*Edited by Edward T. Zawada Jr. and Sohail Abdul Salim*

Renal diseases can be explosive and difficult to control or silent but progressive. Those that create acute and chronic renal failure become serious and often life-threatening burdens to patients and their families. Advanced diseases can be resistant to treatment and extremely expensive to manage. Early and precise diagnosis is the best solution to these problems. Renal biopsy is often the most efficient method for accurate diagnosis to allow disease-modifying therapy with steroids, anti-rejection immunosuppressive medication, plasmapheresis, or the new immunomodulating biological drugs. This book is a primer on the rationale for precise diagnosis of renal diseases to allow the greatest chance of stabilizing or remitting these diseases, which will otherwise create a permanent need for dialysis or transplantation. This collection of reports documents the type of disease that can be identified by renal biopsy. Reports from authors around the world describe their experience with the techniques, risk, and benefits of renal biopsy. These reports conclude that renal biopsy has become a universally used, safe, same-day, outpatient procedure that can give suggestions for treatments to prevent potentially devastating consequences.

ISBN 978-1-78985-131-1

Renal Diseases

Published in London, UK © 2020 IntechOpen © rightdx / iStock


**Meet the editor**

After obtaining his MD at the Universitat Autònoma de Barcelona, Pere-Joan Cardona started an internship in Clinical Microbiology in 1994, in the Hospital Germans Trias i Pujol, where he was familiarized with the problem of TB in its clinical and diagnostic challenges. Invited by Ian Orme at CSU, he was trained on the development of TB experimental models. PhD was obtained in 1999. He

became Head of the Experimental Tuberculosis Unit at the Institut Germans Trias i Pujol of Badalona and Assistant Professor of Microbiology at UAB. He has been involved for the last 15 years in the study of the pathophysiology of TB infection. Additionally, he has authored 70 peer-reviewed publications in the field and is responsible for the development of different experimental models in mice, guinea pigs, goats and mini-pigs, and in the development of new drug regimens and vaccines against TB.

Contents

**Preface IX**

Chapter 1 **Multi-Drug/Extensively Drug**

Claude Kirimuhuzya

Chapter 3 **A New Hope in TB Treatment:**

Héctor R. Morbidoni

Chapter 5 **An Approach to the Search**

Juan Bueno

Chapter 7 **New Antitubercular Drugs**

Chapter 4 **In Search of El Dorado:**

Chapter 2 **Chemotherapeutic Strategies and**

**Part 1 Strategies for New Drug Discovering 1**

**Resistant Tuberculosis (Mdr/Xdr-Tb):**

**Targets Against Resistant TB 33**

Ruiru Shi and Isamu Sugawara

**Current Trends and Strategies in the Development of Novel Anti-Tubercular Drugs 107**

Chapter 6 **Antitubercular** *In Vitro* **Drug Discovery: Tools for Begin the Search 147**

**Renewed Global Battle Against Tuberculosis? 3**

Neeraj Shakya, Babita Agrawal and Rakesh Kumar

**The Development of the Newest Drugs 93**

**for New Drugs Against Tuberculosis 137**

**Designed by Molecular Modification 169**  Jean Leandro dos Santos, Luiz Antonio Dutra, Thais Regina Ferreira de Melo and Chung Man Chin

Fernando R. Pavan, Daisy N. Sato and Clarice Q.F. Leite

## Contents

#### **Preface** XI

X Contents

## Preface

In 1957, a Streptomyces strain, the ME/83 (*S.mediterranei)*, was isolated in the Lepetit Research Laboratories from a soil sample collected at a pine arboretum near Saint Raphaêl, France. This drug was the base for the chemotherapy with Streptomicine, which demonstrated in 1980 to have a 100 per cent efficacy rate after being used together with two or three other drugs during the first two months of treatment in addition to an extra four month treatment combined with Isoniazid. The euphoria generated by the success of this regimen lead to the idea that TB eradication would be possible by the year 2000. Thus, any further drug development against TB was stopped. Unfortunately, the lack of an accurate administration of these drugs originated the irruption of the drug resistance in *Mycobacterium tuberculosis*. Once the global emergency was declared in 1993, seeking out new drugs became urgent. In this book, diverse authors focus on the development and the activity of the new drug families.

> **Dr. Pere-Joan Cardona**  Institut Germans Trias i Pujol (IGTP) Catalunya, Spain

**Part 1**

**Strategies for New Drug Discovering**


**Orchid Biochemistry 2.0**

Editor

**Jen-Tsung Chen**

MDPI Basel Beijing Wuhan Barcelona Belgrade Manchester Tokyo Cluj Tianjin

*Editor* Jen-Tsung Chen Department of Life Sciences National University of Kaohsiung Kaohsiung Taiwan

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *International Journal of Molecular Sciences* (ISSN 1422-0067) (available at: www.mdpi.com/journal/ ijms/special issues/Orchid Biochemistry 2).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-4652-0 (Hbk) ISBN 978-3-0365-4651-3 (PDF)**

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**3. Results**

Aim 1: Between January 2019 and December 2019, the team conducted 15 in-situ simulations (goal 24) in our Maternal and Newborn Services Division, averaging roughly one simulation per month. The main challenge to conducting in-situ simulations was a persistently high census in the NICU that limited the ability of sta ff to participate without potentially compromising the care of actual patients.

Aim 2: In 2019, 70% of our sta ff (goal 80%) attended two or more simulated scenarios. Seven of the sta ff (26%) attended only one simulation during that time. We fell short of our goal primarily due to medical leave or per diem schedules.


**10. Oral rehydration during exercise**

Based on the physiological responses to warm and hot conditions described previously, it is suggested that dehydration prior to and during exercise should be prevented or countered. This would reduce extra thermal and cardiovascular strains during exercise in the heat and the risk of developing heat-related illnesses. In general, oral rehydration with solutions containing 0.1–0.2% of NaCl and 4–8% of CHO based on thirst sensation is recommended, but, during exercise, we need to prevent the addition of 2% of body weight reduction [7]. Elderly persons or persons who work or undertake high-intensity exercise in warm and hot conditions should start fluid ingestion prior to and during the early period of work before thirst is perceived [7]. Special care should be taken by diabetics so that hyper and hypoglycemia are not induced with oral rehydration during exercise and hyperthermic conditions.


*Edited by Constantin Volosencu*

The book promotes new research results in the field of modern actuators and their applications. New coverage of dielectric barrier discharge plasma actuators, polymeric microgripper based on the cascaded V-shaped electrothermal actuators, ionic polymer actuators, wideband actuators and energy harvesters, electromagnetic actuators and shape memory alloy actuators are comprehended. The book is structured in four sections: design, fabrication and simulation; control systems; medical applications and fault detection. Seven chapters are published following a rigorous selection process. In the first section, a study carried out to investigate experimentally and by numerical simulations a microscale plasma actuator; the design, fabrication, numerical simulations, and experimental investigations of a polymeric microgripper designed using the cascaded V-shaped electrothermal actuators; a review of the development of ionic polymer actuator with introduction of two kinds of typical polymer actuators—ionic polymer-metal composites and bucky gel actuator—with their basic principle and fabrication process and typical applications and a methodology of designing and testing wideband actuators and energy harvesters, treated as one mechanical resonator, with a discussion on shock harvester, resonant harvester and energy transmission system, are presented. The second section has a chapter dedicated to modeling, system identification and control of electromagnetic actuators with main focus on the actuators used in magnetic levitation, in fuel injection systems and in variable valve timing. The third section presents a study focused on quantifying the decline in tactile sensation associated with diabetic neuropathy and developed a measurement device that used a thin-shaped memory alloy wire as the actuator. The fourth section includes a chapter presenting a twolevel fault diagnosis and root-cause analysis scheme for a class of interconnected invertible dynamic systems, which aims at detecting and identifying actuator fault and causes.

ISBN 978-1-78923-428-2

Actuators

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


*4.2. Simulated Results*

Simulations were performed with the Saber simulator. Figure 5 shows the simulated results at 1200 rpm. In the IRFOC, the d-axis current, referred to as the magnetic current, is kept constant in the below-based speed region. According to Equation (5), the rotor flux should be kept constant. A step load is set at 2.0 s with 30 Nm and at 4.0 s with 60 Nm. Figure 5a–c depicts the simulated results when the rotor resistance *R*∗ *r* is used in the slip angular velocity calculation is set as 0.5*Rr*, 0.8*Rr* and 1.5*Rr*, respectively. The rotor field-oriented angle is compensated from 1.0 s. The rated flux level is set to 0.73 Wb based on the motor parameters. The flux level varies because of the inaccurate rotor field-oriented angle without compensation. This effect can lead to the degradation of dynamic and stable performance. After compensation, we observe that the flux intensity under the three

simulation conditions could reach 0.73 Wb at 2.0 s. The flux level could be kept almost constant during the load step.

**Figure 5.** Flux and compensated angle at 1200 rpm with 30 Nm and 60 Nm step loads at 2.0 s and 4.0 s, respectively. (**a**) *R*∗ *r* = 0.5*Rr*; (**b**) *R*∗ *r* = 0.8*Rr*; (**c**) *R*∗ *r* = 1.5*Rr*.

According to Equation (18), the predictive model needs stator resistance and inductance. Inductance is almost constant. The variation in the stator resistance is neglected in the proposed algorithm. To verify that the neglect is accepted, a simulation was performed. In the simulation, the stator resistance was changed from 0.374 Ω to 0.748 Ω linearly, and the rotor resistance was set to 0.5*Rr*. A step load is also set at 2.0 s with 30 Nm and at 4.0 s with 60 Nm. Compared with the simulated results in Figure 5a, the rotor flux and compensated angle indicate little difference, as shown in Figure 6. The flux level with angle error compensation, regardless of whether the stator resistance is changed, is much better than that without compensation. Although the maximum deviation of the compensated angle is almost 2.5 rad when the stator resistance changed to twice the nominal resistance, the deviation of the rotor flux level is only 0.03 Wb. In the application, this small difference of the rotor flux level could be neglected, and the algorithm proposed here is almost not affected by the variation in the stator resistance.

