\r\n\tThe knowledge of the production potential of innumerable chemical compounds produced by plants generates great expectations and concrete possibilities so that in the not too distant future, science can make available new technological processes compatible with the current needs. Among the numerous chemically diversified compounds, the most polar varieties that range from non-polar, encompassing hydrocarbons, such as polyphenols, found in the most different fractions such as essential oils, fixed oils, and extracts, all have a promising potential for treating different endemic diseases.
\r\n
\r\n\tIn this book, entitled: Essential Oils - Advances in Extractions and Biological Applications, experimental study, or in-silicon study, we will try to address the most varied possibilities of using natural products to control diseases, as well as applications. Priority will be given to secondary metabolites produced by plants, and especially essential oil. The focus will be on the issues related to the functions of natural products and their applications. We will also address the biosynthetic pathways of production by plants.
\r\n
\r\n\tOverall, the book will provide its readers with a broad and diverse mirror of the importance that secondary metabolites produced by plants represent the safety of plants and the possibilities for innovative biotechnological approaches that will make all the difference to new potential industrial products.
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1. Introduction
Malnutrition is an increasing health problem among children in most developing African countries, especially among rural infants [1]. These young children are most vulnerable due to their high nutritional requirements for growth and development. This is on the backdrop that over 60% of the world population in Africa live in rural areas [2]. In many poor communities in Africa such as Ghana, a severe form of malnutrition called Kwashiorkor is common. Malnutrition is problematic because many children younger than 5 years in rural areas are exposed to multiple challenges including the high poverty situation of parents and unstimulating home environments. This can affect feeding practices. As a result, these children are disadvantaged leading to malnutrition, poor health, and early childhood stunting. These conditions detrimentally affect their cognitive, motor, and social–emotional development. The effects extend into poor cognitive and educational performance.
Children who perform poorly in school, have attention deficit, have poor future prospects, and subsequently have low incomes; as adults, they exhibit poor cognitive utility living lives below their intellectual capabilities. Attention problems resulting from malnutrition-related challenges persisting to adulthood are an indication of the continuation of poor brain development over the life span of a person. Infantile malnutrition may have long-term effects on attentional processes for over 40 years after its initial establishment. Once established it is difficult to reverse even with excellent long-term nutritional rehabilitation and provision of independent socioeconomic conditions in childhood and adolescence. The challenge with malnutrition, poor cognitive development, and related poverty carry over effects continues to adulthood leading to poor care for children, thus contributing to the intergenerational transmission of poverty and poor standard of living [3, 4, 5]. The objective of this chapter is to explore alternative nutritional sources to increase the cognitive development of children in rural areas in Africa, especially Ghana.
2. Factors influencing brain development in children
Historically, higher IQ children are found in urban areas. It has been observed that although both low-income rural and low-income urban children show working memory deficits compared with their high-income counterparts, the low-income urban children exhibited symmetrical verbal and visuospatial working memory deficits compared to their high-income urban counterparts. These results suggest that different types of poverty are associated with different working memory abilities. Memory deficiency is predominately enhanced in rural locations where children are deprived of good nutritional benefits. It is estimated that over 200 million children under 5 years are not fulfilling their developmental potential in south Asia and sub-Saharan Africa, which have more nutritional deficiencies especially in rural areas [6].
For the same level of socioeconomic status in assessing executive functions and non-verbal intelligence performance in 5-year-old children, children in rural settings performed consistently worse than children in urban settings. Both parents’ educational level and poverty greatly accounted for poor cognitive functions. In the US lower omega-6 to omega-3 ratios are associated with eating healthy diets in both adults and children. However, adults with a college degree showed much lower ratios than those without a college degree. The situation depicts the impact of knowledge level, financial ability, and exposure to education on eating habits, which are more experienced in parts of Latin America and Sub-Saharan Africa [6, 7]. In China, the urban community setting showed a significant protective effect on cognitive impairment. Cognitive underdevelopment was often prevalent in underprivileged communities and these are poorly addressed in rural settings. It is stated that malnutrition early as far as 3 years has an association with poor cognition at age 11. This prompts the need to promote early childhood nutrition and address factors affecting nutritional choices since they enhance long-term cognitive development and school performance, especially in children with potential for multiple nutritional deficits as may happen in rural areas [8].
2.1 How rural children develop lower cognition level
Cognition is defined as the processes by which an individual processes information through skills of perception, thinking, memory, learning, and attention. There is more evidence that connects improved nutrition and optimal brain function, with studies showing that brain development is faster in the early years of life compared to the rest of the body. The conditional availability of breast milk early in life may account for faster brain development. However, brain development is affected by quality, timing, and the regional requirement for a particular nutrient at that time in the brain. Nutrients provide building blocks that play a critical role in cell proliferation, DNA synthesis, and neurotransmission, and hormone metabolism. Therefore, children that are malnourished through the mother may be at higher risk [7, 8].
Urban children are taller and heavier than rural counterparts in almost all low-income and middle-income countries. The urban–rural difference is largest in Andean and some central Latin American countries such as Peru, Honduras, Bolivia, and Guatemala. A similar trend in some African countries including Niger, Burundi, and Burkina Faso; and in Vietnam and China. The disparity between urban and rural children’s growth is clear with urban children in China, Chile, and Jamaica looking taller than their rural folks. All over the world, the heaviest children live in cities and the most underweight in rural areas. Between 1985 and 2011, the urban advantage in height fell in southern and tropical Latin America and South Asia, but changed little or not at all in most other regions. The urban–rural weight differential also decreased in southern and tropical Latin America, but increased in East and Southeast Asia and worldwide. Overall, the weight gain of urban children outpaced that of rural children [9].
While listing other factors such as poor health care and improved access to affordable stable food and its supply as the major contributing factors to poor cognitive development in rural areas, the quality of nutrition was largely lacking in rural areas. Here, the foods lack adequate amounts of limited nutrients such as proteins, fatty acids, and vitamins. In Ghana, children with the highest levels of total n-3 and docosahexaenoic acid (DHA) were three and four times, respectively, more likely to pass at least one condition of the dimensional change card sort (DCCS) test of executive function than those with the lowest DHA levels. The results of this study indicate an association between n-3 FAs and high-level cognitive processes in children two to six years of age, providing an impetus for further studies into possible interventions to improve essential fatty acids (EFA) status of children in developing countries [10]. The effects of docosahexaenoic acid/omega-3 long-chain essential fatty acid on externalizing behavior are more mixed. Other micronutrients known for their impact on brain development and cognition include zinc, iron, vitamin B, and protein deficiencies, which lead to low IQ and later high antisocial behavior in children. However, the more indicators of malnutrition there is, the greater the antisocial behavior resulting from children. From animal and human findings, it is clear that malnutrition impairs neurocognitive functioning by reducing neurons, alternating neurotransmitter functioning, and increasing neurotoxicity. The neurocognitive impairments predispose to externalizing behavior. Poor nutrition contributes to the development of child behavior problems (Figure 1) [11, 12, 13].
Figure 1.
Nutritional and social factors influencing brain development (Source: Osendarp [11]). Many factors contribute to appropriate development of children and in turn impacts on sensory, social, emotional and cognitive performance of children. However, nutritional factors are easier are easier to address but they must be culturally, economically and socially accepted to make impact in rural communities.
3. Relationship between omega-3 fatty acid and children brain
The growth and development of children is key in developing countries mostly concentrated in sub-Saharan Africa. Rural children are poorly malnourished and this is not only affecting their growth but also cognitive ability. According to Akinwumi Adesina, President of the African Development Bank in the 2017 Global Nutrition Reports, “Nourishing the SDG’s”, he said “Africa’s economic progress is being undermined by hunger, malnutrition and stunting, which cost at least US$25 billion annually in sub-Saharan Africa, and leave a lasting legacy of loss, pain and ruined potential”. He continued, “stunted children today lead to stunted economies tomorrow”. Hence poor health of children both born and unborn can have strong implications on personal and national growth. The causes of malnutrition are in parts directly related to inadequate dietary intake. Other factors include diseases, household food security, maternal and childcare, health services, and the environment. While most nutrition interventions are delivered through the health sector, non-health interventions such as appropriate dietary or nutrient type can also be critical. Actions should target the different causes to reach sustainable change, which requires a multi-sectoral approach [14].
Adequate levels of omega-3 essential fatty acids are vital before conception, during pregnancy, and during breastfeeding to ensure a perfect start to life. Mothers need to produce not just enough vital DHA and EPA for themselves and their own body but also enough for the new life and vital organs of their baby too, particularly the brain, nervous system, and eyes. Omega-3 long-chain polyunsaturated fatty acids have been observed as important constituents of young and maturing brain cells of children and therefore are considered crucial for brain development in utero and early infancy. In children older than 2 years of age, epidemiological evidence suggests an association between psychiatric or neurodevelopmental disorders and omega-3 fatty acid deficiencies. In neonates, a deficiency is associated with visual impairment, abnormalities in the electroretinogram, and delayed cognitive development [10, 15, 16].
