**1. Introduction**

Soybean [*Glycine max* (L.) Merr.], an important oil crop, accounted for 28% of the total vegetable oil consumption in the world in 2019 [1]. The last 7 years have seen 22.5% increases in soybean oil consumption worldwide (**Figure 1**) and soybean oil production is expected to rise in the future. Commodity soybean seed contains 20% oil, 40% protein, and 12% soluble carbohydrates on a dry weight basis [2]. Fatty acid compositions of food and oil have received considerable attention in the past few decades for human nutrition and health concerns. Hence, improving the FA composition of the soybean oil is crucial to reduce the risks of associated coronary heart and other diseases.

#### **Figure 1.**

*Soybean oil consumption worldwide from 2013/14 to 2019/20 (in million metric tons). Retrieved from https:// www.statista.com/statistics.*

#### **1.1 Fatty acid composition of important oil crops**

Oil and fatty acids are essential elements for the development and growth of all the creatures. These elements are the structural components of the cellular membrane, as well as play a pivotal role in storing energy and involved in the cellular signaling processes. Known natural resources of oil and fatty acids are plants, animals, and oleaginous microorganisms. Oil and fatty acids played a crucial role in human life in several ways as food and fuel resource, mostly as a nutritional element of the diet. Edible oilseed crops (palm, olive, rapeseed, canola, sunflower, and soybean) primarily contain five fatty acids: palmitic acid (16:0), stearic acid (18:0), oleic acid (18:1), linoleic acid (ω-6, 18:2), and α-linolenic acid (ALA, ω-3, 18:3) in their seed oil. The fatty acid composition varies with the oilseed plant species as given in **Table 1**. Fatty acids accumulated in plants as a form of triacylglyceride, which consists of three fatty acids linked to glycerol as a backbone through ester bond [4, 5]. The triacylglyceride is a key source of renewable energy in the form of reduced carbon used as food, feedstock, and fuel.

In the plant fatty acids, basic biosynthetic pathways are well established but FA/lipid operating between the plastid and the endoplasmic reticulum remains to be determined [6, 7]. The fatty acid biosynthesis takes place in the chloroplast stroma of leaves and proplastids of seeds by *de novo* and further incorporation of triacylglyceride occurs in the endoplasmic reticulum [8]. Besides, polyunsaturated fatty acids (PUFA) synthesis in higher plants takes place by both prokaryotic and eukaryotic enzymatic pathways [7, 9]. These pathways are regulated through diverse sets of genes.

#### **1.2 Polyunsaturated fatty acids in soybean**

Soybean seed primarily contains two saturated fatty acids, which are palmitic acid, and stearic acid, and three unsaturated fatty acids, which are oleic acid, linoleic acid, and ALA. The relative ratio of these fatty acids commonly found in cultivated soybean are, 11% of palmitic acid, 4% of stearic acid, 23% of oleic acid, 55% of linoleic acid, and 8% of ALA [10]. However, wide variation for fatty acids content has been reported in several studies [11–13]. Commonly, the proportion

*Breeding Strategy for Improvement of Omega-3 Fatty Acid through Conventional Breeding… DOI: http://dx.doi.org/10.5772/intechopen.95069*

#### **Table 1.**

*Fatty acid composition of major oilseed crops.*

ratio of fatty acids in soybean is influenced by the genotypes as well as the environmental factors which are very crucial to determine the entire quality of the oil.

Linoleic acid and ALA are the PUFA, also termed essential fatty acids (EFA), present in the soybeans. Due to the presence of two or more double bonds between the carbons of fatty acid chains, PUFA are distinguished from saturated and monounsaturated fatty acids. The EFA is primarily classified into two forms, ω-6 and ω-3, which are metabolically interlinked but functionally diverse. ω-6 and ω-3 comprise long chains of carbon atoms with a carboxyl group at one end of the chain and a methyl group at the other end.

ω-3 fatty acids have a carbon–carbon double bond located between the third and fourth carbon atoms from the methyl end of the chain. ω-3 fatty acids exhibit *cis*-*trans* isomerism with its extension to *E*-*Z* configuration [14]. ALA, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) are the three important ω-3 fatty acids. The carbon backbone of these contains 18 carbon atoms, 20, and 22 for ALA, EPA, and DHA, respectively. Their structures are shown in **Figure 2**. ALA is the precursor of DHA and EPA, which are essential for the growth and development of the brain and retina in humans [15].

#### **1.3 Sources of ω-3 fatty acids**

Seafood products such as fish are the main sources of ω-3 fatty acids (ALA, EPA, and DHA). However, they are not a routine part of the traditional diet in many countries. The ω-3 fatty acid is abundantly available in nature and found in most of the oilseed crops. The ALA is also synthesized in the plants found in green leafy vegetables, and in the seeds of flax, as rapeseeds (*Brassica campestris*), chia (*Salvia hispanica*), perilla (*Perilla frutescens*), walnut (*Juglans sinensis*), and soybean. The ω-3 is dietary EFA for humans; however, because of the absence of delta-12 and delta-15 desaturase enzymes, humans and other animals are unable to synthesize ω-6 and ω-3 fatty acids. Therefore, these EFAs need to acquire through diet or dietary supplements [16].

**Figure 2.**

*Major three* ω*-3 polyunsaturated fatty acid structure: Three unsaturated fatty acids are shown with the cis configuration of the double bonds with a methyl end and a carboxyl (acidic) end represented with commonly followed nomenclature numerical scheme.*

### **1.4 Importance of ω-3 fatty acid and ω-6/ω-3 ratio**

Several studies reported the nutritional and health benefits of ω-3 in humans [17]. Besides, ω-3 fatty acids are known for therapeutic uses and to offer protection against numerous diseases [15]. Thus, the nutritional value of ω-3 fatty acids is now widely accepted. Earlier diets comprised meat, plants, eggs, fish, nuts, and berries, which contained substantial amounts of ω-3 fatty acid [17, 18]. With the changes in dietary habits, consumption of ω-6 fatty acid was enhanced, which consequently reduced the level of ω-3 fatty acids in human. Thus, the contemporary diets are now comprised of a high intake of saturated and ω-6 fatty acids, decreased ω-3 fatty acid intake, and an overuse of salt and refined sugar [19]. These dietary changes led to diets with an undesirable ω-6/ω-3 ratio up to 20:1 [20]. Ultimately, an altered ratio of ω-6/ω-3 is considered unhealthy and reported to be the prevalent cause of prothrombotic and proinflammatory diseases, such as atherosclerosis, obesity, and diabetes [16, 21–23]. Several studies have reported a positive correlation between lower ω-6/ω-3 ratio and reduced risks of cardiovascular disease (CVD), cancer, including breast, colon, prostate, liver, and pancreatic cancers, inflammation, favor apoptosis and exert antiproliferative effects cancers [24].

The balanced ω-6/ω-3 ratio is an important determinant in decreasing the risk for CVD [17]. Increased intake of linoleic acid is known to interfere with the incorporation of EPA and DHA (which have the most potent inflammatory effects) in cell membrane lipids, and causes platelet aggregation and oxidation of low-density lipoprotein. Intake of ω-3 fatty acids may help in preventing the development of CVD as well as other associated diseases. Therefore, it is highly essential to increase the intake of ω-3 fatty acids and to reduce the consumption of ω-6 fatty acids. It has been estimated that the present Western diets have a ω-6/ω-3 ratio of 15-20:1, which is highly imbalanced. Several studies in animals, such as *Caenorhabdtis*

*Breeding Strategy for Improvement of Omega-3 Fatty Acid through Conventional Breeding… DOI: http://dx.doi.org/10.5772/intechopen.95069*

*elegans,* rats, mice, and pigs have shown the importance of balanced ω-6/ω-3 ratio [25–27]. Experimental studies suggested the changes in mucosal inflammation and reduced the patient suffering from an increased ratio of ω-6/ω-3. The combination of rheumatoid arthritis drug treatment and an adequate ω-6/ω-3 ratio has been suggested to cause significant changes in inflammatory markers [24]. Such studies have provided evidence for the need to have a balanced ratio of ω-6/ω-3 (1:1 to 2:1) [20, 24]. Besides, ω-6 and ω-3 fatty acids are involved differentially in the cellular process. It was reported that ω-6 fatty acids increase triacylglyceride content in the cell through altering membrane permeability; whereas, ω-3 fatty acids lower fat deposition in adipose tissues by suppressing lipogenic enzymes and increasing β-oxidation [28].


**8. Optimizing lipid productivity**

Increasing microalgal lipid productivity improves both the economics and energy balance of the process. The land area and size of culture vessels required, as well as the energy and water requirements for large-scale algal culture are strongly dependent on algal productivity. With a higher productivity, lower cultivation, mixing, pumping and harvesting volumes would be required to yield the same amount of product, resulting in lower cost and energy requirements. More concentrated cell suspensions could also make downstream processing more efficient. The genetic characteristics of an algal species determine the range of its productivity. The levels reached in practice within this range are determined by the culture conditions. The two main approaches to enhancing productivity are: 1. selection of highly productive algal species and 2. designing and maintaining optimal conditions for productivity.

biomass to combustion of the fuel. Lardon et al. (2009) conducted a life-cycle analysis of a hypothetical algal biodiesel production facility. Two different culture conditions: fertilizer feeding and nitrogen starvation, as well as two different extraction options: dry or wet, were investigated. The study confirmed the potential of microalgae as an energy source, but highlighted the necessity of decreasing energy and fertilizer consumption. Energy inputs, such as the energy required for mixing and pumping, the embodied energy in the materials used and the energy cost of harvesting and processing must be minimized. Recycling of material and energy from waste streams is also important wherever feasible (Scott et al., 2010). The use of nitrogen stress, as well as the optimization of wet extraction were indicated as desirable options. The anaerobic digestion of residual biomass was also suggested as a

We conducted a LCA on a hypothetical algal biodiesel process. Biomass production in three different reactor types (open ponds and two types of closed reactor: horizontal tubular and vertical tubular) was evaluated. In all cases, harvesting was modeled as an initial settling step followed by centrifugation. Hexane extraction was used to recover the oil, with the residual biomass sent for anaerobic digestion and the resulting energy from biogas production recycled to the process. The hexane was recovered and the oil converted to biodiesel using an enzymatic process. The basis chosen was production of 1000 kg of biodiesel from *Phaeodactylum tricornutum*. The net energy return (the energy embodied in the biodiesel produced divided by the energy input required) was positive (1.5) for the open pond, neutral (0.97) for the horizontal tubular reactor and negative (0.12) for the vertical tubular reactor. In this model, open ponds were the most energetically favorable reactor type, yielding 50% more energy than was put in. Horizontal tubular reactors required an energy input equivalent to the output, and vertical tubular reactors were the most unfavorable, requiring several times the energy input as that in the product, where system

The overriding energy input in the process was found to be that required to run the reactor. Reactor energy was by far the most dominant determinant of the overall process energy requirement. This was largest in the vertical tubular reactor as these were continually mixed by gas sparging. Energy required for pumping between unit processes was also significant, particularly at lower biomass concentrations due to the larger volume of culture to be processed. The major energy inputs in downstream processing were that embodied in the lime used as a flocculation agent, and the energy required for solvent recovery. Lipid

Increasing microalgal lipid productivity improves both the economics and energy balance of the process. The land area and size of culture vessels required, as well as the energy and water requirements for large-scale algal culture are strongly dependent on algal productivity. With a higher productivity, lower cultivation, mixing, pumping and harvesting volumes would be required to yield the same amount of product, resulting in lower cost and energy requirements. More concentrated cell suspensions could also make downstream processing more efficient. The genetic characteristics of an algal species determine the range of its productivity. The levels reached in practice within this range are determined by the culture conditions. The two main approaches to enhancing productivity are: 1. selection of highly productive algal species and 2. designing and maintaining optimal conditions for productivity.

productivity and species choice had a significant impact on the energy balance.

way of reducing external energy usage and recycling of nutrients.

optimization was not conducted.