**Figure 6.** Rotor flux and compensated angle at 1200 rpm with the variable stator resistance.

In the IRFOC, the q-axis current reflects the developed torque when the motor runs at a constant speed. Therefore, the q-axis current should be proportional to the torque. That is, the q-axis current with a 60 Nm load should be twice the value with a 30 Nm load at 1200 rpm. The q-axis current with different loads is presented in Figure 7. After compensation, the q-axis current is changed from approximately 15 A to 30 A when the load rises from 30 Nm to 60 Nm regardless of the *R*∗ *r* set. However, with no compensation, the q-axis current is different from the same load and is not proportional to the torque. The simulated current is compared in Table 2.

**Figure 7.** The q-axis current comparative waveforms at 1200 rpm with a 30 Nm load at 2.0 s and 60 Nm load at 4.0 s. (**a**) Current with compensation; (**b**) Current without compensation.

In Table 2, *R*∗ *r* is the rotor resistance, which is used to calculate the slip angular velocity in the program; *Rr* is the actual resistance of the induction machine; *iqs*1 and *iqs*2 are the values of the q-axis current when the loads are 30 Nm and 60 Nm, respectively. If the rotor field orientation is accurate, then the ratio of *iqs*2 and *iqs*1 should be 2. It can be seen that the q-axis current is not proportional to the load when the rotor resistance is not the actual value. After compensation, the q-axis current is almost proportional to the load.

Figure 8 shows the simulation results at 3000 rpm. A step load at 20 Nm was set at 2.0 s. When *R*∗ *r* = 0.5*Rr*, the speed could no longer be kept at 3000 rpm without compensation. This is different from the simulation result at 1200 rpm shown in Figure 7 because the voltage is limited to the supply torque current in the field-weakening region if the rotor flux level is not sufficiently reduced, as shown in Figure 8b. The actual flux without compensation when *R*∗ *r* = 0.5*Rr* was much higher than the normal level. Therefore, the voltage could not supply enough q-axis current when the speed was 3000 rpm, and then the speed was decreased. This finding means that an inaccurate field-oriented angle can affect the maximum output torque of induction machines. This results of q-axis current can also be seen in the following experimental results at 1200 rpm. Although the flux levels with compensation are not constant, the variable in Figure 8a is much smaller than that without compensation, as shown in Figure 8b.

**Figure 8.** Rotor flux, q-axis current, and d-axis current comparative waveforms at 3000 rpm with a 20 Nm load at 2.0 s. (**a**) Rotor flux and d/q current with compensation; (**b**) Rotor flux and d/q current without compensation.

Figure 9 compares the waveform with and without the proposed compensation. In the simulation, the rotor resistance began to change from 0.268 Ω to 0.536 Ω linearly at the instant of 2.0 s during the following 2 s interval. Then, the rotor resistance was changed back to 0.268 Ω linearly at the instant of 4.0 s during the next 2 s interval. After compensation, the torque, rotor flux, and q-axis current are almost the same. There are some fluctuations in the flux, especially when the resistance was changed instantly. This change is mainly because the compensation of the proposed algorithm requires some time to realize. In practice, the rotor resistance cannot be changed so fast. The proposed algorithm has enough time to regulate the slip coefficient. The simulation at 3000 rpm yielded similar results, as shown in Figure 10.

**Figure 9.** Rotor flux, d-axis, and q-axis current comparative waveforms at 1200 rpm with a 60 Nm load.

**Figure 10.** Rotor flux, d-axis, and q-axis current comparative waveforms at 3000 rpm with a 15 Nm load.


**7.5** *Spinacia oleracea* **Linn**

*Spinacia oleracea* commonly known as spinach is endowed with a number of medicinal properties [92]. Ethnopharmacological studies proposed that *Spinacia oleracea* seeds have promising antioxidant, neuroprotective, anti-epileptic, antialzheimer and anti-inflammatory effects [93–95]. The study evaluated the protective effects of *Spinacia oleracea* seed extract in an experimental model of ketamineinduced schizophrenia in mice. Ketamine was used to induce stereotyped psychotic symptoms in mice. Behavioral studies (locomotor activity, stereotypy, immobility duration and memory retention) were carried out followed by biochemical, neurochemical and cellular alterations in the brain. Chronic treatment with *Spinacia oleracea* seed extract significantly attenuated stereotyped behavioral symptoms in mice. Biochemical estimations revealed that the extract reduced lipid peroxidation and restored total brain proteins. Likewise, *Spinacia oleracea* remarkably reduced dopamine levels, acetylcholinesterase activity & inflammatory surge serum tumor necrosis factor (TNF-α) and increased the levels of GABA and reduced glutathione in mice. The results of the study indicated that the extract could ameliorate ketamine-induced psychotic symptoms in mice, signifying a protective effect in the treatment of schizophrenia. Moreover, its protective effect in schizophrenia may be associated with its regulating effect on dopamine, GABA, acetylcholinesterase enzymes, glutathione and malondialdehyde levels [67].

#### **7.6** *Terminalia macroptera* **Linn**

*Terminalia macroptera* Guill. & Perr. (Combretaceae) is a medicinal plant used commonly in Africa. Ethnomedicinal report from Mali mentions the decoction of leaves of *T. macroptera* in treatment of epilepsy [96], and anxiolytic effects of *T. macroptera* has also been reported by [97]. The study was carried out to investigate the antipsychotic effects of *T. macroptera* in an experimental model of ketamineinduced psychosis in mice. Ketamine and apomorphine were used to induce stereotyped psychotic behavioral symptoms in mice. Behavioral studies (stereotype behavior, locomotor activity, immobility duration and memory retention) were carried out to investigate the protective effect of the ethyl acetate fraction of *T. macroptera* on ketamine-induced psychotic symptoms, repeated treatment with the ethyl acetate fraction for 7 consecutive days significantly attenuated stereotyped behavioral symptoms, immobility duration and memory deficit in mice. The study revealed that *T. macroptera* could ameliorate psychotic symptoms indicating protective effects in psychosis. Agent that ameliorate ketamine induced psychotic symptoms are generally thought to act in a similar manner as atypical antipsychotics [12].

#### **7.7** *Crassocephalum bauchiense* **(Hutch.) Milne-Redh**

*Crassocephalum bauchiense* is a medicinal plant with diverse medicinal uses. The leaves decoction of *C. bauchiense* is effective in the treatment of epilepsy, cerebral

*Medicinal Plants Used in the Management of Psychosis DOI: http://dx.doi.org/10.5772/intechopen.100224*

malaria, cerebral deficit, anxiety and behavioral disturbances in mentally retarded children. Likewise, an aqueous extract of the whole plant is useful in the treatment of insomnia, psychosis and other central nervous system disorders, [98, 99]. The antipsychotic effects of *C. bauchiense* extracts were evaluated using the apomorphine animal model of psychosis. The ability of the leaves extracts of *C. bauchiense* to modify the duration of akinesia was observed in the catalepsy test. Furthermore, gamma-aminobutyric acid concentrations in the brain of treated mice were also estimated. The aqueous extract and the alkaloid fraction of *C. bauchiense* attenuated the apomorphine-induced stereotypy and fighting, and had significant fall of the body temperature. In biochemical experiments, the concentration of the inhibitory amino acid, gamma-aminobutyric acid, was significantly increased in the brain of animals treated with the aqueous extract of *C. bauchiense.* The results revealed that the antipsychotic and sedative properties of *C. bauchiense* are possibly mediated via the blockade of dopamine D-2 receptors and GABAergic activation [54].

#### **7.8** *Alpinia zerumbet* **(Pers.) Burtt. et Smith**

*Alpinia zerumbet* has important physiological and pharmacological functions, such as antioxidative [100], anticancer [101], anti-inflammatory [102], and antianxiety [103]. In phytotherapy, *A. zerumbet* is used to treat neuropsychiatric symptoms such as depression, stress and anxiety, but it is only recently that the central nervous system (CNS) effects of the essential oil from the plant leaves have been studied [10]. The essential oil of *A. zerumbet* (50, 100 and 200 mg/kg i.p.) was administered once to mice to evaluate antipsychotic activity assessed by ketamineinduced hyperlocomotion, hypnotic activity induced by sodium pentobarbital, antioxidant effects (determination of lipid peroxidation and GSH levels), as well as variations in nitric oxide levels (determination of nitrite content). The result revealed that the extract at doses of 100 and 200 mg/kg prevented ketamine hyperlocomotion, and at a dose of 200 mg/kg decreased sleep latency, while all doses increased sleeping time. The in-vitro antioxidant capacity of the oil caused a reduction in lipid peroxidation and increase in glutathione levels. The extract also prevented the decrease in nitrite content caused by oxidative stress. The findings indicate antipsychotic and antioxidant effects of the essential oil of *A. zerumbet* that may have promising efficacy for the treatment of schizophrenia [10].

#### **7.9** *Albizia zygia* **(DC.) J.F. Macbr. (Leguminosae)**

*Albizia zygia* is one such plant with numerous medicinal uses. A decoction of the roots is used for the treatment of insanity [104]. Several compounds including two novel oleanane-type saponins, zygiaosides A and B, were lately isolated from the roots of *A. zygia* [105].

*A. zygia* effects were assessed against apomorphine-induced cage climbing, ketamine induced hyperlocomotion, enhanced immobility, impaired social interaction as well as novel object recognition. The propensity of the extract to induce catalepsy and to attenuate haloperidol-induced catalepsy were also investigated. Findings revealed that *A. zygia* extract significantly attenuated apomorphineinduced climbing behavior as well as ketamine-induced hyperlocomotion, immobility and object recognition deficits. Furthermore, the extract had no cataleptic effect. The root extract of *A. zygia* therefore exhibited antipsychotic-like activity in mice with potential to alleviate positive, negative and cognitive symptoms of schizophrenia [51].


