**1. Introduction**

Heart failure (HF) affects approximately 6 million adults in the United States (US) and is projected to affect over 8 million persons over the age of 18 years by the year 2030 [1]. Of these, it is estimated that the prevalence of advanced heart failure (American College of Cardiology/American Heart Association Stage D) ranges between 250,000 and 300,000 individuals. Between 1988 and 2021, over 83,000 heart transplants were performed in the US. Among those patients, a ventricular assist device (VAD) was used in over 20,000 or approximately 25% of transplantations [2]. Pediatric patients (≤17 years old) comprised 31% of total transplantations, and approximately 9% of children required a VAD as a bridge to transplantation (BTT) [2].

A description of noteworthy milestones in the history of cardiac surgery and mechanical support must include the meaningful advances led by Dr. John H. Gibbon in the 1950s. These advances laid the foundation for the use of cardiopulmonary bypass (CPB) and circulatory assist devices to support patients with perioperative complications and prolonged hemodynamic recovery [3, 4]. Since the early days of mechanical circulatory support (MCS), VADs have become a standardized alternative strategy to bridge to hemodynamic recovery, destination therapy, a bridge during decision-making for the next steps in management, or as a BTT [5].

The first clinical use of a LVAD was reported by Liotta *et al*. in 1963, in a patient with cardiac arrest the morning after aortic valve replacement. The intrathoracic pump was still functioning 4 days postoperatively when the patient died due to brain damage, a complication of cardiac arrest they experienced prior to LVAD implantation [6]. In 1964, The National Institutes of Health (NIH) became actively involved in the development of mechanical assist devices with the inception of the Artificial Heart Program [7]. By 1966, the first successful pneumatically driven paracorporeal left ventricular assist device (LVAD) was employed by DeBakey et al. to support a patient following cardiac surgery. The first human heart transplant was performed by Dr. Christiaan Barnard in 1967, and shortly afterward the use of artificial ventricle technology was initiated as a bridge to support patients until a donor heart could be found [8–10]. Concurrently, the idea of replacing the entire organ using an "artificial pump" came to clinical practice in 1969 by Cooley *et al*. who reported the first use of a total artificial heart (TAH) as a BTT. However, this device was only able to be retained for a few days due to adverse events such as infection, thrombosis, and hemolysis [11].

The establishment of the National Heart, Lung, and Blood Institute (NHLBI) by the NIH in the 1970s promoted the development of implantable devices intending to provide longer mechanical support [7]. In 1978, the first LVAD was used by Norman et al. for nearly 6 days as a BTT [12]. The first TAH intended for permanent support was implanted in 1984 by DeVries et al. with the patient being supported for 112 days before succumbing to sepsis [13].

The first successful BTT case using a VAD was reported by Portner *et al.* in 1984 using the Novacor (Baxter Healthcare Corporation, Oakland, CA) implantable electrical LVAD in a patient with ischemic end-stage heart disease [14]. By the mid-1990s, the FDA approved multiple pulsatile devices allowing patients to recover from hemodynamic compromise [15] (**Figure 1**). Subsequently, in the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) trial, a new indication for mechanical support was explored and the trial revealed

*A Historical Review of Mechanical Circulatory Support DOI: http://dx.doi.org/10.5772/intechopen.110525*

that patients supported by a LVAD exhibited an 81% improvement in 2-year survival compared to medical therapy in patients with advanced heart failure who were not candidates for heart transplantation [5, 16]. The results of this trial led to the approval of the HeartMate VE LVAD device for destination therapy in 2003.

As the prevalence of advanced heart failure increased over the past decades, utilization of LVADs became essential to improve pre-transplant illnesses, improve quality of life, and enhance survivorship—a phenomenon primarily driven by advances in device design, patient characteristics, implantation techniques, and long-term management of adverse effects [17, 18].


4.2.4. Multivariate Data Analysis

The integral of each included bin was normalized to the summed integral of all included bins. The normalized data was then utilized for multivariate data analysis using SIMCA-P<sup>+</sup> (V11.0 and 13.0, Umetrics AB, Umea, Sweden). Initially, for the overview of data distribution and detection of possible outliers, Principal Component Analysis (PCA) was performed on mean-centered data using two principal components. Then, Projection to Latent Structure Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structure Discriminant Analysis (OPLS-DA) were performed on unit-variance scaled data by using grouping information as Y-matrix [59]. Two PLS components were calculated for PLS-DA models, while one PLS and one orthogonal component were used for

OPLS-DA models. Both supervised models were validated using a 7-fold cross-validation method [59]. Further assessments of model quality were also performed, including a permutation test with 200 permutations for PLS models [60] and ANOVA of the cross-validated residuals (CV-ANOVA) tests for OPLS-DA models [61].

