**1. Introduction**

Fuel ethanol helps to reduce harmful emissions from vehicles, contributing to the fight against climate change and the pursuit of clean mobility [1]. Moreover, as a kind of renewable energy made from sustainable biomass materials, fuel ethanol is an ideal substitute for the non-renewable fossil fuels [2]. Thus, fuel ethanol is expected to play a key role in the formation of a sustainable energy system. China is the largest consumer of fossil fuels and attaches great importance of the development of the fuel ethanol industry. In 2018, the fuel ethanol production capacity in China reached 3.22 million tons. China has become the third-largest fuel ethanol consuming and producing country, following Brazil and the United States [3]. However, China accounted for just over 3% of global production in 2018. The development of the fuel ethanol industry in China still faces many challenges (e.g., technical uncertainty, demand uncertainty, and feedstock uncertainty) [4–6]. The Chinese government announced a new nationwide ethanol mandate that will expand the mandatory use of E10 fuel (gasoline containing 10% ethanol) from 11 trial provinces to the entire country by 2020. If China were to meet the national mandate of E10, it would require an extra 12 million tons of fuel ethanol production capacity, which is about four times that of its current production capacity. Therefore, exploring the nexus among the fuel ethanol industry, the uncertain technology system, and the uncertain market demand will be conducive to clarifying the growth mechanisms of the fuel ethanol industry and then improving public policy to accelerate the growth of the fuel ethanol industry in China.

Many scholars have already analyzed the di fferent factors that a ffect the development of the fuel ethanol industry. These factors mainly include technology change [7–10], market demand [11–13], feedstocks [8,14,15], renewable energy infrastructures [16,17], energy policies [18–21], and economic, social, and environmental impacts [22–25]. These works mainly used case studies [8,10], econometric methods [9,11,12,19], and simulation methods [7,13]. Although the existing literature provides important information on the relationships between the fuel ethanol industry growth and its drivers, there are still some limitations. Firstly, most studies do not consider the adverse impacts of the fuel ethanol industry on its drivers and the interrelationships among the driving factors. The ignorance of the above interactions may lead to a misunderstanding of the growth mechanisms of the fuel ethanol industry [26]. Secondly, the fuel ethanol industry is an emerging industry in China. Therefore, the econometric methods used in the existing studies could not be applied to analyze the development of the fuel ethanol industry in China due to the lack of statistical data. Lastly, although simulation is an ideal method to quantitatively analyze the development of emerging industries, such as the fuel ethanol industry, this method is often questioned because of the subjectivity of its parameter settings [27].

In addressing these limitations, this paper employs a history-friendly evolutionary model to explore the interactions among the fuel ethanol industry and its driving factors and analyze the impacts of di fferent policies on the evolution of the fuel ethanol industry in China. The contributions of our work are reflected in three aspects. First, we argue that there are multidirectional causalities among the fuel ethanol industry, the technology system, and the market system. Therefore, we applied a coevolutionary framework to model the above relationships. Under this framework, each party exerts selective pressures on the others, thereby a ffecting each other's evolution [28]. Second, we developed a history-friendly model to depict the above coevolutionary relationships related to the fuel ethanol industry in China. The parameters of the history-friendly model are set based on the historical evolutionary characteristics of the industry but not on historical statistics. Therefore, this method can be used to analyze the growth of the fuel ethanol industry, which lacks historical statistics in China. At last, we further analyzed the impacts of entry regulation, production subsidy, R&D subsidy, and ethanol mandate policy on the evolution of the fuel ethanol industry in China.

The rest of the paper is structured as follows. Section 2 reviews the driving factors that a ffect the growth of the fuel ethanol industry. Section 3 features a history-friendly model based on the coevolutionary framework, which describes the interactions among the fuel ethanol industry, the technology system, and the market system. In Section 4, we first run the baseline simulation to select the values of the parameters that can reflect the historical characteristics of the fuel ethanol industry and then use this model to further analyze the impacts of several typical fuel ethanol industry policies, while Section 5 contains concluding remarks and policy implications.


*Edited by Adnan Mustafa and Muhammad Naveed*

This edited volume, *Soil Contamination - Recent Advances and Future Perspectives,* provides a comprehensive overview of soil contamination. Chapters address such topics as using biochar to improve soil quality, electrochemical analysis of pesticides and heavy metals in the environment and food, nitrate leaching in soils, the ability of bacteria to remove heavy metals and polycyclic aromatic hydrocarbons, and much more. This book is a useful resource that opens new possible research paths for further novel developments in the field.

*J. Kevin Summers, Environmental Sceinces Series Editor*

ISBN 978-1-80356-827-0

Soil Contamination - Recent Advances and Future Perspectives

ISSN 2754-6713

Published in London, UK © 2024 IntechOpen © Jian Fan / iStock


**2. Methodology**

Arksey and O'Malley's scoping review framework was adopted in our literature review [20]. The scoping review methodology involves five different stages: (i) identifying a research question, (ii) identifying relevant studies, (iii) the selection of the studies, (iv) charting of data, and (v) summarizing and reporting of the results.

This scoping review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [21]. A PRISMA chart was constructed (Figure A1). The databases used in the literature search were: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Medline, PubMed, Scopus, Google Scholar and Web of Science. Our search strategy can also be found in Figure A2.

Studies that revolved around four concepts: (i) CMs, (ii) Scheduling and rescheduling, (iii) Codeine and (iv) Pharmacist, which were published in the English language between 10 March 2014 and 10 March 2019, were included. A timeframe of five years was selected due to the increasing trend of the use of CMs by consumers over this period [22]. Terminologies used in the search strategy for the four respective concepts are shown below:

Research articles that did not follow IMRAD (Introduction, Methods, Results, and Discussion) format were excluded from the study. Letters, editorials, and commentaries were also excluded. Search term criteria and terminologies were agreed upon among the four authors, and one author performed the literature search. The same author who conducted the literature search completed the title and abstract screening of the yield, along with the removal of duplicates. The review of full text articles was performed by four authors.


**2. Properties of jet fuel**

Turbine engine fuel for aviation uses is a petroleum based fuel similar in properties to kerosene, although newer biofuels and synthetic fuels are under development. Liquid hydrocarbon based fuels are considered ideal for transportation use because they are more easily handled than solids and have a higher energy density than gasses. In most cases, fuel for aviation purposes is of a higher quality than fuels for heating or road transportation purposes. The higher quality requires a greater degree of refining to significantly reduce the level of sulfur, nitrogen and oxygen containing components. It is still thought that the heteroatom containing compounds are responsible for many of the issues of deposit formation and low temperature oxidation.

Fuel for turbine engines is produced meeting one of several specifications. Early turbine engines ran on kerosene based fuel. Over time, a wide cut fuel incorporating gasoline and kerosene was developed for use by the US military, because it was thought availability would be better. It was found, however to increase casualties and evaporation of fuel. It is still currently used in cold regions under the Jet B specification. In the rest of the world, commercial aviation has used a kerosene based fuel under the specification Jet A, Jet A-1 [2] or TS-1. Some

**Table 1.** Physical properties of some common jet fuels.

properties of each of the commercial jet fuels are shown in **Table 1**. The US military also has a number of different fuel designations; although JP-5 and JP-8 are the most commonly used and are kerosene based fuels.

compatibility issues are possible. Other aspects of storage standards require that the fuel remain free of contaminants, including surfactants, other petroleum products, microorganisms and dyes. All of these types of contaminants can arise from many sources, but careful

The storage and delivery of aviation fuels is carefully regulated and must meet certain standards Including maintaining the purity of the fuel and safety of the aircraft and ground crew [1]. In this chapter, the contamination of fuel from only a few sources will be considered; in particular water contamination, contamination with solids formed by reaction to the fuel with oxygen and contamination due to microorganisms. Finally, some of the challenges associated

Turbine engine fuel for aviation uses is a petroleum based fuel similar in properties to kerosene, although newer biofuels and synthetic fuels are under development. Liquid hydrocarbon based fuels are considered ideal for transportation use because they are more easily handled than solids and have a higher energy density than gasses. In most cases, fuel for aviation purposes is of a higher quality than fuels for heating or road transportation purposes. The higher quality requires a greater degree of refining to significantly reduce the level of sulfur, nitrogen and oxygen containing components. It is still thought that the heteroatom containing compounds are responsible for many of the issues of deposit formation and low

Fuel for turbine engines is produced meeting one of several specifications. Early turbine engines ran on kerosene based fuel. Over time, a wide cut fuel incorporating gasoline and kerosene was developed for use by the US military, because it was thought availability would be better. It was found, however to increase casualties and evaporation of fuel. It is still currently used in cold regions under the Jet B specification. In the rest of the world, commercial aviation has used a kerosene based fuel under the specification Jet A, Jet A-1 [2] or TS-1. Some

Place of use United States Western Europe Parts of Canada and Alaska

**Property Jet A Jet A-1 Jet B**

Flash point 38°C 38°C 28°C Freezing point −40°C −47°C −51°C Energy content 42.8 MJ/kg 42.8 MJ/kg 42.8 MJ/kg Aromatics 25% 25% 25%

**Table 1.** Physical properties of some common jet fuels.

Type of fuel Kerosene Kerosene Wide cut

Density 0.775–0.840 g/cm3 0.775–0.840 g/cm3 0.750–0.801 g/cm3

storage can minimize the hazards associated with some.

with the transition to alternate fuels will be discussed.

**2. Properties of jet fuel**

194 Flight Physics - Models, Techniques and Technologies

temperature oxidation.

While these properties can be met by a number of different fuel sources, the composition of the fuels can be quite different. In many cases, it is the trace heteroatom containing compounds that negatively affect the chemistry of the fuel, especially under storage conditions. In many instances, additives are included in the fuel in order to reduce the reactivity of the heteroatom containing compounds, but the additives themselves may contribute to the reactivity of the fuel. Several of the common additives found in jet fuels are described in **Figure 1** and **Table 2** [3].

**Figure 1.** Chemical structures of some common additives found in fuels.

**Table 2.** Some common additives found in jet fuel.


*2.3. Evaluation of Target Gene Transcription*

Specific mRNA synthesis of target genes was evaluated in eukaryotic cells 293T transfected with pEV.CTL and pEV.Th using MATra-A reagent according to the manufacturer's instruction (PromoKine, Heidelberg, Germany). Cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) with 10% FBS. 48 h after transfection mRNAs were isolated from cells with a kit for RNA isolation (Promega, Madison, WI, USA). Before reverse transcription all RNA samples were treated with RNase-free DNase. cDNAs were obtained by reverse transcription using RevertAid H Minus First Strand cDNA Synthesis Kit (Thermo Scientific, Berlin, Germany). Further, the obtained cDNA carried out PCR with the use of specific primers to gene *EV.CTL* (fCTL—AACTCAGGCACTCTTCCTGC, rCTL—TCGTACCGGAATCTCAGGGT) and gene *EV.Th* (fTh—ACGTTGACAAGCTGAGGAGG, rTh—GAGAGTCCTCAGCCCAGAGA). The amplification product was analyzed by electrophoresis in 1% agarose gel.