*Article* **Cell-Penetrating Doxorubicin Released from Elastin-Like Polypeptide Kills Doxorubicin-Resistant Cancer Cells in In Vitro Study**

**Jung Su Ryu 1 , Felix Kratz 2 and Drazen Raucher 1, \***

**Abstract:** Elastin-like polypeptides (ELPs) undergo a characteristic phase transition in response to ambient temperature. Therefore, it has been be used as a thermosensitive vector for the delivery of chemotherapy agents since it can be used to target hyperthermic tumors. This novel strategy introduces unprecedented options for treating cancer with fewer concerns about side effects. In this study, the ELP system was further modified with an enzyme-cleavable linker in order to release drugs within tumors. This system consists of an ELP, a matrix metalloproteinase (MMP) substrate, a cell-penetrating peptide (CPP), and a 6-maleimidocaproyl amide derivative of doxorubicin (Dox). This strategy shows up to a 4-fold increase in cell penetration and results in more death in breast cancer cells compared to ELP-Dox. Even in doxorubicin-resistant cells (NCI/ADR and MES-SA/Dx5), ELP-released cell-penetrating doxorubicin demonstrated better membrane penetration, leading to at least twice the killing of resistant cells compared to ELP-Dox and free Dox. MMP-digested CPP-Dox showed better membrane penetration and induced more cancer cell death in vitro. This CPPcomplexed Dox released from the ELP killed even Dox-resistant cells more efficiently than both free doxorubicin and non-cleaved ELP-CPP-Dox.

**Keywords:** drug delivery; tumor targeting; elastin-like polypeptide; cell penetrating peptide; matrix metalloproteinase; doxorubicin resistance

#### **1. Introduction**

It is acknowledged that current conventional chemotherapy is mostly comprised of cytotoxic drugs, which have a strong anticancer efficacy but cause collateral damage to non-tumor tissues. These unwanted side effects are usually a dose-limiting factor for chemotherapy and are a main reason for the unsatisfactory prognosis of the therapy. Many efforts have been made to resolve these problems, usually by attempting to raise the therapeutic index of the chemotherapy.

Curing cancer is certainly one of the greatest challenges of our time, and to confront it our knowledge of cancer has grown greatly over the last decades. In recent years, there has been a surge of new technologies for cancer treatment such as molecular targeted therapies (i.e., anti-tyrosine kinase and anti-HER2) [1], immunotherapies such as cancer vaccines or anti-PD1 [2,3], sophisticated radiation therapy [4], and advanced tumor-targeting technologies such as nanoparticles and antibody–drug conjugates. These technologies could make a big difference in the way we treat cancer, bringing us closer to being able to "cure" this disease. In particular, the nanosized drug delivery technologies have been significantly improved, and many of them are currently being used to solubilize the drugs, bypass immune surveillance, sensitize current therapies, and target tumor tissues [5,6]. A tumor-targeting technology delivers drugs specifically to tumor tissue so that the concentration of drugs in the tissue will increase compared to the concentration in normal tissue. This allows more

**Citation:** Ryu, J.S.; Kratz, F.; Raucher, D. Cell-Penetrating Doxorubicin Released from Elastin-Like Polypeptide Kills Doxorubicin-Resistant Cancer Cells in In Vitro Study. *Int. J. Mol. Sci.* **2021**, *22*, 1126. https://doi.org/10.3390/ijms22031126

Academic Editor: Angela Stefanachi Received: 23 December 2020 Accepted: 20 January 2021 Published: 23 January 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/).

opportunity for a drug to express its activity on tumor cells, resulting in the selective death of cancer cells with tolerable side effects.

An elastin-like polypeptide (ELP) is a thermo-responsive bio-polymeric carrier for targeted drug delivery. ELPs derived from tropoelastin consist of repeats of pentapeptides (mainly comprised of valine, proline, and glycine) [7,8]. The repeats of these hydrophobic amino acids permit an ELP to have a unique re-arrangement of molecules in response to the surrounding temperature, which is a thermoresponsive phase transition. At low temperature, an ELP remains a monomer and is soluble in solution; however, it co-acervates and precipitates in solution when the ambient temperature rises above its phase-transition temperature [9–11]. This co-acervation can also be reversed by decreasing the temperature of the ELP solution. Thus, this reversible phase transition of an ELP is mainly controlled by temperature, and the ELP is highlighted as a controllable carrier for the delivery of anticancer drugs in active tumor-targeting strategies.

Additionally, and ELP exploits an "enhanced permeability and retention (EPR) effect" and can progressively accumulate in tumor tissue due to the abnormal histological structures of the tumor. These unique properties turn the ELP itself into a drug carrier that can exploit both the EPR effect and tumor targeting using the hyperthermic technique [12]. Furthermore, the ELP has been modified by the addition of cell-penetrating peptides (CPPs) to allow enhanced cellular uptake, improved penetration of physiological barriers like the blood–brain barrier, and preferential intracellular distribution such as in the cytoplasm or the nucleus [13,14]. Many previous researchers have verified the potential of this polymer [15–18], and animal studies have demonstrated that ELPs are able to deliver a sufficient amount of a drug to the tumor area to produce significant tumor reduction efficacy in combination with the use of local hyperthermia.

In this study, we further modified an ELP drug delivery system to release drugs in response to an enzyme that is abundant in tumor tissue (Figure 1). The suggested system is composed of an ELP; a matrix metalloproteinase (MMP) substrate, mmpL; a cellpenetrating peptide, CPP; and a 6-maleimidocaproyl amide derivative of doxorubicin, Dox (Figure 1A). We report the potential use of this strategy, an MMP-responsive ELP drug delivery system releasing CPP-Dox, to overcome Dox resistance by investigating the cellular uptake and anti-proliferation properties of the proposed system.

**Figure 1.** Elastin-like peptide (ELP) drug delivery system. (**A**) The proposed ELP system consists of an elastin-like polypeptide, matrix metalloproteinase (MMP)-cleavable linker, cell-penetrating peptide (CPP), and cargo molecules. The constructs are digested by the MMP, releasing the CPP cargo molecule. (**B**) The hypothetical model proposed in this system. The ELP constructs can form aggregates and release the CPP cargo molecule in hyperthermic tumors (T, temperature).

#### **2. Results**

#### *2.1. Incubation of ELP-mmpL-CPP with MMP-2 Produces Cleaved CPP*

Figure 2A depicts how ELP-mmpL-CPP would be cleaved by MMP, producing ELP (60 kDa) and cleaved CPP (Tat peptide, 3 kDa), while the other construct, ELP-CPP-Dox (63 kDa), would not be digested by MMP. This hypothesis was verified by the following experiments. MMP-2 was used for the digestion in this experiment since MMP-2 is involved in the degradation of extracellular matrices in tumors and is overexpressed in most tumors compared with normal tissues [19]. After incubation of each rhodamine (rho)-labeled construct (ELP-mmpL-CPP-rho and ELP-CPP-rho) with MMP-2, the reactant was run on SDS-PAGE and analyzed by both silver staining and fluorescence scanning (Figure 2B). Silver-stained gels revealed that MMP-digested ELP-mmpL-CPP-rho produced two bands (lane 2 in the left panel); the upper one for ELP (60 kDa) and the other for cleaved CPP-rho (3 kDa), while ELP-CPP-rho digestion produced only one band (lane 1 in the left panel) which represents undigested ELP-CPP-rho. However, when the gel was scanned for fluorescence, each reactant showed only one band. Since rhodamine was conjugated to the C-terminal of the CPP (Tat peptide), MMP digestion produced one single fluorescent band (CPP-rho, 3 kDa) without ELP (lane 2 in the right panel), while a band of undigested ELP-CPP-rho fluoresced at around 63 kDa, as with the silver-stained gel (lane 1 in the right panel). ② ① ② ①

**Figure 2.** MMP-2 digestion of ELP-mmpL-CPP. (**A**) ELP-mmpL-CPP-rhodamine and ELP-CPPrhodamine were incubated with MMP-2 for 4 h in ZnCl2 buffer (pH 7). (**B**) MMP-2 incubation of the constructs produced ELP-CPP-Dox (63 kDa, upper band) and CPP-rhodamine (3 kDa, lower band). Left panel: silver-stained gel; Right panel: fluorescence-scanned gel. Dox: 6-maleimidocaproyl amide derivative of doxorubicin; mmpL: MMP substrate.

#### *2.2. MMP-2 Digestion Increases the Cellular Uptake of CPP -Rhodamine in Breast Cancer Cells*

MMP digestion will produce CPP-rhodamine (rho), which is smaller than the whole construct, ELP-mmpL-CPP-rho. This smaller size would facilitate its cellular uptake. Cells treated with MMP-digested ELP-mmpL-CPP-rho and ELP-CPP-rho, respectively, were analyzed for uptake ability via flow cytometry. In Figure 3A, cells treated with cleaved CPP-rho (from ELP-mmpL-CPP-rho) showed up to five times higher uptake rates than the ELP-CPP-rho-treated group in three cancer cell lines. This improved cellular uptake was also evident in observation with a fluorescence microscope (Figure 3B).

**Figure 3.** Cellular uptake rate of cleaved CPP-rhodamine in breast cancer cells. (**A**) Each cellular uptake rate was measured through flow cytometry (\* *p* < 0.05). (**B**) Merged image of Dox (red) and DAPI (blue). (**C**) Cytotoxicity of cleaved CPP-Dox on breast cancer cells. Cells were treated with ELP-CPP-Dox and ELP-mmpL-CPP-Dox, both digested by MMP incubation.