There are three types of omega 3 fatty acids (alpha-linolenic acid (ALA), eicosapentaenoic (EPA) acid, and docosahexaenoic acid (DHA), each of which is needed in the human diet because of their unique beneficial qualities on health and brain development in children. The ALA is carried by plants and is efficiently converted to the EPA in human tissues. The capacity to up-regulate ALA conversion to DHA in women is assumed important for meeting the demands of the fetus and neonate during pregnancy [17]. However, most pregnant women and children in rural areas may not be getting enough omega-3 fatty acids because the major dietary source that contains n-3, which is seafood is not available. Seafood is restricted to coastal areas. On the part of rural people, their purchasing power restricts them to less seafood and in urban areas, the major restriction is two servings a week. These fatty acids are especially critical during pregnancy for the development of the baby’s brain, nervous system, and retinas. Studies have shown that pregnant women whose diets were higher in docosahexaenoic acid had offspring with higher IQ scores while infants who did not get enough omega-3 fatty acids from their mothers during pregnancy are reported to be at risk of developing vision and nerve problems. Children with attention-deficit/hyperactivity disorder (ADHD) may have low levels of certain essential fatty acids (including EPA and DHA) [18]. In a clinical study of nearly 100 boys, those with lower levels of omega-3 fatty acids had more learning and behavioral problems (such as temper tantrums and sleep disturbances) in comparison with boys with normal omega-3 fatty acid levels. All these are possible because omega-3 fatty acids improve metabolic responses such as low-density-lipoprotein (LDL) oxidations of blood glucose and these have direct functions on the brain [19].
3.1 Omega3 fatty acids intake of children in Africa and Ghana
Omega-6 and omega-3 fatty acids (FAs) and their ratio have been shown to affect cognitive function in children and older adults. It is expected that most children would consume at least the recommended amount of alpha-linolenic acid (ALA; omega-3) for their age and gender without consuming high amounts of linoleic acid (LA; omega-6). However, children often do not consume sufficient eicosapentaenoic acid (EPA; omega-3) and docosahexaenoic acid (DHA; omega-3), which form the bulk of the directly usable form of omega-3 fatty acids needed for brain development. Currently, it is not stated what the daily intake of omega-3 FA is in a typical African population by country but there are spatial data for certain locations. However, in developed countries, there are data to show intake. The average American consumes about 1.6 grams of omega-3 FA daily and about 1.4 grams of this comes from ALA, while 0.1-0.2 grams from EPA and DHA. Additionally, the American Heart Association recommends that healthy adults eat fish at least twice weekly because of the large amounts of omega-3 FA. The World Health Organization (WHO) also recommends a daily EPA and DHA intake of 0.3-0.5 grams and a daily ALA intake of 0.8-1.1 grams [20].
The overall omega-3 fatty acid intake of children in three different geographical communities in South Africa has shown that daily intakes are low in children from all communities studied. The median combined intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) was 50, 55, and 3 mg a day in the 3 communities, which are very far below the typical recommended intake between 100 and 200 mg per day for children from 2 to 6 years. It is general to see higher consumption levels of omega-6 fatty acids rather, which is known to suppress endogenous production of EPA and DHA from ALA (alpha-linolenic acid). In Ghana pregnant and nursing mothers often consume a lot of melon seeds (Neri and Egushi) with the sole aim of increasing their breast yield and quality. However, it has been demonstrated that the two seeds rather contain higher levels of omega-6 fatty acids and non-existent levels of omega-3 fatty acids. The largest nutritional benefit derived from the seeds is the higher content of amino acids, which could increase children’s general development but may not have a greater impact on cognitive development [21, 22, 23].
In the Gambia, omega-3 intake of children below 36 months was as low as 140 mg per day compared to children in Britain. However, there was no variation in the omega-6 intake. Additionally, the growth of children by body weight in Britain was superior from infancy to 36 months compared to children in the Gambia, emphasizing a deficiency in omega-3 fatty acids in supporting children’s growth and development both maternally and after weaning up to 36 months in a typical developing country. In several other Africa countries including Nigeria, Congo, South Africa, Burkina Faso, and Egypt there is evidence of lower maternal milk omega-3 levels, which appears to emphasize generally the lower omega-3 intake in children chronicled so far. The lower omega-3 intake is reflected in the diets taken by these people, which have higher amounts of omega-6 rather than omega-3 fatty acids [24].
In Ghana especially Northern Ghana, often characterized by increased poverty, the diets of children are mainly from cereals and fruits, with the intake of fats and proteins below adequate levels. In characterizing for whole-blood fatty acids levels of Northern Ghanaian children, specifically at Savelugu-Nanton district and to assess the association between FA levels and growth parameters, it was reported that a high level of Mead acid levels, a type of omega-9 fatty acids was evidence of essential fatty acids deficiency. Besides, the level of stunting in children was as high as 29%. This high level of stunting was also noticed along with significantly low DHA, total n-3, and the omega-3 index in stunted children compared to non-stunted children [25]. In a similar study, with children in southern Ghana of the same age, residing in communities in the Upper Manya Krobo district the blood omega-3 FA levels were rather significantly higher while omega-6 FA levels were lower compared to the previous study [26].
The mean level of DHA and the omega-3 index, for example, were 2.62% and 4.55%, respectively in the northern Ghana population compared to 5.09% and 8.03% in the southern Ghana population. Similar to the Mead acid level, this was lower indicating a lower EFAD compared to the Northern children. The disparity was due to this community’s high consumption of fish and seafood compared to Northern Ghana children accounting for lower stunting. The proximity of the southern population to a fishing community could have accounted for the availability of fish as the major supplier of omega-3 FA rather than affordability, with both indicators being problematic in most rural communities. It appears there is a higher rate of EFA deficiency in the northern compared to the southern children. Comparative observation showed the southern children in this location also described as a fishing community had similar or higher omega-3 index than children in some developed countries. It is therefore clear from the two types of research that children in northern Ghana and by extension children in rural communities located far away from fishing vicinities were more at risk of not meeting their omega-3 FA need. Therefore, any intervention project should well focused on where there is a need for omega-3 FA supplementation. Since the above studies did not correlate the omega-3 measurements to cognition, a more detailed data collection of such a relationship was recommended [25, 26].
3.2 Bridging the gap in omega-3 fatty acids intake in children
Marine fish such as mackerel, salmon, sardine, herring, and smelt, both fresh and dry are excellent sources of EPA and DHA and influences the total available omega-3 fatty acids in diets greatly. Similarly, plant seeds and oils such as flaxseed oil, perilla oil, canola oil, soybean oil are good sources of omega-3 fatty acids, especially ALA but most of these seed/oils are not tropically available and therefore are not known in rural areas of most African countries. Sources such as Soybeans, sunflower, and palm oils contain varying quantities of omega-3 fatty acids but have large quantities of omega-6 fatty acids [27].
In Urban areas, there are fish capsules available in stores that could be taken to provide children with omega-3 FA and increase development. However, per some assumptions, rural people and people living in most developing countries will less likely accept capsule supplements due to various beliefs and cultural systems. It must be noted that the above sources of omega-3 FA are only given to children when they above 2 years in most cases. Therefore, providing omega-3 FA in eggs has been proposed in various circles as an alternative to fish sources. This is because they will find it to be more natural as they may not think about the deep scientific work done in incorporating the omega-3 FA into the eggs but they will know that egg is produced when feed is given to chickens and this is a natural process of producing eggs [28]. In developing countries, it is always the norm to increase food intake in rural areas and not necessarily food quality. However, concerns are rift on the quality of food because the levels of omega-6 FA are usually high in Africa foods. Therefore, new approaches to developing quality intake are long overdue. The idea in this chapter includes the exploration of the nutritional enrichment of eggs with omega-3 fatty acids, which will be largely accepted by rural folks. Even in a developed country like the USA, it has been observed that overall, omega-3 fatty acids intake as supplements was low among individuals with lower educational attainment and income levels. Many people were also at higher risk of lower omega-3 fatty acids supplements and fish intake with people in such disadvantaged positions [28]. This could be worse in rural areas of Africa where the family fish is rationed during meals. While supplemental use increased EPA + DHA intake only 7.4% of individuals consistently took the supplements. Additionally, daily supplementation of ≥450 mg DHA + EPA per day increased omega-3 index to >6%, which is described as a more likely show of efficacy on cognition in children and adolescents [29]. The Omega-3 index assesses the omega-3 status by analyzing the erythrocyte fatty acids, at least longer-term intakes over approximately the previous 120 days. This omega-3 index was proposed by Harris and von Schacky to reflect the content of EPA + DHA in erythrocyte membranes expressed as a percentage of total erythrocyte fatty acids [30].