**5.1. Range-free localization in one dimensional spaces**

In this part, we investigate the range-free localization of sensors in a network deployed in a one dimensional topology.

### *5.1.1. Background*

6 Will-be-set-by-IN-TECH

equal distribution in space. In (5a) the terms involving *xi* write 2 ∑*j*∈**N**(*i*)(*xi* − *xj*)*T*(*xi* − *xj*). The minimization of 2 ∑*j*∈**N**(*i*)(*xi* − *xj*)*T*(*xi* − *xj*) in terms of *xi* tends to adapt *xi* to the center formed by all *xj* for *j* ∈ **N**(*i*). The second term in (5a) is essentially a barrier term and approaches to infinitely large when the solution tends to violate the inequality constraints

(3) with *R* replaced by *R* − *δ*, i.e., the solution of *xi* obtained by solving (2) with *R* replaced by

The ODE model (6) is a distributed one since the update of *xi* in (6) only depends on *xj* for *j* ∈ **N**(*i*), i.e., the position estimations of the neighbor sensors. Therefore, communication

**Remark 2.** *The ODE model (6) is initialized with the ultimate output of the ODE model I (3) with R replaced by R* − *δ in (4) with δ* � *R. The goal is to ensure the ultimate output of Model I strictly locates inside the open set formed by (1), which is necessary for the barrier term in (5) to restrict the*

According to Theorem 1, the ODE model I with *R* replaced by *R* − *δ* ultimately converges to a

with which we conclude that (*xi* − *xj*)*T*(*xi* − *xj*) ≤ (*R* − *δ*)2 *< R*2 for *i* ∈ **N**(*j*). With the effect

with an initialization inside the feasible set will still stay inside this set. We have the following

*ultimate output of the ODE model I (3) with R replaced by R* − *δ in (4) with δ* � *R, stays in the open*

In this section, simulations are used to verify the two ODE models in both the one dimensional

*R*2−(*xi*−*xj*)*T*(*xi*−*xj*)(*xi* − *xj*) in the model II (6), the ultimate solution of (6)

*c*0 *R*2 − (*xi* − *xj*)*T*(*xi* − *xj*)

*i* means that *xi* is initialized with *x*�

*i* (6)

(*xi* − *xj*)

*i* is the ultimate output of Model I

. *�*2 *>* 0 is a scaling

*i*

2 for*i* ∈ **N**(*j*) (7a)

*i*

*, which is the*

*xk* = *x*¯*k* for *k* ∈ **B** (7b)

given in (1). This term works to restrict the solution in the feasible set.

 1 +

About the initialization of the ODE model, we have the following remark,

(*xi* − *xj*)*T*(*xi* − *xj*) ≤ (*R* − *δ*)

**Theorem 2** ([11])**.** *The ODE model II (6) with �*2 *>* 0*, c*0 *>* 0*, initialized with x*�

*j*∈**N**(*i*)

We use the following gradient based dynamics to solve (5):

*xk* = *x*¯*k* for *k* ∈ **B**

where *xi* is the position estimation of the *i*th blind sensor, *x*�

*x*˙*i* = −*�*2 ∑

*xi*(0) = *x*�

*R* − *δ* in (4). The expression *xi*(0) = *x*�

factor and *c*0 *>* 0 is a positive constant.

only happens between neighbor sensors.

*solution always stays inside the feasible region.*

solution in the following set,

of the barrier term *c*0

*set constructed by (1).*

**5. Simulations**

theorem to state this point rigourously,

space and the two dimensional space.

 For example, WSNs for highway monitoring [1, 9, 21] are often deployed along the highway direction and thus form a one dimensional deployment topology, as sketched in Fig. 3. Other applications, such as WSNs for bridge health monitoring [32] and WSNs along a tunnel [5] for traffic safety, can also be put into the category of one dimensional localization problem.

### *5.1.2. Simulation setup and simulation results*

We consider a wireless sensor network with one dimensional deployment. There are 4 beacon sensors deployed at 0m, 166.6667m, 333.3333m and 500.0000m, and 16 blind sensors deployed at 26.6011m, 56.1963m, 83.3216, 119.9182m, 147.6692m, 176.9903m, 208.3049m, 238.5405m, 263.6398m, 290.4771m, 320.4868m, 355.1442m, 384.0493m, 407.3632m, 440.0192m and 470.5006m, respectively. The communication range of sensors is 50m.

For the dynamic models, the state values of Model I are randomly initialized. We choose *�*1 = 105, *�*2 = 20 × 105 as the scaling parameters, the coefficient *c*0 = 5. The shrinking constant *δ* is chosen as 5. Fig. 4 plots the transient behavior of the position estimation by Model I. From this figure, we can clearly see that the estimation converges with time. For Model II, it is initialized with the output of Model I by replacing *R* with *R* − *δ*. As *R* ≈ *R* − *δ* in this simulation, Fig. 4 and Fig. 5, which shows the transient to obtain the initial position estimation for Model II, demonstrate similar behaviors. Fig. 6 shows the transient of the position estimation by Model II. By comparing the final values and the initial values in Fig. 6, it can be found that the values tends to equal distances between neighbors. The position estimation results are shown in Fig. 7. It can be observed that both models result in estimations meeting the nonlinear inequalities (neighbor sensors are within a distance of 50m). However, the result by Model I may break

**Figure 4.** Transient of the position estimation by Model I in the one dimensional localization problem. Triansent to obtain the initial value for Model II

**Figure 5.** Transient to obtain the initial position estimation for Model II in the one dimensional localization problem.

8 Will-be-set-by-IN-TECH

Output of Model I

0 0.5 1 1.5 2

time/s

Triansent to obtain the initial value for Model II

0 1 2

time/s

**Figure 5.** Transient to obtain the initial position estimation for Model II in the one dimensional

**Figure 4.** Transient of the position estimation by Model I in the one dimensional localization problem.

0

0

100

200

300

x/meters

localization problem.

400

500

100

200

300

x/meters

400

500

x 10 −4

x 10−4

**Figure 6.** Transient of the position estimation by Model II in the one dimensional localization problem.

**Figure 7.** Position estimation results in the one dimensional localization problem.

the real order, i.e., as shown in Fig. 7 the sixth sensor from the left actually locates to the left of the eighth one while the estimated position of the sixth sensor by Model I is to the right of the eighth sensor. However, the performance is improved by using Model II and the estimation results follows the real order. For better comparisons of Model I and Model II in the sense

of estimation error, we use the Root-mean-square error *E*1 defined as *E*1 = ∑*n i*=1 ||*x*ˆ*i*−*xi*||2 *n* (where *x*ˆ*i* and *xi* represent the estimated value and the true value of the *i*th sensor's position), and the maximum absolute error *E*2 defined as *E*2 = max*i*=1,2,...,*n*{||*x*ˆ*i* − *xi*||} to evaluate the estimation error. Ten independent simulations with random initializations are performed and the estimation errors are calculated for all runs. As shown in Table 1, the error *E*1 is around 40 and the error *E*2 is around 80 for Model I with the simulation setup. In contrast, the estimation errors for Model II are much lower, which are about 10 for *E*1 and 26 for *E*2 in the ten simulation runs. This result demonstrates the advantage of Model II over Model I for position estimation of sensors by introducing heuristic information. Also note that there are only 4 beacon sensors in contrast to 16 blind sensors, meaning that the ratio of beacon sensors to blind sensors is 25%. For such a low beacon vs. blind sensor ratio, the estimation errors *E*1 and *E*2 for both Model I and Model II, especially for Model II, as shown in Table 1, are acceptable for rough estimations of sensor positions in applications.

Estimation Error of Model I Estimation Error of Model II

**Table 1.** Estimation errors for Model I and Model II in different simulation runs of the one dimensional localization problem.

### **5.2. Range-free localization in two dimensional Spaces**

In this part, we investigate the range-free localization of sensors in a network deployed in a two dimensional topology.

### *5.2.1. Background*

Most existing literatures deal with the general localization problem in two dimensional space. The one dimensional sensor localization problem investigated in the last section falls into this category by fixing the value of sensor positions along one dimension. Localization in applications, such as wildlife monitoring [7], WSN aided robot navigation [6, 19] and animal tracking [27], can be abstracted as two dimensional localization problems.

10 Will-be-set-by-IN-TECH

the real order, i.e., as shown in Fig. 7 the sixth sensor from the left actually locates to the left of the eighth one while the estimated position of the sixth sensor by Model I is to the right of the eighth sensor. However, the performance is improved by using Model II and the estimation results follows the real order. For better comparisons of Model I and Model II in the sense

(where *x*ˆ*i* and *xi* represent the estimated value and the true value of the *i*th sensor's position), and the maximum absolute error *E*2 defined as *E*2 = max*i*=1,2,...,*n*{||*x*ˆ*i* − *xi*||} to evaluate the estimation error. Ten independent simulations with random initializations are performed and the estimation errors are calculated for all runs. As shown in Table 1, the error *E*1 is around 40 and the error *E*2 is around 80 for Model I with the simulation setup. In contrast, the estimation errors for Model II are much lower, which are about 10 for *E*1 and 26 for *E*2 in the ten simulation runs. This result demonstrates the advantage of Model II over Model I for position estimation of sensors by introducing heuristic information. Also note that there are only 4 beacon sensors in contrast to 16 blind sensors, meaning that the ratio of beacon sensors to blind sensors is 25%. For such a low beacon vs. blind sensor ratio, the estimation errors *E*1 and *E*2 for both Model I and Model II, especially for Model II, as shown in Table 1, are

Estimation Error of Model I Estimation Error of Model II

 E1 E2 E1 E2 41.2948 84.0870 9.9419 23.7890 44.5053 84.4876 10.2123 27.2300 43.7765 80.0258 13.2497 26.1105 44.3971 83.3457 11.1692 26.9130 40.9815 83.3549 12.4231 28.3951 40.7992 79.4815 10.8819 25.6199 39.5168 86.9493 11.4397 26.0199 37.4373 65.4550 10.4166 24.0404 42.9898 83.3865 11.4330 26.6570 44.0201 78.4496 13.2752 28.7782

**Table 1.** Estimation errors for Model I and Model II in different simulation runs of the one dimensional

In this part, we investigate the range-free localization of sensors in a network deployed in a

Most existing literatures deal with the general localization problem in two dimensional space. The one dimensional sensor localization problem investigated in the last section falls into this category by fixing the value of sensor positions along one dimension. Localization in applications, such as wildlife monitoring [7], WSN aided robot navigation [6, 19] and animal

∑*n*

*i*=1 ||*x*ˆ*i*−*xi*||2 *n*

of estimation error, we use the Root-mean-square error *E*1 defined as *E*1 =

acceptable for rough estimations of sensor positions in applications.