#### *2.3. Cleaved CPP-Dox Kills Breast Cancer Cells More Efficiently than Non-Cleaved ELP-CPP*

Rhodamine was replaced by doxorubicin to investigate whether improved uptake of cleaved CPP would contribute to cytotoxicity. Figure 3C compares the cytotoxicities of MMP-2-digested ELP-mmpL-CPP-Dox and ELP-CPP-Dox against three cancer cell lines. Improved cytotoxicity was observed in MMP-2-digested ELP-mmpL-CPP-Dox-treated cells than those treated with ELP-CPP-Dox. These results suggest that the MMP digestion of ELP-mmpL-CPP-Dox results in increased uptake of cargo molecules and facilitated the death of cancer cells by cleaved CPP-Dox.

#### *2.4. Cleaved CPP-Dox Deposits in and Kills Dox-Resistant Cancer Cells*

To investigate whether cleaved CPP-Dox is able to penetrate and kill even Doxresistant cancer cells, comparisons of cytotoxicities and uptake rates of MMP-cleaved CPP-Dox were made between Dox-resistant cells (NCI/ADR, MES-SA/Dx5) and Doxsensitive cells (MCF7, MES-SA).

Figure 4A shows the validated Dox resistance in NCI/ADR and MES-SA/Dx5, and cleaved CPP-Dox from ELP-mmpL-CPP-Dox showed more cell killing than ELP-CPP-Dox at 4 µM Dox equivalence. Confocal microscopic images of NCI/ADR cells show that cleaved CPP-Dox from ELP-mmpL-CPP-Dox was taken up by NCI/ADR more than the other constructs (i.e., free Dox and ELP-CPP-Dox; Figure 4B). This was also confirmed by flow cytometry (Figure 4C). The uptake rate of MMP-digested CPP-Dox in NCI/ADR was almost doubled compared with the uptake rates of free Dox and ELP-CPP-Dox. These results suggest that MMP-cleaved CPP-Dox can penetrate and kill even Dox-resistant cancer cells, probably with the help of a CPP (Tat peptide). One limitation of this experiment is that 4 µM of a doxorubicin-equivalent dose is the maximum concentration that can be reached from the current cleavage assay protocol; further optimization of the protocol may

enable the generation of a higher concentration of each drug and calculation of IC50 to compare the cytotoxicity of each treatment.

**Figure 4.** Cytotoxicity of CPP-Dox against Dox-resistant cancer cells. (**A**) Free Dox killed Dox-sensitive cancer cells (MCF7 and MES-SA), while it spared Dox-resistant NCI/ADR and MES-SA/Dx5. (**B**) Cytotoxicities of CPP-Dox and ELP-CPP-Dox in NCI/ADR and MES-SA/5DX at 4 µM Dox equivalence. (**C**) Confocal microscopic images show that CPP-Dox penetrated into NCI/ADR. (**D**) Flow cytometry, 60% increased uptake in CPP-Dox in comparison with ELP-CPP-Dox and free Dox. \* *p* < 0.05.

#### *2.5. MMP-Releasing HT-1080 Can Cleave ELP-mmpL-CPP-rho and Take Up Cleaved CPP-rho*

Given that an MMP-cleaved CPP-Dox can inhibit proliferation in Dox-resistant cancer cell lines, this ELP-mmpL-CPP-Dox system was further validated using HT-1080, a fibrosarcoma cancer cell producing endogenous MMP-2 and MMP-9. This experiment showed that the ELP-mmpL-CPP construct could also be digested by the endogenous MMP enzyme and release CPP cargo molecules. MMP-releasing HT-1080 cells were incubated with either ELP-mmpL-CPP-rho or ELP-CPP-rho for 4 h, and each group of treated cells was processed either for flow cytometry or fluorescence microscopy. In flow cytometry, cells incubated with the ELP-mmpL-CPP-rho group had twice the rhodamine signal of the ELP-CPP-rho group. However, this increased uptake was reversed by pretreatment with GM6001, an MMP catalytic inhibitor (Figure 5A). This finding was further confirmed by fluorescence microscopy, with the rhodamine particles being found in the nucleus of HT-1080 cells treated with ELP-mmpL-CPP-rho (Figure 5B). Uptake of these particles, as in the flow cytometry experiment, was also abolished by GM6001 pretreatment. GM6001 prevents MMP digestion, and undigested ELP-mmpL-CPP-rho was likely washed off the cells during the rinsing step. These results indicate that ELP-mmpL-CPP-rho was digested by intrinsic MMP released from HT-1080 cells, and that the resultant cleaved CPP-rho penetrated the HT-1080 cells.

**Figure 5.** Cellular uptake rate of CPP-rhodamine in MMP-expressing HT-1080 cells. (**A**) Localization of CPP-rho (fluorescence microscopy, 20x) in cultured HT-1080 cells. The arrows indicate the CPPrhodamine in the cells. (**B**) Flow cytometry showing increased uptake in cleaved CPP-rho in cells. \* *p* < 0.05.

#### **3. Discussion**

Our tumor-targeted drug delivery system using an ELP delivers anticancer cargo molecules specifically to the tumor site by exploiting the enhanced permeability and retention (EPR) effect along with the active thermal targeting approach [15–17,20]. This thereby increases the relative concentration of the cargo drugs in tumors and improves the therapeutic index of the drugs, alleviating unacceptable toxicity to the patients [12,21]. A striking example of this targeting can be found in previous studies [20], in which fluorescently labeled CPP-ELPs were administered into S2013 tumor-bearing mice. One group of animals received hyperthermic treatment with infrared (IR) lasers on tumors immediately after injection of CPP-ELP so that the temperature in the tumor core reached 42 ◦C, while the other group was exempt from hyperthermic treatment. This study demonstrated that the IR heating of tumors created 2–3 times greater tumor accumulation of CPP-ELP as well as the penetration of this protein into the tumor tissues. Given the thermoresponsive behavior of the ELP, the aggregation of CPP-ELP in hyperthermic tumors resulted in an increase of the construct's concentration in the tumors. These results strongly suggest that the ELP preferentially accumulated in tumors in response to local hyperthermia.

We further improved the ELP system in this study to release payloads in response to additional external stimuli—that is, matrix metalloproteinases. This novel system is comprised of four components: an ELP, an MMP-2-cleavable linker, CPP (Tat peptide), and doxorubicin as a payload. The linker is a substrate of MMP-2, designed to be cleaved by MMP-2 so that the ELP system can eventually release a complex of the payload (doxorubicin) and a CPP (CPP-Dox) in tumor tissues. The involvement of MMPs, which are zinc-containing endopeptidases, in cancer biology has been extensively discussed in a variety of publications [22–25]. Especially, increased expression and activity of MMP-2 and MMP-9 subtypes in tumors are known to be related to the degradation of basement membranes—an essential step in tumor invasion and in enhancing angiogenesis. For example, Tutton et al. reported that MMP-2 expression was significantly increased in colorectal cancer tissues compared to matched normal colon tissue as measured by ELISA [26]. High levels of MMPs in tumors will facilitate the release of CPP-Dox out of the renovated ELP complex and provide an additional, secondary tumor-targeting opportunity compared to the previous ELP delivery system. This system thus becomes a triple-targeting strategy when used along with the EPR effect and local hyperthermia. Specifically, this cleavable ELP construct still contains the ELP molecule until it is digested by MMPs at tumor tissues. This late cleavage process will allow CPP-Dox to benefit from ELP by EPR, and from the thermo-targeting. ELP is expected to allow the proposed construct to be initially targeted to the tumor site by the local application of mild heat. Then, ELP-mmpL-CPP-Dox will be fully digested by MMP to release CPP-Dox (Figure 1B), followed by improved cellular uptake by cancer cells and increased cancer-cell death.

This MMP-cleavable system displays a couple of other advantages in delivering chemotherapeutic molecules. First, when the MMP-cleavable ELP-CPP-drug is digested by MMP in tumor tissue, small fragments (CPP-Dox) will be produced. Since the molecular weight of the released CPP-Dox (<3 kDa) is one-twentieth that of the parental ELP construct (60 kDa), it will quickly infiltrate into adjacent tumor cells, as can be seen in other studies [27,28]. This hypothesis was examined by cell-uptake assays in this study. Cells treated with CPP-rho, which is a digested product from ELP-mmpL-CPP-rho, showed more rhodamine uptake than the cells treated with undigested ELP-CPP-rho. It is also demonstrated in Figure 5 that the ELP-mmpL-CPP-rho could be digested by endogenous MMPs and taken up by HT-1080 cells. Reversal of this uptake by GM6001 (an MMP inhibitor) indicates that the cell uptake of rhodamine by HT-1080 relies on the catalytic activity of MMP. This increased uptake was reflected in the enhanced cytotoxicity of MMP-cleaved CPP-Dox in breast cancer cells. After MMP-2 digestion, ELP-mmpL-CPP-Dox killed more cancer cells than did ELP-CPP-Dox (Figure 3C).

A second advantage of this system is that the released CPP-Dox still takes advantage of the abilities of the CPP to facilitate uptake by the cells and to penetrate physiological barriers like the blood–brain barrier. More importantly, there is an increasing number of studies reporting the role of CPPs in overcoming the multidrug resistance (MDR) of cancer cells, which has been one of main hurdles that doxorubicin has faced. The use of doxorubicin, one of the most effective chemotherapy agents since the 1960s, has been compromised by the development of MDR in patients [29,30]. MDR involves increased efflux, decreased uptake, and enzymatic drug metabolism (e.g., glutathione S-transferase) of chemotherapeutic drugs such as doxorubicin [31]. An elevated expression of active drug transporters in cancer cells is known to be a major resistance mechanism [32]. The coupling of chemotherapeutic drugs to peptides such as CPPs have been suggested as the solution for these problems, since this strategy may alter the cellular uptake pathway and circumvent ABC-transporter-mediated drug efflux, allowing drugs to accumulate at high concentrations in drug-resistant cells, leading to an improved therapeutic index and fewer adverse effects [28,33–35]. Specifically, CPP-Dox developed by Liang et al. [35] showed a 59% uptake rate in Dox-resistant MCF7 cells, while 90% of free Dox was lost during cell penetration, leading to a considerable improvement in the IC50 of doxorubicin. In line with these reports, our current study also demonstrates that cleaved CPP-Dox showed greater cellular uptake by Dox-resistant NCI/ADR in comparison with free Dox and ELP-CPP-Dox (Figure 4C). Like Dox-sensitive breast cancer cells, this increased uptake also led to an enhanced cytotoxicity of cleaved CPP-Dox against NCI/ADR and MES-SA/Dx5 (Figure 4B).