**8. Optimizing lipid productivity**

The choice of algal strain is a key consideration. The diversity of algal species is much greater than that of land plants (Scott et al., 2010) allowing selection of species best suited to the local environment and goals of the project. Although there have been several screening programs, building on the work of the Aquatic Species Program (Sheehan et al., 1998), the majority of strains remain untested, few species have been studied in depth and the data reported in the literature is often not comparable due to the different experimental procedures used. We conducted a broad literature review of the growth rates and lipid contents of 55 promising microalgal species under both nutrient replete and limited conditions. The original study (Griffiths & Harrison, 2009) has been extended here through the use of two key assumptions to convert data into common units of biomass and lipid productivity.

Lipid productivity is determined by both growth rate and lipid content. Lipid content (P) was typically reported as percentage dry weight (% DW). Data presented in pg lipid.cell-1 was discarded if no cell weight was available for conversion. Growth rates were reported as doubling time (Td) or specific growth rate (µ). These were inter-converted according to Equation 1.

$$T\_d = \frac{\ln 2}{\mu} \tag{1}$$

Standard units of g.L-1.day-1 were chosen for biomass productivity. Specific growth rate (µ, in units of day-1) can be converted to volumetric biomass productivity (QV, in g.L-1.day-1) where the biomass concentration (X, in g.L-1) is known (Equation 2). Biomass productivity is often reported on the basis of surface area (QA), in units of g.m-2.day-1. This can be converted to QV using Equation 3 where the depth (D, in m) of the culture vessel can be calculated from the reactor geometry.

$$Q\_V = \mu \times X \tag{2}$$

$$Q\_V = \frac{Q\_A}{D \times 1000} \tag{3}$$

Lipid productivity (QP) was infrequently reported in the literature, and was generally reported in g.L-1.day-1 or mg.L-1.day-1. This parameter could be calculated from volumetric biomass productivity (QV, in g.L-1.day-1) and lipid content (P in % DW) where appropriate data were available (Equation 4).

$$Q\_P = Q\_V \times P \tag{4}$$

The calculation of lipid productivity for the majority of species necessitated two assumptions:

The average literature values for the 55 species are shown in Table 4. Among the species with the highest reported lipid productivity were *Neochloris oleoabundans*, *Navicula pelliculosa*, *Amphora*, *Cylindrotheca* and *Chlorella sorokiniana* (Fig. 3). Other findings were that green algae (Chlorophyta) generally showed an increase in lipid content when nitrogen deficient, whereas

Advantages and Challenges of Microalgae as a Source of Oil for Biodiesel 193

diatoms and other taxa were more variable in their response, although all those subjected to silicon deprivation showed an increase in lipid content. This increase in lipid content, however, does not necessarily translate into increased lipid productivity due to decreased growth rates under nutrient stress conditions. Response of biomass productivity to nutrient

Fig. 3. Average calculated (grey bars) and literature (empty bars) biomass productivity for the 20 most productive species investigated (adapted from Griffiths & Harrison, 2009)

In Fig. 4, the impact of biomass productivity and lipid content on calculated lipid productivity is analyzed through correlation. A relationship is demonstrated between lipid productivity and biomass productivity. All species with a high biomass productivity (above 0.4 g.L-1.day-1), and all but one above 0.3 g.L-1.day-1, have a high lipid productivity, greater than 60 mg.L-1.day-1. However, there are a few species with high lipid productivity despite an average biomass productivity, indicating that lipid content is also a factor. Lipid content correlates poorly with lipid productivity, indicating that lipid content alone is not a good indicator of suitability for biodiesel production. There are several species with low lipid productivity despite an aboveaverage lipid content (> 22%). The species with high lipid productivities (> 60 mg.L-1.day-1) range in lipid content from 16% DW to 51%. Further, species with high lipid content (> 30%)

Once the species has been chosen, the next critical factor is the optimisation of culture conditions. In addition to optimal temperature and pH, conditions that maximize autotrophic growth rate are optimal light, carbon and nutrient supply. Microalgal lipid accumulation is affected by a number of environmental factors (Guschina & Harwood 2006; Roessler 1990), and often enhanced by conditions that apply a 'stress' to the cells. Lipids appear to be synthesised in response to conditions when energy input (rate of photosynthesis) exceeds the capacity for energy use (cell growth and division) (Roessler 1990). Enhanced cell lipid content has been found under conditions of nutrient deprivation (Hsieh & Wu, 2009; Illman et al., 2000; Li et al., 2008; Shifrin & Chisholm, 1981; Takagi et al.,

vary in lipid productivity between 15 and 164 mg.L-1.day-1.

deprivation is variable between species and further investigation is necessary.

a Key to taxa: C = Chlorophyta, Cy = Cyanobacteria, D = Dinophyta, E = Eustigmatophyta, Eg = Euglenozoa, H = Haptophyta, O = Ochrophyta, Pr = Prasinophyta, b Key to media: F = Freshwater, M = Marine, S = Saline

Table 4. Growth and lipid parameters of 55 species of microalgae, along with their taxonomy and media type (adapted from Griffiths and Harrison, 2009). The average of literature values for lipid content under nitrogen (N) replete and deficient growth conditions, doubling time (Td), and areal (QA) and volumetric (QV) biomass productivities are shown in columns 4 to 8. Average biomass productivity calculated from Td, µ, QA and QV is shown in column 9, and calculated and literature lipid productivity in columns 10 and 11 respectively. Blanks represent no data available

a Key to taxa: C = Chlorophyta, Cy = Cyanobacteria, D = Dinophyta, E = Eustigmatophyta, Eg = Euglenozoa, H = Haptophyta, O = Ochrophyta, Pr = Prasinophyta, b Key to media: F = Freshwater, M = Marine, S = Saline Table 4. Growth and lipid parameters of 55 species of microalgae, along with their taxonomy and media type (adapted from Griffiths and Harrison, 2009). The average of literature values for lipid content under nitrogen (N) replete and deficient growth conditions, doubling time (Td), and areal (QA) and volumetric (QV) biomass productivities are shown in columns 4 to 8. Average biomass productivity calculated from Td, µ, QA and QV is shown in column 9, and calculated and literature lipid productivity in columns 10 and 11 respectively. Blanks represent no data available

diatoms and other taxa were more variable in their response, although all those subjected to silicon deprivation showed an increase in lipid content. This increase in lipid content, however, does not necessarily translate into increased lipid productivity due to decreased growth rates under nutrient stress conditions. Response of biomass productivity to nutrient deprivation is variable between species and further investigation is necessary.

Fig. 3. Average calculated (grey bars) and literature (empty bars) biomass productivity for the 20 most productive species investigated (adapted from Griffiths & Harrison, 2009)

In Fig. 4, the impact of biomass productivity and lipid content on calculated lipid productivity is analyzed through correlation. A relationship is demonstrated between lipid productivity and biomass productivity. All species with a high biomass productivity (above 0.4 g.L-1.day-1), and all but one above 0.3 g.L-1.day-1, have a high lipid productivity, greater than 60 mg.L-1.day-1. However, there are a few species with high lipid productivity despite an average biomass productivity, indicating that lipid content is also a factor. Lipid content correlates poorly with lipid productivity, indicating that lipid content alone is not a good indicator of suitability for biodiesel production. There are several species with low lipid productivity despite an aboveaverage lipid content (> 22%). The species with high lipid productivities (> 60 mg.L-1.day-1) range in lipid content from 16% DW to 51%. Further, species with high lipid content (> 30%) vary in lipid productivity between 15 and 164 mg.L-1.day-1.

Once the species has been chosen, the next critical factor is the optimisation of culture conditions. In addition to optimal temperature and pH, conditions that maximize autotrophic growth rate are optimal light, carbon and nutrient supply. Microalgal lipid accumulation is affected by a number of environmental factors (Guschina & Harwood 2006; Roessler 1990), and often enhanced by conditions that apply a 'stress' to the cells. Lipids appear to be synthesised in response to conditions when energy input (rate of photosynthesis) exceeds the capacity for energy use (cell growth and division) (Roessler 1990). Enhanced cell lipid content has been found under conditions of nutrient deprivation (Hsieh & Wu, 2009; Illman et al., 2000; Li et al., 2008; Shifrin & Chisholm, 1981; Takagi et al.,

Advantages and Challenges of Microalgae as a Source of Oil for Biodiesel 195

processes). Current research is focussed on achieving this through a combination of biological and engineering approaches. The major challenges currently being addressed are:

Developing cost and energy-efficient methods of harvesting dilute suspensions of small

Decreasing the overall energy and cost requirements, particularly for pumping, gas

 Decreasing environmental footprint through recycling of water, energy and nutrients. These topics have captured the imagination of several researchers and some innovative solutions are being investigated. The overall goal of biofuel production is to optimise the conversion of sunlight energy to liquid fuel. In algal cultivation, techniques to improve light delivery include manipulating the reactor design, the use of optics to deliver light to the centre of the reactor, optimising fluid dynamics to expose all cells to frequent light flashes, increasing the efficiency of photosynthesis and carbon capture (e.g. enhancing the carbon concentrating mechanism), and using mixed-species cultures to utilise different intensities

One of the major problems with light delivery is poor penetration of light into dense cultures due to mutual shading by the cells. Under high light conditions, microalgal cells absorb more light than they can use, shading those below them and dissipating the excess energy as fluorescence or heat. In nature, this confers individual cells an evolutionary advantage, however, in mass production systems it is undesirable as it decreases overall productivity. It would be advantageous to minimize the size of the chlorophyll antennae in cells at the surface, so as to permit greater light penetration to cells beneath (Melis, 2009). Reducing the size of the light harvesting complexes through genetic modification has been shown to improve productivity (Nakajima et al., 2001). The goal now is to engineer cells that

Although the TAG content of cells can be enhanced by manipulation of the nutrient supply, there is a tradeoff between growth and lipid production. For optimum productivity, cells that can maintain a simultaneously high growth rate and lipid content are required. Strategies to achieve this include screening for novel species, and genetic engineering of well characterised strains. The genes and proteins involved in regulation of lipid production pathways are currently being investigated through synthetic biology and the modelling of carbon flux through metabolism. Key enzymes and branch-points can then be manipulated to improve productivity. For example, carbohydrate and lipid production compete directly for carbon precursors. Shunting carbon away from starch synthesis by downregulation of the enzyme ADP-glucose pyrophosphorylase in *Chlamydomonas* has been shown to enhance

The challenge of harvesting small algae cells from dilute suspensions has yet to be solved in a cheap, energy efficient manner. Ideally the addition of chemical agents that impede the recycling of the culture medium and nutrients should be avoided. A series of methods is likely to be used e.g. flocculation followed by sedimentation, or settling followed by

Improving resource utilization and productivity through a biorefinery approach

 Increasing productivity in large-scale outdoor microalgal culture Minimizing contamination by predators and other algal species Mitigating temperature changes and water loss due to evaporation

Optimizing supply of light and CO2

Producing valuable co-products

or wavelengths of light (Scott et al., 2010).

change antennae size according to light intensity.