**5.2. Range-free localization in two dimensional Spaces**

tracking [27], can be abstracted as two dimensional localization problems.

localization problem.

*5.2.1. Background*

two dimensional topology.

**Figure 8.** True positions of sensors in a typical simulation run of the two dimensional WSN localization problem.

In the simulation, we consider a 100 × 100*m*2 square area with 9 beacon sensors uniformly deployed (the beacon sensors are deployed along the perimeter and at the center, with relative coordinates [0, 0]m, [60, 0]m, [120, 0]m, [0, 60], [60, 60], [120, 60], [0, 120], [60, 120], [120, 120] respectively.) and 20 blind sensors randomly deployed (see Fig. 8 for the deployment of sensors in a typical simulation run). The maximum communication range of sensors are chosen as *R* = 50m.

As to the dynamic models, we choose the scaling factors 1 = 2 = 105, the connection weight *wij* equals 5 for connections with a beacon sensor and 1 otherwise for Model I, the relaxation parameter *delta* = 0.5 for Model II and the coefficient *c*0 = 1 for Model II.

Fig. 9 and Fig. 10 show the time histories of the position estimations by Model I along x-direction and that along y-direction respectively. From the figures we can observe that after a short period of transient, the estimation results converge to constant values. The time histories of the position estimations along x-direction and y-direction estimations by Model II are plotted in Fig. 11 and Fig. 12, respectively. Compared to the transient of Model I, the change of state values in Fig. 11 and Fig. 12 are much milder. The adjustment of values refine the initial estimation with the tendency to even distributions under the communication connectivity constraints. With time elapses, the estimation results by Model II converge. It is worth noting that the ultimate values by Model II shown in Fig 11 and Fig. 12 are not exactly uniformly distributed. This is due to the compromise of the heuristic information driving to even

**Figure 9.** Time history of the position estimation in x-direction by Model I in the two dimensional WSN localization problem.

distribution in space and the inequality constraints imposed by communication connectivity. The final position estimations by Model I and Model II are shown in Fig. 13 and Fig. 14, respectively. It can be observed that the estimated results shown in both figures are within the area covered by the circle centered at the true position with a radius *R* = 50m, which verifies the effectiveness of Model I and Model II in modeling the communication connectivity constraint. On the other hand, it is clear that the estimation results shown in Fig. 14 outperforms the results shown in Fig. 13 thank to introducing heuristic information in Model II. For better comparisons of Model I and Model II for estimating sensor locations in two dimensional scenario, the Root-mean-square error *E*1 defined as *E*1 = ∑*n i*=1 ||*x*ˆ*i*−*xi*||2 *n* and the maximum absolute error *E*2 defined as *E*2 = max*i*=1,2,...,*n*{||*x*ˆ*i* − *xi*||}, both of which are the same as the definition in the one dimensional case, are used to evaluate the estimation error. Ten independent simulations with random initializations are performed and the estimation errors are calculated for all runs. As shown in Table 2, the error *E*1 is around 20 and the error *E*2 is around *r*0 for Model I with the simulation setup. In contrast, the estimation errors for Model II are about 10 for *E*1 and 20 for *E*2 in the ten simulation runs, which are much lower than the results obtained by Model I and again verifies the advantage of Model II in position estimation.

### **5.3. Discussions**

In the above two subsections, we considered the range-free localization problem in one dimensional case and two dimensional case respectively. In some applications of WSNs, higher dimensional cases [24] (see Fig. 15 for the sketch of a typical three dimensional one

12 Will-be-set-by-IN-TECH

0 0.5 1 1.5 2

time/s

**Figure 9.** Time history of the position estimation in x-direction by Model I in the two dimensional WSN

distribution in space and the inequality constraints imposed by communication connectivity. The final position estimations by Model I and Model II are shown in Fig. 13 and Fig. 14, respectively. It can be observed that the estimated results shown in both figures are within the area covered by the circle centered at the true position with a radius *R* = 50m, which verifies the effectiveness of Model I and Model II in modeling the communication connectivity constraint. On the other hand, it is clear that the estimation results shown in Fig. 14 outperforms the results shown in Fig. 13 thank to introducing heuristic information in Model II. For better comparisons of Model I and Model II for estimating sensor locations in two

maximum absolute error *E*2 defined as *E*2 = max*i*=1,2,...,*n*{||*x*ˆ*i* − *xi*||}, both of which are the same as the definition in the one dimensional case, are used to evaluate the estimation error. Ten independent simulations with random initializations are performed and the estimation errors are calculated for all runs. As shown in Table 2, the error *E*1 is around 20 and the error *E*2 is around *r*0 for Model I with the simulation setup. In contrast, the estimation errors for Model II are about 10 for *E*1 and 20 for *E*2 in the ten simulation runs, which are much lower than the results obtained by Model I and again verifies the advantage of Model II in position

In the above two subsections, we considered the range-free localization problem in one dimensional case and two dimensional case respectively. In some applications of WSNs, higher dimensional cases [24] (see Fig. 15 for the sketch of a typical three dimensional one

dimensional scenario, the Root-mean-square error *E*1 defined as *E*1 =

−20

localization problem.

estimation.

**5.3. Discussions**

0

20

40

60

x/meters

80

100

120

140

x 10−5

∑*n*

*i*=1 ||*x*ˆ*i*−*xi*||2

*n* and the

**Figure 10.** Time history of the position estimation in y-direction by Model I in the two dimensional WSN localization problem.

Estimation Error of Model I Estimation Error of Model II

**Table 2.** Estimation errors for Model I and Model II in different simulation runs of the one dimensional localization problem.

**Figure 11.** Time history of the position estimation in x-direction by Model II in the two dimensional WSN localization problem.

**Figure 12.** Time history of the position estimation in y-direction by Model II in the two dimensional WSN localization problem.

14 Will-be-set-by-IN-TECH

0 0.5 1 1.5 2

time/s

0 0.5 1 1.5 2

time/s

**Figure 12.** Time history of the position estimation in y-direction by Model II in the two dimensional

**Figure 11.** Time history of the position estimation in x-direction by Model II in the two dimensional

−20

−20

WSN localization problem.

0

20

40

60

y/meters

80

100

120

140

WSN localization problem.

0

20

40

60

x/meters

80

100

120

140

x 10−5

x 10−5

**Figure 13.** Position estimation results by Model I in the two dimensional WSN localization problem.

**Figure 14.** Position estimation results by Model II in the two dimensional WSN localization problem.

**Figure 15.** A typical WSN in normalized three dimensional space.

after normalization along three axial directions), such as building monitoring [35], underwater acoustic sensor networks[2], may be encountered. For example, the localization problem of sensors for building monitoring is actually defined in a three dimensional space since sensors are deployed in two dimensions on each floor and the whole network constructed by sensors on different floors forms a three dimensional one. Also, sensors in the underwater acoustic sensor networks are often deployed at different depth, with different longitude and latitude and thus form a three dimensional sensor network.

The presented two models in this chapter admit the higher dimensional localization problems as we did not specify the number of dimensions in the problem formulation and the model works for all possible dimensions.

As demonstrated in simulations, Model II outperforms Model I in the sense of estimation error for the cases with the same simulation setup. However, it is notable that Model II requires an extra dynamic model for the initialization, which is at the cost of implementation complexity and longer computation time. Fortunately, the dynamic models can be implemented with either digital or analog devices and thus the computation can be completed in a very short time. For example, the simulation examples for the two dimensional localization problem showed that it takes a time of 10−5 level for the dynamic models to converge. As to the implementation complexity, more hardware devices, such as summators, multipliers, dividers, integrators, etc, are needed to fabricate the analog circuit of Model II than that of Model I.


**4.1 Copper as fungicide**

First uses of fungicide reach back 400 years ago where simple water solutions (salts) were frequently used for cereal seed treatments. Not earlier than at the beginning of 20th Century more complex organic chemical solutions were introduced (Bordeaux and Burgundy mixture etc.). Nowadays a considerable number of phytochemical companies offer numerous and different classes of fungicides, where copper fungicides according to their antifungal and antibacterial effects play an important role. The efficiency of copper fungicides, particularly aggressive in moist media, is caused by denaturation of protein structures (secondary and tertiary) of fungi and bacteria, and consequently the interruption of their functions. Copper forms compounds in the oxidation states of +1 (cuprous) and +2 (cupric), but trivalent copper in aqueous solution splits rapidly.

Classification of copper fungicide according to chemical structure:

#### **4.1.1 Copper sulphate (cupric sulphate)**

Copper(II) sulphate is a very versatile chemical compound with a wide use in industry but also in horticulture where it exists in a various forms that differs in degree of hydration. Cupric sulphate is the most frequently used form of Cu in pesticides against many diseases listed in Table 3. It is produced industrially by treating copper metal or its oxides with sulfuric acid, or with placing of copper in a solution of nitric or sulfuric acids as well as in water. The chemical form copper sulphate pentahydrate (CuSO4 x 5H2O), a fungicide, mixed with hydrate lime (Ca(OH)2), is called Bordeaux mixture or with sodium carbonate (Na2(CO)3) Burgundy mixture. Both Cu solutions are used more than 200 years yet, especially in fungi disease control in horticulture (vineyards, orchards etc.).

