**5. Conclusions**

In conclusion, a total of 24 *Dof* genes were identified from physic nut, and these *Dof* genes were further divided into three major groups based on the phylogenetic inference. The gene structures, conserved motifs, gene duplicated events, selection pressures, and expression profiling of these *JcDof* genes were analyzed. A genome comparison discovered that the expansion of the *Dof* gene family in physic nut mainly resulted from segmental duplication, and this expansion was mainly subjected to positive selection. The expression profile demonstrated the broad involvement of *JcDof* genes in different hormonalor abiotic stressed treatments. Among them, three genes (*JcDof-1*, *JcDof-8*, and *JcDof-10*) exhibited significant responses to the BA treatment. Furthermore, many *JcDof* genes were significantly responsive to the salt and drought treatments. On the whole, this study provides an extensive resource for understanding the *Dof* genes in physic nut.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1422-0067/19/6/1598/ s1.

**Author Contributions:** C.L. conceived and supervised this study. C.L. and P.W. designed the experiments. P.W. carried out the experiments, and analyzed and interpreted the data. J.L., X.G., D.Z. and A.L. participated in the discussion and provided valuable advice and practical contributions. C.L., P.W. and J.L. wrote the manuscript. All authors reviewed, edited, and approved the final manuscript.

**Acknowledgments:** This work was supported by the following grants: National Natural Science Foundation of China (grant nos. 31471220, 91440113), Start-up Fund from Xishuangbanna Tropical Botanical Garden, and 'Top Talents Program in Science and Technology' from Yunnan Province.

**Conflicts of Interest:** The authors declare no conflict of interest.


Meet the editors

Dr. Stawicki is a Professor of Surgery and Chair of the Department of Research & Innovation, St Luke's University Health Network, Bethlehem, Pennsylvania. A specialist in general surgery and surgical critical care, he has co-authored nearly 700 scholarly works, including more than 20 edited books. In addition to national and international medical leadership roles, Dr. Stawicki is a member of numerous editorial boards. His areas of expertise

include blockchain technology, patient safety, academic leadership, mentorship, trauma/critical care, and sonography.

Dr. Michael S. Firstenberg is a thoracic surgeon at the St. Elizabeth Medical Center (Ascension), Appleton, Wisconsin. He attended Case Western Reserve University Medical School, Cleveland, Ohio, received his general surgery training at University Hospitals in Cleveland, and completed thoracic surgery fellowships at The Ohio State University and The Cleveland Clinic. He is an active member of the Society of Thoracic Surgeons (STS),

American Association of Thoracic Surgeons (AATS), American College of Cardiology (ACC), and American College of Academic International Medicine (ACAIM), for which he served as president in 2021–2022. He has authored more than 250 peer-reviewed manuscripts, abstracts, and book chapters and has edited several textbooks and lectured worldwide on topics ranging from medical leadership, COVID-19, endocarditis, and extra-corporeal membrane oxygenation (ECMO).

## Contents

#### **Chapter 9 137**

The Use of Virtual Reality Simulations in Nursing Education, and Patient Safety *by Sule Biyik Bayram and Nurcan Caliskan*


**5. Conclusions**

We found that the forest-steppe migration generally followed Holocene climate change in the Tien Mountains. Specifically, the forest belt where *Picea schrenkiana* dominates has a very low pollen taxa diversity, characterized by high richness and low evenness, which plays a key role in the mountainous diversity. By detecting the diversity change of the deposition sites, we found that in the process of coping with warm and dry climate during the early Holocene from 12,000 to 8000 yr BP, the forest belt moved upward, causing a reduced forest belt influence on, or contribution to, the low-elevation deposition sites, as the observed diversities around the two lakes were high. In this case, the forest-steppe ecotone moved upward accordingly. During the warm and wet middle Holocene from 8000 to 4000 yr BP, the forest belt with low taxa evenness expanded and widened, as the observed diversities around the two lakes were very low. As such, the forest-steppe ecotone moved downward accordingly. As the late Holocene became cold and wet from 4000 yr BP onwards, the forest belt moved downward, resulting in a reduced forest belt influence on or contribution to the sites, leading to the high diversities observed. Thus the forest-steppe ecotone moved further down. Moisture loss may pose the greatest threat to the narrow forest-steppe ecotone, considering the climatic niche space and the limited living space for humidity-sensitive taxa. This study highlights that temperature and moisture co-influence the forest belt change, which further determines the migration of the forest-steppe ecotone.

**Author Contributions:** H.L. and Y.C. proposed the idea and designed the study. Y.C. analyzed the data and wrote the first draft. Y.C., H.L., H.W., Q.H., Y.H., K.D. and Z.D. discussed the results and contributed to improving the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, Grant Number 41790422, 41901092. It was also funded by the Fundamental Research Funds for the Central Universities of China, Grant Number GK202003069.

**Conflicts of Interest:** The authors declare no conflict of interest.


Preface

Clean energy is defined as energy obtained from renewable and zero-emission sources; it also refers to a range of environmentally friendly energy options derived from renewable, low-emission sources such as solar geothermal energy, hydropower, ocean power, and wind power. Unlike fossil energy sources, clean energy sources do not run out and constantly renew themselves. For this reason, clean energy is also called sustainable energy. Other natural resources such as plants and animal wastes and hydrogen are also among the elements from which clean energy is obtained. Unlike depleting fossil energy sources, clean energy is less costly. It does not harm the environment, contributes to the protection of nature, and is the most efficient use of nature. Today, countries adopt clean energy technologies and infrastructure, invest in renewable energy sources, and prioritize energy efficiency practices to accelerate the transition to an affordable, reliable, and sustainable energy system.

Clean energy is one of the most effective ways to combat climate change. While clean energy can be defined as energy obtained from sources that do not pollute the air, produce greenhouse gas emissions, or cause any harm to nature, green energy is the energy obtained from natural sources. On the other hand, renewable energy is produced from continuously renewable and inexhaustible sources. The direct use of renewable energy in reducing carbon emissions is more on the agenda today. Effective improvements in energy efficiency, the importance of which is often overlooked, are also considered. Issues such as the greater inclusion of electric vehicles in the transportation system, the widespread use of heat pumps in residences, the inclusion of clean hydrogen and its derivatives in the energy system, and the use of bioenergy, carbon capture, and storage technologies come to the fore.

Hydrogen, one of the clean energies, has the potential to solve the growing energy crisis today due to its clean, renewable, high-energy density, and non-carbon fuel properties. Hydrogen is a synthetic fuel that can be produced from various raw materials such as water, fossil fuels, and biomass using primary energy sources. During the production phase, there are many alternative production technologies such as steam recovery, waste gas purification, electrolysis, photo processes, thermochemical processes, and radiolysis. Hydrogen has a variety of uses, including nonpolluting vehicles, fuel cells, home heating systems, and aircraft. Furthermore, using hydrogen as an energy carrier is a long-term option for reducing global carbon dioxide emissions by obtaining high-value hydrocarbons through carbon dioxide hydrogenation. The costs of a hydrogen-based system, however, are still high. Therefore, research on hydrogen, which is abundant in nature, continues so that hydrogen-based systems, particularly for long-term energy storage, can become commercially attractive. The use of hydrogen energy as a complement to traditional energy sources also aids in the implementation of low-carbon solutions.

This edited collection consists of two sections and nine chapters. The first section includes case studies for hydrogen production research. The second section presents research on gasification, a process in which a limited amount of oxygen, air, air-water vapor mixture, or enriched oxygen-containing air is introduced to carbon-containing materials such as coal and biomass, which is used to obtain combustible gases such as methane and hydrogen.

Chapter 1 introduces the topic. Chapter 2 proposes a new photocatalyst to replace an organic photocatalyst for hydrogen generation from water and hydrogen sulfide. Chapter 3 discusses various routes to produce solar-based hydrogen, namely, photocatalytic, photo-electrocatalytic, and photobiological decomposition. It also discusses mitigation measures to prevent charge carriers from recombination during the photocatalyst process. Chapter 4 focuses on the use of hydrogen in aviation, the modifications needed to adapt an existing gas turbine to use hydrogen, and a CFD simulation of an existing gas turbine burning hydrogen. Chapter 5 discusses the use of hydrogen oxyfuel in glass and metal industries as a source of heat. The authors suggest an improvement of the exhaust gas stream for energy recovery and to decrease emissions. Moreover, they examine safety issues in the use of hydrogen energy and the acceptance of the community. Chapter 6 studies the effect of hydrogen and carbon monoxide on aluminosilicate refractory. Chapter 7 reviews recent advances in supercritical water gasification of pulping black liquor for hydrogen production. Chapter 8 criticizes minimizing carbon dioxide emissions from coal gasification processes. Finally, Chapter 9 discusses how to improve sustainable hydrogen production yield in hydrothermal gasification processes through novel metal catalysts.