In summary, the modified ELP-CPP-Dox was cleaved by incubation with an intrinsic or extrinsic MMP enzyme. MMP digestion produced CPP-Dox (or rhodamine), which showed better membrane penetration and induced more cancer cell death *in vitro*. This CPP-complexed Dox released from an ELP killed even Dox-resistant cells more efficiently than both free doxorubicin and non-cleaved ELP-CPP-Dox. This pilot study emphasizes the extra functionalities of the ELP drug delivery system. The novelty of this study is improvement in the drug delivery efficiency of ELP and demonstration of ELPs' potential in multiple-targeting strategies.

#### **4. Materials and Methods**

#### *4.1. Design of Construct and Protein Preparation*

The ELP used in this study consists of 150 repeats of VPGXG with guest residues (amino acid at position X) of Val, Gly, and Ala in a 5:3:2 ratio. ELP coding sequences were modified by the addition of the "Tat" cell-penetrating peptide sequence (YGRKKRRQRRR), an MMP-cleavable sequence (PLGALG), and three Gly-Gly-Cys residues to the C-terminus

of the ELP for the conjugation with Dox (Table 1). For an MMP-uncleavable control, six Gly residues were used instead of the MMP-cleavable sequence. All constructs were expressed in the *Escherichia coli* strain BLR (DE3) using pET 25b as an expression vector, and were purified by repeated inverse transition cycling.

**Table 1.** Construct sequences.

#### *4.2. Conjugation of ELP Constructs with Doxorubicin or Fluorescent Probes*

Protein (100 µM) in PBS was reduced with 1 mM of tris-(2-carboxyethyl) phosphine (TCEP, Molecular probes) for 30 min at room temperature. Conjugation with 200 µM of the 6-maleimidocaproyl amide derivative of doxorubicin or tetramethylrhodamine-5-iodoacetamide dihydroiodide (molecular probes) was followed by incubation at 4 ◦C overnight. Conjugated peptides were purified by inverse transition purification as described previously [9], and the concentration and the labeling efficiency were assessed by UV–visible spectrometry (UV-1600, Shimadzu). Concentrations of labeled ELP polypeptides were determined using the following equations:

for Dox conjugation

$$\text{protein}, \text{ M } = \frac{\text{Abs} \, 280 \text{nm} - (0.71 \times \text{Abs} \, 495 \text{nm})}{6890 \text{M} - 1 \text{cm}^{-1}}$$

for tetra-methyl-rhodamine conjugation

$$\text{protein}, \text{ M} = \frac{\text{Abs} \, 280 \, \text{nm} - (0.17 \times \text{Abs} \, 541 \text{nm})}{5690 \text{M} - 1 \text{cm}^{-1}}$$

#### *4.3. Cell Culture*

MDA-MB-231, MCF7, NCI/ADR, MES-SA, MES-SA/Dx5, and SKBR3 cell lines were obtained from ATCC. HT-1080 was a generous gift from Dr. Michael Herbert of the University of Mississippi Medical Center. All cell lines were grown and maintained at 37 ◦C, 5% CO2 in Dulbecco's Modified Eagle's Medium with 10% fetal bovine serum.

#### *4.4. Cleavage Assays*

Recombinant human pro-MMP-2 (Enzo life science) was activated with 2.5 mM 4 aminophenylmercuric acetate at 37 ◦C for 2 h. Then, 1 µg of each ELP construct was incubated with the pre-activated MMP-2 (10 pmol) for 4 h in a reaction buffer (50 mM Tris, 200 mM NaCl, 10 mM CaCl2, and 10 mM ZnCl2, pH 7.5). After the reactions, the samples were loaded and separated on an SDS-PAGE gel, and each peptide's cleavage pattern was confirmed by silver staining and by scanning the fluorescence of the gel with the IVIS Live Animal Imager (Caliper Life Sciences).

#### *4.5. Flow Cytometry Analysis of Cellular Uptake*

Cells were incubated with each treatment for 2 h at 37 ◦C, rinsed with PBS, and collected by trypsinization. Intracellular fluorescence was measured using a Gallios Flow Cytometer (Beckman Coulter) after trypan-blue quenching as described previously [36]. Forward- versus side-scatter gating was used to remove cell debris from the analysis, and the mean cellular fluorescence intensity was recorded. The mean cellular fluorescence was corrected for differences in labeling efficiencies among polypeptides, and the results shown are an average of at least 3 experiments with bars representing the standard error of the mean (SEM).

#### *4.6. Cytotoxicity Test*

Cells were plated in a 96-well plate and treated with a range of concentrations of each treatment for 24 h at 37 ◦C. After further incubation with fresh media for 48 h, cell viability was assessed using the MTT assay (Sigma). Briefly, a 0.5 mg/mL solution of thiazolyl blue tetrazolium bromide dissolved in PBS was added to each well and the plates were incubated for 4 h at 37 ◦C. Formazan formed by mitochondrial reduction was dissolved in 100 µL of DMSO, and its absorbance was read at 570 nm. The survival rate of each group was calculated in comparison to a vehicle-treated control group.

#### *4.7. Confocal Microscopy*

Cells (103 cells/chamber) were plated in 2-well Lab-Tek CC2 chamber slides (Nunc). After 24 h incubation at 37 ◦C, the cells were treated with each treatment for two hours at 37 ◦C. The cells were washed three times with PBS, fixed with cold 4% paraformaldehyde, and stained with DAPI (molecular probe) for 10 min at room temperature to visualize the nucleus. Distribution of each molecule was investigated by laser scanning confocal microscopy with a 60x oil immersion objective (Leica).

**Author Contributions:** Conceptualization, J.S.R.; methodology, J.S.R.; investigation, J.S.R.; resources, D.R. and F.K.; writing, J.S.R.; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** Research reported in this publication was supported by the National Science Foundation (NSF, IIP- 1640519), the National Cancer Institute (1R21CA229943-01A1) and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM121334. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.

**Acknowledgments:** We would like to express our great appreciation to Rebecca B. Mackey of the UMMC for her editorial support. Access to animal imaging equipment was provided by the Animal Imaging Core Facility at the University of Mississippi Medical Center.

**Conflicts of Interest:** Drazen Raucher is a founder/owner of Thermally Targeted Therapeutics, Inc. Felix Kratz is Vice President of CytRx Corporation.

#### **Abbreviations**

#### **References**


**Correlation Matrix**

To discover the pattern of intercorrelations among variables, we examine the correlation matrix. That is given in Table 7:

Note : Figures reported in this table are correlation coefficients.


**2. Irrigation Modernisation and Automation**

The terms irrigation modernisation and automation have become common in irrigation literature in the recent decades, and in some cases have been used interchangeably. In the context of this paper, modernisation is the process of replacing ageing irrigation infrastructure and methods, often with new or "modern" equipment and technologies that have been developed in the recent past. The aims of irrigation modernisation include water saving and improved water delivery, and reduced operating and labour costs, which leads to sustainable agricultural production and enhanced livelihoods of farmers [6]. On the other hand, automation of irrigation systems is the use of equipment that allows the irrigation process to proceed with minimum human involvement, except for periodic inspections and routine maintenance. In Australia, modernisation and automation have been undertaken in the water distribution networks at the farm level as well. The nexus between automation and modernisation, and WUE will be explored in this paper.


*4.2. Phase Subtraction*

The separated temperature-induced strain and temperature data was fitted subject to the Fourier decomposition. Furthermore, the temperature fitting curves of the 1st order, 4th order, and 8th order decomposition on 20 April 2017 are shown in Figure 7a. With the increase of the Fourier expansion order, the consistency was better, as shown in Figure 7b. When the data was expanded to the 6th order, the accuracy requirement was met.

**Figure 7.** *Cont*.

**Figure 7.** The Fourier curve fitting results (20 April 2017).

The direct consequence of the time-lag phenomenon is the decline in correlation between the temperature and the temperature response. The hysteresis loop area directly reflects the degree of the time-lag phenomenon. Specifically, the larger the area, the more significant the lag effect. Therefore, the hysteresis loop area and the correlation coefficient can be used as indicators to verify the effectiveness of subtracting the time-lag phenomena by the Fourier series expansion method.

With the Fourier fitting method, the phase difference of the measured temperature and strain data on 22 July 2017 (summer) was calculated. The lag time of the S8 strain measuring point and T4 temperature measuring point can be calculated through Equation (9). As a result, the lag time was approximately 176 min in the summer season. Then, a translation of the temperature data by the lag time achieves the goal of subtracting the time-lag phenomenon. The temperature and strain correlation before and after the subtraction is shown in Figure 8. Using the same method to subtract the time-lag phenomenon in winter, Figure 9 is obtained. Meanwhile, through Equation (9), the lag time was calculated to be around 129 min in the winter season. As mentioned before, the displacement and temperature data for steel bridges possess a lag time of approximately 45 min in the research by Guo et al. [21]. As a result, the lag time in concrete structures is longer than that in steel structures.

(**a**) Before the phase difference is translated

(**b**) After the phase difference is translated

**Figure 8.** Temperature vs. strain plots in summer (22 July 2017).

**Figure 9.** Temperature vs. strain plots in winter (27 November 2017).

By calculating the hysteresis areas and lag time of raw data and phase difference eliminated data, Table 1 was obtained. From Table 1, the hysteresis curve and lag times in winter with those in summer, respectively, were compared. Consequently, after subtracting, the hysteresis loop was notably reduced and the correlation coefficient was significantly improved. Therefore, the Fourier fitting method was verified to be effective.