3.3 Omega-3 eggs enrichment efforts in Ghana and Africa
The assembling on a least-cost basis a poultry layer diet with the inclusion of omega-3-fatty acid source has been explored at the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, to produce eggs, which will be cheaper than other sources of omega-3 fatty acid sources in increasing the brain development of children in rural areas and sustain healthy living. Between 2014 and 2016, this study was successfully conducted in Ghana with the incorporation of omega-3 fatty acids in eggs through the diets of laying chickens in Ghana, the first in the Sub-Saharan Africa region. The Grand Challenges Canada, saving brains grant for research, supported this proof of concept. The technique was accomplished through poultry production techniques of feeding poultry diet formulated with the inclusion of flaxseed oil, a concentrated source of the omega-3 fatty acids group called ALA, which appreciably can be converted to EPA and DHA in infants that are easily usable by the body [31]. The eventual fatty acids deposited in the eggs were analyzed and proven with laboratory procedures through gas chromatography technology. Upon completion of the research, it was realized that the control diet which did not contain flaxseed because of the inclusion of fishmeal in the diet contained 466 mg per 50 g egg while the 3% flaxseed oil inclusion in poultry diet treatment together with some fishmeal yielded 1,206 mg omega-3 fatty acids per 50 g egg. Considering that, the research yielded more omega-3 fatty acids even with the control diet there is a lot of progress made in ensuring adequate nutrition for rural children. The idea is on the premise that the amount of omega-3 varies with the number of foods containing omega-3 s that are eaten by hens but must be bioavailable to humans. The target is to make these eggs available in rural areas. Interestingly, in this study, there was a high yield of ALA compared to EPA and DHA. In children above 2 years, it is understood that the conversion rate of ALA to EPA is moderate but very limited to DHA. However, the higher the level of ALA and lower the level of LA, the increase in the conversion of ALA into EPA or DHA, with the conversion more dependent on absolute amounts but not their ratio [32]. Interestingly, there is evidence that the local, indigenous people of Africa seemed to have retained the genetic ability to activate the necessary dehydrogenase and elongation enzymes to make EPA and DHA from the readily available ALA substrate, which makes the above study a potential solution to the omega-3 FA deficiency for Ghanaian and Africa children [33].
4. Eggs as alternative carrier of omega-3-fatty acids in rural areas
The egg is one of the cherished sources of nutrients in human food and it plays a vital role in a healthy life. It has low-calorie content and supplies most nutrients including proteins when consumed. A 60 g egg contains 61.5% (36.9 g) albumen, 29% (17.4 g) yolk and 9.5% (5.6 g) shell. A 60 g chicken egg contains about 5.5 to 6 g lipids, which is found in the egg yolk [34]. The fatty acid profile of a hen’s egg shows that 44% of the lipids in eggs are MUFA, followed by 29% saturated fatty acid and 11% PUFA [35]. Conventional hen diets result in eggs with a n:6: n:3 ratio of about 13:1, much higher than recommended for optimal health [36]. However, an enriched egg can provide upwards of 500 mg total n:3 PUFA, which may contain up to 290 mg of combined EPA and DHA. Both n:3 and conventional eggs contain the same amount of energy, protein, saturated fat, and monounsaturated fats but the n:3 eggs contain more LA, AA, ALA, DHA when compared with conventional eggs. On average, a commercially available n:3-enriched egg contains about 75 mg DHA for an egg from a flax-fed hen, and 125 mg DHA from a hen fed both flaxseed and fish oil (Table 1) [37].
Nutrient
N:3 Egg
Conventional Egg2
Energy (calories)
70
70
Protein (g)
6
6
Total fat (g)
5.0
5.0
Saturated fat (g)
1.5
1.5
Monosaturated fat (g)
2.0
2.0
Total n:6 fatty acids(g)
0.8
0.6
Linoleic acid(mg)
6405
540
Arachidonic acid(mg)
305
80
Total n:3 fatty acid (g)
0.43
0.1
Alpha-linoleic acid (mg)
1745
31
Eicosapentanoic acid (mg)
175
1
Docosapentanoic acid (mg)
105
4
Docosahexanoic acid (mg)
1085
344
Cholesterol (mg)
195
195
Table 1.
Nutrient content of enriched and conventional eggs.
A fortified egg has the unique advantage of combining almost all the primary nutrients from animals for humans. These include innately providing the highest quality protein and amino acid proportions, fats and essential fatty acids, vitamins, and minerals. Eggs also have a unique capacity to concentrate added essential nutrients and phytonutrients and effectively deliver them with high bioavailability [19]. Additionally, eggs are highly acceptable among rural folks and therefore its substitution for nutrients supplements for children is highly recommended. Besides eggs are cheaper sources of concentrated nutrients. In the past, the WHO has recognized the need to improve the process by which health-related recommendations are developed including nutrition using the best available evidence. With this, the WHO Nutrition Guidance Expert Advisory Group was formed with experts from WHO Advisory Panels and other experts in the fields of epidemiology, nutrition, public health, pediatric medicine, and program implementation. We recognize that nutritional cognitive development innovation should provide a solution for children and must follow proper guidelines [38]. An additional proposal from the WHO includes adopting priority nutrition outcomes such as complementary feeding counseling and active feeding, growth monitoring and promotion, and supplementary feeding or food-based interventions. That is why we have spent the time to provide proof that it is possible to intervene in cognitive retardation with nutritionally fortify eggs, which will complete the protein and other essential nutrients in supporting children’s development. This is because the special functions of the brain are reflected in a higher need for nutrients such as choline, folic acid, iron, zinc, and special fats (e.g. gangliosides, sphingolipids, and docosahexaenoic acid (DHA)) and we emphasize the essential fatty acids. For eggs enriched with omega-3 fatty acids, the health advantages include high antioxidants levels, due to high n-9 monounsaturated fatty acids (MUFA) or n-3 polyunsaturated fatty acids (PUFA), with lowered n-6 PUFA, and n-6: n-3 ratio over regular eggs [8].
4.1 How much omega-3 is required by children through eggs
For children between 6 months to 8 years the total ALA omega-3 fatty acids need is between 0.5 g to 0.9 g per day [39]. In the research at KNUST, a 50 g egg enriched with 3% flaxseed oil provides 1.2 g of the total omega-3 per day with a high level of ALA in addition to EPA and DHA. Besides, the majority of the omega-3 fatty acids contain other types of omega-3 fatty acid eggs that are readily available for direct utilization by children for improved health using the enrichment procedure adopted, compared to available enrichment procedures that use only flaxseed or algae that enriches mostly ALA. Increasing omega-3 eggs intake of this local enrichment intake for a period of weeks to months could result in an increase in the proportion of eicosapentaenoic acid (EPA; 20:5n-3) in plasma lipids, in erythrocytes, leukocytes, platelets in children and in breast milk with potential to increase newborn health [40].
Protein intake combines with omega-3 FA intake for child growth and development. An average egg contains about 6–7 grams of proteins. However, the protein content depends on egg size. The average protein requirement for a child’s development ranges from 1.12 g/kg/day at age 6 months to 0.74 g/kg/day at 10 years, with a small decline towards the adult value thereafter. The corresponding values for the safe level are 1.43 g/kg/day at 6 months and 0.91 g/kg/day at 10 years. These values show that just one 50 g egg a day will provide over 5 g more protein for a child per day [41]. This means eggs contain so much protein for a child’s development and is a cheaper source of protein. So just one egg in 5 days is far enough and village people can afford this. So considering the wastage of protein/omega-3 in the human metabolism pathway, one egg for 2 days at most is sufficient for a child’s development if other sources are excluded. Out of 43 countries in sub-Saharan Africa, only nine countries have an average consumption of eggs that is higher than 2 kg while in most of Asia and the Americas, people eat at least twice that amount, and in Japan, it is 19.1 kg. Expanding the poultry business using eggs will increase production because consumer perception will change. The potential increase in egg intake will greatly affect the egg value chain (grain producers, hatcheries, feed manufacturers, poultry farmers, veterinary stores, and egg sellers). This happened in South Africa in 2010; an increase in egg consumption led to higher prices, production, and jobs [42].
In 2015, the MDGs expired, yet average nutritional problems in Ghana were still high with about 20% of children being stunted, 9% is wasted and 14% being underweight. The approaches used in the MDGs included vaccinations, NGO interventions, and aids. However, Ghana did not appear to achieve MDG 4 (reducing child mortality at end of 2015) due mostly to hunger and poor nutrition (MDG 3). According to the World Food Programme, eradicating hunger and malnutrition is one of the great challenges of our time, and not having enough – or the wrong food causes suffering and poor health, as well slows progress in education and employment.
The project of enriching eggs with omega-3 FA has the potential to meet the now SDGs 1 to 5 including no poverty, zero hunger, good health, and well-being, quality education, and gender equality respectively in very vulnerable people in our society especially women and children (unborn and children under 5 years). There is considerable evidence that the DHA content of the maternal diet is the most important determinant of the amount of DHA secreted in milk, which increases children’s health. Therefore, it is no surprise that infants fed a DHA-fortified formula have better eyesight than infants fed a formula without it. However, these nutrient supplements are not available to rural parents and make them a vulnerable population. The greater portion of the diets of rural people in Ghana similar to the Western population contains greater amounts of omega-6 fatty acids (oil palm, starchy foods, overcooked vegetables) leading to poor health [43]. By eating eggs especially the omega-3 fortified eggs it will also give them a good balance of omega 3 to 6 ratio. With the focus on children, and women living in rural areas there is a greater potential to provide quality nutrition and health care.
4.2 Omega-3 egg impacts the egg value chain in Africa
The idea of developing the omega-3 eggs through the feed of poultry layers will expand the egg value chain. This includes enhancement of the activities of operators of breeder farmers (layer breeder flocks to produce fertile eggs for a hatchery), hatcheries, commercial egg producers (have layer flocks to produce table eggs), egg marketers, feed millers and feed processors, or feed ingredient dealers and primary crop farmers, veterinary drug dealers and consumers of the eggs. Eggs are also useful to many households, especially rural communities that have bigger malnutrition challenges. In Ghana specifically, the Government school feeding program, free senior high school program, and various public institutions including hospitals need eggs as higher immune booster especially with the COVID 19 pandemic where patients need to eat good quality food to increase their immune system. The omega-3 egg value chain can address the marketing challenges of egg farmers and increase its intake in the whole Sub-Saharan Africa Region and Africa.