Bordeaux mixture is produced by dissolving copper sulphate and hydrated lime in water, mostly in a concentration of 1 %; in relation 1:1:100 means 1 kg of copper sulphate and 1 kg of hydrated lime is dissolved in 100 litres of water. Copper sulphate contains approx. 25 %

Copper plays an important role not only in pathogen controlling but also in soil fertilization, animal nutrition etc. Excessively long copper use in plant protection but also in terms of fertilisation in modern agriculture lead to copper accumulation in soils, where copper surplus is considered if the soils contain more than two parts per million available copper, bearing in mind the physiological and biochemical needs of the plants. On the other hand the same soil conditions in context of copper deficiency can also affect nutritional statuses of

First uses of fungicide reach back 400 years ago where simple water solutions (salts) were frequently used for cereal seed treatments. Not earlier than at the beginning of 20th Century more complex organic chemical solutions were introduced (Bordeaux and Burgundy mixture etc.). Nowadays a considerable number of phytochemical companies offer numerous and different classes of fungicides, where copper fungicides according to their antifungal and antibacterial effects play an important role. The efficiency of copper fungicides, particularly aggressive in moist media, is caused by denaturation of protein structures (secondary and tertiary) of fungi and bacteria, and consequently the interruption of their functions. Copper forms compounds in the oxidation states of +1 (cuprous) and +2

Copper(II) sulphate is a very versatile chemical compound with a wide use in industry but also in horticulture where it exists in a various forms that differs in degree of hydration. Cupric sulphate is the most frequently used form of Cu in pesticides against many diseases listed in Table 3. It is produced industrially by treating copper metal or its oxides with sulfuric acid, or with placing of copper in a solution of nitric or sulfuric acids as well as in water. The chemical form copper sulphate pentahydrate (CuSO4 x 5H2O), a fungicide, mixed with hydrate lime (Ca(OH)2), is called Bordeaux mixture or with sodium carbonate (Na2(CO)3) Burgundy mixture. Both Cu solutions are used more than 200 years yet,

Bordeaux mixture is produced by dissolving copper sulphate and hydrated lime in water, mostly in a concentration of 1 %; in relation 1:1:100 means 1 kg of copper sulphate and 1 kg of hydrated lime is dissolved in 100 litres of water. Copper sulphate contains approx. 25 %

especially in fungi disease control in horticulture (vineyards, orchards etc.).

animals and consequently their life expectancy.

**4.1.1 Copper sulphate (cupric sulphate)**

(cupric), but trivalent copper in aqueous solution splits rapidly. Classification of copper fungicide according to chemical structure:

**4.1 Copper as fungicide**

copper as metal, therefore the content of Cu in 1 % Bordeaux mixture varies between 0 and 25 %. On the other hand 1 % Burgundy mixture is made by dissolving 1 kg of copper sulphate in 50 litres of water and separately 125 kg washing soda (or 0.475 kg soda ash) in 50 litres of water. After that the soda solution has to be slowly added to the copper sulphate solution with stirring. Both mixtures have been effective in controlling most of the fungi diseases of plants, applied in a time span of 10 to 20 days, depending on fungi species, plant, weather conditions, age and phenological stage of plants etc. According to the manner of Cu use it is important that first spraying is done before the diseases is expected and continued at regular intervals throughout the susceptible period. The protective coating of Cu mixtures prevents spores germination and consequently their damages caused by penetration through tissues of unprotected parts of plants. An effective spraying scheme has to ensure permanent copper coating over susceptible plant parts, what demands accuracy of the timing of the spraying, especially at precipitation (> 30 mm) and at intensive fruit growing (fruit enlargement).

  List of diseases and pathogens at some horticultural plants suitable to control with copper fungicides

Copper in Horticulture 271

a black-brown crystal or amorphous powder. It is used in making fibres and ceramics and for welding fluxes. Cu2O presents one of the principal oxides of copper. The most common preparation of cuprous oxide is by oxidation of copper metal 4 Cu + O2 → 2 Cu2O; 2 Cu + O2 → 2 CuO, commercially however also by reduction of copper(II) solutions with sulfur dioxide, the final product of which is reddish mineral cuprite. Cuprous oxide is commonly used as a pigment (colouring of porcelain and glass), a fungicide (seed dressings) and an antirust protection agent for marine paints. Available on the market are copper(II)

Synonyms of cuprous oxide are yellow cuprocide; red copper oxide; dicopper monoxide; dicopper oxide; brown copper oxide; copper hemioxide; Copper nordox; copper protoxide; copper suboxide; cuprite; cuprocide; fungimar; dikupferoxid (German); óxido de dicobre

Copper(II) chloride is a light brown solid chemical compound with the formula CuCl2 and has the potential of slowly absorbing moisture and forming a blue-green dihydrate., The aqueous solution prepared from copper(II) chloride contains a range of copper(II) complexes depending on concentration, temperature, and the presence of additional chloride ions. Copper(II) chloride occurs as a very rare mineral in nature, tolbachite and the dihydrate eriochalcite, more common however are mixed oxyhydroxide-chlorides, like atacamite Cu2(OH)3Cl. There are few preparations of cupric chloride known, used as

CuCl2 is a yellowish to brown, deliquescent powder soluble in water, alcohol and ammonium chloride, used as a mordant in dyeing and printing textiles. CuCl consists of fine grey-black pearls with size of a few hundred µm and a copper content of 64 %. Copper(II) chloride dihydrate (CuCl2 x 2H2O) is built up of blue-green crystals, soluble in water and has a copper content of approx. 37 %. Anhydrous copper(II) chloride is a brown crystal powder, soluble in water and highly hygroscopic, with a copper content of approx. 47 %. Synonyms of cupric chloride; copper(II) chloride; dichlorocopper, Kupferdichlorid

Copper(I) chloride, known also as lower chloride of copper with the formula CuCl (Mr = 99.03 g mol-1) or Cu2Cl2 (Mr = 198.05 g mol-1), is a white solid substance partially soluble in water, but totally in concentrated hydrochloric acid. In middle of 17th century cuprous chloride was first produced by Robert Boyle from mercury(II) chloride and metal Cu: HgCl2 + 2 Cu → 2 CuCl + Hg. Later Proust J.L. prepared CuCl by heating CuCl2 at red heat in absence of air, causing it to lose half of its combined chlorine, followed by removing residual CuCl2 by rinsing with water, and by the application which was widely used for heating and lighting. During the 19th and early 20th Centuries the acidic solution of CuCl

oxides with a copper content of around 78 %.

(Spanish); oxyde de dicuivre (French).

fungicide in agriculture as well:

**4.1.5 Cuprous chloride**

a. chlorination of copper: Cu + Cl2 + 2 H2O → CuCl2(H2O)2,

(German); dicloruro de cobre (Spanish); dichlorure de cuivre (French).

b. treatment of Cu hydroxide, oxide or Cu(II) carbonate with hydrochloric acid, c. anhydrous CuCl2 prepared directly by the union of copper and chlorine and d. by crystallization of torrid dilute hydrochloric acid, cooling in CaCl2-ice bath.

**4.1.4 Cupric chloride**

#### **4.1.2 Copper acetates**

Copper(II) acetate is also known as cupric acetate with the chemical formula Cu(OAc)2, where OAc- stands for acetate (CH3CO2 -). Since ancient times copper acetates of some form have been used as fungicides (greenish colour), whereas nowadays more frequently as reagents for syntheses of many inorganic and organic compounds. Copper acetate is odourless and efflorescent. It is soluble in alcohol and slightly soluble in ether and glycerol. The commercially available copper acetates are these in hydrated forms (Cu(C2H3O2)2 H2O); Mr = 199.67 g mol-1) which contain one molecule of water for each atom of Cu. The production of copper acetates first appeared in France, where the term of "verdigris" (*verte grez*) was applied. At grape remaking and wine production copper metal sheets were used, and when the surface of copper sheets exposed to the air for some days, they had become coated with verdigris, which were removed, and then replaced. The obtained product was known as blue verdigris and in a concentration of 1 kg of basic copper acetate in 500 litres of water; it was used as a fungicide. It contains approx. 32 % of copper and crystallises with 1 mol crystal water. Nowadays it is prepared by dissolving cupric oxide or a copper carbonate in acetic acid; however it can be produced also by treating a copper sulphate solution with lead acetate or by the reaction of acetic acid and copper with subsequent crystallization.

Until the 19th century, verdigris coincidentally happened to be the most vibrant green pigment available and was therefore frequently used in painting. Copper acetates of different chemical compositions make up pigments, colours from green to green-blue, bluegreen, and finally blue. Reactions with copper acetate vary among substances such as the following: copper acetates dissolve in mineral acid, alkalis convert them into blue copper hydroxide, oils, resins and proteins react to form green transparent copper oleates, resinates, and proteinates.

Neutral Verdigris is neutral copper acetate which occurs when basic acetates are dissolved in acetic acid, or when basic verdigris is ground up with strong acidic acid. Basic verdigris forms from the combination of air, water vapour, acetic acid vapour, and copper or copper alloy mix. It forms a solid of blue, or blue-green material. It is often made up of fine needles. The chemical formula for basic verdigris can include all of the following (Kühn, 1997):

> [Cu(CH3COO2]2. Cu(OH)2. 5H2O (blue) Cu(CH3COO)2. Cu(OH)2. 5H2O (blue) Cu(CH3COO)2. [Cu(OH)2]2 (blue) Cu(CH3COO)2. [Cu(OH)2]3. 2H2O (green)

Synonyms of cupric acetate are acetate cuivrique (DOT French); acetato cuprico (DOT Spanish); acetic acid cupric salt; acetic acid, cupric salt; copper acetate; copper acetate (Cu(C2H3O2)2); copper acetate (Cu (MCO2)2); copper acetate (Cu(OAc)2); copper diacetate; copper (II) acetate; copper (II) diacetate; copper (II) acetate; crystallized verdigris; crystals of venus; cupric acetate monoydrate; cupric diacetate; neytral verdigris; and venus copper.

#### **4.1.3 Cuprous oxide**

Cuprous oxide is an inorganic compound of two known forms: copper(I) oxide (Cu2O; Mr = 143.08 g mol-1), in nature present as cuprite, dark red crystals or granular powder, formed on copper by heat, and cupric oxide copper(II) oxide (CuO; Mr = 79.55 g mol-1),

Copper(II) acetate is also known as cupric acetate with the chemical formula Cu(OAc)2,

have been used as fungicides (greenish colour), whereas nowadays more frequently as reagents for syntheses of many inorganic and organic compounds. Copper acetate is odourless and efflorescent. It is soluble in alcohol and slightly soluble in ether and glycerol. The commercially available copper acetates are these in hydrated forms (Cu(C2H3O2)2 H2O); Mr = 199.67 g mol-1) which contain one molecule of water for each atom of Cu. The production of copper acetates first appeared in France, where the term of "verdigris" (*verte grez*) was applied. At grape remaking and wine production copper metal sheets were used, and when the surface of copper sheets exposed to the air for some days, they had become coated with verdigris, which were removed, and then replaced. The obtained product was known as blue verdigris and in a concentration of 1 kg of basic copper acetate in 500 litres of water; it was used as a fungicide. It contains approx. 32 % of copper and crystallises with 1 mol crystal water. Nowadays it is prepared by dissolving cupric oxide or a copper carbonate in acetic acid; however it can be produced also by treating a copper sulphate solution with lead acetate or by the reaction of acetic acid and copper with subsequent crystallization.

