for hospitalization in older adults. Usually, as studies only include diagnosed HF cases, the true prevalence is likely to be higher [1, 2]. Although some progress has been made in reducing mortality in patients with HF, rates of rehospitalization continue to rise and approach 40–50% within 1 year after discharge [3, 4]. To reduce mortality and rehospitalization rate, it is widely recognized that, in addition to optimizing medical and device therapies for HF, attention should also be given to how HF care is delivered. Several position papers that cover non-pharmacological

management, discharge planning, and standards for delivering HF care.

Appropriate self-management by patients with HF plays an important role in the prevention of HF decompensation and improvement of survival and quality of life (QOL) [5]. A literature review stated that most HF treatment figure on self-management intervention and focus on self-management strategies, such as to become more informed about their illness and be actively engaged in their own care, which

Professor and Chairman, Department of Biomedical Science Education, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA

> Professor, Department of Medicine, University of California, Los Angeles, CA, USA


*2.2. Bioinformatics Studies*

#### 2.2.1. Quality Assessment of RNA-seq Data, Mapping and Read Annotation

All processing and analysis on raw data were performed using Ubuntu 20.00 (64-bit) and open-source software available through the R/Bioconductor. After check and quality control of paired-end reads with the final version of MultiQC (https://github.com/ewels/ MultiQC, 25 January 2022) and Trimmomatic (http://www.usadellab.org/cms/?page= trimmomatic, 25 January 2022), the remaining reads as clean reads were mapped to the genome reference GRCh37 (hg19) using the star (https://github.com/alexdobin/STAR/ releases, 10 January 2022) package, and mapping efficiencies accounted for 98.50%. The counting of transcripts was also performed with Htseq-count (https://htseq.readthedocs. io/en/release\_0.11.1/count.html, 10 January 2022).


**1. Introduction**

The observation of the Sun, the solar system, and the vast regions laying outside are commonly called *astronomy*.

In the whole range of observations of our neighborhood (**Figure 1**), we study the Sun, the Moon, the rocky planets, and the gas giants. In the last decades, the space probes sent around the solar system have enhanced our exploration capacity, that is, to go from hazy photographs to high-resolution mapping of most of our planetary bodies, even of their moons. Additionally, dwarf planets (Ceres and Vesta) and asteroids have been visited and mapped. This, in itself, it is a unique civilizational achievement in terms of exploration.

In between Mars and Jupiter, the asteroid belt is found. Further and mostly after Neptune's orbit is the Kuiper belt. Eventually, the very possible Oort cloud, a reservoir of visiting comets, is vastly beyond the orbit of (90377) Sedna (**Figure 1**).

Our solar system is located in between two arms of a spiral galaxy, within what is often called a "finger" named the *Orion spur* (**Figure 2**). The center of our galaxy, the *Milky Way*, is the seat of a supermassive black hole (SMBH) called *Sagittarius A\** [3].

#### **Figure 1.**

*Extracted from ref. [1], this is the solar system. The upper left is defined by Jupiter's orbit, upper right reaches Pluto's orbit, the Kuiper belt, and the perigee of (90377) Sedna. Bottom right is the orbit of (90377) Sedna, which is barely seen in the bottom left alongside the inner Oort cloud, which is thought to be the source of comets. Image credit: NASA/CalTech.*

**Figure 2.** *The Milky Way. Extracted from ref. [2], image credit: NASA/Adler/U. Chicago/Wesleyan/JPL-Caltech.*

Our galaxy is located on a fringe of what could be called *mycelium* filaments. In other words, detectable matter at the cosmological level, tends to agglomerate in threads, interconnected by groups of larger material aggregations, not unlike the spread of fungal mycelium in Earth's soil, punctuated by the presence of "nodes," from which mycelium filaments extend. Of particular importance to our galaxy "suburb" is a large area void of matter (**Figure 3**). The *Local Void* has been mapped synthetically with a great resolution recently [4].

The overall observable universe is a sphere, centered on the location of the one observing. For us, it is planet Earth. When mapped, this sphere, so far, extends outwards in a radius of 46.5 billion light-years (440 Ym), as a comoving distance. The

#### **Figure 4.**

*Extracted from ref. [6], scanning from the dark energy spectroscopic instrument (DESI), from the center, earth, toward the further seen so far, about one billion years after the big bang.*

sloan digital sky survey (SDSS), mapping of the observable universe [5], was done not by direct distance, but in signed velocity from the observation point, deducting mostly from the red-shifting of the observations. The faster the positive red-shifting velocity, the further away the colocation to the observer (**Figure 4**).


**2.2 Classification of PHA**

PHAs are classified into three classes short, medium, or long chain length (scl, mcl, and lcl), respectively. It is based on the number of carbon atoms as short-chain-length PHA (scl-PHA), medium-chain-length PHA (mcl-PHA), and long-chain length PHA (lcl-PHA). Scl-PHA refers to PHA comprised of monomers having 5 or fewer carbon atoms [19]. These include 3-hydroxybutyrate and 3-hydroxyvalerate. The mcl-PHA is comprised of monomers having 6 to 14 carbon atoms. These include 3 hydroxyhexanoate, 3-octanoate, and 3-hydroxydecanoate. The lcl-PHA, which is uncommon and least studied, consists of monomers with more than 14 carbon atoms [20] (**Figure 1**).

**Figure 1.** *General structures of polyhydroxyalkanoates.*