> **Murat Eyvaz** Associate Professor, Department of Environmental Engineering, Gebze Technical University, Kocaeli, Turkey

> > **Dr. Yongseung Yun** Institute for Advanced Engineering, South Korea

Section 1

Hydrogen Energy Processes

**Dr. Ahmed Albahnasawi** Department of Environmental Engineering, Gebze Technical University, Kocaeli, Turkey Section 1


2. Background

Assume that the noisy speech x is a linear additive mixture of the clean speech s and the

where x(t) is the time-domain mixture signal at sample t, and s(t) and n(t) are the time-domain speech and interferer signals, respectively. The speech enhancement algorithm attempts to suppress noise without distorting speech and obtain the enhanced speech components ^s from the noisy signal and reconstruct the original clean speech. In other words, speech enhancement algorithms try to reduce the impact of background noise on the speech signal. Most traditional speech enhancers are implemented in the short-time Fourier transform (STFT) domain with X ¼ j j STFT x t f g ð Þ γ where γ = 1 gives the magnitude of spectrum or the power spectrum by γ = 2. The inverse Fourier transformation then is used to convert the estimated speech to the time domain, assuming that the phase of the interferer can be approximated with the phase of

The speech enhancement techniques mainly focus on removal of noise from speech signal. The various types of noise and techniques for removal of those noises are presented [5–13]. The famous spectral subtraction technique [5] extracted the clean speech spectrum based on the principle that the noise contamination process is additive. The major advantage of the spectral subtraction method is their simplicity by subtracting an estimation of the interfere spectrum from the observed mixture spectrum [5, 6]. The main problem with the magnitude spectral subtraction is that it does not attenuate noise sufficiently negative magnitude by error in the

Filtering techniques [7, 8] or short-time spectral amplitude (STSA) estimators [9] or estimators based on super-Gaussian prior distributions for speech DFT coefficients are [10–13] the statistical models assumed for each of the speech and noise signals that estimate the clean speech from the noisy observation without any prior information on the noisy type or speaker identity. However, in the case of nonstation of background noise, these methods face much

Recently, dictionary learning (DL) techniques, which build dictionary consisting of atoms and represent a class of signals in terms of the atoms, have been shown to be effective in machine learning, neuroscience, and audio processing [17–20]. In speech enhancement, the dictionary models utilize specific types of the a priori information considered for both the speech and noise signals [21–25]. This class of methods assumes that a target spectrogram can be generated from a set of basis target spectra (a dictionary) through weighted linear combinations. Generally, this approach decomposes the time-frequency representations (the power or magnitude spectrogram) of noisy speech in terms of elementary atoms of a dictionary. One of the key issues in dictionary-based speech enhancement is how to precisely learn a dictionary. Dictionary learning methods are commonly based on an alternating optimization strategy, in which the signal representation is fixed, and the dictionary elements are learned; then the sparse signal representation is found, while the dictionary is fixed. Two popular methods have appeared to determine a dictionary within a matrix decomposition including sparse coding

difficulty in estimating the noise power spectral density (PSD) [14–16].

[26] and nonnegative matrix factorization (NMF) [27].

x tðÞ¼ s tð Þþ n tð Þ (1)

interfere n as defined in the following equation:

72 Active Learning - Beyond the Future

the mixture [4].

subtraction.

Dictionary learning performs approximate matrix factorization of a data matrix into the product of a dictionary matrix and a coding matrix, under some sparsity constraints on the coding matrix. Dictionary learning is the generalization of gain-shape codebook learning. Signal vectors are represented as linear combinations of multiple dictionary atoms, allowing for lower approximation error while maintaining equal dictionary size. Two relatively different methods are described for how to form the dictionary from the given data including sparse representation (SR) and nonnegative matrix factorization (NMF).


2.5. Antibacterial effects

2.3. Characterization

266 Titanium Dioxide - Material for a Sustainable Environment

Italia S.r.l., Milan, Italy).

2.4. Photocatalysis testing

2.4.2. Nitrogen oxides

for Europe (range 100–200 ppb).

2.4.1. Volatile organic compounds

GC System, Agilent Technologies Italia S.p.A, Milan, Italy).

Mineralogical analyses were done by laser Raman microspectroscopy (Raman; inVia Raman Microscope, green laser, 514 nm, 20, 1.5-μm beam diameter, Renishaw, Wotton-under-Edge, Gloucestershire, UK). Microstructural analyses were done by field emission gun scanning electron microscopy (Field emission gun scanning electron microscopy (FEG-SEM); LEO 1525, 15, or 20 kV accelerating voltage, LEO Electron Microscopy Inc., Thornwood, NY, USA). Elemental analyses were done by paired energy dispersive spectroscopy (EDS; Bruker Quantax, Bruker

VOC photodegradation reactions were conducted in a Pyrex glass cylindrical reactor with a diameter of 200 mm and an effective volume of 5 L [38]. A 100-cm2 digital inkjet-printed sample or jet-sprayed sample was placed at the bottom of the reactor and exposed to a 500-W iron halogen lamp (HG 500, 315–400 nm, UV-A, Jelosil, Milan, Italy). The reactor had two entrances, one for VOC loading and the other for sampling by a gas chromatograph (490 Micro

Ethanol and toluene were selected as model pollutants, and the initial concentration for both

Two different setups were used—one under static conditions [39] and the other under flowing conditions [40]. A chemiluminescence NOx monitor (Teledyne API, Model 200E, San Diego, CA, USA) provided online detection of NO and NO2 concentrations. The batch reactor consisted of a Pyrex glass cylinder of volume of 25 L; this is described elsewhere [39]. The tile (2 20 cm) was located at the bottom of the cylinder and an overhead 500-W iron halogenide lamp (HG 500, 315–400 nm, UV-A, Jelosil, Milan, Italy) provided irradiation of 20 W/m2

inlet gas consisted of NO2 (0.6% in N2) mixed with air of relative humidity (RH) 40%, which allowed equilibration between NO and NO2 to be reached quickly. The NO2 concentration was set at 1000 ppb (static) or 250 ppb (flowing) and tests lasted 6 h each. The continuous flow reactor allowed testing of larger tiles (60 60 cm) [40]. A Thermo-Hygro Meter (HT-3006A, Metravi, Calcutta, India) measured both temperature and relative humidity, which ranged between 40 and 50%. Two iron halogen lamps (Jelosil, model HG 500), at 770-mm center-tocenter distance, which was designed to irradiate the sample surface with the same 20 W/m2 light intensity, were used. The total NOx gas flow was in the range of 140–180 NL/h, which is in accordance with the standard concentration based on the limiting values specified in Directive 2008/50/EC of the European Parliament and of the Council on Ambient Air Quality and Cleaner Air

was 400 ppm for each test. The photodegradation tests were conducted for 6 h.

The photocatalytic tiles were tested using Escherichia coli (E. coli, ATCC 8739) according to ISO 27447:2009 Fine Ceramics—Test Method for Antibacterial Activity of Semiconducting Photocatalytic Materials. Each strain was inoculated into a nutrient agar slant, incubated for 16–24 h at 37 1C, and then transferred to a new nutrient agar slant and again held at 37 1C for 16–24 h. An appropriate quantity of bacteria was dispersed in 1/500 nutrient broth (NB) to obtain a count of 6.7 105 to 2.6 106 cells/mL. Tile samples of dimensions 50 50 mm were rinsed with distilled water and autoclaved at 121C for 30 min prior to testing in order to remove any organic residue on the surfaces. For each strain, six tile samples without (controls) and six with photocatalytic coating were investigated. A volume of 0.15 mL of bacterium suspension was placed on each specimen and covered with an inert and non-water adsorbent film of dimensions 40 40 mm; the film transmitted >85% of radiation in the range of 340–380 nm. Each specimen was placed in a 100-mm diameter Pyrex Petri dish containing a moistened paper filter to prevent drying of the suspension and covered with a 1-mm thickness borosilicate glass slide, also with radiation transmission of >85% in the range of 340–380 nm.

A fluorescent UV lamp (18 W, Royal Philips, Amsterdam, Netherlands) was used for the testing at an intensity of 0.25 mW/cm2 for 8 h. A viability count was performed by dilution and plating on nutrient agar incubated at 37C for 48 h. More details are reported elsewhere [37].


**Selected Papers from the pHealth 2021 Conference, Genoa, Italy, 8-10 November 2021**

Editors

**Bernd Blobel Mauro Giacomini**

Basel ' Beijing ' Wuhan ' Barcelona ' Belgrade ' Novi Sad ' Cluj ' Manchester

*Editors* Bernd Blobel Medical Faculty University of Regensburg Regensburg Germany

Mauro Giacomini DIBRIS University of Genoa Genoa Italy

*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 *Journal of Personalized Medicine* (ISSN 2075-4426) (available at: www.mdpi.com/journal/jpm/special issues/pHealth 2021).

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

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

**ISBN 978-3-0365-9257-2 (Hbk) ISBN 978-3-0365-9256-5 (PDF) doi.org/10.3390/books978-3-0365-9256-5**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license.


**4. Discussion**

This study focused on the socio-economic assessment of the attractiveness of Arctic territories to potential migrants. We should note that the regions under consideration are significantly heterogeneous in terms of most indicators. The Murmansk region is characterized by the highest population, which is largely due to its milder climatic conditions, proximity to European countries, and developed infrastructure. The lowest population for the 9 studied years was in the Nenets Autonomous district, whose indicator values are 17–19 times lower than those in the Murmansk region. The rest of the indicators are also scattered for different Arctic regions. However, the territories under consideration have a common problem: the outflow of the working population to more favorable climatic zones.

The main limitations of the study are discussed below.