TAG content 10-fold (Li et al., 2010).

microalgal cells

Developing cheap and efficient reactor designs

transfer, mixing, harvesting and dewatering

2000), high light intensity (Rodolfi et al., 2009), high temperature (Converti et al., 2009); high salt concentration (Takagi et al., 2000) and high iron concentration (Liu et al., 2008).

Fig. 4. Correlation of calculated lipid productivity with (a) biomass productivity and (b) lipid content under nutrient replete conditions

Nitrogen (N) deprivation is the most frequently reported method of enhancing lipid content, as it is cheap and easy to manipulate. N deficiency has a reliable and strong influence on lipid content in many species (Chelf, 1990; Rodolfi et al., 2009; Shifrin & Chisholm, 1981). Unfortunately, stress conditions that enhance lipid content, such as nitrogen deprivation, typically also decrease the growth rate, and thus the net effect on lipid productivity must be ascertained (Lardon et al., 2009). Maximum biomass productivity and lipid content in *Chlorella vulgaris* occur under different conditions of nitrogen availability, suggesting that a two-stage cultivation strategy may be advantageous. From studies we have conducted on *C. vulgaris*, it appears that an intermediate level of nitrogen limitation creates the optimum balance between biomass and lipid production. The optimum cultivation strategy tested was batch culture, using a low starting nitrate concentration (between 250 and 300 mg.L-1 nitrate), ensuring that nitrogen in the medium was depleted towards the end of exponential growth. Other cultivation strategies (e.g. two-stage batch, fed-batch or continuous) were found not to improve upon the productivity achieved in N limited batch culture.

Although high lipid productivity is a key factor in species selection, other characteristics such as ease of cultivation, tolerance of a range of environmental conditions (particularly temperature and salinity), flue-gas contaminants and high O2 concentrations, as well as resistance to contaminants and predators are likely to be equally as important.


*Review* **Regulatory Mechanisms of Plant Growth-Promoting Rhizobacteria and Plant Nutrition against Abiotic Stresses in Brassicaceae Family**

**Arshad Jalal 1, Carlos Eduardo da Silva Oliveira 1, Fernando Shintate Galindo 2, Poliana Aparecida Leonel Rosa 1, Isabela Martins Bueno Gato 1, Bruno Horschut de Lima 1 and Marcelo Carvalho Minhoto Teixeira Filho 1,\***

**Abstract:** Extreme environmental conditions, such as abiotic stresses (drought, salinity, heat, chilling and intense light), offer great opportunities to study how different microorganisms and plant nutrition can influence plant growth and development. The intervention of biological agents such as plant growth-promoting rhizobacteria (PGPRs) coupled with proper plant nutrition can improve the agricultural importance of different plant species. Brassicaceae (Cruciferae) belongs to the monophyletic taxon and consists of around 338 genera and 3709 species worldwide. Brassicaceae is composed of several important species of economical, ornamental and food crops (vegetables, cooking oils, forage, condiments and industrial species). Sustainable production of Brassicas plants has been compromised over the years due to several abiotic stresses and the unbalanced utilization of chemical fertilizers and uncertified chemicals that ultimately affect the environment and human health. This chapter summarized the influence of PGPRs and nutrient management in the Brassicaceae family against abiotic stresses. The use of PGPRs contributed to combating climate-induced change/abiotic factors such as drought, soil and water salinization and heavy metal contamination that limits the general performance of plants. Brassica is widely utilized as an oil and vegetable crop and is harshly affected by abiotic stresses. Therefore, the use of PGPRs along with proper mineral nutrients management is a possible strategy to cope with abiotic stresses by improving biochemical, physiological and growth attributes and the production of brassica in an eco-friendly environment.

**Keywords:** microorganisms; stressful conditions; sustainability; abiotic stresses; nutrition; Brassicaceae


*3.5. BCFs and Management Concentration of Fluopyram in Soil*

The 95% confidence intervals of the BCF of scallion were determined from soil residues at DAP 0 and scallions at DAP 48, as shown in Table 5. The BCFs of scallions were in the range of 0.21–0.24 and 0.14–0.18 in trials A and B, respectively, which were lower than that of bell pepper leaves (0.32) and higher than that of tomato leaves (0.12) [7]. These results indicate that the uptake and accumulation capacity of fluopyram by scallions was intermediate between that of bell pepper leaves and tomato leaves.

**Table 5.** Bioconcentration factors (BCFs) of fluopyram calculated from residue estimates (95% confidence interval) in soil and scallions.

Based on the MRL in the crop and BCFs in this study, the management concentration in soil (MCsoil) was determined, which is the maximum tolerable level in soil caused by pesticides used during the cultivation of the preceding crop, thereby minimizing residual pesticide levels in rotational crops grown in contaminated soils and producing safe agricultural commodities. In the Republic of Korea, the MRL for fluopyram arising from unintentional contamination is set at 0.2 mg/kg for leaf-and-stem vegetables, including scallions. The minimum MCsoil for fluopyram was calculated as 0.83 mg/kg from the upper 95% confidence limit of the BCF. Therefore, the management concentration for fluopyram in the soil is proposed as less than 0.8 mg/kg. This threshold for fluopyram in the soil is likely to be set as a precautionary measure to ensure that scallions grown in soil contaminated with fluopyram will not be exposed to the MRL in scallions. If the concentration of fluopyram in the soil is found to be higher than the proposed management level of 0.8 mg/kg, appropriate measures should be taken to mitigate the associated risks. These measures may include delaying planting of seedlings, tillage, and soil dressing. These actions are generally taken to reduce the level of fluopyram in the soil to an acceptable level, in order to protect the rotational crops and the environment.


**2. Procedure of EMG detection**

a replacement for the lost body part [5, 8]. The meal assistive robot [9], assistive wheel chair [10] and assistive humanoid robot [10] are some examples of the other types of assistive robots. Both orthoses and prostheses directly interact physically and cognitively with the wearer. Therefore, they are expected to provide physiological and mental comfort to the user, without letting the user feel a difference while functioning/assisting correctly to perform the required motion. However, the controlling of the robot according to human motion intention is not an easy task [11, 12]. Therefore, in the integration of the human with a robot, the selection of a proper control input signal to reflect the correct motion intention would be very important. So far research is being carried out considering different biological signals such as Electromyog‐ raphy (EMG), Mechnanomyogram (MMG), Electroencephalography (EEG) Electrooculogra‐ phy (EOG) and Electrocorticogram (EcoG) [13, 14, 15] as the main input signal to the robot controller. Among them, the EMG signal, which is the measurement of the electrical activity of muscles at rest and during contraction, has obtained promising results in the case of controlling robotic prostheses and orthoses by correctly interpreting the human motion intention [16, 17]. Basically, EMG based control is a sophisticated technique concerned with detection, processing, classification and application of EMG to control assistive robots. EMG can be applied to control assistive robots in various manners by considering data acquisition, feature extraction and classification [18]. Some of the available exoskeleton robots controlled based on EMG are the orthotic exoskeleton hand [19], exoskeleton robot for tremor suppression [20], SUEFUL-7 [6] etc. In addition, DEKA Arm [21], Saga Prosthetic Arm [22], and Manus Hand [23] are some of the available prosthetic devices with EMG based controlling. Many researchers from different institutions have been working on EMG based control methods over several decades and their contribution has greatly influenced a new era of EMG based control

This chapter presents a comprehensive review of EMG based control methods of recent upperlimb orthoses and prostheses. Initially, it explains the detection and processing of EMG and available EMG extraction systems. Next, ways of categorization of EMG based control methods are explained. Here EMG based control methods are categorized in detail, based on the structure of the controller algorithm, as pattern recognition based controls and non-pattern recognition based controls. Most of available control methodologies used with assistive robots are pattern recognition based controls [18, 19, 24, 26, 27]. The control methods belonging to non-pattern recognition based controls [28-30] are rare. Further, comparison of EMG based control methods of upper-limb exoskeleton robots and upper-limb prostheses are presented considering the features of the control method. In addition, the conclusion and future direc‐

The next section focuses on the procedure of EMG detection and processing. Section 3 describes the categorization of EMG based control methods. The review of EMG based control methods of upper-limb exoskeleton robots and upper-limb prostheses together with a comparison of control methods are presented in section 4. The final section briefly outlines the conclusion

tions of EMG based control methods of assistive robots are described.

of assistive robots [5, 24, 25].

238 Electrodiagnosis in New Frontiers of Clinical Research

and future directions.

Detection of EMG signals can be done mainly in two ways, namely non-invasive and invasive [5]. Surface EMG (sEMG) electrodes are used for the former, while intramuscular EMG (iEMG) electrodes are used for the latter. Placement of surface EMG electrodes is comparatively easier than intramuscular EMG electrodes. However noise and other disturbances are inherent in surface EMG detection [5]. Intramuscular EMG electrodes are placed close to the Motor Unit Action Potentials (MUAP), and as a result the influence of other disturbances is not dominant. It provides a better accuracy and repeatability of the EMG signal [25]. As shown in Fig. 1, the EMG extraction process includes several steps. The initial step is the selection of the most significant muscle of the human body relevant to the required motion. After the muscle is selected, the next important step is the placement of electrodes. In the case of sEMG, the electrodes should be placed in the belly area of the muscle for maximum signal extraction.