The OPLS-DA models were interpreted as back-transformed and color-coded correlation coefficients loadings plots [62] (MATLAB 7.0, The Mathworks Inc., Natick, MA, USA), where the colors indicate the significance of differentiating metabolites, with a warm color (e.g., red) being more significant than a cool color (e.g., blue). The cutoffs for correlation coefficients were chosen on the basis of discrimination significance (*p* < 0.05), e.g., a cutoff value (|r|) of 0.602 was corresponding to the sample number (n) of 10. Differentiating metabolites were also summarized in a heat map, color-coded with the Pearson correlation coefficients from the OPLS-DA models (MeV version 4.9.0).


**Acknowledgment**

We would like to thank the library staff from Texas Tech University Health Sciences Center, Lubbock for their extraordinary support and assistance. On a special note, we would like to thank again Carrie Gassett who showed great interest and support in our project.

## **Author details**

Thomas Nelius, Christopher Winter, Julia Willingham and Stephanie Filleur

Texas Tech University Health Sciences Center, Departments of Urology and Microbiology, Lubbock, USA

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**A. Appendix**

The thresholds for the linear model fitted

sqrt hc ð Þ� ð Þ scale þ shape þ log scale ð Þþ log shape ð Þ 2 þsqrt shape ð Þ ∗ scale

The multiply R-squared for all these models is equal to 1 corrected to the sixth decimal place. The square of the fitted values for these models provides an starting estimate of the threshold for the respectively in-control ARL of 100, 200, 300, or 400. This can be further revised using simulation (**Table A1**).

#### **Table A1.**

*Fitted models for predicting the thresholds when the scale parameter is between 0.02 and 0.046 (inclusive) and the shape parameter ranges from 0.6 to 1.4 (inclusive) for the EWMA statistic used in this chapter with smoothing constant α = 0.1.*

**Author details**

**195**

Ross Stewart Sparks\* and Hossein Hazrati-Marangaloo

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Exponentially Weighted Moving Averages of Counting Processes When the Time between Events…*

*DOI: http://dx.doi.org/10.5772/intechopen.90873*

Data61, CSIRO, Sydney, NSW, Australia

provided the original work is properly cited.


**4.3 Seed stratification**

Stratification is used mainly to alleviate dormancy in temperate plant species. The stratification approach depends on the cause of dormancy in a species. For example dormancy due to immature embryos is broken via warm stratification; physiological dormancy is relieved by cold stratification while combined warm and cold stratification is effective for seeds that have both immature embryos and physiological dormancy [76].


**5.1. CBR traffic**

In the first situation, the simulations were conducted with CBR traffic (1 MB), the transmission consists hop-by-hop by four routing protocols: AODV, OLSR, HWMP Proactive and HWMP Reactive. By the analyze of the throughput, achieved better performance result by HWMP Reactive (Figure 9). This result is because of the protocol in this scenario constantly keep checking the best route and always find a solution when faced with a new, always managing to optimize the flow through the best link at any given time.

**Figure 9.** Comparison of CBR traffic throughput for the four routing protocols

The result of the hybrid routing protocol show the better results in comparison with other protocols presented here. In other protocols, it takes a long time to find a best route for data flow and sometimes, take congested routes, which reduces the throughput of the network.


*Edited by Fahmina Zafar and Eram Sharmin*

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**1. Introduction**

It is well-known that the Boost converter can operate in three modes, named CCM, critical conduction mode (CRM), and DCM, related to the energy stored in the inductor. In CCM/DCM, the stored energy is strictly/non strictly greater than zero, and CRM stands for zero energy during an infinitesimal period. Many authors have considered the Boost converter's modeling and stabilization in CCM with a resistive load since no wide variations or constant power phenomena were expected at the load side. Some only mention that DCM operation should be avoided or treated with special care, and others have limited the DCM operation's study to its stationary behavior [1–6]. A brief survey of relevant DCM, CRM, and CCM models and controllers for the Boost converter is presented in the following.