**Table 1.** Comparison of indicators before and after phase difference elimination.

With the proposed method, the time-lag phenomenon can be reduced. Furthermore, the long-term stable relationship between temperature load and the structural corresponding response can be more clearly reflected. Specifically, the same temperature and strain measurement points of four days in the four seasons of 2017 were selected to draw the temperature–strain curve. Then, the subtracted data curve was plotted in Figure 10. It is clear to see, although the specific distribution difference of strain varying with temperature exists in different seasons, the hysteresis loops in four seasons possess similar slopes overall. In particular, by eliminating the phase difference, the reduced curve can more clearly reflect the corresponding linear relationship characteristics of temperature-induced effects and temperature in different seasons. This feature reflects the specific mapping relationship between temperature and structure response and it is related to structure physical properties, such as structural geometry and material properties.

In summary, the wavelet decomposition and reconstruction method is useful and handy to extract temperature-induced strain. The lag time in a concrete structure is longer than that in a steel structure. Moreover, according to the comparison of hysteresis loop areas and correlation coefficient before and after subtracting the time-lag effect, the effectiveness was verified.

**Figure 10.** Temperature–strain curve and the subtracted data curve in different seasons.


4.8.3. Recommendations

Equipment for resistance training typically includes:

An initial intensity of 30–40% of 1-RM for upper body and 50–60% of 1-RM for the lower body is recommended. The general recommendations for resistance training according to the American College of Sports Medicine (modified) are given in Table 35 [12].

**Table 35.** ACSM resistance training recommendations.

Abbreviations: ACSM—American College of Sports Medicine; 1-RM—one repetition maximum. Source: Adapted from [12].

The AACVPR and ACSM recommendations for the commencement of resistance training and acceptable load are summarized in Table 36 [1,12].

**Table 36.** Commencement of resistance training and acceptable load.

Abbreviations: AACVPR—American Association of Cardiovascular and Pulmonary Rehabilitation; ACSM—American College of Sports Medicine; CABG—coronary artery bypass graft surgery; MI—myocardial infarction; PPM—permanent cardiac pacemaker. Source: Adapted from [1,12].

Resistance training can be implemented a few weeks after myocardial infarction (after at least 1 week of well-tolerated aerobic training); however, it should be postponed following cardiac surgery until full sternum stability—i.e., for 3 months. In the case of individuals with a very low functional capacity or muscular atrophy, resistance training should commence simultaneously or before the aerobic component to increase muscle power. Resistance training can be performed as an independent session or may be used as part of warm-up or cool-down phases. Sessions should be performed 2–3 times a week with at least 48 h separating training for the same muscle groups [80].

Considering the findings of recent studies, high-intensity dynamic strength training is recommended, as it leads to greater muscle strength improvement than low-intensity exercise, and, if executed properly, has been demonstrated as safe [81]. It has been postulated that dynamic high-intensity resistance training elicits enhanced myofibrillar protein synthesis, subsequently leading to greater gains in muscle mass compared to dynamic low-intensity training [82,83].

### 4.8.4. Strength Testing

Strength testing prior to the commencement of resistance training enables appropriate load assessment. Typical approaches to determining appropriate resistance training intensity include [8]:

60% of 1-RM = 17 repetitions possible; 70% of 1-RM = 12 repetitions possible; 80% of 1-RM = 8 repetitions possible; 90% of 1-RM = 5 repetitions possible; 100% of 1-RM = 1 repetition possible.

Based on recent studies, for a precise 1-RM estimation, the use of no more than 10 repetitions has been suggested during strength testing [84]. In addition, the rating on the perceived exertion scale can be a valuable adjunct to control the intensity of resistance training [85].

To facilitate load estimation, dedicated equations for 1-RM estimation from multiple RM tests have been proposed [86,87]—e.g., 1-RM = (1 + 0.0333 × repetitions) × applied weight.

Typically, 8–12 repetitions improve muscle strength, whereas 15–20 repetitions improve endurance.


Meet the editor

Mousumi Sen has a doctorate degree from the Indian Institute of Technology, Delhi, India. She is currently an Assistant Professor in the Department of Applied Chemistry, Amity University. Her research focuses on the prediction of pollutant dispersion from industrial areas, development of effective and sustainable methods for the removal of inorganic and organic pollutants from polluted water, food chemistry, heavy metal detoxification,

composites/nanocomposites, water research, bio-inorganic chemistry, and nano chemistry. She has published numerous peer-reviewed research articles in journals of high repute as well as edited book chapters, authored a book, edited a book, and conference proceeding papers in her credit.

Contents

**Section 1**

*by Ali Salman Ali*

**Section 2**

**Section 3**

**Section 4**

Current Development

Energy-Related Applications

Nanocomposite Materials

*by Mousumi Sen*

**Preface XI**

Nanomaterials: Synthesis and Applications **1**

**Chapter 1 3**

**Chapter 2 17**

**Chapter 3 37**

Nanomaterials in Energy Storage **57**

**Chapter 4 59**

Preparation Methods of Nanoparticles: Energy Related Applications **75**

**Chapter 5 77**

Nanocomposite Materials: Sustainable Environment **105**

**Chapter 6 107**

Biological Synthesis of Nanoparticles Using Endophytic Microorganisms:

Modern Trends in Uses of Different Wastes to Produce Nanoparticles

Application of Nanomaterials in Environmental Improvement

Nanotechnology in the Service of Solar Energy Systems *by Farzaneh Ghasemzadeh and Mostafa Esmaeili Shayan*

Ultrasound-Assisted Preparation Methods of Nanoparticles for

*by Christos Vaitsis, Maria Mechili, Nikolaos Argirusis, Eirini Kanellou, Pavlos K. Pandis, Georgia Sourkouni, Antonis Zorpas and Christos Argirusis*

and Their Environmental Applications *by Salah Abdelbary and Hadeer Abdelfattah*

*by Omar Messaoudi and Mourad Bendahou*

## Contents

Preface

This book on *Nanotechnology and the Environment* includes an introduction to nanotechnology contributed to by world-renowned specialists in the field of nanomaterials, renewable energy, and environmental sciences. This book is a collection of advanced areas of research on all aspects of nanotechnologies. Nanoscience, nanotechnology, and environmental research are truly multidisciplinary and international efforts, which cover a wide range of scientific disciplines such as physics, chemistry, biology, biomedical, medicine, materials sciences etc. Nanomaterials that usually range in size from 1 to 100 nm in at least one dimension. The properties of nanomaterials are different from those of macro particles because the chemical and physical properties are very dissimilar when the dimensions are at the nano meter range and this reflects in production of very unique physiochemical properties like shape, size, dispersion, surface functionalization or de-functionalization etc. The core scientific principles and applications of this interdisciplinary field bring together chemists, physicists, materials scientists, and engineers to meet the potential future challenges such as renewable energies for sustainable development, new technologies for preparation of advanced materials, and environmental protection. This also shows a huge amount of potential application in solar cells, fuel cells, secondary batteries,

Application of nanotechnology in solar cells reflects awareness about the newly developed nanomaterials. The new architectures for solar cells focus on nanotechnology by making transparent solar cells as compared to the conventional materials. The application of nanotechnology-based photo catalysts in solar cells i.e., uses of photo nano catalyst have been appreciated as very interesting and of course environmentally sustainable i.e., self-cleaning process using nano photo

The potential of nanotechnology in agricultural sciences using the revolutionary application of nano fertilizers have proved to be economically feasible and socially sustainable technology as compared to conventional materials. The application of nanotechnology-based nano fertilizers has potential to reduce nutrient loss due to leaching, emissions, and long-term incorporation by soil microorganisms i.e.,

Modern trends in uses of different wastes to produce nanoparticles and its environmental applications reflects a healthy awareness about the newly developed nano materials. The sequences of the uses of the new architectures for synthesis of different nanoparticles such as nano-cellulose, metals and metal oxide nanoparticles, carbon nanoparticles and nano-fibers using different methods on agricultural wastes and industrial wastes have been developed. Also discussed is the self-cleaning process using nano materials from wastes by using food wastes in nanoparticle

capacitors, MOFs etc.

self-cleaning process using nano fertilizers.

synthesis as compared to the conventional materials.

catalyst.


**Abstract**

Risk is unavoidable, so quantification of risk in any institution is of great importance as it allows the management of an institution to make informed decisions. Lack of risk awareness can lead to the collapse of an institution; hence, our aim in this chapter is to cover some of the ways used to quantify risk. There are several types of risks; however, in this chapter, we focus mainly on quantification of operational risk using parametric loss distributions. The main objective of this chapter is to outline how operational risk is quantified using statistical distributions. We illustrate the application of parametric loss distributions' risk quantification using "Taxi claims data" which seems to best fit one of the loss distributions and fully illustrate how to quantify this specific data. More importantly, we also illustrate how to implement quantification of risk for two other scenarios: (i) if we assumed the underlying distribution is unknown and use the nonparametric empirical distribution approach, and (ii) when using the generalized extreme value (GEV) distribution approach. The latter two scenarios were not the main objective but were done in an effort to compare our results with some of the more commonly used techniques in real-world risk analysis scenarios.

**Keywords:** risk quantification, loss distributions, parametric, nonparametric, value-at-risk


**4. Experiments**

#### *4.1. Experimental Datasets*

We evaluated the proposed architecture on three publicly available HSI datasets, which are frequently used for pixel-wise HSI classification. These datasets are Indian Pine (IP), Pavia University (PU), and Kennedy Space Center (KSC). These datasets were captured by different sensors, thus having different spatial resolutions and a different number of bands. Each dataset has a different category (class), and each class has a different number of instances. The details of the datasets are provided below:

314,368 pixels, but only 5122 pixels have ground-truth information (as shown in Table 1b). The dataset's spatial resolution is 18 meters per pixel, and its band number is 174.