**Figure 1.** EMG signal extraction process

The electrode should be placed onto the relevant muscle after cleaning the skin surface. There are a few types of surface electrodes, some of which need a gel [31] to be applied between the skin and the electrode and some [32] which instead use an adhesive tape to ensure proper contact between the muscle and the electrode. Signals from several electrodes are then fed into the input box and subsequently passed to the amplifier. The output of the amplifier can be fed into a computer via data cards or any other data communication interface and can be recorded or manipulated in the required way. In most EMG based control methods a raw EMG signal is processed to extract the features of the signal. Several feature extraction methods are available for this purpose [5]: mean absolute value, mean absolute value slope, waveform length, zero crossings, root mean square value, *etc*. The features of the raw EMG signal have to be extracted in real time to use EMG as input signals to the controller of the assistive robots. Most robots use Root Mean Square (RMS) as the feature extraction method of raw EMG mainly due to ease of analyzing real time information of EMG signal.

There are a number of commercially developed EMG acquisition systems available [31-34] (see Fig. 2). They could be used for both medical and research purposes. Some of the leading EMG acquisition system manufacturers are Nihon Kohden Co. [31], Delsys [32], BioSemi [33], and Cambridge Electronic Design [34]. The Delsys EMG system shown in Fig. 2(a) is widely used in research, whilst the Nihon Kohden EMG system shown in Fig. 2(b) is widely used for medical applications. Nevertheless, there are other models in Nihon Kohden which also support for research [31]. The BioSemi EMG system shown in Fig. 2(c) could also be used in research applications. The next section will explain the classification of EMG based control methods

(a)Delsys [32] (b)Nihon Kohden[31] (c)Bio Semi [33]

Data Segmentation

Data

**Figure 5.** EMG processing sequence in non-pattern recognition based control

**3. Categorization of EMG based control methods**

**Figure 4.** EMG processing sequence in pattern recognition based control

acuqisition

acuqisition

(see Fig.5).

Feature

Recent Trends in EMG-Based Control Methods for Assistive Robots

http://dx.doi.org/10.5772/56174

241

Extraction Classification EMG

Segmentation Non pattern recognition method EMG

Control methods of assistive robots based on EMG can be categorized mainly according to the input information to the controller, architecture of the control algorithm, output of the controller and other ways. Figure 3 shows the ways of categorization of EMG based control methods. In this chapter, the EMG based control methods of assistive robots are categorized based on the architecture of the control method. Considering the EMG signal processing method in the architecture of the controller the EMG based control methods can be categorized mainly as pattern recognition based and non-pattern recognition based [5, 25]. Control methods of many assistive robots are designed with pattern recognition based control methods and it provides an accurate control action compared to non-pattern recognition based EMG based control [5]. However, several intermediate steps (see Fig.4) are applied with a pattern recognition method such as data segmentation, feature extraction, and classification [5, 25]. The accuracy of pattern recognition based control is greatly improved by methods of feature extraction and classification [5, 25]. Robots such as, SUEFUL-7 [6], NEUROExos [27], W-EXOS [11], DEKA Arm [21], Saga Prosthetic Arm [22], Manus Hand [23] are on pattern recognition based control. The non-pattern recognition based control method involves only a few steps

**Figure 2.** Available EMG acquiring systems

**Figure 3.** Control method classification for assistive robots

**Figure 4.** EMG processing sequence in pattern recognition based control

(a)Delsys [32] (b)Nihon Kohden[31] (c)Bio Semi [33]

Control Model Input Output

> Dynamic model Muscle model On off control Proportional control Proportional+Derivative

Fuzzy control Neuro-fuzzy control Impedance control Admittance control

Proportional+Derivative+Integral

Current

Voltage

**Figure 2.** Available EMG acquiring systems

240 Electrodiagnosis in New Frontiers of Clinical Research

Biological signal (EMG/EEG/EOG/MMG)

Non-biological signal (Force/Torque signal)

**Figure 3.** Control method classification for assistive robots

**Figure 5.** EMG processing sequence in non-pattern recognition based control


**2. WHO histological classification**

Numerous classifications for thymic neoplasms, based on different histological and clinical criteria, have been proposed in the past. For universal agreement purposes, the International Committee of the World Health Organization (WHO) developed a classification system that distinguishes five histological subtypes of thymomas that differ in both morphological and clinical presentation and which correlate with invasion [1]. The WHO classification is increasingly more malignant from A to B3. Thymomas are composed of a mixture of neoplastic epithelial cells and normal T lymphocytes and exhibit a resemblance to the normal thymic architecture, which cannot be found in other organs. In contrast, thymic carcinoma (in the past considered as a thymoma type C) is similar to carcinomas found outside the thymus. Although most thymomas have an excellent prognosis, they can be locally invasive and are (although rarely) able to spread to lymph nodes or extrathoracic sites, so the term "benign" should be avoided.

Type A thymoma is composed of spindle cells and a few to no lymphocytes. About 24% of the type A thymomas are found in patients suffering from myasthenia gravis, and pure red cell aplasia may occur. Eighty percent of type A thymomas are Masaoka-Koga stage I on presentation (for explanation on the Masaoka-Koga staging system, see paragraph on staging). Stage IV is rare in this histological subtype [1, 3]. The prognosis of type A thymoma is excellent with 5- and 10-year survival rates close to 100% [10].

Type AB thymoma is composed of a mixture of lymphocyte-poor type A thymoma and more lymphocyte-rich type B. Approximately 14% of type AB thymomas are associated with the development of myasthenia gravis. Paraneoplastic pure red cell aplasia has also been also reported. Seventy-two percent is Masaoka-Koga stage I at presentation, 22% stage II, and 6% stage III. As in WHO type A disease, stage IV is rare [1]. Overall 5- and 10-year survival is reported 80–100% for stages I and II.

Type B1 thymoma is composed of thymic epithelial cells which are practically indistinguishable from the normal thymus cortex and medulla. A prominent population of immature lymphocytes is present. Like the other types, type B1 thymoma is associated with myasthenia gravis, but additionally hypogammaglobulinemia and pure red cell aplasia have been reported. Invasion in adjacent organs occurs in 12% of patients [11]. Complete surgical resection is possible in 94% of the cases with a recurrence rate of 10%. Ten-year survival rate is >90% [10].

Type B2 thymomas are usually at a more advanced stage. They are predominantly composed of large, polygonal tumor cells in the background of a large population of immature T lymphocytes. The most frequent manifestations (up to 80%) are symptoms arising from myasthenia gravis. Pure red cell aplasia, hypogammaglobulinemia, and other paraneoplastic autoimmune diseases have been described. Up to 53% are stage Masaoka-Koga I or III at presentation, and 8.2% is

**77**

*Thymoma and Thymic Carcinoma*

and 100% [1].

survival [1, 10, 14].

loss are possible.

mune syndromes.

of thymoma [15–17].

the thymoma is uncommon [18].

**3.1 Paraneoplastic autoimmune syndromes**

**3. Clinical presentation**

*DOI: http://dx.doi.org/10.5772/intechopen.87132*

stage IV. In 5–15% of cases, they are non-resectable, and recurrence rates are 9% after complete resection [10, 12]. Reported 10-year survival rates are between 50

Like the other types of thymomas, the most frequent association is myasthenia gravis. Pure red cell aplasia and hypogammaglobinemia are rare. The majority of patients are Masaoka-Koga stage II or III at presentation, and stage IV occurs in

Thymic carcinomas are atypical, invasive epithelial tumors that show resem-

Squamous cell carcinomas and undifferentiated carcinomas are the most common subtypes. They are composed of polygonal or round epithelial cells with mild atypia. Invasion of adjacent structures has been reported to occur in 83% of cases. In contrast to thymomas, paraneoplastic syndromes are very rare. Thymic carcinomas have the worst survival rate of the thymic malignancies with only 35% 5-year

Patients with a thymus malignancy can present themselves with complaints due to local compression/invasion or due to a paraneoplastic autoimmune phenomenon,

Local symptoms are related to the site and size of tumor and compression or invasion on the surrounding tissue. Patients can present themselves with symptoms as chest pain, cough, dysphagia, or stridor. A superior vena cava syndrome can cause swelling of the face and arms and dyspnea. Dyspnea can also be the result of unilateral or bilateral phrenic nerve palsy and pleural or pericardial involvement. The latter can also elicit tachycardia [3]. Systemic symptoms such as fever or weight

About 40–50% of thymomas are associated with a variety of paraneoplastic autoimmune syndromes, and over 30 associations have been described [1, 15, 16] (**Table 1**). Up to 25% deaths in thymoma are due to the complications of autoim-

The most common paraneoplastic syndrome is myasthenia gravis, followed by

Ten to twenty percent of patients with myasthenia gravis have a thymoma, and 30–50% of patients with thymoma have myasthenia gravis. It is a neuromuscular disease that leads to varying degrees of muscle weakness. It is caused by autoantibodies that interfere with the acetylcholine receptors in the neuromuscular junction of the voluntary muscles, but the exact mechanism how has yet to be discovered. Systemic symptoms consist of fatigue and general muscle weakness leading to troubles with walking. Myasthenia gravis can antedate the diagnosis of thymoma, be diagnosed concurrently, or occur after thymectomy, with or without recurrence

Pure red cell aplasia is a profound non-regenerative anemia, characterized by a severe reduction in reticulocytes and absence of erythrocyte precursors in the bone marrow. Five to fifteen percent of patients with thymoma have pure red cell aplasia [16]. It is more common in older women. Remission following surgical excision of

pure red cell aplasia and hypogammaglobinemia (Good syndrome).

but ~40% of the thymic neoplasms are asymptomatic incidental findings.

20%. Ten-year survival rates range between 50 and 70% [10, 12, 13].

blance to carcinomas outside the thymus and show a lot of differentiation.

Type B3 thymomas are almost always invasive and unresectable at presentation.

#### *Thymoma and Thymic Carcinoma DOI: http://dx.doi.org/10.5772/intechopen.87132*

*Thymus*

mediastinum, they can also be found in other mediastinal compartments, as well as

Approximately 1000 new cases are diagnosed per year in Europe [6]. Patients are usually between 20 and 70 years of age, with a peak in 30–40 years for those with myasthenia gravis and 60–70 years for those without myasthenia gravis. Incidence is similar in men and women [3, 7]. There are no known risk factors [3, 7], although clusters of thymoma are described in patients with multiple endocrine neoplasia type 1 (MEN1) syndrome [8] and an association between the Epstein– Barr virus and myasthenia gravis in thymoma patients has been suggested [9].