It is evident that the migration attractiveness of the Arctic region to the working population is determined by a range of indicators. However, not all indicators can be quantified. This is due to the fact that people rarely base their choices only on quantitative indicators in the decision-making process. In particular, it seems doubtful that a potential immigrant would analyze regional statistics on indicators such as GRP per capita, investment in fixed assets, or infant mortality rates before moving to a region. The decision to move is made on the basis of the individual's principles, life attitudes, beliefs, and other personal characteristics that cannot be quantified, as mentioned in [71].

Thus, the migration attractiveness of a region also depends on a range of non-measurable personal characteristics, such as tolerance to difficult weather conditions. Our research does not separate the influence of quantitative and qualitative parameters on migration processes, as the qualitative characteristics of the research object are difficult to formalize. This fact also prevents their use for assessing the attractiveness of the region together with socio-economic indicators. In addition, only measurable parameters can be analyzed with the proposed research methods. For these reasons, we limited our research to the investigation of quantitative characteristics of the object. Additional research covering qualitative characteristics in order to analyze their influence on migration processes in the Russian Arctic and assess the attractiveness of regions would provide further insight into the problem of migration outflows. Nevertheless, we consider it possible to identify key economic and social indicators that can be used to objectively assess the attractiveness of the Russian Arctic regions based on statistical data, which was performed in the framework of this study.

The second limitation is that this study focuses only on simple linear regression relationships between migration indicators and the indicators that cause their variation. In this regard, as dependent variables of the models, we studied only those indicators that have a close linear correlation with migration. The presented models do not account for other factors. In the future, we plan to apply complex-valued non-linear econometric models and include a wider range of indicators. In addition, it is possible to apply an individual approach to each region separately, adjusting the list of indicators for building regression models depending on their individual characteristics.

Finally, the research was limited by the relatively small amount of retrospective data for the following key reasons: (1) the investigated time series data must be stationary, and (2) statistical data are not publicly available for all analyzed regions. Since migration processes are very sensitive to changes such as legislative and legal regulations, social benefits, and economic incentives, we considered time series that were formed in relatively stable conditions, which limits the study timeframe. Moreover, we faced a data acquisition problem when trying to separate the statistical data for areas located beyond the Arctic Circle from the whole administrative region's dataset. For this reason, we had to analyze only those regions that are fully located beyond the Arctic Circle. In addition, some Arctic regions do not have any publicly available unified statistical information within the 2010–2019 timeframe, which also limited our research.

There is also one disputable point that should be mentioned. The choice of specific indicators for assessing the social and economic attractiveness of regions and building regression models may seem controversial. However, in the course of the study, the authors conducted a thorough analysis and selected attractiveness indicators based on the works of Russian and foreign researchers, specifics of the studied regions, and possibilities of accessing quantitative information for their assessment. According to our assessment, the list of selected indicators best characterizes the attractiveness of the Arctic region to migrants from other territories, especially Russian ones. Choosing equal weight coefficients for all indicators is debatable, although it prevents the excessive influence of any indicator on the result.

The objectivity of the research in selecting indicators for estimating the attractiveness of regions was achieved by using a number of independent experts and conducting correlation analyses to determine indicators that have a significant linear relationship with migration processes. The use of complex-valued econometric methods makes this study unique, since this is the first time that the tools of a complex-valued economy are used to simulate migration processes in the Arctic regions. The procedure for evaluating regional attractiveness is also new, as it involves a unique list of indicators identified during the research, which are specific to Arctic regions.

Thus, the results of this study are the six economic and six social indicators identified on the basis of a thorough literature review and expert surveys; the assessments of the linear relationship between each indicator and migration processes in the Arctic regions; four complex linear regression models that can be used to predict the number of arrivals and departures in the region; and evaluation of the attractiveness of four Arctic regions in terms of their social and economic development.

The key findings of the research are as follows: (i) all Russian Arctic regions are characterized by a stable outflow of the population throughout the 10 studied years; (ii) migration processes are more linearly dependent on social indicators than on economic ones, which confirms the importance of the former; (iii) the four presented complex linear models produced better forecasting results than the simple extrapolation of the trends, so linear complex-valued models are suitable for predicting migration processes in the Arctic regions of Russia; (iv) the Chukotka Autonomous district has the highest social attractiveness, while economic incentives for migration are the highest for the Yamalo-Nenets Autonomous district.

The unexpected result of the study was that among all Arctic regions, the Chukotka Autonomous district took the lead in terms of social attractiveness. It should be emphasized that Chukotka is the most sparsely populated region of Russia and the most distant from the center. According to the Russian Statistical Agency, the area of the region is 4% of the total area of Russia, the population in 2021 is 49,527 people (0.03% of the population of Russia), and the population density is only 0.07 people/km2. The region is very rich in mineral resources, so all socio-economic indicators are at a high level. The finding that the Yamalo-Nenets Autonomous district emerges at the top in terms of economic indicators is more predictable since oil and gas resources, the basis of the national budget, are concentrated in this region. The region is in 7th place for GRP among all regions of the country. However, due to the larger population (547.01 million people) and the population density (0.71 people/km2) compared to Chukotka, the Yamalo-Nenets Autonomous district has a lower score in terms of social attractiveness.

The scientific contribution of this article to the study of migration processes in Arctic regions is the use of complex-valued econometric models to predict future migration flow. These studies were conducted for the first time, and the calculations proved to be effective and have potential practical applications.

We suggest that this method be tested on a larger dataset on migration.


**2.4 Clustered regularly interspaced short palindromic repeat/CRISPR/Cas9 nuclease system**

The CRISPR-Cas system, like TALENS, draws its inspiration from biology. The CRISPR-Cas nucleases had first been found in the adaptive immune system function of archaea and bacteria. CRISPR codes for "spacer" RNA molecules that create associations between CRISPR-associated (Cas) nucleases and instruct them to break down external nucleic acids. The spacer segments within those biological systems were obtained from bacteriophage components caused by a bacterium the prokaryote progenitor. The system's aiming specialization is based upon basic genetic platform principles. A lateral or segmental short sequencespecific element is necessary for a target sequence site to be accepted and bisected; the above offers assurance that the prokaryote utilizing the CRISPR/ Cas system will not focus its genome; these representations are just not available among exogenously linked patterns. The *Streptococcus pyogenes* CRISPR-Cas9 system (CRISPR-*Sp*Cas9) was the first to be discovered for genetic manipulation, and also the term "CRISPR-Cas9" is generally used to describe this technology [32–34]. To avoid misunderstanding, CRISPR-Cas9 can only be used to relate to properties that CRISPR-SpCas9 and its orthologs share. The designed CRISPR-Cas9 system is made up of two parts—(1) the Cas9 nuclease and (2) a single guide RNA (sgRNA), which is made up of two RNA molecules—the spacer-containing CRISPR RNA (crRNA), and they serve to enhance crRNA (tracrRNA), which itself is needed again for retired moiety's growth and development. The sgRNA

#### **Figure 1.**

*Home page of CHOPCHOP bioinformatics tool for designing of sgRNA.*

#### **Figure 2.**

*Preliminary stage for the identification of off target regions of our desired genomic sequence.*

#### **Figure 3.**

*Intermediate stage for identification of off target regions of our desired genomic sequence. In this view, we can identify the %GC, levels of off targets and primer sequences.*

leads the nuclease combination to a specific DNA location, causing the identical nucleotide sequence to be cleaved [20, 21]. Cas9 has a composite morphology, having dual nuclease domains, RuvC and HNH [14, 20, 35]. The CRISPRCas9 system has gone through several modifications in the area of plant genome engineering methods [36], including simplicity of design, cloning, and transport into *Enhancement of Agricultural Crops: A CRISPR/Cas9-Based Approach DOI: http://dx.doi.org/10.5772/intechopen.100641*

**Figure 4.**

*Final stage of off target analysis or our desired genomic sequence.*

plant cells, which also has resulted in a significant success rate in genome editing technology. The design of guide RNAs using the CHOPCHOP software tool is summarized in **Figures 1–4**.


**4. Conclusions**

against biofilm-grown *S. aureus* and the effects of the oil on biofilm formation. The biofilm inhibitory concentrations (0.125–0.500%, v/v) and biofilm eradication concentrations (0.25–1.0%, v/v) were twofold or fourfold greater than the concentration inhibitory planktonic growth. Sub-inhibitory concentrations of the oils from oregano prevented biofilm

*Nigella sativa* is a herbaceous plant cultivated in many countries in the world [88]. Crude extract and seed essential oil possess antibacterial activity against several bacteria [89]. The antibacterial effect may be due to the presence of the two important active compounds of *N. sativa*, thymoquinone and melanin [90]. The activity of *N. sativa* extract against clinical isolates MRSA was investigated by Hannan et al. [91]. They showed that all MRSA isolates were sensitive to *N. sativa* extract at a concentration of 4 mg/disc and MIC was in the range of 0.2–0.5 mg/mL. On the other hand, the multidrug resistant *S. aureus* strains isolated from nasal and milk samples of cows and buffalo were completely inhibited by *N. sativa* extract at concentration of 40 μg/mL on disc and MIC values were between 0.3 and 2.5 mg/mL [92]. Essential oil of cinnamon and cinnamaldehyde, which is main chemical constituent of this oil, also showed activity against MRSA. Essential oil from *Cinnamomum osmophloeum* (clone B) had an excellent inhibitory effect with the MIC of the essential oil and cinnamaldehyde against MRSA from human stand at 250 μg/mL [93]. The antimicrobial activity of cinnamon essential oil and *trans*-cinnamaldehyde against *Staphylococcus* spp. from clinical mastitis of cattle and goats was not dependent on the antibacterial susceptibility profile. However, the best antimicrobial activity was showed with *trans*-cinnamaldehyde and this compound could