Until the 19th century, verdigris coincidentally happened to be the most vibrant green pigment available and was therefore frequently used in painting. Copper acetates of different chemical compositions make up pigments, colours from green to green-blue, bluegreen, and finally blue. Reactions with copper acetate vary among substances such as the following: copper acetates dissolve in mineral acid, alkalis convert them into blue copper hydroxide, oils, resins and proteins react to form green transparent copper oleates, resinates,

Neutral Verdigris is neutral copper acetate which occurs when basic acetates are dissolved in acetic acid, or when basic verdigris is ground up with strong acidic acid. Basic verdigris forms from the combination of air, water vapour, acetic acid vapour, and copper or copper alloy mix. It forms a solid of blue, or blue-green material. It is often made up of fine needles. The chemical formula for basic verdigris can include all of the following (Kühn, 1997):

[Cu(CH3COO2]2. Cu(OH)2. 5H2O (blue) Cu(CH3COO)2. Cu(OH)2. 5H2O (blue) Cu(CH3COO)2. [Cu(OH)2]2 (blue) Cu(CH3COO)2. [Cu(OH)2]3. 2H2O (green) Synonyms of cupric acetate are acetate cuivrique (DOT French); acetato cuprico (DOT Spanish); acetic acid cupric salt; acetic acid, cupric salt; copper acetate; copper acetate (Cu(C2H3O2)2); copper acetate (Cu (MCO2)2); copper acetate (Cu(OAc)2); copper diacetate; copper (II) acetate; copper (II) diacetate; copper (II) acetate; crystallized verdigris; crystals of venus; cupric acetate monoydrate; cupric diacetate; neytral verdigris; and venus copper.

Cuprous oxide is an inorganic compound of two known forms: copper(I) oxide (Cu2O; Mr = 143.08 g mol-1), in nature present as cuprite, dark red crystals or granular powder, formed on copper by heat, and cupric oxide copper(II) oxide (CuO; Mr = 79.55 g mol-1),

**4.1.2 Copper acetates**

and proteinates.

**4.1.3 Cuprous oxide**

where OAc- stands for acetate (CH3CO2

a black-brown crystal or amorphous powder. It is used in making fibres and ceramics and for welding fluxes. Cu2O presents one of the principal oxides of copper. The most common preparation of cuprous oxide is by oxidation of copper metal 4 Cu + O2 → 2 Cu2O; 2 Cu + O2 → 2 CuO, commercially however also by reduction of copper(II) solutions with sulfur dioxide, the final product of which is reddish mineral cuprite. Cuprous oxide is commonly used as a pigment (colouring of porcelain and glass), a fungicide (seed dressings) and an antirust protection agent for marine paints. Available on the market are copper(II) oxides with a copper content of around 78 %.

Synonyms of cuprous oxide are yellow cuprocide; red copper oxide; dicopper monoxide; dicopper oxide; brown copper oxide; copper hemioxide; Copper nordox; copper protoxide; copper suboxide; cuprite; cuprocide; fungimar; dikupferoxid (German); óxido de dicobre (Spanish); oxyde de dicuivre (French).


**Author details**

Juraj Jablonický1 \*, Danela Uhrínová1 , Juraj Tulík1 and Ján Polerecký2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Transport and Handling, Faculty of Engineering, Slovak University of Agriculture in Nitra, Slovak Republic

2 S-EKA Ltd. is a Designated Service (Technical Service) for the Emission Control of Road Motor Vehicles, Nitra, Slovak Republic


**5. Discussion**

The criteria for using suits designed specifically for a person with scoliosis is like the use of rigid bracing, in that best outcomes are achieved in early intervention when children have good spinal mobility. When used in older adolescents and adults, it is sensible to encourage the patient to complete a course of physiotherapeutic scoliosis specific exercises (PSSE), which includes auto-correction in 3D, training in activities of daily living (ADL), stabilising the corrected posture and patient education. These exercise protocols have level II evidence, both for independent use and in conjunction with spinal bracing in patients with curves less than 45° Cobb angles and are also suggested prior to surgery to help improve outcomes [9].

In a testimonial, one patient reported improved balance, upright standing and patient confidence. The use of PSSE, combined with an enhanced power scoliosis DEFO suit provided a mechanism to ensure maximum vertebral movement, combined with the unique proprioceptive and long term low level force, known to be the most effective for muscle stretching [46]. When the suit was first applied the patient reported loss of balance, which indicated that the suit was having the desired effect and her mother reported a reduction in the pain she was experiencing [52]. After 3 years, the suit reduced the scoliosis progression and scoliosis surgery at the end of DEFO treatment was no longer required (**Figure 16**).

**Figure 16.** *Sixteen-year-old lady in structural scoliosis suit.*

Improvements in walking have shown that improved proprioception and provision of a flexible supportive exoskeleton can improve image of self, reduced hip sway, hip force closure and to reduce the known mechanisms of increased lordosis to lock the spine, enabling smother movement [33, 40, 53]. People with scoliosis can learn a new sitting position by the mechanism of continual motor learning carried on throughout life. This can be initiated by external forces including poor sitting position, spinal injuries, strokes as well as left or right-handed wheelchair controls. The use of dynamic orthoses, therefore, have an important role to play in centralising the spine without the use of rigid force.

The mechanisms utilised in DEFOs dynamic orthotics became more objective than subjective, with more research and reports of use. It is important to understand the mechanism for the changes observed in the patients and their reaction to the intervention. For a number of years, the outcomes detected were put down to improved patient stability from the compressive forces on the hip and shoulder, coupled with the distraction of the shoulders as seen in proprioceptive neuromuscular facilitation (PNF) [54]. There is evidence that PNF on the trunk can reduce chronic low back pain in adult women, if applied in the sitting position [55]. There are two commonly used exercises. Rhythmic stabilisation training (RST) which uses isotonic (muscle working within normal contraction range) contraction of the agonistic (opposing) muscle patterns which results in co-contraction of the antagonists. The isometric contraction of the muscles is provoked into working by pushing or pulling an immovable object. This prevents muscles shortening in length and improves fitness and builds up strength. It is used where weakness is a primary factor. The combination of isotonic exercises (COI) is used to evaluate and develop the ability to carry out purposeful and controlled movements, involving alternating concentric (circular), eccentric (non-circular) and isometric movement to treat strength deficiency and range of motion. Four weeks of RST and COI proprioceptive neuromuscular facilitation have shown increased muscle endurance, decrease in back pain intensity, as well as improved functional ability.

There are several different mechanisms that appear to be working together to provide the DEFO's positive reported outcomes and therefore it is important to understand the different concepts of treatment. The orthoses initiate change where the effect is likely to be constrained by the linear range of elasticity of the fabric or "dynamic" [56]. The fabrics are designed to grip the skin while allowing air and moisture through without slippage. A DEFO uses these properties by griping onto the skin and therefore transfers the torsional resistance from the different reinforced fabric layers, which are designed to have different linear orientations. These forces are conveyed through the skin and soft tissue directly to the muscles and skeleton beneath with set movements expectations. For instance, if there is a long spinal curve to the right, set pattern options are applied to the left side of the trunk to resist stretch, therefore providing a stiffer area in the suit resulting with less of a lean to the right.

It is understood that slight compression of the trunk rather than distraction (as used in rigid and semi rigid spinal orthoses) can provide a stabilising effect on low core stability in children presenting with cerebral palsy. The same effect can be initiated in adults.

New innovations are signaling that the use of textile fabric materials can have positive spinal correction. Computer modeling has been developed to geometrically model scoliosis through finite element modeling (FEM). It is a numerical technique used to perform finite element analysis (FEA) of any given phenomenon (https:// www.simscale.com/blog/2016/10/what-is-finite-element-method/)*.* The use of 2D x-ray clinical data on appropriate textile materials, measuring physical and mechanical properties were used to determine the performance of the textile brace in terms of Cobb angle through FEM simulation. The results showed that textile

### *The Use of a Dynamic Elastomeric Fabric Orthotic Intervention in Adolescents and Adults… DOI: http://dx.doi.org/10.5772/intechopen.96391*

materials with banded parallel fabric layers provide good softness and air permeability (key for compliance in hot climates). There was high capacity to provide a 14.4% Cobb angle improvement, when used on a teenage patient with adolescent idiopathic scoliosis presenting with a 62° typical double curve [57]. This provides some medical and biological engineering evidence of the orthotic effect.

This study also highlighted the need for cosmetic appearance and the effects on patient compliance. Rigid braces have been proven to be effective in reducing scoliosis progression [4], however there have been issues with compliance particularly from discomfort caused by the brace and psychological stress from the visual impact. Patient involvement in brace design and aesthetics have shown improved compliance [58], so the use of elastomeric fabrics will further enhance this patient based involvement and provide scoliosis suits which are more comfortable in both adolescents and adults alike thereby improving activities of daily life.

There is evidence for the use of DEFO scoliosis management in neuropathic onset scoliosis and early indicators that the same orthoses can be effective in adolescent idiopathic scoliosis. The key thing to remember is the need for flexibility of the spine to be able to use dynamic movement to facilitate a spinal scoliosis angle reduction, vertebral rotational control and improved cosmetic outcome. The most effective treatment plan would be to combine PSSE with the use of scoliosis specific DEFO suits to start the treatment early in the onset rather than wait for the 25° Cobb angle starting point, that has historically been suggested. This highlights the need for early intervention and further research on this specific scoliosis presentation.


*2.2.1 Cyanine dyes*

Cyanine dyes are found to be important functional dyes due to their typical optical properties, and act as sensitizers in solar cells, photography, and laser discs [30]. A significant property of cyanine dyes is the affinity for biological structures, specifically for DNA, and possesses wide color change, high photostability and increased fluorescent intensity when bound to biological structures [31]. Due to high fluorescence quantum yields and high molar extinction coefficients, they have been extensively used in cell imaging and gel staining techniques. Typically, cyanine dyes are obtained by heating a mixture of substituted quaternary salts with bisaldehyde or bis-imine. Accordingly, a series of cyanine dyes **43a-g** were synthesized by the condensation of quaternary salts of quinoline derivatives with 1*H*-indole-3-carbaldehydes in the presence of piperidine under solvent-free microwave irradiation at 126–329 W in 89–98% yields in only 2–5 min. The fluorescence spectra of the dyes showed absorption maxima (λabs) at 453–471 nm. However, in the presence of DNA, a

bathochromic shift (red shift) at 483–499 nm was observed. Further, the living cell imaging experiments of the dyes **43b**, **43e**, and **44** have shown preferable staining of the head of the human sperm containing the nuclear DNA. Also, the motility of the sperm didn't slow down which indicated low cell cytotoxicity. Hence, these dyes could be used as potential fluorescent probes for labeling DNA to measure human sperm viability [31]. Likewise, the condensation of benzothiazole with quarternary salts of quinoline which upon coupling with the tertiary diamine linkers gave tetracationic analogous (bis-intercalators) of monomethine cyanine dyes **45**. The steady-state fluorescence spectral studies of **45** revealed greater labeling affinity toward DNA and proved for singlet oxygen sensitization property, and found to be a potential candidate for photodynamic therapy [32].