Numerous classifications for thymic neoplasms, based on different histological and clinical criteria, have been proposed in the past. For universal agreement purposes, the International Committee of the World Health Organization (WHO) developed a classification system that distinguishes five histological subtypes of thymomas that differ in both morphological and clinical presentation and which correlate with invasion [1]. The WHO classification is increasingly more malignant from A to B3. Thymomas are composed of a mixture of neoplastic epithelial cells and normal T lymphocytes and exhibit a resemblance to the normal thymic architecture, which cannot be found in other organs. In contrast, thymic carcinoma (in the past considered as a thymoma type C) is similar to carcinomas found outside the thymus. Although most thymomas have an excellent prognosis, they can be locally invasive and are (although rarely) able to spread to lymph nodes or extrathoracic

Type A thymoma is composed of spindle cells and a few to no lymphocytes. About 24% of the type A thymomas are found in patients suffering from myasthenia gravis, and pure red cell aplasia may occur. Eighty percent of type A thymomas are Masaoka-Koga stage I on presentation (for explanation on the Masaoka-Koga staging system, see paragraph on staging). Stage IV is rare in this histological subtype [1, 3]. The prognosis of type A thymoma is excellent with 5- and 10-year

Type AB thymoma is composed of a mixture of lymphocyte-poor type A thymoma and more lymphocyte-rich type B. Approximately 14% of type AB thymomas are associated with the development of myasthenia gravis. Paraneoplastic pure red cell aplasia has also been also reported. Seventy-two percent is Masaoka-Koga stage I at presentation, 22% stage II, and 6% stage III. As in WHO type A disease, stage IV is rare [1]. Overall 5- and 10-year survival is reported 80–100%

Type B1 thymoma is composed of thymic epithelial cells which are practically indistinguishable from the normal thymus cortex and medulla. A prominent population of immature lymphocytes is present. Like the other types, type B1 thymoma is associated with myasthenia gravis, but additionally hypogammaglobulinemia and pure red cell aplasia have been reported. Invasion in adjacent organs occurs in 12% of patients [11]. Complete surgical resection is possible in 94% of the cases with a

Type B2 thymomas are usually at a more advanced stage. They are predominantly composed of large, polygonal tumor cells in the background of a large population of immature T lymphocytes. The most frequent manifestations (up to 80%) are symptoms arising from myasthenia gravis. Pure red cell aplasia, hypogammaglobulinemia, and other paraneoplastic autoimmune diseases have been described. Up to 53% are stage Masaoka-Koga I or III at presentation, and 8.2% is

recurrence rate of 10%. Ten-year survival rate is >90% [10].

in the neck, lung, pleura, and thyroid, due to ectopic thymic tissue [1, 4, 5].

**2. WHO histological classification**

sites, so the term "benign" should be avoided.

survival rates close to 100% [10].

for stages I and II.

**76**

stage IV. In 5–15% of cases, they are non-resectable, and recurrence rates are 9% after complete resection [10, 12]. Reported 10-year survival rates are between 50 and 100% [1].

Type B3 thymomas are almost always invasive and unresectable at presentation. Like the other types of thymomas, the most frequent association is myasthenia gravis. Pure red cell aplasia and hypogammaglobinemia are rare. The majority of patients are Masaoka-Koga stage II or III at presentation, and stage IV occurs in 20%. Ten-year survival rates range between 50 and 70% [10, 12, 13].

Thymic carcinomas are atypical, invasive epithelial tumors that show resemblance to carcinomas outside the thymus and show a lot of differentiation. Squamous cell carcinomas and undifferentiated carcinomas are the most common subtypes. They are composed of polygonal or round epithelial cells with mild atypia. Invasion of adjacent structures has been reported to occur in 83% of cases. In contrast to thymomas, paraneoplastic syndromes are very rare. Thymic carcinomas have the worst survival rate of the thymic malignancies with only 35% 5-year survival [1, 10, 14].


**5.5. The agrometeorological bulletin and improved decision-making processes in Paraguay**

Extreme weather events such as river floods, severe storms, droughts and below-freezing low temperatures are strongly linked to the onset of El Niño Southern Oscillation (ENSO). Extreme events primarily affect river streamflow and cause numerous direct and indirect impacts on many key sectors of the Paraguayan economy: agriculture, ground and river transportation, potable water, construction, electricity, and recreation. Several studies have demonstrated that the potential impacts of these extreme events and their economic consequences are directly

related to the magnitude of ENSO episodes. Historically, Paraguay has experienced above normal mean temperatures and rainfall amounts, as well as unusually heavy rainfall events, during El Niño (or ENSO positive phase) episodes. The resulting floods have affected thousands of individuals, damaged numerous houses, public buildings and highways, and submerged entire crops, cattle grasslands and livestock farms. Recent examples include the catastrophic events that took place in the Paraguay and Paraná rivers, particularly during the El Niño 1982-83, 1991-92 and 1997-98 years. In the flat Paraguayan savannahs, rainfall extreme events have also caused a proliferation of *Aedes aegypti* mosquito breeding sites, increasing the incidence of classic dengue fever. This has been particularly true in the central, northeastern and eastern portions of this country, where climatic conditions are suitable for the successful development of *Aedes* mosquitoes. During La Niña events (or ENSO negative phase), the country usually experiences the opposite (i.e. rainfall deficits and well below normal ambient temperatures). The concomitant droughts cause a decline in dairy production, an increase in the rate of desertification of arable land, and a rise in the occurrence of grassland and forest fires. They also decrease hydropower generation, increase the pollution of rivers and pools of stagnant water, and limit fluvial transportation, thereby increasing import/export transport fees and diminishing the trade of goods. Moreover, long dry spells affect sunflower, maize, soy, cotton, and wheat production, thus reducing the revenue from these key agricultural activities. All these impacts, although mainly the ones affecting the agriculture sector, prompted the creation of a multidisciplinary group led by the Paraguayan Meteorological and Hydrological Service, and the Risk Management Unit at the Ministry of Agriculture and Livestock Farming. Collaboratively, the group issues an agro-meteorological bulletin, every month or at other intervals depending on specific needs, following the approach presented in the decision-making information system below. Activities include (see Figure 11) the Risk Management at the Latin American Observatory 551

assessment of available climate information, seasonal and ENSO forecasts, and medium- to long-term potential impacts on local agricultural production. Key information is then shared with local farmers and smallholders through technicians, advisors and on-the-ground

Up to date, the total number of end-users has reached over 2,000 local, regional and international groups including, among many others, the South American Common Market (MERCOSUR) and organizations in the United States of America, Canada and European countries. Numerous technicians, decision-makers, researchers, and students also use this

In central Chile, the *secano* zone involves a total surface of 4.362 km2, and a human population of 54.450 habitants (6.25% of the regional population). Considered as a rural area, the *modus vivendi* depends basically on agriculture. In the coastal secano of the O´Higgins Region, the total surface designated to crops and plantations is on the order of 22.800 hectares. The majority of this land possesses forage plants (27%), cereals –especially wheat- (21%), fruits (20%), vines and vineyards (25%), and in lesser proportion legumes (5%) and vegetables (2%) [26]. Presently, the cattle production is on the order of 536.170

The secano zone shows a dry season that varies between 6 and 8 months per year. Precipitations take place between April and September, with values around the 500-600 mm/year [27]. Reports on droughts in Chile go back to the times before the Spanish Colonies, with a strong impact in the agriculture production throughout the history of the country [28]. In a period of 400 years, 25% has been reported as dry and half of them as extremely dry [29]. The mean probability is therefore on the order of one dry year every 4 years. Quintana and Aceituno [30] have recently discussed trends of decrease in

Traditionally, the drought management in Chile has been contingent. Nonetheless, in the first decade of the XXI century, government actions in this regard have increased, orienting efforts towards an integrated management of extreme climate events. For example, this originated the National Plan for Civil Protection in 2002. The formal approval of the National Climate Change Strategy in 2006 and the respective Action Plan in 2008 have revealed the adaptation needs of the different productive sectors of Chile, but also the most vulnerable territories and populations. All this created a positive scenario for the establishment of improved policies for climate risk. Three important examples after the extreme drought of 2008 are the creation of the Comité Interministerial de Recursos Hídricos (Inter-ministry Water Resources Committee), the Comisión Nacional de Emergencias Agrícolas y Riesgos Agroclimáticos (National Committee for Agriculture Emergencies and Agro-Climate Risk) and the Sistema Nacional de Gestión del Riesgo Agroclimático (the National System for Agro-Climate Risk Management,

precipitation for central Chile of 10-30% for the second half of the XX century.

found at http://www.minagri.gov.cl/agroclimatico/comision\_nacional.php).

sectoral information in their individual routine activities.

animals, most of them being sheep.

**5.6. Climate risk of droughts in Chile and its effect on agriculture**

experts.

**Figure 11.** The Paraguayan monthly agrometeorological bulletin (available online at: http://www.meteorologia.gov.py/) and the decision-making information system.

assessment of available climate information, seasonal and ENSO forecasts, and medium- to long-term potential impacts on local agricultural production. Key information is then shared with local farmers and smallholders through technicians, advisors and on-the-ground experts.

Up to date, the total number of end-users has reached over 2,000 local, regional and international groups including, among many others, the South American Common Market (MERCOSUR) and organizations in the United States of America, Canada and European countries. Numerous technicians, decision-makers, researchers, and students also use this sectoral information in their individual routine activities.


**2.2.3 HPLC analysis**

412 Macro to Nano Spectroscopy

Ba Bc Bg Bm Bp Bphy Bs Btr Bt

250 350

300

Btr 225, 250

Table 2. Wavelengths (nm) with statistically significant differences (p <0.05) among the species. *B. articulata* (Ba), *B. crispa* (Bc), *B. gaudichaudiana* (Bg), *B. microcephala* (Bm), *B. penningtonii* (Bp), *B. phyteumoides* (Bphy), *B. sagittalis* (Bs), *B. triangularis* (Btr) and *B. trimera*

A TLC analysis was initiated of the dichloromethane extracts of *B. articulata*, *B. gaudichaudiana* and *B. sagittalis* used to perform UV-Visible spectra using the mobile phase DCM: Hexane: MeOH (4:2:1) in order to complement the studies carried out by PCA of the spectrophotometric data and to find potential markers of the species that could not be

> 325 425

> 325 425

> 300 325 350

350 300

300 425

250 350

300 325

 225, 250, 300, 325 350

 225, 250 300, 325 350

225, 250 300, 325 350, 375

> 225 425

> 300 325 425

225, 250, 300, 325, 350, 375

> 250 350 375

> 300 325 375

250, 300 325,350 375

325, 350 375, 425

250

325

Bm 250

Bp 250

Bphy 250

Bs 225

325 350 375

250, 300, 325, 350 375

> 300 325 350

Ba 300

Bt

(Bt)

**2.2.2 TLC analysis**

325

Bg 250

Bc 300

HPLC analysis was carried out on the same extracts as used for the studies with UV-Visible spectrophotometry and for TLC in *B. articulata*, *B. gaudichaudiana* and *B. sagittalis.* The chromatophotographic profiles showed the main peaks with the following retention times for *B. articulata*: 2.32, 3.00, 3.22 and 3.30 min; for *B. gaudichaudiana*: 2.26, 3.00, 3.12 and 3.30 min and for *B. sagittalis*: 2.26, 2.40, 3.00, 3.12 and 3.30 min. The retention time for the apigenin marker was 3.00 min and for genkwanin was 3.30 min. These peaks appear in all three species studied. In *B. gaudichaudiana* and *B. sagittalis* there is also a peak at 3.12 min, and this retention time corresponds to the luteolin marker (Figure 10).