The number of research on antibacterial properties of extracts from medicinal plants against MRSA increased in recent years. These researches are conducted in different countries and show that extracts of plants are rich source of unique phytochemicals with activity against MRSA. Among recently investigated plant was *Schinus areira* L., which grows naturally in Argentina, Peru, Bolivia and Northern Chile. The essential oil from leaves and fruits of two specimens of *S. areira* differ in chemical profile. The limonene-rich oil isolated from the leaves and fruits had potent antibacterial effect on MRSA. When using 3.2 and 15 μL/mL (MICs value) of essential oil from leaves and fruits, respectively, the complete inhibition of MRSA growth was observed. Leaves and fruits oils showed bactericidal action after incubation for 24 h with 20 and 40 μL/mL, respectively. On the other hand, the α-phellandrene-rich fruit oil,

According to research of Endo and Dias Filho [96], MRSA is also sensitive to berberine (plant alkaloid) which is used in Chinese medicine. MICs values of berberine ranged from 62.5 to 250 μg/mL and MBC values were the same or twofold above the MIC. Highly potent anti-MRSA activity with MIC values in range of 25–50 mg/mL was detected among Libyan medicinal plants such as *Cistus salviifolius, Salvia officinalis, Pistacia atlantica, Arbutus pavarii* and *Myrtus communis* [97]. Significant anti-MRSA activity was documented in many studies on extracts of plants used in traditional medicine in Brazil. A mixture of hydrolyzable tannins from *Punica granatum* and the naphthoquinones α-lapachone I and α-xyloidone II from *Tabebuia avellanedae* showed antibacterial activity against all *S. aureus* strains tested, including MRSA isolates [98].

having a lower content of limonene, was inactive against MRSA [95].

formation by *S. aureus* strains [82].

214 Frontiers in Frontiers in Staphylococcus Aureus *Staphylococcus aureus*

be used in the treatment of mastitis [94].

Staphylococci belong to the most important pathogens for both humans and animals. The number of antibiotics effective in treatment of infections caused by these pathogenic bacteria is rapidly decreasing. Many centuries of observation and the use of bee products and essential oils in folk medicine as well as the results of advanced scientific research carried out during the last several decades clearly confirm high antimicrobial, including antistaphylococcal activity of these products. We have no doubt that they are an interesting and promising alternative to classical antibiotics and should be more seriously considered as therapeutic agents.


**4. Conclusion**

The synthesis of a new geopolymer-type material using a hydrothermal process was carried out after the preparation of base materials such as silica and alumina. This work describes the synthesis and valuation of FAES in the raw and activated states as well as sand from the dunes of southern Algeria for the preparation of geopolymer mortars by alkaline activation. The characterization and preparation of sodium silicate (Na2SiO3) were carried out in specialized laboratories in Algeria. Chemical analysis by XRF shows that the sand of the dunes is very rich in quartz ranging from 90.04% to 99.16% silica; in addition, a low concentration of other oxides was observed. However, microscopic SEM observations of the sand revealed the presence of pores with differing morphologies (e.g., rounded, elongated, and angular). The influence of FAES is important with respect to water penetration, and this performance is better than that of ordinary Portland cement. In addition, we observed very good fire resistance with a reduction in CO2 emissions. SEM also revealed uniform and correct distribution of the eggshells over the whole matrix phase. It was also found that there is good adhesion between the sand particles and eggshell particles, due to the sand being rich in aluminum and silicon. From the alkaline activation of eggshells, it was found that FAES can be used with sand from dunes to obtain geopolymers and hybrid geopolymers that can be used as green and durable concrete.


**3. Miscellaneous**

2-Benzoylpyridine N(4)-cyclohexyl thiosemicarbazone-Anti-proliferative activity of the ligand and its indium complexes was tested against human hepatocellular carcinoma. The studies revealed that the indium complex has better activity than the others [78]. N4-(2-Hydroxy-5-chlorobenzyliden e)-2-amino-5-chlorobenzophenone thiosemicarbazone; N4-(2-Hydroxy naphthalene-1-carbaldehyde)-2-amino-5-chlorobenzophenone TSC and its complexes of Ru(II) cytotoxic activity was studied and reported that, all the complexes showed better in vitro cytotoxic activity against MCF-7, Hop62, MDA-MB cell lines [79]. 7-Chloroquinoline thiosemicarbazone and its complexes of Ga(III), Cytotoxic and antimalarial activity were tested and proved that the complex shows 31 times better activity on colon cancer cell line than etoposide. The complex has better antimalarial activity against *Plasmodium falciparum* than lumefantrine [80].

2-Acetylpyridine-N(4)-Orthochlorophenyl thiosemicarbazone and their complexes of Ga(III), Sn(IV), Pd(II) and Pt(II) Cytotoxic activity was reported. The results indicate the ligands and metal complexes showed better cytotoxic activity [81]. (Z)-(2-((1,3-Diphenyl-1H-pyrazol-4-yl) methylene) and hydrazinyl) (pyridin-2-ylamino) methane-thiol thiosemicarbazones and their complexes of Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II) Cd(II) and Zn(II) complexes had strong antioxidative property. Ni(II) complex shows excellent activity against HepG2 and MCF-7 cancer cell line [82]. Cd(II) and its complexes of 2-Acetylyuridine-N4 substituted thiosemicarbazones and their Cytotoxic activity against human breast cancer cell lines was tested and found that one of cadmium complexes has better activity than the cisplatin drug [83]. Bi(III) and its complexes of 2,6-Diacetylpyridine bis (4N-methylthiosemicarbazone) have much more anti-bacterial and anticancer activity than its parent ligand against *Bacillus cereus* and *Salmonella Typhimurium* and K562 leukemia cells [84]. Ru(II) and its complexes of Benzaldehyde thiosemicarbazones and their Cytotoxic activity were evaluated against the WHCO1 esophageal cancer cell line. Among the tested complexes two had moderate activity against the cancer cell line [85]. 2-Acetylpyridine thiosemicarbazone; 2-Acetylpyridine N(4)-methyl TSC; 2-Benzoylpyridine TSC; 2-Benzoyl pyridine N(4)-methyl TSC; 2-Benzoylpyridine N(4)-phenyl TSC and its complexes of Mn(II), Co(II), Zn(II) was tested against its cytotoxicity and reported that compounds show significant activity against K562 leucocythemia cancel cell line [86].

2-Pyridineformamide thiosemicarbazones, 2-formyl and 2-acetyl acetyl pyridine thiosemicarbazones and its complexes of Ga(III) antiproliferative activity were evaluated against human cancer cell lines (MCF-7, T24, A549 and mouse L-929). 2-acetyl pyridine thiosemicarbazone shows higher activity than others against all cancer cell lines [87]. Kowol et al. [72] studied the anticancer activity of 2-Acetylpyridine N, N-dimethyl TSC, 2-acetyl pyridine N-pyrrolidinyl TSC, acetyl pyrazine N, N-dimethyl TSC, acetyl pyrazine N-pyrrolidinyl and acetyl pyrazine-N-piperidinyl TSC and its complexes of Ga(III), Fe(III). Cytotoxic activity of the ligands was enhanced by the chelation with Ga(III) while weakening with Fe(III). Noblia et al. [88] reported cytotoxic activity of 5-Bromo salicylaldehyde semicarbazone; 2-Hydroxy-naphtalen-1-carboxaldehyde semicarbazone and their

vanadium complexes. The results indicate that the complexes had selective activity against TK-10 cell line. Anticancer activity of ortho-Naphthaquinone thiosemicarbazone and its Cu(II),Ni(II), Pd(II) and Pt(II) complexes were tested against MCF7 human breast cancer cell lines and results revealed that chelation of metal ion into ligand was enhanced its activity [89]. Casas et al. reported the cytotoxic activity of Formylferrocene thiosemicarbazones and their Au(III) complexes were evaluated against HeLa cell lines and found that antiproliferative activity was similar to cisplatin, a commercial drug.

Arion et al. [90] reported cytotoxic activity of 2-Acetylpyridine 4 N-dimethyl TSC and its Ga(III) complex against human cancer cell lines SW480, SK-BR-3, and 41 M were tested and results revealed that complexes show slightly higher activity than ligand. Antiproliferative activity of Acenaphthenequinone thiosemicarbazone and its Ni(II), Fe(II), Cu(II), Zn(II)were reported and found that Cu(II) complex shows better antiproliferative activity than others. Perez et al. [91] tested the cytotoxic activity of p-Isopropyl benzaldehyde and methyl 2-pyridyl ketone thiosemicarbazones and their Zn(II), Cd(II) against various cell lines. Results indicate that zinc complex was proven as a more potent antitumor agent. Jayasree and Araindakshan [92] reported the antitumor activity of Acetoacetanilide thiosemicarbazone and its Mn(II), Zn(II), Cd(II), Co(II), Fe (IIII) complexes against Ehrlich Ascites tumor cells and found that metal complexes were more active than free ligand. Mohan et al. [93] studied the antitumor activity of 2,6-Diacetylpyridine bis(N4-azacyclic thio semicarbazones) and their Mn(II), Fe(III), Fe(II), Co(II), Ni(II), Cu(II), Zn(II), Pt(II) complexes against P 388 lymphocytic leukemia test system in mice shows that copper complex shows better activity than others.