#### *2.2.2 Naphthalimide dyes*

1,8-Naphthalimide dyes are proved to be important fluorescent compounds due to their greater photostability and high fluorescent quantum yield. The basic spectral properties of these dyes depend on the polarization of naphthalimide molecule as a result of electron donor-acceptor interaction occurring between the substituents at the C-4 position and the carbonyl groups of the imide ring. Generally, 1,8-naphthalimide dyes are prepared *via* the substitution reaction of naphthalimides with various nucleophiles. The aromatic nucleophilic substitution reaction of 4-bromo-*N*-alkylnaphthalimides with amines, alkoxides, and thiols in the presence of KF/Al2O3 under solvent-free microwave irradiation yielded corresponding fluorescent dyes **46–48** which exhibited increased fluorescent intensity in the polar solvents [33]. Similarly, the derivatives **49** and **50** were obtained by the substitution reaction of 1,8-naphthalimides with primary amines. These dyes were further evaluated for the free radical scavenging properties against 2,2-diphenyl-1-picrylhydrazyl (DPPH). The results showed IC50 values at lower concentrations than the common synthetic antioxidant 2,6-ditertiarybutyl-4-methylphenol (BHT) [34].

#### *2.2.3 Coumarin dyes*

Coumarin dyes have been found commercial significance due to their intense fluorescence and are widely employed as fluorescent brighteners [35]. A one-pot

**67**

*Microwave Synthesized Functional Dyes DOI: http://dx.doi.org/10.5772/intechopen.94946*

microwave promoted synthesis of benzimidazol/benzoxazol functionalized coumarin dyes (**51a-e**) was developed which involved the reaction of 4-diethylam

ino-2-hydroxybenzaldehyde, diethylmalonate, and *o*-phenylenediamine/ *o*-hydroxyaniline in *n*-pentanol within 3 min. The synthesized dyes showed higher fluorescence emission intensity and the dyes **51a-c** were further investigated for the effects of ink media on the fluorescence properties. Dye **51b** has exhibited an intense green fluorescence at 531 nm for mixed very long alkyl resin and maleic varnish in 60:40 ratios at 1% (w/w) concentration, and the fluorescence emission intensity of the dye reduced by 11% after 30 h of exposure to light. As a result, the dye **51b** could potentially be used in security offset ink [36]. Similarly, one-pot three-component microwave-assisted reaction of 7-diethylamino-coumarin ethylidene malononitrile, aromatic aldehydes and malononitrile to get highly fluorescent 3,5-disubstituted-2,6-dicyanoaniline coumarin dyes **52a-d** at 80°C in good yields in 2 min are prepared. The optical and thermal screening studies of **52a-d** exhibited excellent photophysical and thermal stability properties [37]. A group of 8-aza-7-hydroxy-4-methylcoumarin dye **53** was synthesized by reacting 2,6-dihydroxypyridine hydrochloride with ethylacetoacetate in the presence of magnesium bromide as a Lewis acid catalyst. This fluorophore is adequately soluble in water and has a high fluorescent quantum yield and showed increased fluorescence in protic solvents at neutral pH, which could be useful in biosensors that are required for finding biologically active compounds [38]. Furthermore, a microwave-assisted Knoevenagel condensation of salicylaldehyde and cyano-N′-methyleneacetohydrazide in the presence of piperidine catalyst gave 3-carbohydrazide coumarin fluorescent dye (**54**) which could be

used to print polyester and polyamide fabrics [35].

Benzimidazole dyes are known to exhibit photophysical, photovoltaic, and optical properties [39]. An approach has been made to synthesize benzimidazoquinolines **55a-c**, substituted with piperidine, pyrrolidine, and piperazine moieties by uncatalyzed amination protocol under microwave heating in relatively high yields (56–90%), which by conventional heating after several days gave **55a-c** only in low yields (<10%). The emission spectra of **55a-c** showed an increase in the fluorescence intensity when interacted with the calf thymus DNA (*ct*-DNA) [40]. The microwave promoted synthesis of bisbenzimidazolyl derivatives upon *N*-alkylation gave water-soluble fluorescent dyes **56a-b**. These dyes proved to be highly selective fluorescent probe toward Zn2+ in aqueous solution and the mixture of dye-Zn2+ could detect picric acid by fluorescence quenching [41]. Under solvent-free microwave irradiation, a series of 2-substituted styryl benzimidazole dyes **57a-g** and **58a-f** were prepared by the condensation of 2-alkyl benzimidazoles with aromatic aldehydes in the

*2.2.4 Benzimidazole dyes*

presence of acetic anhydride [42].

#### *Microwave Synthesized Functional Dyes DOI: http://dx.doi.org/10.5772/intechopen.94946*

*Microwave Heating - Electromagnetic Fields Causing Thermal and Non-Thermal Effects*

bathochromic shift (red shift) at 483–499 nm was observed. Further, the living cell imaging experiments of the dyes **43b**, **43e**, and **44** have shown preferable staining of the head of the human sperm containing the nuclear DNA. Also, the motility of the sperm didn't slow down which indicated low cell cytotoxicity. Hence, these dyes could be used as potential fluorescent probes for labeling DNA to measure human sperm viability [31]. Likewise, the condensation of benzothiazole with quarternary salts of quinoline which upon coupling with the tertiary diamine linkers gave tetracationic analogous (bis-intercalators) of monomethine cyanine dyes **45**. The steady-state fluorescence spectral studies of **45** revealed greater labeling affinity toward DNA and proved for singlet oxygen sensitization property, and found to be a potential candidate for photodynamic

1,8-Naphthalimide dyes are proved to be important fluorescent compounds due

Coumarin dyes have been found commercial significance due to their intense fluorescence and are widely employed as fluorescent brighteners [35]. A one-pot

to their greater photostability and high fluorescent quantum yield. The basic spectral properties of these dyes depend on the polarization of naphthalimide molecule as a result of electron donor-acceptor interaction occurring between the substituents at the C-4 position and the carbonyl groups of the imide ring. Generally, 1,8-naphthalimide dyes are prepared *via* the substitution reaction of naphthalimides with various nucleophiles. The aromatic nucleophilic substitution reaction of 4-bromo-*N*-alkylnaphthalimides with amines, alkoxides, and thiols in the presence of KF/Al2O3 under solvent-free microwave irradiation yielded corresponding fluorescent dyes **46–48** which exhibited increased fluorescent intensity in the polar solvents [33]. Similarly, the derivatives **49** and **50** were obtained by the substitution reaction of 1,8-naphthalimides with primary amines. These dyes were further evaluated for the free radical scavenging properties against 2,2-diphenyl-1-picrylhydrazyl (DPPH). The results showed IC50 values at lower concentrations than the common synthetic antioxidant 2,6-ditertiarybutyl-4-methylphenol

**66**

therapy [32].

(BHT) [34].

*2.2.3 Coumarin dyes*

*2.2.2 Naphthalimide dyes*

microwave promoted synthesis of benzimidazol/benzoxazol functionalized coumarin dyes (**51a-e**) was developed which involved the reaction of 4-diethylam ino-2-hydroxybenzaldehyde, diethylmalonate, and *o*-phenylenediamine/ *o*-hydroxyaniline in *n*-pentanol within 3 min. The synthesized dyes showed higher fluorescence emission intensity and the dyes **51a-c** were further investigated for the effects of ink media on the fluorescence properties. Dye **51b** has exhibited an intense green fluorescence at 531 nm for mixed very long alkyl resin and maleic varnish in 60:40 ratios at 1% (w/w) concentration, and the fluorescence emission intensity of the dye reduced by 11% after 30 h of exposure to light. As a result, the dye **51b** could potentially be used in security offset ink [36]. Similarly, one-pot three-component microwave-assisted reaction of 7-diethylamino-coumarin ethylidene malononitrile, aromatic aldehydes and malononitrile to get highly fluorescent 3,5-disubstituted-2,6-dicyanoaniline coumarin dyes **52a-d** at 80°C in good yields in 2 min are prepared. The optical and thermal screening studies of **52a-d** exhibited excellent photophysical and thermal stability properties [37]. A group of 8-aza-7-hydroxy-4-methylcoumarin dye **53** was synthesized by reacting 2,6-dihydroxypyridine hydrochloride with ethylacetoacetate in the presence of magnesium bromide as a Lewis acid catalyst. This fluorophore is adequately soluble in water and has a high fluorescent quantum yield and showed increased fluorescence in protic solvents at neutral pH, which could be useful in biosensors that are required for finding biologically active compounds [38]. Furthermore, a microwave-assisted Knoevenagel condensation of salicylaldehyde and cyano-N′-methyleneacetohydrazide in the presence of piperidine catalyst gave 3-carbohydrazide coumarin fluorescent dye (**54**) which could be used to print polyester and polyamide fabrics [35].


**6. Conclusions and outlook**

Membrane-based separations have emerged as an economically favorable alternative due to its small footprint, reduced capital, and energetic cost. Membranes for C3+ hydrocarbon recovery from natural gas have been practiced in the chemical and petrochemical industries, the improved C3+ hydrocarbon separation and removal process from natural gas will be beneficial of these industries.

This chapter summarizes C3+ hydrocarbon separation and removal from natural gas using polymeric membrane-based technologies. More specifically, it addresses the removal of C3H8 and C4H10 from CH4 by perspective polymer architectures based on reverse-selective glassy polymers with stiff chains and rubbery polymers with flexible chains. At present PDMS, POMS and poly(ether-*b*-amide) rubbery polymer membranes are used by industries in the field for C3+/CH4 separation. However, improved mixed gas selectivity of C3+/C1 with high flux will have a positive impact of the economics of the C3+ hydrocarbon recovery from natural gas. In case of reverse selective glassy polymers, membrane separation performance can be improved by introducing new groups or additives into membrane matrix, however, the loss of permeances due to physical aging restrains their applications in industry. This chapter also accounts the effect of the chemical structure of rubbery polymers on their permeation properties for C3+ hydrocarbons recovery from natural gas. Modified siloxane rubbery polymers-based membranes may be seen in future in the field for enhanced C3+ hydrocarbons recovery from natural gas since these membrane materials display improved mixed gas C3+/CH4 selectivity than


3.1.3. Blue Dot Network

In November 2019, the US, Australia, and Japan came together to establish what is now known as a trilateral BDN to help develop and promote quality infrastructure in the Indo-Pacific region and around the world. Focusing on transparency and sustainability, the BDN aims to set a standard of excellence in infrastructure development. Hansbrough [19] argued that the BDN is primarily a vision of what global infrastructure should look like. In the eyes of many observers, the BDN is also seen as an alternative to China's BRI, or a counter to the rising debt traps and low-quality infrastructure that boost quantitative and nontransparent aspects of the projects see, for example, [12,20–23].