Quality Control of Herbal Medicines with Spectrophotometry and Chemometric Techniques

– Application to *Baccharis* L. Species Belonging to Sect – Caulopterae DC. (Asteraceae) 415

Fig. 10. Original chromatograms obtained by HPLC of *B. articulat*a (Ba), *B. gaudichaudiana*

The growing tendency to use high quality, standardized plant extracts, with a guarantee of security and efficacy, is going to continue. Therefore, all efforts should be directed at researching the chemical-pharmalogical profiles of the extracts and combinations and in

(Bg) and *B. sagittalis* (Bs),. Compounds: 1, apigenin; 2, genkwanin; 3, luteolin.

**3. Conclusion**

rationalizing their therapeutic applications.

Fig. 9. TLC of *B. articulata (Ba), B. gaudichaudiana (Bg)* and *B. sagittalis (Bs)*. Chromatograms were sprayed with NP-PEG and observed under UV 365 (A) or under UV 254 without chemical treatment (B). A, apigenin; G genkwanin; L: luteolin. Mobile phase: DCM: Hexane:MeOH (4:2:1)

Fig. 10. Original chromatograms obtained by HPLC of *B. articulat*a (Ba), *B. gaudichaudiana* (Bg) and *B. sagittalis* (Bs),. Compounds: 1, apigenin; 2, genkwanin; 3, luteolin.


**Author details**

reduction of OLEDs and performance enhancement. The potential inexpensive synthesis methods, OLEDs fabrication process, flexibility and lightweight make them one of the promising materials for energy devices. A large number of phosphorescent and fluorescent organic materials have been already synthesized for their use in OLEDs. Recently, TADF materials have been introduced in OLEDs fabrication to achieve 100% IQE and high EQE, which can be

120 Light-Emitting Diode - An Outlook On the Empirical Features and Its Recent Technological Advancements

The actual development of organic TADF emitters achieves a status that surpasses a simple curiosity. The new OLED framework based on highly efficient TADF materials opens a new field of applications for display and lightening. These materials, based on separated donor-acceptor moieties in one molecule, leads to a unique luminescent process of a triplet harvesting in the excited state with a further huge increase of internal efficiency up to the 100% limit. This simple way to tailored pure organic emitters is really one of the most recent advances in chemistry and OLEDs filed. Achieving very high external efficiencies without using an expensive and rare transition metals, puts the organic emitters towards new possibilities. Particularly important is the application in lighting from large area emitters, a field that is until now, a technological problem. The possibility to have a full range color emitters with high efficiency (as shown, and particularly in blue) is also an outstanding achievement towards a new device design and application, particularly in white emission. Nevertheless, improving the efficiency towards the theoretical efficiency limit, can only be achieved with a deep understand of the TADF process in these organic molecules. This is due to the fact that the interactions between energy levels of TADF emitter and the host can condition the emission due to the TADF process itself as referred before. Thus, the next steps must be focused on the physical models for this *rISC* pathway and the relationship with the molecular structure. In the OLEDs point of view, the design of reliable host materials that fulfills the requirements needs to allow an efficient luminesce emission form the

TADFs have been studied and used for OLEDs so far, and this field is relatively young but it has developed significantly during the past 5 years. By the incorporation of TADFs 40–50% EQE can be achieved. However, at the present stage despite of numerous characterization techniques to understand the TADF behavior, more rigorous efforts are required for their understanding and use for commercially production. The fabrication cost of the OLEDs based on TADF emitter should be less to make them major candidate in both display and lightening industry and the cost is critical. To reduce the cost, it is necessary to develop solution processed OLED fabrication methods and synthesis of polymeric or dendrimeric TADF emitters. These TADFs have already been found a significant role in next-generation displays and lightning materials and their use can only be realized as their synthesis characterization and

The authors would like to acknowledge the i3N support from UID/CTM/50025/2013 and the EXCILIGHT Project from the European Union's Horizon 2020 research and innovation pro-

gram under the Marie Sklodowska-Curie grant agreement No 674990.

achieved near 50%.

TADFs. A new era is opened.

device fabrication progresses.

**Acknowledgements**

Manish Kumar1,2, Miguel Ribeiro1 and Luiz Pereira2 \*

\*Address all correspondence to<EMAIL_ADDRESS>
1 CeNTI – Centre for Nanotechnologies and Smart Materials, R. Fernando Mesquita, Portugal

2 Department of Physics and i3N – Institute for Nanostructures, Nanomodulation and Nanofabrication, University of Aveiro, Portugal


*2.3. The Influence of Di*ff*erent Physicochemical Parameters on the Biosorption of Co2*+ *by PLEM*

Experiments to evaluate the effect of several physicochemical variables on the biosorption of Co2+ by *PLEM* were carried out in 500 mL Erlenmeyer flasks. They contained 120 mL of a solution with a known concentration of Co2+ at a predetermined pH value. Subsequently, an addition was made of 0.12 g of PLEM at a certain particle size, thus achieving a biosorbent concentration of 1 g (dry weight) L−1 . The suspensions were left at 18 ◦C (rt) for 2 h under constant agitation at 140 rpm in an orbital shaker (All Sheng™, Hangzhou Allsheng Instruments Co, Ltd., Hangzhou, China). The pH of the solutions was adjusted to the desired value and maintained constant throughout the assay by adding 0.1 M HCl and 0.01 M NaOH.

Firstly, the pH varied (2, 3, 4, 5, 6, and 7), while maintaining the initial concentration of Co2+ (*Cini*) at 100 mg L−1 and the particle size of *PLEM* at 0.3–0.5 mm. Later, distinct particle sizes (0.3–0.5, 0.5–0.8, 0.8–1.4, 1.4–2.0, and 0.3–0.8 mm) were utilized, while maintaining *Cini* at 100 mg L−1 and the pH at 7.0. Finally, different initial values of *Cini* (10, 20, 40, 60, 80, 100, 200, and 300 mg L−1 ) were used, while maintaining the pH at 7.0 and the particle size at 0.3–0.8 mm.

During the experiment, samples were taken at various exposure times and filtered to afford a solution free of biosorbent. The filtrate of each flask was diluted properly for the posterior quantification of the cobalt concentration. From the values obtained, the biosorption capacity of Co2+ by *PLEM* was calculated at a series of exposure times using Equation (1):

$$q = \frac{V}{M}(\mathbb{C}\_{\text{ini}} - \mathbb{C}) \tag{1}$$

where *q* (mg g−1 ) is the capacity of biosorption of Co2+, *V* (L) is the total volume of the solution, *M* (g) is the biosorbent mass, and *Cini* and *C* (mg L−1 ) correspond to the initial concentration of Co2+ in the solution and its concentration at time *t* (h), respectively. When the system reaches equilibrium, *t* = *teq*, *C* = *Ceq* and *q* = *qeq*. Based on the values of biosorption capacity found, the most suitable pH of the solution and the best particle size for the removal of Co2+ were selected for the rest of the biosorption experiments. For each of the parameters examined, controls free of biosorbent were established and analyzed for possible changes in the concentration of cobalt.

### *2.4. Kinetic Modeling of the Biosorption of Co2*+ *by PLEM*

For the kinetic modeling of the biosorption of Co2+ by *PLEM*, the equations of pseudo-first-order, pseudo-second-order, and fractional power were employed (Table 1).

#### *2.5. Biosorption Isotherm Studies at Di*ff*erent Temperatures*

In 125 mL flasks were poured 30 mL of solutions of Co2+ at distinct concentrations (20, 40, 60, 80, 100, 200, and 300 mg L−1 ), adjusting the pH to 7.0. Then 0.03 g of *PLEM* (particle size = 0.3–0.8 mm) was placed in each flask to ensure a concentration of 1 g L−1 of *PLEM*. The suspensions were left for 2 h at 18, 30, 40, 50, or 60 ◦C to reach biosorption equilibrium. Subsequently, the samples from each flask were filtered, and the residual concentration of Co2+ was quantified in each filtrate. With the experimental results of the biosorption capacity found at equilibrium (*qeq*) and the residual concentration of cobalt at equilibrium (*Ceq*) for each initial concentration of metal assayed (*Cini*), the isotherm for adsorption was calculated. It was then possible to select the best mathematical model for describing the experimental behavior. With this objective in mind, models of two (Langmuir and Freundlich) and three parameters (Sips and Redlich-Peterson) were used (Table 1).

#### *2.6. Determination of the Thermodynamic Parameters*

The thermodynamic parameters examined were the changes in Gibbs free energy (∆*G* 0 , J mol−1 ), in standard entropy (∆*S* 0 , J mol−1 K −1 ), and in standard enthalpy (∆*H*0 , J mol−1 ). With the data on the isotherms for biosorption at equilibrium, the coefficient of distribution (*Kd,* L g−1 ) was obtained for each temperature and concentration assayed using Equation (2) [21]:

$$K\_d = \frac{q\_{eq}}{\mathcal{C}\_{eq}} \tag{2}$$

In the graph of Ln *Kd* vs. *Ceq* for each temperature, the point at which the ordinate crosses the origin corresponds to Ln *K*0 (*K*0 being the sorption constant at equilibrium, L g−1 ). These values were substituted in Equation (3) to find the change in Gibbs free energy [22]:

$$
\Delta G^0 = -RT\,\ln K\_0 \tag{3}
$$

where *R* is the constant of the ideal gases (8.315 J mol−1 K −1 ), and *T* is the absolute temperature (K) during biosorption. The change in standard entropy (∆*S* 0 ) was found by Equation (4):

$$
\Delta S^0 = \frac{\partial \Delta G^0}{\partial T} \tag{4}
$$

The slope of the graph of ∆*G* 0 vs. *T* indicates the mean value of ∆*S* 0 . The change in the standard enthalpy was furnished by Equation (5):

$$
\Delta \mathbf{G}^0 = \Delta H^0 + T\Delta \mathbf{S}^0 \tag{5}
$$

### *2.7. Desorption of Co2*+ *from the Biosorbent*

To evaluate desorption, the biosorbent was first saturated by exposing *PLEM* (1 g L−1 , with a particle size of 0.3–0.8 mm) to a solution of Co2+ (300 mg L−1 , pH 7.0, rt) under constant agitation at 140 rpm for 2 h. Upon completion of this time, the biosorbent was washed with deionized water several times to eliminate the excess cobalt and then dried in an oven at 60 ◦C for 48 h. Finally, it was stored in hermetically-sealed bottles until further use.