**2.1 Aquaculture in Tunisia**

In Tunisia, according to the General Directorate of the Environment and Quality of Life [18], 5 types of aquatic livestock farming activities currently exist: marine fish farming, inland fish farming, fish farming in geothermal waters, shellfish farming and tuna grow-out. These aquaculture activities date back to the 1960s and they started with the rearing of sea bass *Disentrarchus labrax* and sea bream *Sparus aurata* in marine waters. At that time, the competent authorities set up a strategy for the exploitation of the dams' reservoirs as a significant hydro-ichthyic support. However, the first attempts to introduce freshwater species into the reservoirs have proven that only carp (*Cyprinus carpio communis*, *Cyprinus carpio specularis*, *C. carpio coriaceus*), roach (*Rutilus rubilio*) rudd (*Scardinius erythrophtalmus*), pikeperch (*Sander lucioperca*), black-bass (*Micropterus salmoides*), mullets (*Chelon ramada*, *Mugil cephalus*) and Nile Tilapia (*O. niloticus*) were able to acclimatize [14, 17, 19]. That said, inland fish farming in water reservoirs and hill lakes in Tunisia remains of paramount importance and contributes with 1000 tons/year in the total fishery production [20].


**4. Molecular docking**

The plethora of diseases discovered ever since and being investigated tirelessly by scientists all over the world ultimately culminates to the sole objective of finding effective solutions. The therapeutic targets in most of the cases are proteins. After knowing their mechanism of actions, how the proteins works and what goes wrong during the diseased state, the next notion is to challenge their functionality with designing some inhibitors. It comes under the domain of drug discovery. And one of the most challenging fields of study is the drug design and development. The complete clinical trials take about 10–15 years of time with billions of dollars expenses for a single drug to reach market. With the completion of human genome project which leads to identification of ever-increasing number of new drug targets (mainly proteins); the efforts were strengthened to find solution to the diseases. Additionally, the availability of 3D structures of protein and protein-ligand complexes made it feasible to carry out research in this area. However, to experimentally screen millions of compounds and their conformers for a single therapeutic target requires enormous amount of time and resources which makes it quite challenging. With the application of computational techniques, the pre-clinical period can be reduced to save valuable assets. The *in-silico* approaches will significantly curtail the time needed for hit identification and also improve the chances of finding the anticipated drug molecules. To facilitate drug design and discovery, several modeling

techniques were available and mostly they are categorized into two main approaches viz. structure-based and ligand-based drug design approaches. The structure-based approach mainly relies on the 3D data of target and the ligand. The ligand-based approach is chiefly adopted in the absence of known experimental structure of the target. In ligand-based approach, the known ligands which were bound to the targets were investigated to decipher the physiochemical and structural properties of the ligands and these were correlated with the anticipated pharmacological activity of the ligands in hand [60].

One of the most extensively utilized computational techniques in the structurebased drug design is molecular docking. Molecular docking is usually achieved by first predicting the molecular orientation or pose of a ligand within the active site of a target and followed by assessing their binding affinity with the usage of a scoring function. The technique is exploited to decipher the interactions between a target and ligand at the atomic level allowing us to describe the behavior of ligands within the active sites of targets as well as to reveal fundamental biochemical processes. Since the first developments of docking algorithms in the 1980s, molecular docking became an indispensable tool in the field of drug discovery [61].

### **4.1 Types of molecular docking**

Molecular docking can be basically categorized into three types: rigid docking, semi-flexible docking and flexible docking. In the rigid docking approach, both the structure of target and ligand does not change. The computation method is relatively modest and chiefly spans the degree of conformational matching, thus it is more apt for investigating macromolecular systems such as protein-nucleic acid and protein-protein systems. The semi/quasi flexible docking approach take flexibility into consideration while docking of the ligand and thus it is more appropriate to deal with the intermolecular interactions of small molecules and proteins. Usually the structure of the ligands can move freely while the target remains rigid or retain few rotatable residues ensuring computational efficiency during the docking process. In the flexible docking method, it is based on the idea that a protein is not always a rigid entity during the course of ligand binding and thus it considers both the protein and ligand as flexible entities. Over the years various methods have been introduced, based on induced fit model and/or conformational sampling.

#### *4.1.1 Scoring function*

One crucial element of any docking algorithm is the scoring function. The scoring function aids in the pose selection and it is involved in distinguishing putative precise binding modes and to filter out the non-binders from the *N* number of generated poses during a docking run. The speed and accuracy of docking programs is also dependent on scoring functions. Further computational efficiency and reliability are points kept in mind while developing any scoring function. There are three categories of scoring functions:

#### i.Force-field based scoring function

This scoring function is based on the concept of molecular mechanics which estimates the potential energy of a system with a mixture of intramolecular and intermolecular elements. In molecular docking, the intermolecular elements are usually considered, with the probable ligand-bonded terms, especially the torsional constituents. The non-bonded constituents include the van der Waals term which

**43**

*Role of Force Fields in Protein Function Prediction DOI: http://dx.doi.org/10.5772/intechopen.93901*

examples of the mentioned scoring function.

iii.Knowledge-based scoring function

ii.Empirical scoring function

empirical scoring functions.

functions [68].

*4.1.2 Sampling algorithms*

i.Shape matching

is defined by Lennard-Jones potential, and the electrostatic term, specified by the Coulomb function. GoldScore [62], AutoDock [63] and GBVI/WSA [60] are few

Empirical function is the sum of different empirical terms such as van der Waals, H-bond, electrostatic, entropy, desolvation, hydrophobicity, etc. Utilizing least square fitting method, they are optimized on a training set of target-ligand complexes to reproduce the binding affinity data. Empirical scoring functions compared to force-field ones are computationally much more efficient owing to their simple energy terms. The first example of empirical scoring function is the LUDI scoring function [64]. GlideScore [65] and ChemScore [66] are other examples of

Knowledge-based functions are directly obtained from the structural information of experimentally solved protein-ligand complexes. The frequencies of interatomic contact and/or distances between the target and the ligand are obtained. The premise for this criterion relies on the assumption that frequency of occurrences will be greater for the ones with more favorable interactions. Pairwise atom-type potentials were generated with the obtained frequency distributions. Further the score is computed by preferred interactions and imposing penalty for repulsive contacts between each pair of atoms in the target and ligand within a set cutoff. Examples of this scoring function are DrugScore [67] and GOLD/ASP

With the advancement in the field of high-performance computing, scientists have also applied artificial intelligence based and machine learning based scoring

Sampling plays the next crucial role in any molecular docking program. With a set therapeutic target, the sampling algorithm will generate a number of conformations (poses) of the small molecule within the docked site of the target. The knowledge of the docked site is considered either from experimental data or predicted with the aid of active site prediction software. As the speed and accuracy of molecular docking plays a role in large virtual screening research works, the area of developing and/or improving existing sampling algorithms have provided ample opportunities for computational scientists. The sampling algorithms can be categorized as: shape matching, systematic search algorithm and stochastic algorithm.

One of the earliest methods designed was the shape matching algorithm for sampling. The criterion implemented in this algorithm is that the molecular surface of the small molecule needs to complement the molecular surface of the binding region of the target. The three translational and three rotational (six degree of freedom) of the small molecule led to spans many probable orientations. Thus, the goal of this algorithm is to place as smoothly and quickly the small molecule into the binding site based on shape complementarity. In this method, the conformation of the small molecule is usually fixed and therefore, this method along with

functions in virtual screening which holds promising outcomes [69].

#### *Role of Force Fields in Protein Function Prediction DOI: http://dx.doi.org/10.5772/intechopen.93901*

*Homology Molecular Modeling - Perspectives and Applications*

of the ligands in hand [60].

**4.1 Types of molecular docking**

techniques were available and mostly they are categorized into two main approaches viz. structure-based and ligand-based drug design approaches. The structure-based approach mainly relies on the 3D data of target and the ligand. The ligand-based approach is chiefly adopted in the absence of known experimental structure of the target. In ligand-based approach, the known ligands which were bound to the targets were investigated to decipher the physiochemical and structural properties of the ligands and these were correlated with the anticipated pharmacological activity

One of the most extensively utilized computational techniques in the structurebased drug design is molecular docking. Molecular docking is usually achieved by first predicting the molecular orientation or pose of a ligand within the active site of a target and followed by assessing their binding affinity with the usage of a scoring function. The technique is exploited to decipher the interactions between a target and ligand at the atomic level allowing us to describe the behavior of ligands within the active sites of targets as well as to reveal fundamental biochemical processes. Since the first developments of docking algorithms in the 1980s, molecular docking

Molecular docking can be basically categorized into three types: rigid docking, semi-flexible docking and flexible docking. In the rigid docking approach, both the structure of target and ligand does not change. The computation method is relatively modest and chiefly spans the degree of conformational matching, thus it is more apt for investigating macromolecular systems such as protein-nucleic acid and protein-protein systems. The semi/quasi flexible docking approach take flexibility into consideration while docking of the ligand and thus it is more appropriate to deal with the intermolecular interactions of small molecules and proteins. Usually the structure of the ligands can move freely while the target remains rigid or retain few rotatable residues ensuring computational efficiency during the docking process. In the flexible docking method, it is based on the idea that a protein is not always a rigid entity during the course of ligand binding and thus it considers both the protein and ligand as flexible entities. Over the years various methods have been introduced, based on induced fit model and/or conformational

One crucial element of any docking algorithm is the scoring function. The scoring function aids in the pose selection and it is involved in distinguishing putative precise binding modes and to filter out the non-binders from the *N* number of generated poses during a docking run. The speed and accuracy of docking programs is also dependent on scoring functions. Further computational efficiency and reliability are points kept in mind while developing any scoring function. There are

This scoring function is based on the concept of molecular mechanics which estimates the potential energy of a system with a mixture of intramolecular and intermolecular elements. In molecular docking, the intermolecular elements are usually considered, with the probable ligand-bonded terms, especially the torsional constituents. The non-bonded constituents include the van der Waals term which

became an indispensable tool in the field of drug discovery [61].