NB: It can be noted from the Table 1 above that both omega-3 enriched eggs and conventional eggs have the same amount of energy, protein, total fatty acids, saturated fatty acids, monosaturated fatty acids and cholesterol. These components of the egg are responsible for energy and protein to enhance growth of children, but may not necessarily increase brain development to increased cognition and future prospects of children. Children need not only to grow but must undergo the proper physiological and psychological developments to make them contribute positively to societal development. The enhancement of cognition and its mechanism of brain function has not being studied very well from the egg nutrient point of view. Since omega-3 fatty acids and the omega-3:omega-6 fatty ratio are known to increase brain development they should be well explored. It can enhance cognition and by extension development of children. The most impact can be experienced in rural communities. The enrichment of eggs in this instance, which leads to increased omega-fatty acids and it major components ALA, EPA, DHA and LA by larger folds is a cheaper alternative to increased brain development, child development and cognition development in rural communities. The production of omega-3 fatty acids eggs is a much cheaper, convenience and culturally accepted method compared to medical administration of omega-3 fatty acids supplements to children to increase development.
5. Conclusions
Omega-3 enriched eggs is proposed to supplement the diets of school-going children below age 6. Additionally, embarking on a public health campaign on the health benefits of eggs, engaging governments to use innovative methods to engage stakeholder to increase the intake of eggs for nutritional improvement of children in rural areas. The prospects of the omega-3 fatty acids include increasing academic performance and future prospects of rural children. In addition to the above, increased production and consumption of eggs, especially with the branding of eggs through omega-3 fatty acids fortifications in the developing countries will increase food and nutrition security, and the development of children in rural areas. Besides, eggs are inexpensive source of high-quality protein, essential vitamins, and minerals that are needed for a healthy diet and a healthy life and important for the nutrition of growing children, as well as being culturally acceptable. The current suggestion could increase business, create jobs within the egg value chain, and open opportunities for all stakeholders in health, agriculture, government, and philanthropic organizations, and be used as a tool for increasing rural development.
Acknowledgments
The authors are grateful for the financial contribution from Grant Challenges Canada, Saving Brains. We are also grateful to the Department of Animal Science and Department of Chemistry, KNUST and V. O. A Farms, Kumasi and all support staff who contributed to the outcomes of research captured in this chapter.
Conflict of interest
The authors declare no conflict of interest.
Notes/thanks/other declarations
Aims of the chapter
This chapter shows how infants and children below 5 years are disadvantaged in rural areas. They are also deprived of appropriate and adequate nutrition. This affects their wellbeing, cognitive development and future prospects. The chapter is a form of communication to opinion leaders and stakeholders to address the long-term effect of infant nutrition in rural areas, especially in Sub-Sahara Africa on cognition and how this lingers on into adult life. Moreover, to be useful, the chapter advocates adequate nutritious food, which is eggs and presents a calculated strategy with research proven omega-3 fortified eggs to reverse memory deficits in children during pregnancy and in breast feeding children. Therefore, the ability to perform excellently in children from rural communities will depend on traditionally approved and acceptable nutritional strategies, which can increase sustainable livelihoods in rural communities. The book chapter is a suggested solution to malnutrition, poverty reduction and livelihoods improvement as sustainable development plan to solving malnutrition in rural children.
Key points
In this book chapter there is strong demonstration that:
By enriching the diet of layer birds with 3% flaxseed oil, there is an increase in specific fatty acids that make up omega-3 and omega-6 fatty acids, resulting in eggs high in omega-3 fatty acids.
Total omega-3 fatty acids were 2.54 times higher in the 3% flaxseed oil eggs than the 0% flaxseed oil eggs. However, the feed cost in producing omega-3 eggs by incorporating 3% flaxseed oil was also 2.25 times higher.
In terms of taste, acceptability, smell of the yolk and texture of the egg yolk, the taste panelist did not notice any difference between the flaxseed oil eggs and the fishmeal eggs, or those with no flaxseed oil included in the diet.
\n',keywords:"Infants, children, rural areas, nutrition, cognitive development, omega-enriched eggs",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76380.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76380.xml",downloadPdfUrl:"/chapter/pdf-download/76380",previewPdfUrl:"/chapter/pdf-preview/76380",totalDownloads:147,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:42,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"November 26th 2020",dateReviewed:"March 29th 2021",datePrePublished:"July 9th 2021",datePublished:"February 2nd 2022",dateFinished:"April 21st 2021",readingETA:"0",abstract:"Good health is a crucial requirement for every child for proper growth and development. To increase their future prospects the exact nutritional intervention is needed to boost the thinking and self-confidence of children. Adequate levels of omega-3 essential fatty acids are vital for children during pregnancy, breastfeeding, and few years post-weaning. This is not just for their perfect growth but including their cognitive development. Poverty levels continue to be high in rural areas and there are nutritional interventions that can be used to reverse the trends. However, omega-3 fatty acids, known to have a greater impact on brain development are not cheap and available in forms that are accessible by the rural poor. With the many complications attached to a rural lifestyle, little is known about culturally accepted local sources of omega-3 fatty acids. Therefore, alternative sources of nutritional intervention including the provision of eggs enriched with appropriate fatty acids, which are readily available, accessible, cheaper, and culturally accepted should be explored for children.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76380",risUrl:"/chapter/ris/76380",book:{id:"10227",slug:"rural-development-education-sustainability-multifunctionality"},signatures:"Jacob Alhassan Hamidu, Charlisa Afua Brown and Mary Adjepong",authors:[{id:"331980",title:"Dr.",name:"Jacob",middleName:null,surname:"Alhassan Hamidu",fullName:"Jacob Alhassan Hamidu",slug:"jacob-alhassan-hamidu",email:"jahamidu.canr@knust.edu.gh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"340947",title:"Ms.",name:"Charlisa",middleName:null,surname:"Afua Brown",fullName:"Charlisa Afua Brown",slug:"charlisa-afua-brown",email:"chalisabrown4@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"340948",title:"Dr.",name:"Mary",middleName:null,surname:"Adjepong",fullName:"Mary Adjepong",slug:"mary-adjepong",email:"madjepong2020@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Kwame Nkrumah University of Science and Technology",institutionURL:null,country:{name:"Ghana"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Factors influencing brain development in children",level:"1"},{id:"sec_2_2",title:"2.1 How rural children develop lower cognition level",level:"2"},{id:"sec_4",title:"3. Relationship between omega-3 fatty acid and children brain",level:"1"},{id:"sec_4_2",title:"3.1 Omega3 fatty acids intake of children in Africa and Ghana",level:"2"},{id:"sec_5_2",title:"3.2 Bridging the gap in omega-3 fatty acids intake in children",level:"2"},{id:"sec_6_2",title:"3.3 Omega-3 eggs enrichment efforts in Ghana and Africa",level:"2"},{id:"sec_8",title:"4. Eggs as alternative carrier of omega-3-fatty acids in rural areas",level:"1"},{id:"sec_8_2",title:"4.1 How much omega-3 is required by children through eggs",level:"2"},{id:"sec_9_2",title:"4.2 Omega-3 egg impacts the egg value chain in Africa",level:"2"},{id:"sec_11",title:"5. Conclusions",level:"1"},{id:"sec_12",title:"Acknowledgments",level:"1"},{id:"sec_15",title:"Conflict of interest",level:"1"},{id:"sec_12",title:"Notes/thanks/other declarations",level:"1"},{id:"sec_12_2",title:"Aims of the chapter",level:"2"},{id:"sec_14",title:"Key points",level:"1"}],chapterReferences:[{id:"B1",body:'Abdel Wahed WY, Hassan SK, Eldessouki R. Malnutrition and its associated factors among rural school children in Fayoum governorate, Egypt. Journal of environmental and public health. 2017 Oct 23;2017'},{id:"B2",body:'Amzat J, Razum O. Rural Health in Africa. InTowards a sociology of health discourse in Africa 2018 (pp. 109-124). Springer, Cham. https://doi.org/10.1007/978-3-319-61672-8_8'},{id:"B3",body:'Galler JR, Bryce CP, Zichlin ML, Fitzmaurice G, Eaglesfield GD, Waber DP. Infant malnutrition is associated with persisting attention deficits in middle adulthood. 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Effect of omega-3 long chain polyunsaturated fatty acids (N-3 LCPUFA) supplementation on cognition in children and adolescents: A systematic literature review with a focus on n-3 LCPUFA blood values and dose of DHA and EPA. Nutrients. 2020 Oct;12(10):3115'},{id:"B30",body:'Harris WS, Von Schacky C. The Omega-3 Index: a new risk factor for death from coronary heart disease?. Preventive medicine. 2004 Jul 1;39(1):212-20'},{id:"B31",body:'Williams CM, Burdge G. Long-chain n− 3 PUFA: plant v. marine sources. Proceedings of the Nutrition Society. 2006 Feb;65(1):42-50'},{id:"B32",body:'Goyens PL, Spilker ME, Zock PL, Katan MB, Mensink RP. Conversion of α-linolenic acid in humans is influenced by the absolute amounts of α-linolenic acid and linoleic acid in the diet and not by their ratio. The American journal of clinical nutrition. 2006 Jun 1;84(1):44-53'},{id:"B33",body:'Mathias RA, Fu W, Akey JM, Ainsworth HC, Torgerson DG, Ruczinski I, Sergeant S, Barnes KC, Chilton FH. Adaptive evolution of the FADS gene cluster within Africa. PloS one. 2012 Sep 19;7(9):e44926'},{id:"B34",body:'Cherian G, Holsonbake TB, Goeger MP. Fatty acid composition and egg components of specialty eggs. Poultry Science. 2002 Jan 1;81(1):30-3'},{id:"B35",body:'Sparks NH. The hen\'s egg-is its role in human nutrition changing?. World\'s Poultry Science Journal. 