According to the US Department of State [24], the BDN is a multistakeholder initiative seeking to bring together governments, the private sector, and civil society to encourage the adoption of trusted standards for quality global infrastructure development in an open and inclusive framework. It also encourages responsible construction and lending practices through international norms. Infrastructure projects have to follow the G20 Principles for Quality Infrastructure Investment, aimed at sustainable lending and borrowing; the G7 Charlevoix Commitment on Innovative Financing for Development; and the Equator Principles, which mandate financial institutions to assess and manage environmental and social risks in a given project. Projects that aim for certification under the BDN will have to give an undertaking that they adhere to these principles. The undertaking will then be scrutinized. Certification by the BDN means that a project is high-quality and has transparent origins, much like an 'organic' label for produce. Likewise, a country that agrees to follow BDN standards signifies that its government values high-quality infrastructure that benefits local communities.

The BDN plans to certify projects around the world (whose investment totals an estimated USD 94 trillion) that meet high-quality infrastructure standard over the next two decades [25]. This will meet the projected infrastructure investment need identified by ADB (2017) up to 2040 [4]. In Asia alone, the investment will require approximately USD 26 trillion from 2016 to 2030, or USD 1.7 trillion per year, if the region is to maintain its growth momentum, eradicate poverty, and respond to climate change [26].

The BDN looks promising for the Mekong Subregion and for the world, as it seeks to build the robust, resilient infrastructure essential to a country's growth and its people's wellbeing [27]. But this remains to be seen. The initiative has not been fully defined and project financing facilities are amongst the many details that have to be clarified [22,25].


**4. Co-expression of other mutant genes with FLT 3/ITD & their predictive value**

The duplicated areas are inconsistent in size (between 3 and 400 bp) and also in location, but the resulting product is always rich in tyrosine residues. It is hypothesized that modification of the length of the JM domain, rather than increase of tyrosine chain, causes enhancement of function of FLT3. Furthermore, an activating point mutation has been identified in the JM domain in immature leukemia cell. Mono Mac 1 and Mono Mac6 causes substitution of valine by alanine at position valine 592 (V592A) [62], but this mutation has not yet been reported in

A different group of genetic makeup has been described in the activation loop within the second tyrosine kinase domain of the FLT3 receptor that normally inhibits the binding of adenosine triphosphate and substrate to the kinase domain when the receptor is in a dormant state. Frequently, these involve mutations at aspartic acid 835 (D835) or isoleucine 836 (1836)

FLT3-ITD mutations are present in 20–25% of adult patients with AML and correlate with higher WBC count at diagnosis, increased relapse risk, and poor prognosis*.* Moreover, simultaneous loss of the wild-type FLT3 allele is associated with significantly inferior outcome and decreased overall survival in FLT3-ITD patients*.* In pediatric AML patients, FLT3-ITD is detected in 11–16% of cases and linked to worse prognosis [30, 63–65]*.* FLT3-ITD mutations more frequently occur in acute prolymphocytic leukemia (30–39%) containing t(15;17), which produces the PML-RARα oncogene. FLT3-ITD receptors are characterized by ligand-independent receptor dimerization and phosphorylation, but the accurate mechanism of this mutation binding and how it causes constitutive kinase activity has not yet been fully elucidated. Both lengthening as well as

Structural analysis of the EphB2 RTK, it described that JM domain takes up a z-helical conformation, which inhibits the activation of kinase and also self-dimerization. FLT3-ITD would affect the hindrance of kinase domain and, with the help of JM domain, would produce auto phosphorylation. In further studies of HSCT bone marrow cells in mice which done by retro virally transduced with FLT3-ITD, which produced oligo clonal band of MPD due to onco-

Activating TKD mutations are noticed in 7–14% of adults with AML, but D835 has no significant correlation with poor outcome. In pediatric group this mutation was observed 3–8%. Clustering algorithms has identified that childhood acute leukemias carrying rearrangements of MLL gene on chromosome 11q23 show overexpression of wild-type FLT3 mRNA easily distinguishing them from conventional pre-B ALL and AML. Interestingly, FLT3-TKD deletions involving codons D835 and I836 were identified more frequently with MLL gene [67, 68]. Patient with

Like FLT3-ITD receptors, FLT3-TKD mutations stimulate ligand-independent receptor activation and promote growth factor independence in 32D cells. However, it is under evaluation

these activating FLT3-TKD alterations expresses higher levels of FLT3 transcripts [69].

shortening of the JM domain result in activation of FLT3 receptor [65]*.*

genic potential of FLT3 ITD mutation [66].

**3.11. TKD mutations**

primary AML blasts.

in exon 20.

72 Myeloid Leukemia

**3.10. FLT3-ITD**

#### **4.1. Prognostic and predictive value of the NPM1, FLT3, and CEBPA genotypes**

NPM1 mutation or combined genotype NPM1mut/FLT3-ITDneg is reported as a favorable prognostic marker for attainment of a complete remission after induction therapy [70–73]. Actually, no data is available for specific chemotherapy for NPM1mut [74].

In a more recent study, NPM1 was shown to act as a co-repressor in retinoic acid-associated transcriptional regulation in a manner such that during retinoic acid-induced cellular differentiation, activating protein transcription factor 2 (AP2) recruits NPM1 to the promoter of certain retinoic acid-responsive genes. The German-Austrian AML Study Group (AMLSG) reported favorable effect of ATRA if given with conventional chemotherapy on complete remission rate, event-free survival, and OS in elderly patients with (non- APL) AML1 [50]. This study finding was coinciding with retrospective data in which beneficial effect of ATRA was restricted to NPM1mut/FLT3-ITDneg patients [75]. So, the genotype NPM1mut/FLT3- ITDneg appears as a predictive genotypic marker for the valuable effect of ATRA in non-APL AML.

FLT3-ITD has been reported consistently as an unfavorable prognostic marker for RFS and OS. Whether other molecular markers, in particular NPM1mut, add to prognostication in FLT3-ITDpos, AML is unclear [76, 77]. It is reported in some studies that genotype NPM1mut/ FLT3-ITDpos shows more favorable prognosis compared with the genotype NPM1WT/FLT3- ITDpos; however, confirmation by other studies are due now [56]. More recent data give insight that outcome is also related to the concentration of the mutant allele and not just its mere presence [77]. However, if NPM1 mutation status was included to the prognostic model, the mutant wild-type ratio of FLT3-ITD was not an important prognostic factor. Currently in randomized multicenter phase III trial [Cancer and Leukemia Group B (CALGB) 10603; clinicaltrials.gov, NCT00651261] wild-type ratio (high versus low) is applied for midostaurin (PKC412) in young adult AML patients.

CEBPA mutations are another genetic abnormality that consistently associated with good prognosis, either in the subset of patients with intermediate-risk cytogenetics or with normal karyotypes [74]. In the context of other molecular markers, the mutated CEBPA alone retained its prognostic significance for RFS and OS; additional mutations did not affect outcome in the CEBPA mut subgroup. This needs validation. Actually, even in the largest cohort of patients analyzed so far in CN-AML, the sample size in the CEBPA mut subgroup was too low for meaningful analysis, in particular to compare the different post-remission strategies (chemotherapy versus autologous SCT versus allogeneic SCT) [74]. Therefore, the prognostic marker CEBPA mut cannot actually be used as a predictive marker.

**5.1. Induction therapy**

allogeneic HSCT [81].

**5.3. Response assessment**

not changed much over the years.

**5.2. Post-remission consolidation chemotherapy**

**5.4. Response assessment during follow-up period**

2 years and then every 3–6 months up to 5 years [77].

**5.5. Role of HSCT as a consolidation strategy**

individual trial design) no further treatment.

The primary objective of induction therapy is attainment of normal bone marrow function. The criteria of CR are a platelet count of >100 × 109/L, neutrophil count of >1 × 109/L and a bone marrow examination with <5% blasts. Patients with persistent >5% blasts in the bone marrow following induction chemotherapy have a poor overall survival (OS) [79, 80]. Despite multiple trials, the standard remission induction therapy consisting of three daily infusions of an anthracycline and cytarabine given as continuous infusion for 7 days (7 + 3 regimen) has

Genomics of Acute Myeloid Leukemia http://dx.doi.org/10.5772/intechopen.72757 75

After achieving complete remission(CR) after induction therapy, disease relapse is a certainly virtual. Median disease-free survival (DFS) in this circumstance is estimated at only 4–8 months. Options for post-remission consolidation therapy include high-dose chemotherapy

After conventional induction therapy with 3 days of an anthracycline and 7 days of cytarabine ("3 + 7") or other recommended regimens according to guidelines, response assessment is

Repeat bone marrow examination is recommended every 3 months for the first 2 years; in some cases, it continues every 6 months for the following 2–3 years. Most relapses occur within 1–3 years after the completion of treatment. Standardized schedule is necessary if MRD monitoring is advised. Blood counts should be repeated every 1–3 months for the initial

Prospective single institution studies comparing allogeneic HSCT as a consolidation treatment in the 1980s and the early 1990s showed lower relapse rates of 181 and improved DFS with allogeneic HSCT in AML patients in CR1, but none conclusively demonstrated a survival advantage [81]. Subsequently, six cooperative group trials prospectively addressed the role of HSCT in AML in CR1 in 1995 [91]. Patients with HLA-identical sibling donors were offered allogeneic transplantation ("Genetic randomization"). Remaining patients were randomized to autologous transplantation, intensive consolidation chemotherapy (ICC) or (depending on

Among these trials, the landmark European Organization for Research and Treatment of Cancer (EORTC)-Gruppo Italiano Malattie Ematologiche Maligne dell'Adulto (GIMEMA) trial showed superior 4-year leukemia-free survival (LFS) with allogeneic (55%) and autologous

commonly performed between day 21 and 28 after the start of therapy [81].