3. PU Dataset: The PU dataset was gathered during a flight campaign over the campus in Pavia, Northern Italy, using a Reflective Optics System Imaging Spectrometer (ROSIS) hyperspectral sensor. The dataset consists of 610 × 610 pixels, with a spatial resolution 1.3 meters per pixel. Hence 207,400 pixels are available in this dataset. However, only 20% of these pixels have ground-truth information, which are labeled into nine different classes, as shown in Table 1c. The number of its spectral bands is 103, ranging from 430 to 860 nm.

**Table 1.** Detailed categories and number of instances of Indian Pines dataset (The colours represent the colour labels that are used in the figures of Section 4.3).

#### *4.2. Experimental Configuration*

In our proposed model, we do standardization (a technique to rescale the data to have a mean of 0 and a standard deviation of 1) in advance, before dividing the data into training and

testing. Hyperparameters are initialized based on previous research or optimized during experiments. We initialized the convolution kernel by using the "He normal optimizer" [66] and applied l2 (0.0001) for the kernel regularizer. We use 1D convolutional kernels of size 5 in the sRN sub-network and 2D convolutional kernels of size 3 × 3 in the saRN sub-network. For the number of filters, we use the same size of filters in each convolution layer, 24. We apply 1D average pooling layer with pool size 2 and 2D average pooling layer with pool size 5 × 5 in the sRN and saRN, respectively. Furthermore, a 50% dropout is applied in both sub-networks. Then, we trained our model using Adam optimizer with a learning rate of 0.0003 [67].

Regarding batch-size, a constant batch-size sometimes results in a tiny mini-batch (see Figure 4a). Meanwhile, in a network with BN layers, there is dependency between the mini-batch elements because BN uses mini-batch statistics to normalize the activations during the learning process [68]. This dependency may decrease the performance if the mini-batch is too small [68,69]. Some approaches can be applied to overcome this problem. The first is to ignore the samples in the last mini-batch. This approach is not viable for the IP dataset because the number of training samples in a category can be very small; for example, with 10% training samples, we only have two training samples in Oats category. Performance will be badly affected if the removed samples are from this category (see Figure 4a). The second approach is by copying other samples from the previous mini-batch. This technique will make some samples appear twice in the training process, and these samples will have more weight. Another approach is by dividing the training size over the intended batch number. For example, if we intend to have three batches so the batch size = training size/3. However, when the training sample is too large, the batch size will be large and thus prone to an out of memory problem. If the training size is too small, the batch size will also be small, having a tiny batch size can decrease the performance. Therefore, in our experiment, we used Equation (10) to compute the batch-size prior to the training process to prevent the occurrence of a tiny mini-batch, where *sb* is the standard batch, *tr* is the training size, and *th* is the threshold (the allowed smallest mini-batch, see Figure 4b). We used *sb* = 256 and *th* = 64 in this paper.

$$
bar{h}\_{\text{siz}\varepsilon} = \begin{cases} s\_{\text{b}\prime} & \text{if } tr \bmod s\_{\text{b}} > th. \\ s\_{\text{b}\prime} + \frac{tr \bmod s\_{\text{b}}}{int (\frac{\text{ft}}{s\_{\text{b}}})} \prime & \text{otherwise.} \end{cases} \tag{10}
$$

**Figure 4.** Example condition when the batch size cannot divide the training size evenly: (**a**) the latest mini-batch size is one, and (**b**) the latest mini-batch size is more than threshold (if the threshold is seven).

In the sRN, we used a 3 × 3 spectral cube and computed its mean instead of using a pixel vector directly to minimize the effect of spectral noise. In contrast to sRN, saRN focus is to get the spatial features; hence, the region size of the input cube gives an impact on the spatial feature representation. In this research, in order to find the optimum saRN input region size, *n*, we experiment on a variable set of *n* {21, 23, 25, and 27} with the number of PCA components set to 30 by using 10% random training samples and repeat the experiment 10 times. Table 2 shows the results of the Overall Accuracy mean and standard deviation, and from this table, we can conclude that each dataset has a different optimum number *n*. For the PU, IP, and KSC dataset, the optimum *n* is 21, 25, and 27, respectively.

We then use the optimum value of *n* to find the optimum number of PCA components, *K*. We experiment with different size of *K* {25, 30, 35, and 40}. The Overall Accuracy (OA)-mean with different values of *K* and 10% training samples are shown in Table 3. The table shows that the optimum *K* of KSC dataset is 25, while, for the IP dataset and PU dataset, the optimum *K* is 35.

**Table 2.** Overall Accuracy of each dataset based on various patch sizes (SoP). The number in bold is the best Overall Accuracy.

**Table 3.** Overall Accuracy based on PCA number. The number in bold is the best Overall Accuracy.

Given the optimal parameters for our proposed method, we perform two experiments to understand the impact of each module of our proposed architecture. The first is an experiment to discover the effect of using the mean in the sRN sub-network. Second, we perform an experiment to evaluate the performance of sRN, saRN, and our proposed architecture.

To demonstrate the effectiveness of our proposed method, we compare our method with the state-of-the-art architectures, which focus on exploring the spectral-spatial features of HSI, namely 3D-CNN [34], SSLSTMs [27], SSUN [23], spectral-spatial residual network (SSRN) [19], and hybrid spectral convolutional neural network (HybridSN) [61]. The SSLSTMs and the SSUN explore the spectral and the spatial features using two different streams, while the 3D-CNN, the SSRN, and the HybridSN extract features using a single stream network based on 3D convolutional layer. The implementation codes of the 3D-CNN (https://github.com/nshaud/DeepHyperX), the SSUN (https://github.com/YonghaoXu/SSUN), the SSRN (https://github.com/zilongzhong/SSRN), and the HybridSN (https://github.com/gokriznastic/HybridSN) are publicly available, letting us execute the codes to produce the classification results with all datasets. For the SSLSTMs, even though the implementation code is not accessible, we wrote the code based on their paper architecture and parameters. To confirm that our implemented code is correct, we tested it on 10% of the training dataset and verified our results with the work of Reference [27]. All experiments except the 3D-CNN were conducted on X299 UD4 Pro desktop computer with the GeForce RTX 2080 Ti Graphical Processing Unit (GPU). The experiment of the 3D-CNN was conducted on Google Colab server because 3D-CNN used the Pytorch framework.

To validate the performance of the proposed model with respect to the training size of each compared model, we performed three different experiments. In all of these experiments, we used 10-fold cross-validation. To guarantee that all of the techniques use the same training indices and testing indices, we created a module to generate the training indices and testing indices by using StratifiedShuffleSplit function available in Keras. The input of this function is the training size percentage and the number of the fold/group (*k*). The output is *k* fold training indices and testing indices, where each fold is made by preserving the percentage of samples for each class. We then saved the training indices and testing indices of each fold in a file. Those files were read by each method during the experiment. Following the protocol in Reference [19], we use the same number of training epoch, 200, for all of the experiments. Regarding the hyperparameters, we used the optimum parameter of each model that has been provided in their respective paper. For the hyperparameters of this proposed approach, we used the optimum settings that have been optimized on 10% training samples, which were provided by Tables 2 and 3.

In conclusion, we divided the experiments into two groups. The first group (experiments 1 and 2) is an ablation analysis to understand the impact of using the mean, and concatenating sRN and saRN in the proposed method with respect to the overall performance accuracy. The second group (experiments 3, 4, and 5) are experiments to understand the effectiveness of the proposed method compared to other previous studies. The details of these experiments are as follows:

**Figure 5.** (**a**–**c**) The train split with 30%, 10%, and 4% training size on Indian Pine (IP) dataset (**d**) the test split.

#### *4.3. Experimental Results*

**Experiment 1**: Table 4 shows the OA-mean and standard deviation of this proposed architecture in two different cases. In the first case, the sRN input of our network is a 3 × 3 cube followed by mean operations (with mean), and the second case, the sRN input of our network is a spectral vector, which was not followed by mean operations (without mean). From the table, we can see that, in 11 cases out of 15, the "with mean" slightly outperform the "without mean". We also found that, in 10 cases out of 15, the "with mean" is more stable than "without mean".

**Table 4.** Comparison between with mean and without mean in our proposed network (Bold represents the best results in the experiment setup).

**Experiment 2**: Table 5 displays the OA-mean and standard deviation of sRN, saRN, and TSRN with various training samples, where the best performance is shown in bold. The table shows that with 30% training data, saRN's performance is slightly better than others. With 10% training samples, TSRN's performance starts to exceed saRN's performance. TSRN's superiority is clearly shown in 4% training samples. When the training size is large (30%), and the train and test sets are sampled randomly over the whole image, the possibility of the training samples become the testing samples' neighbor is high. Other spatial features, such as line and shape, are clear, too. See Figure 5a, suppose the center of the red window is the testing sample, we can easily predict its label by seeing its spatial features. However, with 10% training samples, predicting the pixel's label only by using its spatial features is slightly difficult (see Figure 5b). The prediction problems are more complicated when the training size is 4%. Figure 5c shows that the spatial features (e.g., neighborhood, shape, line) alone cannot perform well. Therefore, with 4% training samples, the TSRN, which also use spectral features, produces much better performance then saRN. Meanwhile, the low performance of sRN on IP dataset and KSC dataset probably because IP and KSC dataset have significantly low spatial resolution 20 m per pixel and 18 m per pixel, respectively. For example, in IP dataset, where most classes are vegetation, one pixel corresponds to the average reflectance of vegetation in 400 m2, which results in a mixture of ground materials. As a consequence, classifying the objects based on spectral information only is difficult.

**Table 5.** Comparison between sRN, saRN, and Proposed (TSRN) with 30%, 10%, and 4% training samples (Bold represents the best results in the experiment setup).