2006 Jun 1;62(2):308-15'},{id:"B36",body:'Scheideler SE, Froning GW. The combined influence of dietary flaxseed variety, level, form, and storage conditions on egg production and composition among vitamin E-supplemented hens. Poultry Science. 1996 Oct 1;75(10):1221-6'},{id:"B37",body:'Gillingham LG, Caston L, Leeson S, Hourtovenko K, Holub BJ. The effects of consuming docosahexaenoic acid (DHA)-enriched eggs on serum lipids and fatty acid compositions in statin-treated hypercholesterolemic male patients. Food research international. 2005 Dec 1;38(10):1117-23'},{id:"B38",body:'World Health Organization. (2013). Essential nutrition actions: improving maternal, newborn, infant and young child health and nutrition. World Health Organization'},{id:"B39",body:'Covington M. omega-3 fatty acids. American family physician. 2004 Jul 1;70(1):133-40'},{id:"B40",body:'Brown CA. Paradigm shift in poultry feeding: the development of omega 3 enriched eggs. 2016. MPhil Thesis, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana'},{id:"B41",body:'Garlick PJ. Protein requirements of infants and children. Protein and Energy Requirements in Infancy and Childhood. 2006;58:39-50'},{id:"B42",body:'Agriculture, Forestry and Fisheries. A profile of the South African egg industry market value chain. 2010. https://www.daff.gov.za/docs/AMCP/EggMVCP2010-11.pdf'},{id:"B43",body:'Simopoulos AP. The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases. Experimental biology and medicine. 2008 Jun;233(6):674-88'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Jacob Alhassan Hamidu",address:"jahamidu.canr@knust.edu.gh",affiliation:'
Department of Animal Science, Kwame Nkrumah University of Science and Technology, Ghana
Department of Biochemistry, Kwame Nkrumah University of Science and Technology, Ghana
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1. Introduction
Darwin’s principle of evolution says that the existence of any creature is based on the law “strongest creature survives.” Before computers have entered the human world, in the 50s, knowledge to apply Darwinian principles for automated problem solving was invented. Darwin also proved that the survival of any organism can be maintained with genetic inheritance, such as reproduction, crossover, and mutation. Thus, Darwin’s evolution theory was deployed by computational optimization algorithm to search for a solution to any real-world optimization problem in a natural way [1].
In the 60s, three various interpretations of this idea were introduced at different places. Evolutionary programming was developed by Lawrence J. Fogel in the USA when John Henry at Holland started his methodology as a genetic algorithm, stimulated by Darwin’s evolutionary concepts. Similarly, Ingo Rechenberg and Hans-Paul Schwefel have invented evolution strategies in Germany. Following this fourth one had emerged as genetic programming, in the early 90s. These four different terminologies are seen as different representatives of one technology called evolutionary algorithms (EAs), which denote the whole field by considering evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas and is well depicted in Figure 1 [1, 2].
Figure 1.
Evolutionary algorithms and their subtypes.
2. Need for evolutionary algorithms
Real-world has many optimization scenarios. Optimization, by definition, is a methodology of making the decision as fully perfect as possible to achieve the maximum possible goal, in an engineering system. Nature is a very good optimizer. An optimization problem can be stated as follows [2, 3].
Any engineering system can be represented with a set of quantities. Certain quantities are usually fixed called as pre-assigned constants. Remaining quantities can be treated as decision variables in the optimization process, xi = {x1, x2, … xn}. ‘f(x)’ is the objective function or goal to be attained, ‘gj(x)’ represent ‘m’ the count of inequality constraints and ‘hj(x)’ represent ‘p’ count of equality constraints to be satisfied for attaining feasibility [3].
In real-world engineering problems, the objective function is discontinuous, nonlinear, non-convex, and multi-modal. Also, the problems are multi-dimensional as the number of design variables are more and they are mixed in type like integer, real, discrete, binary. Hence, the search space is not smooth. It may require accessing look-up table data for objective function evaluation. The constraint functions are very complex and the amount of violation of each constraint does not cover the same range, which requires normalization [4].
In general, the optimization problems are categorized based on the existence of constraints, nature of the decision variables, permissible values of the design variables, nature of the equations involved, deterministic nature of the variables, separability of the functions, number of objective functions, etc. Some of them are static optimization problem, dynamic optimization problem, linear programming problem, convex programming problem, nonlinear programming problem, geometric programming, quadratic programming problem, separable programming problem, multi-objective optimization problem, single-variate optimization problem, multi-variate optimization problem [5].
Two different techniques to find the solution for optimization problems are mathematical programming techniques and meta-heuristic techniques. When derivative-based mathematical programming methods are applied in solving nonlinear programming problems, there are several shortcomings. Traditional optimization methods:
Yields results that are caught at premature convergence, that is, local optima often, due to the search space with multi-modality.
Requires mathematically well-defined objective and constraint functions.
Requires existence of derivatives for objective function and constraint functions.
Find difficulty to handle mixed variables.
For a real-world optimization problem, the surface plot obtained even for optimizing two design variables gives greater number of local minima/maxima. Figure 2 depicts the surface plot obtained for the design optimization of the distribution transformer problem with two decision variables, width of the core leg, height of the core window, by minimizing the transformer lifetime cost (TLTC) of the transformer. It is very clear from Figure 2 that the real-world problem is very complex with its multi-modal search space [3].
Figure 2.
Search space for minimization of TLTC objective optimizing two decision variables.
Of course, when it is a multi-dimensional engineering problem, which requires optimization of more decision variables, the multi-modality cannot be imagined. So, the conventional derivative-based mathematical programming technique cannot handle such complex nature of the optimization problem, accurately. It may yield a feasible design; however, it will not be an optimal solution. If there is no knowledge or little knowledge about the behavior of the objective function related to the presence of local minima, location of feasible region, infeasible region in the multi-dimensional parameter space, it is advisable to start the meta-heuristic technique, which is a stochastic strategy [2, 3].
Of all the meta-heuristic techniques, evolutionary algorithms (EAs) are especially effective in the solution of high-complexity, non-convex, nonlinear, multi-dimensional, mixed variable, multi-objective, constrained optimization problems, for which a traditional mathematical model is difficult to build, where the nature of the input/output relationship is neither well defined nor easily computable. The stages of EAs have not yet been investigated in detail steps with illustration, despite their performances are better in terms of convergence, consistency in obtaining the solution, and computational speed in solving any multi-modal problems.
Hence, this chapter discusses in detail the step-by-step evolutionary process that happens behind the optimization algorithm. It highly helps to find solution for any multi-modal real-world engineering optimization problem, by optimizing design objective, while satisfying simultaneously various constraints imposed by international standards and user specifications.
3. Known optimization problems
Evolutionary optimization algorithms minimize or maximize an objective function and they are search algorithms. The algorithm checks all the way through a large search space of possible solution set for the optimal best solution. In day-to-day practical life as well as professional life, there are numerous activities that seek optimization. Some of the common well-known real-world optimization problems are the traveling salesman problem, classification problem, economic power dispatch, base station location problem, antenna design, scheduling problem, etc.
In traveling salesman problem, a salesman wants to visit all the towns, with information of list of towns and distances between all the towns. The constraint is that each town has to be visited only once. The optimization problem statement is searching for the optimum shortest distance/route that the salesman travel and visits each town exactly only once and returns to the place where he started [6].
Base station location problem is setting radio and optimizing maximum coverage. Given a set of spots for installing base stations, feasible configurations for every base station, antenna tilt, maximum power, antenna height, sectors orientation, etc. along with the information of traffic and strength of the signal propagation, the optimization algorithm is to choose the right location and appropriate configuration of the base station such that the installation cost is minimum while meeting the traffic demand simultaneously [7].
The job of optimal generator maintenance scheduling problem is to find out the optimum period for which, the generator units must be taken offline for maintenance over the stipulated time horizon, so that the operating costs involved are minimized, meeting the maintenance constraints during the considered time period such as load demand, maintenance window, maintenance duration and manpower availability [8].
Previous research works have applied only machine learning techniques for the prediction and classification of any disease/tumor. However, nowadays due to the capability of evolutionary algorithms, such classification problems have been stated as an optimization problem and solved using the integrated machine learning-optimization technique. Thus, optimal classification problem aims to select optimum elite features from intelligent liver and kidney cancer diagnostic systems of huge data sets, by filtering the redundant features, minimizing the error rate, in order to improve the quality of heart disease classification [9].