For the desorption of Co2+ from *PLEM*, diverse solutions were tested as eluents: Water at rt (H2O rt, the control), water at 60 ◦C (H2O 60 ◦C), various acidic solutions (HCl, H2SO4, HNO3, C2H2O4, KH2PO4, and NH4Cl) and three alkaline compounds (NaOH, NaHCO3, and K2HPO4). The concentration of all compounds was 0.1 M. Desorption was carried out by placing 120 mL of one of the distinct eluent solutions in each Erlenmeyer flask and adding the saturated biosorbent at a concentration of 1 g L−1 . The material was maintained under constant agitation at 140 rpm and 18 ◦C for 2 h, collecting and filtering samples from each of the flasks at different times. The concentration of desorbed metal on each filtrate was quantified. The percentage of desorption at time *t* was calculated with Equation (6) [23]:

$$E\_D(\%) = \frac{V(\mathcal{C}\_D - \mathcal{C}\_{ini})}{M \, q\_{eq}} \times 100\tag{6}$$

where *Cini* and *CD* (mg L−1 ) are the initial concentration of metal in the solution (*t* = 0 h) and the concentration of Co2+ eluted from the solution at time *t*, respectively, and *qeq* (mg g−1 ) is the amount of Co2+ retained per gram of biosorbent (determined experimentally). The results of the percentage of the desorption were compared to select the adequate solution for eluting Co2+ from *PLEM*.

#### *2.8. Biosorption-Desorption Cycles*

*PLEM* was saturated with Co2+ for 2 h, as described in the previous section. Upon completion of this time, samples of the solution were taken to assess the biosorption capacity of *PLEM* in the first stage (Equation (1)). Subsequently, the saturated biosorbent was washed, dried, and subjected to the desorption of Co2+ (as already explained) by putting 1 g L−1 of the material in a solution with the selected eluent and leaving it under constant agitation at 140 rpm and rt for 2 h. Samples were then taken to quantify the concentration of Co2+ in the solution and calculate the percentage of desorption for the first cycle (Equation (6)). *PLEM* was washed with deionized water and dried at 60 ◦C for 48 h to be submitted to posterior cycles. Three cycles of biosorption/desorption were carried out under the same conditions, allowing for the comparison of the capacity of biosorption and percentage of desorption from one cycle to another.

### *2.9. Scanning Electron Microscope Coupled to Energy-Dispersive X-ray Spectroscopy (SEM-EDX)*

The possible changes in the structure and composition of the surface of *PLEM*, due to the process of biosorption and the posterior desorption of Co2+ were explored on a scanning electron microscope (SEM). The three types of samples of *PLEM* (unexposed to Co2+, saturated, and desorbed in the first cycle) were dried for 24 h at 60 ◦C. Subsequently, they were covered with carbon to be later observed with a JEOL high-resolution scanning electron microscopy (HR-SEM) (model JSM7800F, Jeol Ltd., Tokyo, Japan) with an acceleration voltage of 5 kV.

#### *2.10. Analytical Methods*

Co2+ was quantified by the dimethylglyoxime (DMG) method, with which a compound is formed with an intensity of color proportional to the concentration of Co2+ present in the solution [24]. The measurement of absorbance was conducted in a Spectronic Genesys UV/Vis 10 spectrophotometer (Thermo Electron Scientific Instruments Corp, Madison, WI, USA) at 400 nm. The concentration of Co2+ was established by constructing metal-type curves with at least 10 distinct known concentrations.

#### *2.11. Statistical Analysis*

Each experiment was performed independently at least twice, and the determinations of residual cobalt were made at least three times, with the aim of attaining the appropriate statistical power. Data are expressed as the mean ± standard deviation (SD) of the values obtained experimentally. Regarding the values from the kinetics of biosorption and the experimental biosorption capacity at equilibrium (*qeq*), differences between groups were examined with two-way ANOVA and Tukey's test (with a confidence interval of α = 0.05) on the GraphPad Prism® Ver 8.4 program 2020 (GraphPad Software Inc, San Diego, CA, USA). The kinetic and equilibrium parameters were scrutinized by nonlinear regression on the same software, selecting the best model in accordance with a variety of error functions: The correlation coefficient (*R* 2 ), the absolute sum of squares (*ASE*), the standard deviation of the residuals (*Sy.x*) and Akaike's information criterion (*AICc*). The data from the three cycles of biosorption/desorption were compared with one-way ANOVA and Dunnett's test (confidence interval, α = 0.05) on the GraphPad Prism® Ver 8.4 program 2020 (GraphPad Software Inc., San Diego, CA, USA).

#### **3. Results and Discussion**

No change in the concentration of Co2+ was found for the *PLEM*-free solutions, used as controls for the evaluation of the influence of the physicochemical conditions herein tested. Thus, the removal of Co2+ from the aqueous solution can be fully attributed to the effect of biosorption produced by *PLEM*.

### *3.1. The E*ff*ect of pH*

The level of pH is one of the physicochemical factors that most influence the biosorption of heavy metals [25]. The pH values of 2–7 were presently employed because the precipitation of cobalt was observed experimentally as of pH 8, likely due to the formation of cobalt hydroxide [26,27]. At each pH value, the biosorption capacity was evaluated with respect to time (Figure 1a). With the pH at 2 or 3, the cobalt removal capacity was near 0.

*3.1. The Effect of pH*

**3. Results and Discussion**

*PLEM*.

2020 (GraphPad Software Inc., San Diego, CA, USA).

ANOVA and Tukey's test (with a confidence interval of α = 0.05) on the GraphPad Prism® Ver 8.4 program 2020 (GraphPad Software Inc, San Diego, CA, USA). The kinetic and equilibrium parameters were scrutinized by nonlinear regression on the same software, selecting the best model in accordance with a variety of error functions: The correlation coefficient (*R*2), the absolute sum of squares (*ASE*), the standard deviation of the residuals (*Sy.x*) and Akaike's information criterion (*AICc*). The data from the three cycles of biosorption/desorption were compared with one-way ANOVA and Dunnett's test (confidence interval, α = 0.05) on the GraphPad Prism® Ver 8.4 program

No change in the concentration of Co2+ was found for the *PLEM*-free solutions, used as controls for the evaluation of the influence of the physicochemical conditions herein tested. Thus, the removal of Co2+ from the aqueous solution can be fully attributed to the effect of biosorption produced by

The level of pH is one of the physicochemical factors that most influence the biosorption of heavy metals [25]. The pH values of 2–7 were presently employed because the precipitation of cobalt was observed experimentally as of pH 8, likely due to the formation of cobalt hydroxide [26,27]. At each pH value, the biosorption capacity was evaluated with respect to time (Figure 1a). With the pH at 2

**Figure 1.** Capacity of biosorption of Co2+ by pretreated *Lemna gibba (PLEM)*: (**a**) At various pH values of the solution (*Cini* = 100 mg L−1 , particle size = 0.3–0.5 mm), (**b**) with distinct particle sizes of *PLEM* (*Cini* = 100 mg L−1 , pH = 7.0), and (**c**) at different initial concentrations of the metal (pH = 7.0, particle size = 0.3–0.8 mm). The continuous lines were predicted by the pseudo-second-order kinetic model.

The sorption capacity was enhanced with each increment in pH from 4 to 7, which can be easily explained by considering the pH of the plant material (1.67), which results in zero point of charge (ζ0) [17]. When the pH of a solution is less than that found at ζ0, the net charge of the surface of the biosorbent is positive. Hence, an electrostatic repulsion exists between the positive charge of both the metal ions and the surface of the biosorbent [28]. In contrast, when a solution has a pH value above that at ζ0, the net charge of the surface of the biosorbent is negative, and there is an attraction with the positively charged metal ion [29]. A pH value of 5–7 herein afforded the fastest biosorption of Co2+ during the first 10 min (0.17 h) of the experiment. After this time, however, the velocity of removal of the metal decreased until reaching equilibrium, at which point the velocity of net transfer was 0. The initial rapid biosorption was due to the greater number of sites on *PLEM* available for the uptake of the sorbate and the higher concentration of Co2+ in the aqueous solution. As time passed, the available sites and the concentration of free cobalt ions were both diminished, leading to a gradual decline in the velocity of the removal of Co2+ until reaching the equilibrium dynamic. It was observed that as the pH increased, the biosorbent removed more Co2+, and therefore, required more time to reach equilibrium

(*teq*). The same phenomenon has been reported for the effect of pH on the biosorption of other divalent metal ions [29].

A summary of the of Co2+ removal capacity at experimental equilibrium (*qeq*), the time to reach equilibrium (*teq*), and the values of the parameters and error functions for each model and at each pH value assayed are provided in Table 2. None of the kinetic models employed fit the experimental results at pH 2 or 3, probably owing to the minimal biosorption of Co2+ under these conditions. At pH 4, a reduction in the removal capacity was only found after 0.75 h (Figure 1a), a time period not included in the process of biosorption. Hence, the corresponding data was not considered when determining the values of the parameters for the kinetic models. With a pH of 4–7, the pseudo-second-order model had the highest correlation coefficient (*R* 2 ) and the lowest values for *ASE, Sy.x*, and *AICc* compared to the other two models (pseudo-first-order and fractional power). The Elovich model was also evaluated, but is not listed in the tables because the *R* 2 was too small, and the parameters obtained had exaggerated *SD* values. Given that a pH of 7 produced the greatest biosorption capacity at equilibrium, this value was used for further testing.

**Table 2.** Kinetic parameters of the biosorption of Co2+ by *PLEM* at various pH values of the solution (*Cini* = 100 mg L−1 , particle size = 0.3–0.5 mm).


**1. Introduction**

Ergonomic Design Thinking (EDT) was originally conceived as a project management model used for both organizational design and project management. Thus, it must be understood as an effective tool for project management optimization in general applications, not only design projects. The main idea is the general concept that makes possible the application of creative reasoning and intensive end-user participation in the course of project management just like designers do when solving problems in their during the creative work process. Conventional planning and decision making methods flow in a linear manner, way different from creative thinking. "HSE," short for health safety and environmental management is a fairly new–and crucial–area in operations management [1], especially for those organizations that fall in the category of complex systems, which require a 360° approach to operations management. HSE decision analysis in such environments is definitely a challenge, since prioritizing becomes difficult due to multiple criteria to assess, risks to ponder and level of severity in terms of collective impact and workstation evaluation.