The authors in [7] developed a low-output voltage Boost converter operating in DCM and designed a controller to stabilize the output by a steady-state analysis. Some authors analyzed the power quality in DCM operation for converters and rectifiers, for instance, in [8–13]. However, they based their analysis on steady-state behavior. Small signal linear models for DCM operation of the Boost converter, including power losses, were developed in [14]; however, the obtained transfer function includes many parameters that are not

**Citation:** Parada Salado, J.G.; Herrera Ramírez, C.A.; Soriano Sánchez, A.G.; Rodríguez Licea, M.A. Nonlinear Stabilization Controller for the Boost Converter with a Constant Power Load in Both Continuous and Discontinuous Conduction Modes. *Micromachines* **2021**, *12*, 522. https://doi.org/10.3390/mi12050522

Academic Editor: Young-Ho Cho

Received: 25 March 2021 Accepted: 1 May 2021 Published: 6 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

easy to acquire. In [15], a frequency control to operate the Boost converter in CRM was proposed. The authors based their study on a linear model of the Boost converter operating in DCM, obtained by a small-signal analysis. On the other hand, in [16], a numerical PSPICE implementation of an isolated full-bridge converter reproduced some CCM and DCM phenomena.

In [17], a nonlinear controller for DCM operation of the Boost converter was developed, whereby the authors obtained such controller from the discrete-time differenceapproximation equations and the steady-state solution of the system. The authors in [18] proposed a controller for the output current of the Boost converter operating in DCM, using a steady-state estimation of the dynamics.

In [19], the authors developed a conduction mode independent (CMI) model for the non-inverting Buck-Boost converter. However, the proposed model depends on the inductor's discharge period, which is an implicit function of the duty cycle and parameters and cannot be extended to the Boost converter. Regardless of the previous, a back-stepping controller for the Buck-Boost converter with a resistive load (non-CPL) was developed.

Some authors have stated unwanted phenomena during the transition between conduction modes. In [20,21], proofs of open-loop bifurcation phenomena in the DCM-CCM boundary were provided. Chaos and bifurcation behaviors for the boost converter were also demonstrated in [22]. Controllers for other converters operating in DCM or CCM with no CPL were developed in [23–25].

Furthermore, it has been reported that a CPL can destabilize the dynamics of the Boost converter in any conduction mode. Recently developed control strategies considered a CPL with single conduction modes or linearized models [26–33]. Unfortunately, it is not difficult to achieve DCM to CCM changes in a Boost converter, with both a resistive load or a CPL and in addition, with a CPL, the conduction mode depends on their power level and output voltage/current. Moreover, linearized models are accurate only in a small region containing the operating point.

It is worth mentioning the work in [34], the authors reported that the Boost converter operating in DCM with a CPL is stable during steady-state. However, the transient stage and CCM to CCM changes cannot be neglected for applications in which wide ranges of input/output voltage and a constant power load (CPL) can coexist.

The authors in [35] presented a current control for the Boost converter feeding a CPL and included a passive compensation (paralleled RC network). They based their analysis on small-signal models valid only in a small region containing the operating point.

From the above state of the art, one can find that the complicated scenario that includes CPLs, and conduction mode changes in the Boost converter, has not been formally studied. The models presented until now are small-signal or steady-state. Table 1 summarizes relevant proposals for converters operating with a CPL or different conduction modes; the checkmark symbol means that the research considers the characteristic. There is a gap for the Boost converter's modeling and control with a CPL, operating in both DCM and CCM. Unfortunately, in biomedicine, microelectromechanical, and other applications, wide CPL and input/output voltage variation ranges could induce changes in the conduction mode and stability properties.

Hence, the contributions of this paper are as follows. Precise Boost converter CMI models (CPL-switched, CPL non-switched, resistive load switched, and resistive load nonswitched) are presented. Through such models, it is shown that the Boost converter's operating point with a CPL is potentially unstable in open-loop; this has not been demonstrated from a CMI perspective to the authors' knowledge. Therefore, a nonlinear stabilizing controller is developed for the CMI model with a CPL. A stability analysis based on a common-Lyapunov function is provided, and numerical and experimental tests are presented to show the proposal's effectiveness. The controller does not depend on frequency control but the CPL power level and the output current, making it easy to implement.

This document is organized as follows. Section 2 presents the development of the mathematical models, and Section 3 its validation. Section 4 is devoted to instability

demonstration for the Boost converter with a CPL. In Section 5, the controller design is presented, and in Section 6, numerical and experimental tests of the closed-loop system are presented. Finally, some conclusions are presented in Section 7.

**Table 1.** Summary of the literature review.


**3. Heat exchanger**

Heat exchanger is an energy (heat) exchange equipment, where it transfers the heat from a working medium to another working medium. Knowing heat exchanger is important in wildly fields as in aerospace, petrochemical industry, refrigeration, and other fields. The optimization design of the heat exchanger is a great significance to industry process to reduce production cost, realize energy conservation, and reduce energy consumption [38]. The development technique for different types of the heat exchanger has been reviewed by many researchers. The development method can be by two ways: passive method and active method. The passive method is to generate swirling flow and disturb the thermal boundary layer by installing vortex generator or tabulators such as baffle, rib, winglet, wing, etc.