Economic power dispatch is a vital optimization problem in power system planning. The aim of the economic dispatch is to schedule the optimum power output for the available generator units of the power system such that the production cost is minimum and power demand is met [10].
4. Optimization process of simple evolutionary algorithm
EA handles a population of possible random solutions. Each solution is represented through a chromosome. The fitness of each chromosome is calculated to call for a competition among the chromosomes. Competition results in the selection of those better chromosomes/solutions (with high fitness value) that are suited for the environment. The process of the first level of filtration based on the fitness value is called parent selection [3]. The selected individuals, that is, parents act as seeds for creating children through genetic inheritance, that is, recombination and mutation. These genetic operators aid the necessary diversity. Few pairs of chromosomes from the parent pool are chosen based on the random probability to undergo crossover for forming offspring. The resulted offspring individuals obtained after crossover are allowed to take up mutation randomly. Different regions of the search space are explored for identifying possible optimal individuals through “recombination and mutation” operation known as “exploration.” The new individuals/children thus formed have their fitness evaluated to compete for survival in the next generation. As an end to an iteration, in a replacement stage, 80% worst solutions of the initial random population are substituted by the best offspring children filtered after survival selection process based on the evaluated fitness value. Over time and several iterations, “natural selection” operation, which is called exploitation leads to the identification of an individual in the population as global optimum. The complete working of the evolutionary algorithm is pictured in Figure 3 [11]. The major steps involved in the process of optimization in an evolutionary algorithm are as follows [1, 3].
Solution representation
Random population generation
Fitness function evaluation
Parent selection
Reproduction—(crossover, mutation)
Survival selection
Replacement
Stopping criteria
Figure 3.
Working of evolutionary algorithms.
5. Iterative process behind evolutionary algorithms
To define problem statement:
Consider an equality function, x + 2y + 3z + 4u = 30. We shall apply the evolutionary algorithm to find the appropriate values for x, y, z, u, such that the equity equation gets satisfied [12].
5.1 Formulation of optimization problem
Formulate objective function: f(k)
The objective/aim is to Minimizef(k) = [(x + 2y + 3z + 4u) − 30].
Identify decision variables/type: In this equity problem, there are four decision variables [x, y, z, u]. Variables that possess a larger influence on the objective function and constraint functions are appropriate ones to be chosen as decision variables.
Find problem dimension:
Total number of decision variables is the problem dimension = 4.
Representation:
A solution generated by an evolutionary algorithm is called a chromosome/individual, which is made up of genes [1]. After selecting the decision variables, and problem dimension, choice of suitable type for these variables is another important task. The nature of the decision variables is completely problem dependent and thus in this example, [x, y, z, u]—they are integers. However, the genes can be mixed like binary, real, integer, discrete variables, etc., depending upon the need of the problem under consideration. Chromosome/solution is thus represented as an integer variable as under:
x
y
z
U
Impose boundary constraint:
This range selection for setting the search space is more often done on a trial basis, in case the problem dimension is high. On contrary, if the objective function is very simple, clear, and possesses straight relationship (mathematical equation) with lesser number of decision variables, then the search space can be decided by inspection [2]. For this example, it is very clear that the integer values of decision variables [x, y, z, u] can be restricted to vary between 1 and 30, in order to speed up the computational search.
5.2 Different stages in optimization process
To illustrate solving a minimization type optimization problem using EA, integer type for decision variables, six for population size, single-point method for crossover, and roulette wheel for selection are assumed. The various stages involved in the process of optimization are given for one iteration in this section [13, 14, 15].
Stage 1: Population generation: Initial solution set
Four genes [x, y, z, u] are generated randomly satisfying the lower and upper limits of the boundary constraint. A chromosome thus generated is a vector comprising of four genes. Chromosome refers to the solution/individual of the formulated optimization problem, while the collection of such chromosomes is referred to as a population. For example, Solution [1] = [x; y; z; u] = [12, 05, 23, 08]. Then, the initial population will have an array of sizes [population size, problem dimension]. That is, [(6, 4)] as shown in Table 1.
Initial random population
Feval [k]
Remarks
Solution [1]
12
05
23
08
93
Solution [2]
02
21
18
03
80
Solution [3]
10
04
13
14
83
Solution [4]
20
01
10
06
46
Best Solution
Solution [5]
01
04
13
19
94
Solution [6]
20
05
17
01
55
Table 1.
Functional evaluation of initial population.
Stage 2: Function evaluation: Feval
All the chromosomes of random population will then go through a process known as fitness evaluation to measure the quality of the solution created by EA. Evaluation of fitness value of chromosome/solution is carried out by calculating the objective function value as Feval = Modulus[f(k)].
fk=x+2y+3z+4u−30.
For Solution1,Feval1=ModulusfSolution1=mod12+2x5+3x23+4x8−30
Feval [1] = mod [(12 + 10 + 69 + 32) − 30] = 93. Similarly, the solutions of entire population can be calculated and tabulated as under in Table 1. It is found that Chromosome 4 has the least objective function value 46.
Stage 3: Parent selection
The chromosomes are selected from the initial population to act as parent for reproduction, based on the fitness of the solution/individual. The selection procedure tells how to choose individuals in the population that will create offspring for the next generation. The fittest solution will have a higher probability to be selected as a parent. Two-step selection process is discussed as follows [13, 15].
To compute the probability of selection: Prob[i]
Probi=Fiti∑i=16Fiti
where,
Fiti=11+Fevali
(to avoid undefined divide by zero error, which may encounter for the optimal solution, it is advisable to add 1 with Feval).
Fit [1] = 1/(1 + Feval [1]) = 1/94 = 0.0106 and so on, till i = 6.
To select the parent pool: Roulette-wheel (RW) selection process:
Initial population
Feval [i]
Fit [i]
Prob [i]
Cum [i]
12
05
23
08
93
0.0106
0.1254
0.1254
02
21
18
03
80
0.0123
0.1456
0.2710
10
04
13
14
83
0.0119
0.1408
0.4118
20
01
10
06
46
0.0213
0.2521
0.6639
01
04
13
19
94
0.0105
0.1243
0.7882
20
05
17
01
55
0.0179
0.2118
1
Total fitness
0.0845
Table 2.
Selection probability computation.
Parent selection is vital for the convergence of optimization algorithm as efficient parents force solutions/individuals to optimality. There are different methods in the process of selecting parents such as stochastic universal sampling, fitness proportionate selection, tournament selection, rank selection, and random selection. In this chapter, roulette wheel selection has been implemented for identifying the right parent pool.
Consider a wheel that is split into ‘6’ pies. Pie refers to the individual in the population. Each solution occupies a portion of the wheel, proportional to its fitness value. It is clear that a fitter solution takes a larger pie on the wheel and has larger probability chance of being selected as a parent when the wheel is made to spin ‘6’ times. Hence, the probability of choosing a chromosome depends on its fitness only. The steps involved in the roulette wheel selection process are:
Compute cumulative probability values for all the solutions—Cum[i].
Allot pie in sequence for all the ‘6’ individuals, based on the cumulative probability. That is, Chromosome 1 has occupied light blue pie with cumulative probability ranging between [0–0.1254]. Chromosome 4 which has the highest fitness value ‘0.0213’ has the highest probability ‘0.2521’ among all the solutions of the population. Naturally, it will take larger sized pie, which is yellow in color on the wheel. It is clearly explained in Figure 4, and Tables 3 and 4.
To arrange the order of chromosomes (6) in the parent pool, equivalent to spinning the wheel ‘6’ times, generate random number ‘6’ times, ‘rand[i]’ < 1, six.
Fit the random number of each place in the respective range of cumulative probabilities and fetch the color of the pie. For example, rand [place 2] = 0.285,which is between Cum [2] and Cum [3]. So, the gray pie which is individual/chromosome [3] will occur in place2 of the parent/mating pool.
Figure 4.
Roulette wheel—Parent selection process.
Position
Chromosome
Initial population
Place 1
I
Solution [1]
12
05
23
08
Place 2
II
Solution [2]
02
21
18
03
Place 3
III
Solution [3]
10
04
13
14
Place 4
IV
Solution [4]
20
01
10
06
Place 5
V
Solution [5]
01
04
13
19
Place 6
VI
Solution [6]
20
05
17
01
Table 3.
Place and position of solution—Before RW selection process.
Position
Chromosome
Population after selection
Place 1
II
02
21
18
03
Place 2
III
10
04
13
14
Place 3
I
12
05
23
08
Place 4
VI
20
05
17
01
Place 5
III
10
04
13
14
Place 6
IV
20
01
10
06
Table 4.
Chromosomes in the MATING POOL after RW selection process.
Stage 4: Crossover
The crossover operation involves three steps: (A) selecting mating chromosomes, (B) determining cut point for crossover, and (C) updating the population.
In this operation pairs of parents are chosen and many children/off-springs are generated using the information available in the gene of the parents. Usually, crossover operation is deployed in EA with high probability(pc). Some of the commonly used crossover operators are whole arithmetic recombination, one point crossover, uniform crossover, multi-point crossover, Davis’ order crossover, etc. In the equity problem chosen for illustration, one-point crossover has been used for offspring creation. In one-point crossover, a randomly point of crossover has been chosen and the tail ends of the parent pairs are swapped to produce new children. The process is evident in Table 5.