**42**

sampling.

*4.1.1 Scoring function*

three categories of scoring functions:

i.Force-field based scoring function

is defined by Lennard-Jones potential, and the electrostatic term, specified by the Coulomb function. GoldScore [62], AutoDock [63] and GBVI/WSA [60] are few examples of the mentioned scoring function.

ii.Empirical scoring function

Empirical function is the sum of different empirical terms such as van der Waals, H-bond, electrostatic, entropy, desolvation, hydrophobicity, etc. Utilizing least square fitting method, they are optimized on a training set of target-ligand complexes to reproduce the binding affinity data. Empirical scoring functions compared to force-field ones are computationally much more efficient owing to their simple energy terms. The first example of empirical scoring function is the LUDI scoring function [64]. GlideScore [65] and ChemScore [66] are other examples of empirical scoring functions.


3.1.2. Dyeing Chemical Modification

Some textile fibers present difficulty in dyeing due to the terminal groups present in their chains, causing some dyes to fail to create interactions, as is the case with polypropylene fibers [99] and vinylon fibers [95]. Other fibers present selectivity for dyes, such as cotton, which is not dyed by acidic and dispersed dyes [108]. However, promoting the modification of the surface of these fibers can cause new possibilities for the interactions between the dye and the fibers [109].

Cyclodextrins are polymers that can cause this chemical modification through incorporation into the fiber [99]. This incorporation can be seen as a pre-treatment for the dyeing or as a finishing, depending on the actions taken after modification. In this section, only the modifications for dyeing will be addressed and, in the next, finishing will be explored.

With cyclodextrins incorporated into the fabric, new groups and pores through which the dyes can fix become available. One fiber that presents difficulty in dyeing is cellulose acetate fiber, due to its compact structure, low content of polar groups, and hydrophobicity [110]. These factors make it difficult for dyes to diffuse in the fiber. To obtain better results in the dyeing process, Raslan et al. [75] treated the cellulose acetate fabric (38.5% acetyl) with monochlorotriazinyl-β-cyclodextrin (MCT-β-CD) using the padding technique to improve its dyeability. As a result, they were able to perform dyeing at a low temperature, improving the color intensity, and they also increased the diffusion of the dye within the fiber by about 70%.

In the case of polyester fibers, some authors have performed the process of acetylation [83] or coating [97] to modify the surface with CDs. This results in an improvement in the solidity of the dyeing [98], in addition to the possibility of dyeing with other classes of dyes. Zhang et al. [97], after performing the modification of polyester fiber, dyed this fabric with cationic dye. The fabric showed a gain in hydrophilicity, a reduction in the dyeing temperature to 70 ◦C, and interaction between the crosslinking carboxylate groups and the cationic dye, in addition to its complexation by the CDs.

Another work that used the modification of the polyester surface with cyclodextrins was carried out by Chen et al. [94]. In this work, the modification enabled a 47% increase in the color intensity in the stamping process, a fact associated with the greater sharpness and depth achieved by the dyes. In addition, the CDs, when chemically bonded to the fabric, can act as an anti-migration agent, because during the drying or curing of polyester fabrics dispersed dyes tend to migrate to the fabric surface and the CDs act as a dye sequestrant, consequently preventing this dyeing defect [83].

In the case of the modification of cotton fiber with cyclodextrins, several routes are possible, but the most used is esterification using citric acid or 1,2,3,4-butane tetra-carboxylic acid (BTCA) [111] as crosslinking. These changes will be covered in more detail in the next section. Rehan et al. [96] carried out the modification of cotton fiber with CDs and citric acid to perform dyeing with acid dye. These dyes present low affinity for the dyeing of cellulosic fiber [108]. After the modification, the authors realized that the dye was adsorbed by the cyclodextrins, which allowed the dyeing to achieve satisfactory solidity. surface and the CDs act as a dye sequestrant, consequently preventing this dyeing defect [83]. In the case of the modification of cotton fiber with cyclodextrins, several routes are possible, but the most used is esterification using citric acid or 1,2,3,4-butane tetra-carboxylic acid (BTCA) [111] as crosslinking. These changes will be covered in more detail in the next section. Rehan et al. [96] carried out the modification of cotton fiber with CDs and citric acid to perform dyeing with acid dye. These dyes present low affinity for the dyeing of cellulosic fiber [108]. After the modification, the authors realized that the dye was adsorbed by the cyclodextrins, which allowed the dyeing to achieve satisfactory solidity.

*Molecules* **2020**, *25*, x 9 of 29

because during the drying or curing of polyester fabrics dispersed dyes tend to migrate to the fabric

In general, the modification of the fiber surface through the insertion of cyclodextrins increases the adsorption of dye and allows a greater variability of dye classes in fibers that have no affinity, often achieving better color standards in multi-fiber items [97,98] and improving the efficiency of the dyeing process for fibers that require greater use of auxiliaries to achieve the proper color standard. In general, the modification of the fiber surface through the insertion of cyclodextrins increases the adsorption of dye and allows a greater variability of dye classes in fibers that have no affinity, often achieving better color standards in multi-fiber items [97,98] and improving the efficiency of the dyeing process for fibers that require greater use of auxiliaries to achieve the proper color standard.

#### *3.2. Textile Finishing 3.2. Textile Finishing*

In the area of textile finishing, cyclodextrins can have many applications; they are able to absorb unpleasant odors, and act as an encapsulation agent for essential oils [38,76,78,112,113], vitamins [114], hormones [77] and biocides [6,115] in order to preserve compounds and/or control their release, as shown in Figure 4. The loading of active ingredients allows the incorporation of specific and desired functions into textile materials, which may act differently under particular uses, such as in medicine [116], cosmetics [117], and engineering [118]. In the area of textile finishing, cyclodextrins can have many applications; they are able to absorb unpleasant odors, and act as an encapsulation agent for essential oils [38,76,78,112,113], vitamins [114], hormones [77] and biocides [6,115] in order to preserve compounds and/or control their release, as shown in Figure 4. The loading of active ingredients allows the incorporation of specific and desired functions into textile materials, which may act differently under particular uses, such as in medicine [116], cosmetics [117], and engineering [118].

**Figure 4.** Use of cyclodextrins in textile finishing. **Figure 4.** Use of cyclodextrins in textile finishing.

In numerous cases, the complexation of active ingredients by CDs improves their physicochemical properties, controls their release, maintains bioavailability, increases shelf life, provides storage conditions, reduces environmental toxicity, increases chemical stability, protects against oxidation, and favors resistance to repeated washing [6,7,114,119,120]. In numerous cases, the complexation of active ingredients by CDs improves their physicochemical properties, controls their release, maintains bioavailability, increases shelf life, provides storage conditions, reduces environmental toxicity, increases chemical stability, protects against oxidation, and favors resistance to repeated washing [6,7,114,119,120].

In order to make it possible to incorporate these active molecules into the textile substrate, there is a need to fix the CDs in the fiber. Several methods have been proposed for the permanent fixation of CDs into textile fibers, and in some cases, there is a need for a first step—the modification of the cyclodextrins—so that they can be incorporated into the fabric. The selection of the best method for In order to make it possible to incorporate these active molecules into the textile substrate, there is a need to fix the CDs in the fiber. Several methods have been proposed for the permanent fixation of CDs into textile fibers, and in some cases, there is a need for a first step—the modification of the cyclodextrins—so that they can be incorporated into the fabric. The selection of the best method for

fixing CDs into a textile substrate depends on different factors, the main ones being reactivity of the cyclodextrins to the final application, and the type of fiber [23,121]. fixing CDs into a textile substrate depends on different factors, the main ones being reactivity of the cyclodextrins to the final application, and the type of fiber [23,121].

*Molecules* **2020**, *25*, x 10 of 29

3.2.1. Preparation of Cyclodextrins 3.2.1. Preparation of Cyclodextrins

Cyclodextrins are capable of forming complexes with a wide range of molecules, but they cannot form a direct covalent bond with textile materials; therefore, some cyclodextrin derivatives have been synthesized with reactive groups to allow them to chemically bond to various substrates [122], as shown in Figure 5. Cyclodextrins are capable of forming complexes with a wide range of molecules, but they cannot form a direct covalent bond with textile materials; therefore, some cyclodextrin derivatives have been synthesized with reactive groups to allow them to chemically bond to various substrates[122], as shown in Figure 5.