**Experiment 3**: Tables 6–8 show the quantitative evaluations of those compared models with 10% training samples. The tables present three generally used quantitative metrics, i.e., Overall Accuracy (OA), Average Accuracy (AA), Kappa coefficient (K), and the classification accuracy of each class. The first three rows show the OA, AA, and K of each method. The following rows show the classification accuracy of each class. The numbers indicate the mean, followed by the standard deviation of each evaluation with a 10-fold cross-validation. The bold, the underlined, and the

italic numbers represent the first-best performance, the second-best, and the third-best performance, respectively. Subsequently, Figures 6–8 display the false-color image, the ground-truth image, and the classification map of each method on Indian Pine, Pavia University and KSC datasets.

**Table 6.** Overall Accuracy, Average Accuracy, Kappa Value, and Class Wise Accuracy of our proposed method versus other methods on IP dataset when using 10% training samples. The best performance is in bold, the second-best performance is underlined, and the third-best is in italic.

**Table 7.** Overall Accuracy, Average Accuracy, Kappa Value, and Class Wise Accuracy of our proposed method versus other methods on Pavia University (PU) dataset with 10% training samples. The best performance is in bold, the second-best performance is underlined, and the third-best is in italic.

**Table 8.** Overall Accuracy, Average Accuracy, Kappa Value, and Class Wise Accuracy of our proposed method versus other methods on Kennedy Space Center (KSC) dataset with 10% training samples. The best performance is in bold, the second-best performance is underlined, and the third-best is in italic.

**Figure 6.** The classification map of IP dataset. (**a**) False color image, (**b**) Ground truth, and (**c**–**h**) Prediction classification maps of 3D-Convolutional Neural Network (CNN) (85.29%), spectral-spatial long short-term memory (SSLSTMs) (95%), spectral-spatial unified network (SSUN) (97.24%), SSRN (98.29%), HybridSN (97.38%), and our proposed architecture (98.69%).

**Figure 7.** The classification map of Pavia University dataset. (**a**) False color image, (**b**) Ground truth, (**c**–**h**) Prediction classification maps of 3D-CNN (94.07%), SSLSTMs (98.50%), SSUN (99.52%), SSRN (99.88%), HybridSN (99.85%), and our proposed architecture (99.94%).

**Figure 8.** The classification map of KSC dataset. (**a**) False color image, (**b**) Ground truth, (**c**–**h**) Prediction classification maps of 3D-CNN (82.21%), SSLSTMs (97%), SSUN (97.10%), SSRN (99.27%), HybridSN (87.46%), and our proposed architecture (99.61%).

**Experiment 4**: Figure 9 presents the graphic of AO-mean obtained from our fifth experiment, where all of those methods are trained on smaller training samples 4%, 6%, and 8%. In the figure, we include the results of our first experiment, where those methods are trained on the 10% samples. The performances of all of the compared methods are displayed using a dotted line, while our proposed method is displayed with a solid line.

**Figure 9.** Overall accuracy of each method for different training data sizes of: (**a**) Indian Pine dataset, (**b**) KSC dataset, and (**c**) Pavia University dataset.

**Experiment 5**: Tables 9–11 show the OA, AA, and K of each method with 30% training samples. On large training samples, almost all of the compared methods produce a high accuracy. The difference is small. Hence, in the table, we report the comparison on each fold for a more detailed comparison. The bold numbers are the best accuracies produced by these methods.

**Table 9.** Fold Overall Accuracy, Average Accuracy, and Kappa Value on IP dataset with 30% training data. The best performance is in bold, the second-best performance is underlined, and the third-best is in italic.

**Table 10.** Fold Overall Accuracy, Average Accuracy, and Kappa Value on PU dataset with 30% training data. The best performance is in bold, the second-best performance is underlined, and the third-best is in italic.

**Table 11.** Fold Overall Accuracy, Average Accuracy, and Kappa Value on KSC dataset with 30% training data. The best performance is in bold, the second-best performance is underlined, and the third-best is in italic.

## *4.4. Discussion*

According to the highlighted results in Section 4.3, one of the first apparent points is that the proposed method is able to produce a high performance for all ranges of training sizes (4%, 6%, 8%, 10%, and 30% training samples). Its OA, AA, and K values are higher compared to 3D-CNN, SSLSTMs, SSUN, SSRN, and HybridSN. The differences get higher as the training sample size is

reduced. With large training samples, e.g., 30% training samples, the performances of these methods are similar.

The quantitative evaluation of those models with 10% training samples are reported in Tables 6–8. These tables show three standard quantitative metrics, i.e., OA, AA, K; and the classification accuracy of each class. More specifically, on Indian Pines dataset (see Table 6), in which class sizes are imbalanced, our proposed method produces the highest OA, AA, and K value, and the proposed approach yields OA 0.46% higher than the second-best method, SSRN. Considering the AA, the difference between the proposed architecture and SSRN is much higher, more than 7%. From Table 6, we can see that TSRN tries to optimize the recognition of each class even though the number of instances in the class is tiny. Hence, it achieves a high accuracy compared to the other methods when classifying C9 (Oats), in which number of instances is 20, which means C9 training samples is 2. For more detailed classification accuracy of each class, TSRN yields the best recognition on eight classes out of 16 classes. Its recognition for the other five classes and two classes are the second- and third-best, respectively.

The results are consistent on the Pavia University dataset, in which characteristics are different from the Indian Pine dataset. In the PU dataset, the number of data for each class is large, with the minimum number of instances on Shadows category equal to 947. As shown in Table 7, our proposed method attains the best OA, AA, and K compared to the other architectures, albeit insignificant disparity. The small gap between TSRN and the second-best method, HybridSN, shows that those methods are very competitive for large training samples. For class recognition, the proposed method achieves the highest accuracy on five out of nine classes in the PU dataset, with an improvement of less than 1%.

In contrast to the IP and PU datasets, the total number of instances of KSC dataset is relatively small. From Table 8, we can see that our proposed approach achieves the best performance. Its OA, AA, and K is ± 0.71, ± 0.94, and ± 0.79 higher compared to the second-best method, SSRN. In contrast, HybridSN yields performance that is not as good as its performance on IP and PU dataset.

The comparison between the proposed architecture and other methods on smaller training samples for IP, KSC, and PU dataset is demonstrated in Figure 9a–c, respectively. These figures reveal that the proposed method achieves the best accuracy even with smaller training samples. The accuracy gap between our method and the second-best method is high on KSC dataset. With 4% training data, our method achieves OA ± 2% higher than the second-best method, SSRN. The difference is smaller on IP dataset and is extremely small on PU dataset. The reason is that the size of the KSC dataset is the smallest compared to other datasets. Four percent training samples in the experiment correspond to 208 training instances on KSC dataset, 410 instances on IP dataset, and 1711 samples on PU dataset.

The performance of TSRN and the other methods with larger training samples, 30%, is shown in Tables 9–11 for IP dataset, PU dataset, and KSC dataset, respectively. In IP dataset, out of ten folds, the proposed method achieves the best OA and K on five-folds, and the best AA on eight folds. Our method also outperforms HybridSN, which presents the best OA on three folds. In PU dataset (see Table 10), the HybridSN shows a slightly better OA than the proposed architecture. HybridSN produces the best OA on six-folds while TSRN produces the best performance within five-folds. In the 2nd fold and 5th fold, their OA is precisely the same. Regarding AA, the proposed method achieves the best AA on six-folds when HybridSN achieves the best AA on four-folds. In terms of K, those two methods yield the best value on five-folds. The same with the result from IP dataset, with KSC dataset, our proposed approach also produces the best performance or the second-best performance in each fold (see Table 11). From these results, we can conclude that on large training samples, those approaches, i.e., TSRN, SSUN, HybridSN, SSRN, SSLSTMs, are very competitive.

Table 12 presents the number of parameters, the model size, the depth, the training time, and the testing time of the other methods with 10% training samples from IP dataset. We do not report the time complexity of 3D-CNN because 3D-CNN was tested on a different machine. However, Reference [61] has shown that 3D-CNN is less efficient compared to HybridSN. Moreover, from Table 12, we perceive that the proposed method is more efficient than HybridSN. In other words, we can conclude that TSRN is much more efficient than 3D-CNN. Compared to 3D convolution-based SSRN [19] and (3D + 2D) convolution-based HybridSN [61], our proposed network has a faster training time and fewer learning parameters (Table 12). Our network model size, in which depth is 24, is 3.3 MB. The size is smaller compared to 61.5 MB of a 7-layer HybridSN and more effective compared to 2.9 MB of a 9-layer SSRN. Note that an increase in the network depth results in a model size increase. From Table 12, we can see that our network, which uses (2D+1D) convolution, can be deeper without increasing the number of parameters by a large number. Such a deeper network can extract richer features. On the other hand, for 3D convolution, the model size and the number of parameters will grow dramatically as the network becomes deeper. As a result, training on very deep 3D-CNN becomes challenging with the risk of overfitting. Our network yields a smaller number of learnable parameters, making it less prone to the overfitting problem especially when small samples are used for training.

**Table 12.** Number of parameters, model size, depth, training time, and testing time on IP dataset on different methods with 10% training samples.


*Edited by Anthony Lupo*

This book highlights some of the most recent research in the climatological behavior of tropical cyclones as well as the dynamics, predictability, and character of these storms as derived using remote sensing techniques. Also included in this book is a review of the interaction between tropical cyclones and coastal ocean dynamics in the Northwest Pacific and an evaluation of the performance of CMIP6 models in replicating the current climate using accumulated cyclone energy. The latter demonstrates how the climate may change in the future. This book can be a useful resource for those studying the character of these storms, especially those with the goal of anticipating their future occurrence in both the short and climatological range and their associated hazards.

ISBN 978-1-83880-361-2

Current Topics in Tropical Cyclone Research

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