To select chromosome:
Population after selection
Population after crossover
02
21
18
03
02
05
17
01
10
04
13
14
10
04
13
14
12
05
23
08
12
05
23
08
20
05
17
01
20
04
13
14
10
04
13
14
10
04
18
03
20
01
10
06
20
01
10
06
Table 5.
Population after and before crossover operation.
Parent chromosome from parent pool that undergoes the mating process is randomly selected and the number of mate solutions is decided using crossover rate, pc. Solution ‘i’ will become a parent, if random number, rand[i]falls below the crossover rate. Let us assume the pc = 25% for solving the problem. Generate number randomly ‘6’ times (population size) below 1.
Thus, for the generated random numbers, three chromosomes/solutions [1, 4, 5] are selected for crossover operation. Hence, the number of crossovers becomes 3, that is, 3 pairs.
Solution [1] >< Solution [4] --- First Crossover
Solution [4] >< Solution [5] --- Second Crossover
Solution [5] >< Solution [1] --- Third Crossover
To determine cut point:
Followed by mating chromosome selection, the next phase is to determine the position of the crossover point. The steps involved are:
Generate random numbers between 1 to (Problem dimension—1) in order to get the crossover point. That is, between 1 and 3. Assume, Cut [1] = 1; Cut [2] = 1; Cut [3] = 2
Parent individuals get cut at the crossover point and their genes are interchanged. For first, second, and third crossovers, parents’ genes are cut at positions 1, 1, and 2, respectively.
First Crossover: New Chromosome [1] = Solution [1] >< Solution [4]
Second Crossover: New Chromosome [4] = Solution [4] >< Solution [5]
Third Crossover: New Chromosome [5] = Solution [5] >< Solution [1]
C. To update the population after crossover:
Stage 5: Mutation
Mutation operation is a small random sharp change in the chromosome necessary to obtain a new solution. Mutation is used to sustain population diversity and it generally has a low probability, pm. Mutation in EA refers “exploration” of search space. It has been proven that mutation operation is crucial for the convergence of the algorithm, however, crossover operation is not so. Some of the commonly used mutation operators are bit flip mutation, swap mutation, scramble mutation, random resetting, inversion mutation, etc. Like the crossover operators, this is not an extensive list since EA designer may deploy a hybrid approach as a combination of these operators or prefer problem-specific mutation operators as more practical.
Calculate the total number of genes in the population = 24 genes.
Calculate number of mutable genes.
Number of solutions that undergo mutations in a population is decided by mutation rate pm. Let, ρm = 10%; Number of mutations = 0.1 × 24 = 2.4 = 2.
Calculate gene positions.
Generate two random numbers below 24, say 12 and 18. Mutable genes and chromosomes are Chromosome [3]-gene 4 and Chromosome [5]-gene 2. This process is seen in Table 6.
The value of mutable genes at the mutation point is substituted with random number, satisfying the boundary constraint of decision variables/genes. That is, between 0 and 30; Say 02, 05.
Population after selection
Population after crossover
Population after mutation
02
21
18
03
02
05
17
01
02
05
17
01
10
04
13
14
10
04
13
14
10
04
13
14
12
05
23
08
12
05
23
08
12
05
23
02
20
05
17
01
20
04
13
14
20
04
13
14
10
04
13
14
10
04
18
03
10
05
18
03
20
01
10
06
20
01
10
06
20
01
10
06
Table 6.
Population after and before mutation operation.
Stage 6: Survival selection and replacement mechanism
After mutation operation one iteration/generation of EA is over. Functional evaluation is again performed on the offspring for survival selection. From the functional evaluation of population after mutation, it is evident that the objective function value of the best solution is reducing—37 in comparison with the minimum objective value—47 of initial random population, as shown in the table. Hence the minimization objective, f(k) = [(x + 2y + 3z + 4u) − 30] is met. This means that the solutions obtained by EA at the end of the first iteration is better than the solutions of random population.
To execute the iteration process continuously, population is to be revised at the end of each iteration, as a final process, which is referred to as replacement mechanism. In each iteration end, 80–90% of best solutions from offspring population (4–5 best children) and 20–10% best solutions from the initial population (2–1 random solution) are selected to form new population for next generation [15]. Chromosomes of the next generation will then become as shown in Tables 7 and 8.
Population after mutation
Feval
Remarks
02
05
17
01
37
Best Solution and survive in the next generation
10
04
13
14
77
Survive in next generation
12
05
23
02
47
Survive in next generation
20
04
13
14
93
Rejected solution
10
05
18
03
56
Survive in next generation
20
01
10
06
46
Survive in next generation
Table 7.
Survival selection.
Population after mutation
Next generation initial population
Feval
Remarks
02
05
17
01
02
05
17
01
37
10
04
13
14
10
04
13
14
77
12
05
23
02
12
05
23
02
47
20
04
13
14
20
01
10
06
47
Replaced solution
10
05
18
03
10
05
18
03
56
20
01
10
06
20
01
10
06
46
Table 8.
Replacement-population for the next iteration.
Stage 7: Stopping the iteration
The optimization process is repeated until when objective function value or decision variables values become stagnant, that is, have no/very little change for a greater number of iterations. Thus, over period, the solution will get converge to the final best minimum optimal solution and the optimization process will be stopped, based on any stopping criteria such as the maximum number of iterations, or maximum number of functional evaluations, etc.
6. Conclusion
The basic processes that occur behind an evolutionary algorithm have been explained and illustrated in this chapter with steps covering solution representation, population generation, functional evaluation, parent selection, genetic operations, offspring evaluations, survival selection, and stopping criteria for a simple optimization problem. This knowledge can be extended very well by researchers across any discipline, working in the field of optimization and for applying evolutionary algorithms to solve any complex engineering problem using computers. Although the process behind EA may appear to be simple, the details of the optimization process form the base and are very much necessary in applying the learned concepts for modifying the existing evolutionary concepts and evolving into better optimization methods in the research level.
\n',keywords:"evolutionary algorithms, genetic operators, non-convex, multi-modal, optimization process",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81369.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81369.xml",downloadPdfUrl:"/chapter/pdf-download/81369",previewPdfUrl:"/chapter/pdf-preview/81369",totalDownloads:12,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 14th 2022",dateReviewed:"March 3rd 2022",datePrePublished:"April 18th 2022",datePublished:null,dateFinished:"April 18th 2022",readingETA:"0",abstract:"Real-world has many optimization scenarios with multiple constraints and objective functions that are discontinuous, nonlinear, non-convex, and multi-modal in nature. Also, the optimization problems are multi-dimensional with mixed types of variables like integer, real, discrete, binary, and having a different range of values which demands normalization. Hence, the search space of the problem cannot be smooth. Evolutionary algorithms have started gaining attention and have been employed for computational processes to solve complex engineering problems. Because it has become an instrument for research scientists and engineers who need to apply the supremacy of the theory of evolution to shape any optimization-based research problems and articles. In this chapter, there is a comprehensive introduction to the optimization field with the state-of-the-art in evolutionary computation. Though many books have described such areas of optimization in any form as evolution strategies, genetic programming, genetic algorithms, and evolutionary programming, evolutionary algorithms, that is, evolutionary computation is remarkable for considering it to discuss in detail as a general class.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81369",risUrl:"/chapter/ris/81369",signatures:"S. Tamilselvi",book:{id:"10694",type:"book",title:"Genetic Algorithms",subtitle:null,fullTitle:"Genetic Algorithms",slug:null,publishedDate:null,bookSignature:"Dr. Sebastian Ventura Soto, Dr. José M. Luna and Dr. Jose M. Moyano",coverURL:"https://cdn.intechopen.com/books/images_new/10694.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-178-4",printIsbn:"978-1-80355-177-7",pdfIsbn:"978-1-80355-179-1",isAvailableForWebshopOrdering:!0,editors:[{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Need for evolutionary algorithms",level:"1"},{id:"sec_3",title:"3. Known optimization problems",level:"1"},{id:"sec_4",title:"4. Optimization process of simple evolutionary algorithm",level:"1"},{id:"sec_5",title:"5. Iterative process behind evolutionary algorithms",level:"1"},{id:"sec_5_2",title:"5.1 Formulation of optimization problem",level:"2"},{id:"sec_6_2",title:"5.2 Different stages in optimization process",level:"2"},{id:"sec_8",title:"6. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs. 3rd ed. Berlin, Heidelberg: Springer; 1996. p. 387. DOI: 10.1007/978-3-662-03315-9'},{id:"B2",body:'Gen M, Cheng R. GA & Engineering Design. Hoboken, New Jersey, United States: John Willey & Sons, Inc.; 1997'},{id:"B3",body:'Tamilselvi S, Baskar S, Anandapadmanaban L, Kadhar K, Varshini PR. Chaos-assisted multiobjective evolutionary algorithm to the design of transformer. Soft Computing. 