**Figure 5.** Modification of β-CD by: (**a**) cyanuric acid; (**b**) itaconic anhydride (IAnh); (**c**) acryloyl chloride and (**d**) laccase/2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) enzyme. **Figure 5.** Modification of β-CD by: (**a**) cyanuric acid; (**b**) itaconic anhydride (IAnh); (**c**) acryloyl chloride and (**d**) laccase/2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) enzyme.

One of the most common reactive derivatives of cyclodextrins is MCT-β-CD, as seen in Figure 5a, synthesized through the reaction between cyanuric chloride and β-cyclodextrin [123]. MCT-β-CD is the most interesting derivative used on cellulosic substrates due to the simple bonding process in relatively mild conditions. The monochlorotriazine groups incorporated into the CDs react by a nucleophilic substitution mechanism, and form covalent bonds with the hydroxyl groups of the cellulose [124]. Another product that can be synthesized from MCT-β-CD is the cyclodextrin polymer One of the most common reactive derivatives of cyclodextrins is MCT-β-CD, as seen in Figure 5a, synthesized through the reaction between cyanuric chloride and β-cyclodextrin [123]. MCT-β-CD is the most interesting derivative used on cellulosic substrates due to the simple bonding process in relatively mild conditions. The monochlorotriazine groups incorporated into the CDs react by a nucleophilic substitution mechanism, and form covalent bonds with the hydroxyl groups of the cellulose [124]. Another product that can be synthesized from MCT-β-CD is the cyclodextrin polymer (6A-O-triazine-β-cyclodextrin), produced by polycondensation using β-CD and cyanuric chloride [125].

Formation occurs due to nucleophilic substitution, in which the hydroxyl groups of the CDs react with the chlorine contained in the cyanuric chloride, and thus form the β-CD copolymer [125]. From the formation of this compound it is possible to create interactions with the hydroxyl groups present in the textile fibers; this occurs by substitution.

The modification of CDs can also be performed using itaconic acid (Figure 5b) containing carboxyl and vinyl groups. This bifunctional compound can be linked to the CDs via an esterification reaction, and its vinyl group can perform polymerization by free radicals [5,122]. Itaconic anhydride is obtained from itaconic acid at 110 ◦C in the presence of sodium hypophosphite [122]. From the modification of the CDs, the end containing the itaconic anhydride is able to bond with the textile fibers through covalent reactions.

Another CD modification for incorporation in textiles can be carried out via a reaction with acryloyl derivative (Figure 5c). The CDs are dissolved in dimethylformamide (DMF), mixed by stirring with triethanolamine (TEA), and reacted with acryloyl chloride dissolved in DMF, forming an acryloyl ester derivative [126]. The compound has a vinyl group on the side chain that is able to react with hydroxyl groups, and can be incorporated into the fibers [23,124].

In addition to the reaction through the incorporation of new chemical groups into the CDs, to make them more reactive hydroxyl groups can be oxidized, as can be seen in Figure 5d. The hydroxyl groups in the polysaccharides can be oxidized by a laccase/2,2,6,6-tetramethylpiperidine-1-oxyl enzyme catalyzed to convert the hydroxyl groups of the CDs into aldehyde groups that are capable of reacting with the amino groups of polyamide, silk, and wool [127].


**5. Hardware design**

**Figure 12** depicts the hardware design of the quadrocopter (**Figure 13**). The brain of the system is composed of two processing units, an AVR 32 bit MCU (microcontroller unit) UC3A and the LP‐180 providing an AMD‐x86 processor and 2 × 1.6 GHz system clock [28].

The CPUs can be seen in the centre of the picture. The MCU interfaces all sensors except those connected via USB and performs the control part with real‐time computing, while the task of the LP‐180 contains all functions with a high computational burden such as object recognition and mapping.

The quadrocopter uses a couple of sensors for obstacle detection and is capable of distance‐ controlled collision avoidance [29]. For object recognition, the C270 camera from Logitech is used [30]. All processing is done on‐board the quadrocopter, so it is capable of a fully auton‐ omous flight.

Autonomous Quadrocopter for Search, Count and Localization of Objects http://dx.doi.org/10.5772/63568 15

**Figure 12.** Hardware design.

Furthermore, the dominating colour within the detected circle is calculated by combining several of the most frequent red, green and blue values. It is notable that the average values are not used because they can be heavily influenced by bright spots on the search target which emerge because of unfavourable lighting conditions. The final target colour does not consist of exactly one set of RGB values but of ranges for each colour channel which are derived from

Furthermore, the algorithm searches for more than one radius. Therefore, the initially detected radius ±2 can be chosen as a target range to compensate for light variations of height during the flight. To allow bigger chances in flying altitude, the radius range would have to be adjusted

The actual search is performed using a resolution of 192 × 144 pixels. This allows quick processing while still preserving all the information necessary for a successful detection.

After taking a picture it is converted into a greyscale image and the Hough detection is performed. The number and quality of detected circles heavily depend on the threshold used during the Hough circle detection. A good value for the required number of votes is 30% of the circumference of the smallest radius. With significantly higher values, target objects tend to get missed far too often because the constant movement of the quadrocopter tends to prevent

All detected circles, i.e., all target candidates, are then analysed for their colour. For each pixel inside a candidate's enclosing circle, it is checked if its RGB values lie within a certain range. The range is determined during the initial scan. For a candidate to get confirmed as a target, a certain percentage of its pixels has to be target pixels. Setting this threshold to about 40% was

**Figure 12** depicts the hardware design of the quadrocopter (**Figure 13**). The brain of the system is composed of two processing units, an AVR 32 bit MCU (microcontroller unit) UC3A and

The CPUs can be seen in the centre of the picture. The MCU interfaces all sensors except those connected via USB and performs the control part with real‐time computing, while the task of the LP‐180 contains all functions with a high computational burden such as object recognition

The quadrocopter uses a couple of sensors for obstacle detection and is capable of distance‐ controlled collision avoidance [29]. For object recognition, the C270 camera from Logitech is used [30]. All processing is done on‐board the quadrocopter, so it is capable of a fully auton‐

the LP‐180 providing an AMD‐x86 processor and 2 × 1.6 GHz system clock [28].

according to the currently measured distance of the quadrocopter to the floor.

the original values.

14 Recent Advances in Robotic Systems

*4.3.2. Search*

a good value here.

and mapping.

omous flight.

**5. Hardware design**

the camera from taking sharp pictures.

**Figure 13.** AQopterI8 picture.


**2.3 Electrochemical (corrosion) characterization**

The electrochemical behaviour of the samples (plates 10 x 10 x 0.05mm) was studied in a standard three-electrode thermostated cell (100 ml volume). The model corrosion medium was 0.1 N H2SO4 ("p.a." Merck) after deaeration with additionally purified argon at 25C*.* A counter electrode, representing a platinum plate (10x10x0.6mm), and a mercury/mercurous sulfate reference electrode (MSE), (EHg/Hg2SO4= +0.642V versus SHE) were used. All potentials in the text are related to MSE. The anodic and cathodic polarization curves were obtained using a 273 EG&G potentiostat/galvanostat and computer-aided processing of the results according to an "Echem" programme, with a potential sweeping rate of 10 mV/s within a potential range from –1.500 to +1.500V*.* The recording of the potentiodynamic curves was carried out starting from the stationary corrosion potential (Est), measured in the absence of external current (at open circuit) in the anode and cathode directions. We used a separate electrode for each recorded curve. The stationary corrosion potential of the samples under investigation was determined by direct measurement of the function "Est–time" at open circuit (with respect to the same reference electrode) after immersing the samples in 0.1 N H2SO4 in the absence and in the presence of Ce4+. The Est was established after a sufficiently long time interval - from few minutes to several decades of minutes until the moment, when the corrosion potential change did not exceed 1-3mV for 5 min.


*3.1. 13C*

The 13C NMR spectrum of the anhydrated cement (Figure 1 (top)) shows a single broad asymmetric line at a chemical shift of about 168.7 ppm. Most likely, this line originates from amorphous calcium carbonate and, possibly, small amounts of calcite and dolomite, which are contained in cement, according to the phase composition obtained from the XRPD analysis (Table 1). An effort to perform component deconvolution in this spectrum was not attempted, because its line shape is quite broad and the 13C chemical shifts of the carbonate compounds fall close to each other [14]. The 13C NMR spectra obtained at different ages of cement hydration show two (except for **CAA** and **CSA** at 1 d) clearly distinguished peaks (Figure 1 (bottom) is an example of the 13C NMR spectrum of the sample C07; the other spectra are provided in Supplementary Figures S1a–S3a). Moreover, the peak at about 168.5 ppm consists of unresolved narrow and broad signals; the deconvolution into two peaks is justified in Supplementary Figure S1c,d. We note, however, that the precision of the deconvolution of unresolved signals might suffer from larger errors and a certain degree of caution should be exercised when analyzing these results. The position of the signal at about 171 ppm remains virtually unchanged over time. However, the narrow component of the signal at 168.5 ppm was slightly shifted to the weak field at the initial stage of hydration, and the broad component was shifted to the strong field throughout the entire hydration period, with the exception of the **CSA** sample, for which this component was shifted, on the contrary, to the weak field. The signals mentioned are contributed also by the presence of calcium monocarboaluminate hydrate, forming during the hydration process. The time dependences of the 13C chemical shift values are shown in Supplementary Figures S1b–S3b in Supporting Information.