MLL partial tandem duplication (PTD) is exclusively found in normal karyotype (CN)-AML with an incidence reported from 5% to 11%. There are no clinical features differentiating MLL-PTD positive versus MLL wild-type patients [78]. Approximately 30–40% of MLL-PTDpositive patients consist of FLT3-ITD mutations, whereas combined existence of CEBPA with NPM1 mutations is rare. MLL-PTD is linked with shorter complete remission duration or worse RFS; however, in these studies, MLL-PTD did not show any effect on OS [77]. Recently, the CALGB reported relationship of MLL-PTD in young adults who received autologous SCT in the first complete remission. Clinical outcomes between the MLL-PTD-positive and the MLL wild-type groups were equivalent. WT1 mutations were reported in 10–12.6% in CN-AML. However, variable results have been mentioned about the prognostic significance of WT1 mutations. Both CALGB and MRC studies reported the prognostic impact of WT1 mutations in young adults with CN-AML. In both studies, patients with WT1 mutations were an independent adverse prognostic factor with inferior RFS and OS in multivariate analysis. This is in contrast to the findings of Gaidzik et al. who did not observe any decreased RFS and OS in relation with WT1 mutations on RFS and OS neither in univariate nor multivariate analysis. Of note, when performing exploratory subset analysis on FLT3-ITD samples, the WT1mut/FLT3-ITD pos genotype appeared to be associated with worse clinical course. One major difference between the three studies is different doses of cytarabine used. Cumulative dose of cytarabine was significantly higher in the trial reported by Gaidzik et al. (in preparation), suggesting that the negative impact ofWT1 mutations reported by others may be overcome by the use of high-dose cumulative cytarabine. On the basis of the current data, the prognostic impact of WT1 mutation remains unclear and its impact on treatment remains to be elucidated in future studies.

Although the majority of studied related to CN patients contained at least one of the already mentioned genetic alterations. In a study of AML done by one group, almost a quarter of patients did not have FLT3-ITD, FLT3-TKD, MLL-PTD, or mutations in the CEBPA or NPM1 genes [77]. Thus, it is likely that unidentified novel gene mutations and/or abnormal gene expression with prognostic significance will be discovered in the future. Expression of the meningioma 1 (MN1) gene might become such a novel prognostic factor. Same group in study reported also high expression of the MN1 gene related to inferior RFS and OS and a higher risk of relapse in CN aged 60 years or younger with de novo or secondary AML. This observation needs confirmation before implementation.


**Appendix A**

Reasons for using eConsulta

*Appendix A.1 Administrative*

	- - The patient provides the results of tests carried out in an external centre in order that they are recorded in their medical history.

	- - The patient communicates changes to their health related to an upcoming temporary disability.
	- - The GP tracks the progress of a temporary disability in conjunction with face-to-face visits.
	- - The patient has an enquiry which the GP thinks ought to be dealt with by a specialist and refers them. They can also report incidents resulting from any referrals made.
	- - The GP resolves incidents relating to the timing of visits.
	- - The GP cancels visits from other clinicians in cases in which the problem has been resolved following completion of the e-consultation.
	- - Validation of appointments with other specialists where the citizen needs more information about the motivation of the appointment.
	- -The patient asks for a report/sick-note while consulting their medical history.
	- - The GP asks the patient for more information in order to prepare the report.
	- - The patient asks for their prescription to be updated if it has been modified by an external specialist, either because they do not use it or because it has expired.
	- - The GP warns the patient that their prescription is about to expire and updates it.
	- - The GP cancels an unnecessary prescription following an e-consultation.


**1. Introduction**

Non-climatic stressors (e.g., demographic and income growth, demand for animal products) and climate change (CC) influence the food system. These climatic and nonclimatic stressors have an effect on the four pillars of food security (availability, access, use, and stability) [1]. From the beginning of the 1990s, the Intergovernmental Panel on Climate Change (IPCC), showed that over the period from 1850 to 2012, the global average temperature had experienced warming of 0.78 ◦C. This global warmingcould be induced by the change in the carbon dioxide (CO2) concentration in the atmosphere from 278 ppm to 379 ppm. The prediction for the end of the 21st century is a global warming that will range between 1.5 ◦C and 2 ◦C [2]. In Africa, in recent decades, temperatures have increased at a rate somewhat faster than the global average temperature. Thus, the 2019 year was identified as one of the three warmest years on this continent [3]. CC is a consequence of global warming, which has adverse effects on fluctuations in annual total precipitations, average temperature, global increase in atmospheric CO2, and sea-level rise. These are some of the major manifestations of CC, which have direct and indirect

**Citation:** Kourat, T.; Smadhi, D.; Madani, A. Modeling the Impact of Future Climate Change Impacts on Rainfed Durum Wheat Production in Algeria. *Climate* **2022**, *10*, 50. https://doi.org/10.3390/cli10040050

Academic Editor: Steven McNulty

Received: 1 February 2022 Accepted: 18 March 2022 Published: 23 March 2022

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

**Copyright:** © 2022 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/).

socio-economic negative impacts on plant development and crop yield (reduction in crop yields by up to 70%) ([4,5]). In general, crop yields will increase in cold areas where low temperature currently limits crop growth. However, heat stress on crops and water scarcity will lead to a decline in yields in warm environments. Warm temperatures and precipitation variability associated with a high frequency of extreme climate events (e.g., droughts, floods, heat waves, etc.) have worsened food insecurity in several regions of the world, especially in Africa ([3,6]). Information related to climate impacts on crops is important for understanding their macroeconomic implications for food security. This climate information allows us to choose e the appropriate adaptation strategies supported by knowledge of the processes that lead to changes in yield, under average and extreme weather conditions [7]. Identifying the drivers of changes and variability in yields can enable the development of targeted adaptation measures such as: (i) insurance solutions against specific weather [8], (ii) support the planning of long-term investments in irrigation infrastructure [9], or (iii) improve reproductive efficiency as the suitability of adaptive traits changes with CC and elevated CO2 concentrations in the atmosphere [10].

In Algeria, 50% of non-irrigated agricultural lands are cultivated with cereals, especially durum wheat, with a low national average grain yield estimated at 17 qha−1 (2000–2020 period). However, these lands are mainly located in the High Plains region, known for its semi-arid climate [11]. Moreover, the decrease in the national production of meat and milk had affected their prices and caused an increase in demand for cereal products. The last are characterized by their subvention prices by the government, especially wheat, which is considered as the main source of protein in the diet of Algerian people [12]. Thus, Algeria meets its national needs for durum wheat with massive imports, with an average annual bill of \$1 billion. These food bills are paid, thanks to oil rent [11].

It is projected in Algeria by the future horizon 2030: (i) an increase in temperatures of +0.9 to +1.3 ◦C and their variability, (ii) an intensification of the frequency of heat waves, and (iii) an accentuation of the variability of precipitations, which will result in an increase in dry and wet episodes by +10% and will be accompanied by a decrease in precipitation of −9 up to −14% [13]. In the future decades, the harmful impacts of the above projected CC will manifest themselves above all else by the increase in the frequency and severity of droughts. This projected drought will threaten crop production, mainly rainfed crops yields, such as durum wheat. Thus, by the future, under the projected CC, the demographic surge will lead to an increase in national wheat needs. Thus, with the fall in oil prices, the satisfaction of national demand for wheat could become a real concern to economic balance and food security in Algeria [14].

Given the importance of wheat in human nutrition and global trade, many studies (e.g., [15–17]) are carried out across the world to assess the impact of CC on wheat yield. These studies used crop models and the Representative Concentrations Pathway scenarios (RCPs) [18]. The results of the above studies could not apply directly in Algeria. Because the CC's impacts on wheat production are specific to each region in the world according to its local climate and to its financial and technical capacity to face the CC impacts. According to [19], in Algeria, the negative impacts of CC on water resources were assessed with the UKHI model (United Kingdom Meteorological Office High Resolution). This assessment study carried out by seasonal climate forecasting showed a decrease in the rainiest area and an increase in the driest ones in Algeria. So, the rainfed crop is very vulnerable to future climate change. As a consequence of the above-cited projected CC in Algeria, a decline in crop yields by −10 to −30% was predicted by 2030 [20]. Despite the strategic role of rainfed wheat in national food security and its high level of vulnerability to the projected CC, the studies of CC assessment impacts on wheat production, using crop models and associated with RCPs scenarios, are very rare in Algeria. With the exception of the study carried out by Rouabhi et al. [21], who used a statistical model to predict durum wheat yields under RCP 2.6 and RCP 8.5, in 2070, at the Setif region. So, the main aim of this research is to improve the available knowledge related to the future CC negative impacts on rainfed wheat in Algeria. This study could help the farmers better understand the CC

issue and its impacts on wheat. This study could help the farmers to choose more resilient and CC-adapted agricultural practices in the future. Furthermore, these kinds of studies will be important to the agricultural stakeholders in preparing adapted policies which will accompany the farmers in their quest to prevent wheat yields losses induced by CC. Thus, these types of studies are very important in preparing a national strategy to adapt rainfed wheat against the projected CC negative impacts. This could help them to protect the national economic balance from the potential negative impacts of food insecurity induced by low national wheat production.


**7. Revascularization for patients with diabetes mellitus and multivessel CAD**

In the BARI 2D trial, the selected revascularization strategy, CABG or PCI, was based on physician discretion, declared independent of randomization to either immediate or deferred revascularization if clinically warranted. They analyzed factors favoring selection of CABG versus PCI in 1,593 diabetic patients with multivessel CAD enrolled between 2001 and 2005. The majority of diabetic patients with multivessel disease were selected for PCI rather than CABG. Preference for CABG over PCI was largely based on angiographic features related to the extent, location, and nature of CAD, as well as geographic, demographic, and clinical factors. [21]

However, with each intervention the benefit is less and the risks and complications are greater than in patients without diabetes. Revascularization for treatment of ST elevation myocardial infarction increases survival. Both interventions relieve symptoms, but neither improves survival except in patients at high risk. In patients with clinically stable chronic coronary disease, survival after CABG or PCI is comparable with that in patients treated with optimal medical therapy alone. Accordingly, evaluation for revascularization can be deferred until signs and symptoms worsen except in patients at high risk. In patients at high risk survival after promptly implemented CABG is greater than that with optimal medical therapy, especially when the diabetes is being treated with insulin sensitizing agents. [22]


# **Food Allergies in Modern Life**

Edited by Sara Manti, Gian Luigi Marseglia and Salvatore Leonardi Printed Edition of the Special Issue Published in *Nutrients*

www.mdpi.com/journal/nutrients

## **Food Allergies in Modern Life**


**Author details**

Sandra Murphy and Kay Ohlendieck\*

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Biology, Maynooth University, National University of Ireland, Maynooth, County Kildare, Ireland