This chapter outlines the general model for carrying out HSE project management approaches in the workplace. In practical terms, it adds up to existing management systems that are designed to comply with industry and government standards. In order to contextualize the theoretical framework, a case study is employed as a guide for the implementation of a specific HSE Management system for the construction industry. In order to achieve that, the work team used a set of strategies set forth by the EDT modeling, as proposed by Santos and Soares [2]. The choice of this industry segment is due to the fact this particular sector is known by the lack of qualification of its workers. As a consequence, it is acknowledged worldwide as a low performance sector in terms of controlling its operational and environmental risks and hazards.

The EDT approach used in the course of the process being described in here was carried out to help the consolidation of a HSE management system for building construction sites. Thus, the complex nature of the work process is dictated not by its operational characteristics or the usual determinants of larger complex systems (i.e., oil refineries, nuclear power plants, medical emergency rooms, construction sites), but because of the difficulties posed by management issues, such as control and enforcement of safety and health practices in this type of work. The building and construction industry struggles to set up and maintain effective action plans and indicators when it comes to health safety and environmental processes, commonly referred to as HSE. In fact, its workers ranks the second most exposed to work accidents of all industrial segments in Brazil [3], just behind the transportation sector.

The consequence of poor control and management of HSE in construction sites leads to a series of problems ranging from plain inconvenience of mishandling or misinterpretation of data to more serious ones such as poor HSE plans and management systems. The end result of this is converted into an unpleasant workplace, health and safety issues and all sorts of environmental problems. Without the continuous optimization of HSE processes, it becomes virtually impossible to positively improve the work conditions for the workers and all those directly or indirectly affected by the outcomes of bad planning, poor management and actual operational actions. The EDT methodology provides the "real work element" for such continuous improvement since it is more palatable and credible for containing and merging the expectations of the workers with the needs of the productive process.


*2.4. The Implicit-Explicit Time Discretization*

As the penalization introduces stiff terms to the equations and for accuracy, we would want them to be as stiff as possible, we introduce an implicit time integration for those terms. With an otherwise explicit time integration scheme, this results in an implicit-explicit time-stepping scheme that can be achieved by splitting the right-hand side of the equations into an explicitly integrated part and an implicitly integrated part. Therefore, to perform time integration of the system, we use a Diagonally Implicit Runge–Kutta (DIRK) scheme with three explicit and four implicit stages as presented in [14]. The following section first considers a single implicit Euler step, to discuss the arising equations that need to be solved in each implicit stage of the higher order time discretization.

We denote the right-hand side by *Q* and employ the superscript *ι* for the implicit part and the superscript *ξ* for the explicit part. By using the conservative quantities as subscripts (*ρ*, *m*, and *e*), we can distinguish the right-hand sides for the different equations. Thus, we get:

$$\frac{\partial \rho}{\partial t} = Q\_{\rho}^{\mathbb{E}} + Q\_{\rho}^{\iota} \tag{18}$$

$$\frac{\partial m\_i}{\partial t} = Q\_{m\_i}^{\mathbb{F}} + Q\_{m\_i}^{\iota} \tag{19}$$

$$\frac{\partial \mathcal{e}}{\partial t} = Q\_{\mathcal{e}}^{\mathbb{F}} + Q\_{\mathcal{e}}^{\iota} \tag{20}$$

and we chose the implicit parts as:

$$Q^t\_\rho = 0\tag{21}$$

$$Q'\_{m\_i} = -\frac{\chi}{\eta} \left( u\_i - \mathcal{U}\_{oi} \right) \tag{22}$$

$$Q\_\varepsilon^t = -\frac{\chi}{\eta\_T} \left( T - T\_o \right) \tag{23}$$

out of Equations (7) and (9).

This choice restricts the implicit computation to the local source terms, which can be evaluated pointwise. Unfortunately, the introduced Brinkman porosity in (7) affects the flux and introduces spatial dependencies. To avoid the need for the solution of an equation system across the whole domain for this dependency, the porosity part will be computed in the explicit time-stepping scheme.

### Observation for the Implicit Part

Considering Equations (18)–(20) only with their implicit parts, we get the following equation system:

$$\frac{\partial \rho}{\partial t} = 0\tag{24}$$

$$\frac{\partial m\_i}{\partial t} = -\frac{\chi}{\eta} \left(\mu\_i - \mathcal{U}\_{oi}\right) \tag{25}$$

$$\frac{\partial \varepsilon}{\partial t} = -\frac{\chi}{\eta\_T} \left( T - T\_\delta \right). \tag{26}$$

Notice that these equations can be solved pointwise as no spatial derivatives appear.

A discretization of these equations in time with a Euler backward scheme yields the solvable equation system:

$$\frac{\rho(t+\Delta t) - \rho(t)}{\Delta t} = 0\tag{27}$$

$$\frac{m\_i(t + \Delta t) - m\_i(t)}{\Delta t} = -\frac{\chi}{\eta} \left( u\_i(t + \Delta t) - \mathcal{U}\_{oi} \right) \tag{28}$$

$$\frac{e(t+\Delta t) - e(t)}{\Delta t} = -\frac{\chi}{\eta\_T} \left( T(t+\Delta t) - T\_o \right). \tag{29}$$

Equation (27) trivially yields *ρ*(*t* + Δ*t*) = *ρ*(*t*). With the implied constant density, we can now solve the equation for the change in momentum (28) and arrive at an explicit expression for the velocity *ui*(*t* + Δ*t*) at the next point in time:

$$\frac{\rho(t+\Delta t)u\_i(t+\Delta t) - \rho(t)u\_i(t)}{\Delta t} = -\frac{\chi}{\eta} \left( u\_i(t+\Delta t) - lL\_{oi} \right) \tag{30}$$

$$u\_i(t+\Delta t) = \frac{\rho(t)u\_i(t) + \frac{\chi\Delta t}{\eta}\mathcal{U}\_{oi}}{\rho(t) + \frac{\chi\Delta t}{\eta}}.\tag{31}$$

Finally, density and velocity at the new point in time can be used to find the new temperature as well by substituting the above results in Equation (29) and solving for the temperature at the next point in time. We find:

$$T(t + \Delta t) = \frac{\frac{\chi \Delta t}{\eta\_T} T\_o + c\_v \rho(t) T(t) + \frac{\rho(t)}{2} (u\_i^2(t) - u\_i^2(t + \Delta t))}{c\_P \rho(t) + \frac{\chi \Delta t}{\eta\_T}}. \tag{32}$$

where *ui*(*t* + Δ*t*) is given by Equation (31).

Thus, this specific choice of terms for the implicit part of the time integration scheme yields a system that can be solved explicitly and without much additional computational effort. However, the implicit discretization allows for arbitrarily small values of *η* and *ηT*. A similar approach was developed by Jens Zudrop to model perfectly electrical conducting boundaries in the Maxwell equations, and more details can also be found in his thesis [15].

To solve the complete system, we then employed the diagonally implicit Runge–Kutta scheme with three explicit stages and four implicit stages [14]. It provides a scheme that is third order in time and L-stable.

Note, that while this approach overcomes time step limitations with respect to the permeabilities *η* and *ηT*, the porosity term changes the eigenvalues of the hyperbolic system and affects the stability.


**3.6. Inserting stubs**

222 Ultra Wideband – Current Status and Future Trends

and operates over the range of 2-6 GHz.

in [50].

**3.5. Using spatial and angular variations**

coupling, isolation and impedance matching are presented.

**UWB-MIMO Systems** - In [46], a diversity antenna operating at a frequency range of 3.1-5.8 GHz is designed consisting of two orthogonal half circles with the radiators placed symmetrically with respect to a protruded T-shaped ground plane, which has a slot at the upper center portion of the ground plane. This slot helps in enhancing the isolation and matching the impedance. In [47], this technique is used in real sense where circular slot antenna with a stepped ground plane is proposed. A stepped ground plane generates nonplanar connections and discontinuous interfaces between the elements and the system ground planes. This strategy has effectively decreased the mutual coupling and provided 10 dB enhancements in isolation characteristics. The antenna consists of four radiating elements

**Narrowband MIMO Systems** - The technique of using spatial and angular variations relative to the antenna elements of array is very commonly used to reduce mutual coupling. It is well demonstrated that by increasing the space between the radiating elements decorrelates them and even the spacing greater than or equal to ߣȀʹ gives mutual coupling less than -20 dB, where ߣ is free space wavelength at the center frequency [48]. However, the spacing becomes less than ߣȀʹ in the case of compact MIMO antennas for portable devices, thus it requires considering the mutual coupling effects to be compensated [49]. Therefore, in addition to separating the radiating elements by some distance, positioning of the radiating elements at different angles with respect to each other helps to reduce mutual coupling by exploiting the diversity in polarization. Chae et al. [27] has presented the detailed study using this technique. Further, the same technique is described and employed

**UWB-MIMO Systems** - Being very simple technique, it has not some specific constraints relating to the bandwidth but with size of the antenna. First of all, this technique is used for UWB diversity antenna by Wong et al. in [51] where the antenna consists of two truncated square monopoles orthogonally and symmetrically printed on the two sides of a T-shaped protruded ground plane as shown in Table 2. This antenna operates over 2.3-7.7 GHz giving isolation more than 20 dB. Using T-shaped ground plane also indicates that the modification of ground plane is an additional technique used together with polarization diversity to enhance the isolation. Recently, Chen et al. have used the similar technique [52]. It presents very compact UWB diversity antenna exploiting polarization diversity. The antenna elements are fed orthogonally and are designed for the lower band of UWB, i.e., 3.1-4.8 GHz. The isolation between two antennas is greater than 20 dB across the bandwidth. Also, the same research group has presented a detailed analysis of two suspended UWB plate antennas operating over 3.0-6.0 GHz in [53] for UWB-MIMO systems. They tested two configurations; (i) when shorting walls are vertically positioned (ii) when shorting walls are horizontally positioned. The effects of the variation of distance between antennas on mutual **Narrowband MIMO Systems** - The technique of using stubs to get better isolation also deals with the ground plane instead of the radiating elements. One or more stubs are inserted to enhance the isolation. To the best of our knowledge, there is no scientific publication presenting the use of this technique for narrowband MIMO systems.

**UWB-MIMO Systems** - The method of inserting stubs is mainly found in the literature for UWB-MIMO antennas. For instance, in [54], two elements diversity planar antenna, with three stubs on the ground plane to improve the isolation, has been proposed particularly for PDA phone. The 10 dB return loss bandwidth is achieved from 2.27 GHz to 10.2 GHz and isolation is always more than 15 dB. Similarly, another printed UWB diversity antenna consisting of two square radiators and a cross stub placed between them on the ground is presented in [55]. The 10 dB return loss bandwidth of the antenna ranges from 3.1 GHz to 10.6 GHz and the isolation between the two ports is higher than 18 dB within 3.3 GHz to 10.5 GHz.