**Figure 1.** 13C NMR spectra of the (**top**) anhydrated Portland-limestone cement, and (**bottom**) cement paste sample without additives on the 7th day of hydration.

According to the literature, all the observed signals correspond to CO3 2− structural units of various amorphous and crystalline modifications of calcium carbonate and dolomite [14–16]: the broad signal at about 168.5 ppm corresponds to amorphous calcium carbonate and to metastable ikaite, which is possibly contained in the samples; the narrow signal corresponds to calcite, dolomite and vaterite; and the signal at about 171 ppm corresponds to carbon atoms in aragonite and vaterite. According to Reference [17], the two narrow signals from carbon nuclei in the structure of vaterite correspond to its two most probable crystal structures.


**4.3.2 Effect of different treatments on total accumulated temperature**

The measured results of the total accumulated temperature of each treatment were shown in table 4-8.

Table 4-8. The total accumulated temperature of each treatment

Soil depth (cm)

0

5

10

15

table 4-12.

depths among treatments

Treat ment

Study on Manufacturing Technology and Performance of Biogas Residue Film 291

Mean difference (℃)

*i y i F y* − *y i E y* − *y i D y* − *y i C y* − *y i B y* − *y*

B 417.5 28.5\* 88.6\*\* 24 13.2 C 404.3 15.3 101.8\*\* 37.2\* D 441.5 52.5\*\* 64.6\*\* E 506.1 117.1\*\* F 389

B 391.7 17.3\* 48.4\*\* 11 2.1 C 389.6 15.2 50.5\*\* 13.1 D 402.7 28.3\* 37.4\*\* E 440.1 65.7\*\* F 374.4

B 359.3 15.8\* 41.2\*\* 11.3 5.6 C 353.7 10.2 46.8\*\* 16.9\* D 370.6 27.1\*\* 27.1\*\* E 400.5 57\*\* F 343.5

B 339.9 5.5 45\*\* 11.8 2.4 C 337.5 3.1 47.4\*\* 14.2\* D 351.7 17.3\* 33.2\*\* E 384.9 50.5\*\* F 334.4

Table 4-10. Multiple comparisons of the total accumulated temperature of different soil

Source SS DF MS F P-value F0.05 Interval 0.5030 2 0.2515 2.4037 0.1405 4.1028 Treatment 3.7059 5 0.7412 7.0833 0.0045 3.3258

Table 4-11 showed that there were significant differences between the yields of each treatment. Multiple comparisons of the eggplant yield among treatments were shown in

Notes: The significance of symbols was as same as table 4-4.

Error 1.04637 10 0.1046

Table 4-11. Analysis of variance of the yield date

Total 5.2553 17

**4.4 The effect of different treatments on eggplant yield**  Analysis of variance of the yield was shown in table 4-11.

A 420.8 31.8\* 85.3\*\* 20.7 16.5 3.3 28.13 40.01

A 391.4 17\* 48.7\*\* 11.3 1.8 0.3 15.6 22.19

A 361.7 18.2\* 38.8\*\* 8.9 8 2.4 15.68 22.32

A 343.4 9 41.5\*\* 8.3 5.9 3.5 13.19 18.76

LSD0.05 LSD0.01

Analysis of variance of the total accumulated temperature of each treatment was shown in table 4-9.

Table 4-9. Analysis of variance of the total accumulated temperature of each treatment

Multiple comparisons of the total accumulated temperature of different soil depths among treatments were shown in table 4-10.

Table 4-10 showed that there were significant differences between plastic film and three kinds of biogas fibre residue film, black film and control while the soil depth was 0cm and 10cm. There were significant differences between the black film and control; there were significant differences between A, B treatment of biogas residue fibre film and control; there were no significant differences between C treatment of biogas residue fibre mulch and control; there were significant differences between black film and C treatment of biogas residue fibre film. While the soil depth was 5 cm, there were significant differences between plastic film and three kinds of biogas fibre residue film, black film and control; there were significant differences between A, B treatment of biogas fibre residue film, black film and control. While soil depth was 15 cm, there were significant differences between plastic film and three kinds of biogas fibre residue film, black film and control; there were significant differences between the black film and C treatment of biogas residue fibre film and control.

While the soil depth was 0 cm, 5 cm, 10 cm, the total accumulated temperature of A, B treatment of biogas residue film obviously increased than control, and C did not significantly increase; while the soil depth was 0cm, the total accumulated temperature of A treatment increased 31.8 ℃, B increased 28.5 ℃, and C increased 15.3℃ than control; while the soil depth was 5cm, the total accumulated temperature of A treatment increased 17℃, B increased 17.3℃, and C increased 15.2℃ than control; while the soil depth was 10cm, the total accumulated temperature of A treatment increased 18.2 ℃, B increased 15.8 ℃, and C increased 10.2 ℃ than control. While the soil depth was 15 cm, the total accumulated temperature of three kinds of biogas fibre residue film nearly closed to control, and was less than the black film and plastic film. There was no significant difference between the three kinds of biogas residue fibre film.

Analysis of variance of the total accumulated temperature of each treatment was shown in

Soil depth cm Source SS DF MS F P-value F0.05

Error 2390.63 10 239.06

Error 735.53 10 73.55

Error 743.86 10 74.39

Error 525.47 10 52.55

Table 4-9. Analysis of variance of the total accumulated temperature of each treatment

Multiple comparisons of the total accumulated temperature of different soil depths among

Table 4-10 showed that there were significant differences between plastic film and three kinds of biogas fibre residue film, black film and control while the soil depth was 0cm and 10cm. There were significant differences between the black film and control; there were significant differences between A, B treatment of biogas residue fibre film and control; there were no significant differences between C treatment of biogas residue fibre mulch and control; there were significant differences between black film and C treatment of biogas residue fibre film. While the soil depth was 5 cm, there were significant differences between plastic film and three kinds of biogas fibre residue film, black film and control; there were significant differences between A, B treatment of biogas fibre residue film, black film and control. While soil depth was 15 cm, there were significant differences between plastic film and three kinds of biogas fibre residue film, black film and control; there were significant differences between the black film and C treatment of biogas residue fibre film and control. While the soil depth was 0 cm, 5 cm, 10 cm, the total accumulated temperature of A, B treatment of biogas residue film obviously increased than control, and C did not significantly increase; while the soil depth was 0cm, the total accumulated temperature of A treatment increased 31.8 ℃, B increased 28.5 ℃, and C increased 15.3℃ than control; while the soil depth was 5cm, the total accumulated temperature of A treatment increased 17℃, B increased 17.3℃, and C increased 15.2℃ than control; while the soil depth was 10cm, the total accumulated temperature of A treatment increased 18.2 ℃, B increased 15.8 ℃, and C increased 10.2 ℃ than control. While the soil depth was 15 cm, the total accumulated temperature of three kinds of biogas fibre residue film nearly closed to control, and was less than the black film and plastic film. There was no significant difference between the three kinds of biogas residue fibre film.

Total 30924.4 17

Total 7036.3 17

Total 6843.37 17

Total 6222.34 17

Interval 3016.57 2 1508.29 6.3092 0.0169 4.1028 Treatment 25517.2 5 5103.44 21.3477 4.84E-05 3.3258

Interval 58.83 2 29.42 0.3999 0.6806 4.1028 Treatment 6241.93 5 1248.39 16.9726 0.000133 3.3258

Interval 324.96 2 162.48 2.1843 0.1633 4.1028 Treatment 5774.55 5 1154.91 15.526 0.000195 3.3258

Interval 432.25 2 216.13 4.113 0.0497 4.1028 Treatment 5264.62 5 1052.92 20.037 6.42E-05 3.3258

table 4-9.

0

5

10

15

treatments were shown in table 4-10.

Notes: The significance of symbols was as same as table 4-4.

Table 4-10. Multiple comparisons of the total accumulated temperature of different soil depths among treatments

#### **4.4 The effect of different treatments on eggplant yield**

Analysis of variance of the yield was shown in table 4-11.

Table 4-11. Analysis of variance of the yield date

Table 4-11 showed that there were significant differences between the yields of each treatment. Multiple comparisons of the eggplant yield among treatments were shown in table 4-12.

Study on Manufacturing Technology and Performance of Biogas Residue Film 293

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with Promising Physical Properties in Comparison with Commercial Polyethylene

the Removel of Pb (II) from Aqueous solution and Radiator Manufacturing

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**6. References**

132 (in Chinese)

Chinese)

Chinese)

(in Chinese)

Chinese)

energy 4:36-38 (in Chinese)

26-27 (in Chinese)

paper making 11-13 (in Chinese)

press 273-281 (in Chinese)

Notes: The significance of symbols were as same as table4-4.

Table 4-12. Multiple comparisons of the eggplant yield among treatments

The results showed that there were significant differences between B treatment of biogas residue fibre film and control, no significant differences between A, C treatment and control, significant differences between black film and control. There were extremely significant differences between the plastic film, A, C treatment and control, there were significant differences between plastic film and B treatment, no significant differences between plastic film and the black film. It can be seen, the yield of B treatment increased 47% as compared with control.