**Compound**

**152**

Methane

Ethane Propane Isobutane

*n*-Butane Isopentane

*n*-Pentane

*n*-Hexane *n*-Heptane

*n*-Octane *n*-Nonane *n*-Decane Carbon monoxide

Carbon dioxide Hydrogen sulfide

Air

Hydrogen

Oxygen

Nitrogen

Water

**Table 1.** *Physical properties of each components.*

H O

2

 18.0153

N2

28.0135

492.5

3200.1

O2

31.9988

731.4

H2

2.0159

190.7

 H2S

—

28.9586

551.9

220.97

399.9 181.43 232.53

705.1

0.0511 0.04975

0.0458 0.5319 0.0367

 34.082

 CO

 CO2

44.01

1070.0 1306.5

 28.01

C10

H22

142.282

304.6 506.7

652.2 220.63

87.76 212.81

C9H20

128.255

330.7

C8H18

114.229

360.7

 C

H7 16

100.202

396.8

C6H14

86.175

436.9

C H5 12

72.149

488.8

 C

H5 12

72.149

490.4

C4H10

58.122

550.9

C4H10

58.122

527.9

C H3 8

44.096

615.5

C H2 6

30.069

706.6

CH4

16.042

667.0

 **Formula**

 **Molecular**

**Critical pressure**

**Critical**

**temperature**

**Critical volume**

**Liquid specific gravity**

**Gas specific**

**Acentric**

**factor**

**(air = 1)** 0.55400

1.03830

1.52270

2.00710

2.00710

2.49140

2.49140

2.97580

3.46020

3.94450

4.42890

4.91330

0.96720

1.51970

1.17690

1.00000

0.06961

1.10500

0.96740

0.62210

 0.3443

 0.0372

 0.0222

0.2140

—

 0.1010

 0.2239

 0.0510

 0.4875

 0.4421

 0.3977

 0.3483

 0.2993

 0.2515

 0.2284

 0.2003

 0.1865

*Inverse Heat Conduction and Heat Exchangers*

 0.1529

 0.0994

 0.0115

**(water = 1)**

(0.3) 0.35643 0.50738 0.56295 0.58408 0.62460 0.63113 0.66404 0.68819 0.70698 0.72186 0.73406 0.79265 0.82203 0.80269 0.87603 0.07087 1.14230 0.80687 1.00000

**(ft3/lb)**

**(°F)**

116.66

89.92 205.92 274.41 305.55

369 385.8 453.3 512.9 564.2 610.8

0.0985 0.0775 0.0728 0.0715 0.0703 0.0685 0.0676 0.0688 0.0682 0.0673

0.693 0.0703 0.0527 0.0343 0.0462

**weight**

**(psla)**

The active method is to add the external power to increase efficiency and heat transfer rate such as vibration. So the use of the active method must consider both benefit of the system and additional power cost [39].

Example 1: In case of study characteristics of fluid flow and heat transfer in the (100) silicon microchannel heat sink, the heat convection capabilities in the phase changes as well as in a single-phase flow and the mechanism of bubble nucleation. In the heat transfer characteristics, the results illustrate that changing in the phase process in the microchannels reduces environment working temperature and absorbs the heat. Six different microchannel geometries are selected for the heat

**Figure 1** shows that the decreasing wall temperature phenomenon during the

On the aspect of fluid flow characteristics, the effects of the viscosity and friction coefficient of the fluid in the microchannels are much significant than the macros. Where the specifications of the sink are registered in **Table 2**, Chip 1–4 are prepared for fluid flow experiment. The friction factor is decreasing with the power

2.Changing in thermophysical properties such as viscosity, surface tension,

4.The modification of wettability and capillary wicking force surface roughness

**Hydraulic diameter (μm) Number of channels**

During the last 2 years, there were some review papers which outlined the subject of boiling heat transfer using nanofluids as a new category in thermal fluids.

Chip 1 400 260 221 10 Chip 2 300 130 150 13 Chip 3 250 184 134 15 Chip 4 200 148 109 19 Chip 5 150 113 83 25 Chip 6 100 78 57 38

transfer experiment as shown in **Table 2**.

*DOI: http://dx.doi.org/10.5772/intechopen.89919*

*Equation of State*

phase change is the same as Peng and Wang [43].

of Reynolds number as shown in **Figure 2** [44].

**Chip name Width (μm)**

**Table 2.**

**Figure 1.**

**155**

*The heat flux and channel wall temperature.*

*Specification of the sink.*

**Wc**

1.Nanoparticle types and concentration in the base fluids

3.The operation condition especially the mass and heat fluxes

**Depth (μm) Hc**

thermal conductivity, density, and heat capacity