2017;21(19):5675-5692. DOI: 10.1007/s00500-016-2145-7'},{id:"B4",body:'Tamilselvi S, Baskar S, Anandapadmanaban L, Karthikeyan V, Rajasekar S. Multi objective evolutionary algorithm for designing energy efficient distribution transformers. Swarm and Evolutionary Computation. 2018;1(42):109-124'},{id:"B5",body:'Tamilselvi S, Baskar S, Sivakumar T, Anandapadmanaban L. Evolutionary algorithm-based design optimization for right choice of transformer conductor material and stepped core. Electrical Engineering. 2019;101(1):259-277'},{id:"B6",body:'Agatz N, Bouman P, Schmidt M. Optimization approaches for the traveling salesman problem with drone. Transportation Science. 2018;4(52):965-981'},{id:"B7",body:'Lakshminarasimman N, Baskar S, Alphones A, Willjuice Iruthayarajan M. Evolutionary multiobjective optimization of cellular base station locations using modified NSGA-II. Wireless Networks. 2011;17(3):597-609'},{id:"B8",body:'Tamil Selvi S, Baskar S, Rajasekar S. An Intelligent Approach Based on Metaheuristic for Generator Maintenance Scheduling—Classical and Recent Aspects of Power System Optimization. 1st ed. Cambridge, Massachusetts, United States: Academic Press; 2018. pp. 99-136'},{id:"B9",body:'Gunasundari S, Janakiraman S, Meenambal S. Multiswarm heterogeneous binary PSO using Win-Win approach for improved feature selection in liver and kidney disease diagnosis. Computerized Medical Imaging and Graphics. 2018;70:135-154'},{id:"B10",body:'Bhattacharya A, Chattopadhyay PK. Solving complex economic load dispatch problems using biogeography-based optimization. Expert Systems with Applications. 2010;37(5):3605-3615'},{id:"B11",body:'De Jong KA. Evolutionary Computation—A Unified Approach. 1st ed. Berlin/Heidelberg, Germany: Springer; 2017. p. 268'},{id:"B12",body:'Hermawanto D. Genetic Algorithm for Solving Simple Mathematical Equality Problem. 2013 [arXiv preprint arXiv:1308.4675]'},{id:"B13",body:'Available from: https://www.tutorialspoint.com/genetic_algorithms/genetic_algorithms_parent_selection.htm'},{id:"B14",body:'Goldberg DE. Genetic Algorithms in Search, Optimization, and Machine Learning. 13th ed. Boston, Massachusetts, United States: Addison Wesley; 1989. p. 432'},{id:"B15",body:'Hancock PJ. An empirical comparison of selection methods in evolutionary algorithms. In: AISB Workshop on Evolutionary Computing 1994 Apr 11. Berlin, Heidelberg: Springer; 1994. pp. 80-94'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"S. Tamilselvi",address:"tamilselvis@ssn.edu.in",affiliation:'
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In relation with their location, these sinuses contribute to the development of the facial structures, jaws and upper airway. During the developmental process of the paranasal sinuses, anatomic variations can occur in consequence of intra and extramural migration of the ethmoid air cells, overpneumatization or hypoplasia of the sinuses and bulging of the neurovascular structures to the sinuses. Some of these anatomic variations may affect the drainage pathways, pave the way for chronic infections and cause difficulties when performing paranasal sinuses surgery. 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He is also a research scholar of Harvard Medical School and University of Iowa hospital. During his substantial experience, he ever worked in Stanford University, University of Pennsylvania, Boston Children's Hospital and Massachusetts Eye and Ear. Besides, he is not only working hard on clinical & basic medicine but also launching out into public health in Taiwan. In recent years, he devotes himself to innovation and telemedicine. He always says that “in theoretical or practical aspects, no innovation is a step backward\". Due to his contribution to biodesign, he was invited into executive committee of Advanced Joint R & D Center in Taiwan",institutionString:"Chairman of Otolaryngology-Head and Neck Surgery, China Medical University Hsinchu Hospital, Taiwan",institution:null},{id:"205510",title:"Dr.",name:"Che-Lun",surname:"Hsu",slug:"che-lun-hsu",fullName:"Che-Lun Hsu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"206912",title:"Prof.",name:"Yaşar",surname:"Özkan",slug:"yasar-ozkan",fullName:"Yaşar Özkan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
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The oxidative polymerization of catecholamines can be triggered by light, chemical and physical methods, thus representing one of the widely explored surface coating methods. The overall objectives of this chapter are to compile the various methods of accomplishing surface coatings and compare the structural diversity of catecholamines. The progress achieved so far on polydopamine (pDA) coatings on electrospun polymers will be discussed. Finally, we will summarize the research efforts on catecholamine coatings for biomedical applications as well as their potential as a high definition coating method.",book:{id:"7256",slug:"dopamine-health-and-disease",title:"Dopamine",fullTitle:"Dopamine - Health and Disease"},signatures:"Rajamani Lakshminarayanan, Srinivasan Madhavi and Christina Poh\nChoo Sim",authors:[{id:"256023",title:"Associate Prof.",name:"Lakshminarayanan",middleName:null,surname:"Rajamani",slug:"lakshminarayanan-rajamani",fullName:"Lakshminarayanan Rajamani"},{id:"270706",title:"Prof.",name:"Madhavi",middleName:null,surname:"Srinivasan",slug:"madhavi-srinivasan",fullName:"Madhavi Srinivasan"},{id:"270707",title:"Dr.",name:"Christina Poh Choo",middleName:null,surname:"Sim",slug:"christina-poh-choo-sim",fullName:"Christina Poh Choo Sim"}]},{id:"47285",doi:"10.5772/58851",title:"Spinal Additives in Subarachnoid Anaesthesia for Cesarean Section",slug:"spinal-additives-in-subarachnoid-anaesthesia-for-cesarean-section",totalDownloads:5764,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"3819",slug:"topics-in-spinal-anaesthesia",title:"Topics in Spinal Anaesthesia",fullTitle:"Topics in Spinal Anaesthesia"},signatures:"Hala M. 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Other human and animal studies indicate that fluoride is a developmental neurotoxicant and that it operates in utero. Economic impacts of IQ loss have been quantified. The objective was to use data from the meta-analysis and other studies to estimate a daily dose of fluoride that would protect all children from lowered IQ, and to estimate economic impacts. We used two methods: traditional lowest-observed-adverse-effect (LOAEL)/no-observed-adverse-effect level (NOAEL); and benchmark dose (BMD). We used 3 mg/L in drinking water as an “adverse effect concentration,” with reported fluoride intakes from food, in the LOAEL/NOAEL method. We used the available dose–response data for the BMD analysis. Arsenic, iodine, and lead levels were controlled for in studies we used. BMD analysis shows the possible safe dose to protect against a five-point IQ loss is between 0.0014 and 0.050 mg/day. The LOAEL/NOAEL safe dose range estimate is 0.0042–0.16 mg/day. The economic impact for IQ loss among US children is loss of tens of billions of dollars.",book:{id:"5894",slug:"neurotoxins",title:"Neurotoxins",fullTitle:"Neurotoxins"},signatures:"John William Hirzy, Paul Connett, Quanyong Xiang, Bruce Spittle\nand David Kennedy",authors:[{id:"215103",title:"Dr.",name:"J. William",middleName:null,surname:"Hirzy",slug:"j.-william-hirzy",fullName:"J. 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The oxidative polymerization of catecholamines can be triggered by light, chemical and physical methods, thus representing one of the widely explored surface coating methods. The overall objectives of this chapter are to compile the various methods of accomplishing surface coatings and compare the structural diversity of catecholamines. The progress achieved so far on polydopamine (pDA) coatings on electrospun polymers will be discussed. Finally, we will summarize the research efforts on catecholamine coatings for biomedical applications as well as their potential as a high definition coating method.",book:{id:"7256",slug:"dopamine-health-and-disease",title:"Dopamine",fullTitle:"Dopamine - Health and Disease"},signatures:"Rajamani Lakshminarayanan, Srinivasan Madhavi and Christina Poh\nChoo Sim",authors:[{id:"256023",title:"Associate Prof.",name:"Lakshminarayanan",middleName:null,surname:"Rajamani",slug:"lakshminarayanan-rajamani",fullName:"Lakshminarayanan Rajamani"},{id:"270706",title:"Prof.",name:"Madhavi",middleName:null,surname:"Srinivasan",slug:"madhavi-srinivasan",fullName:"Madhavi Srinivasan"},{id:"270707",title:"Dr.",name:"Christina Poh Choo",middleName:null,surname:"Sim",slug:"christina-poh-choo-sim",fullName:"Christina Poh Choo Sim"}]},{id:"59036",title:"Nursing Care for Persons with Drug Addiction",slug:"nursing-care-for-persons-with-drug-addiction",totalDownloads:2150,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Persons with drug addiction (PDDs) may exhibit symptoms affecting the central nervous system. Multidisciplinary treatment teams may offer the most updated treatment and care. Pharmacotherapy is one standard treatment, effective in managing psychotic symptoms with supportive psychosocial interventions. As part of the health-care team, nurses deal with PDD on a 24-hour basis. Quality nursing care is essential for improving quality of life, health status, and continued abuse-free status of PDD.",book:{id:"6404",slug:"drug-addiction",title:"Drug Addiction",fullTitle:"Drug Addiction"},signatures:"Ek-uma Imkome",authors:[{id:"219235",title:"Associate Prof.",name:"Ek-Uma",middleName:null,surname:"Imkome",slug:"ek-uma-imkome",fullName:"Ek-Uma Imkome"}]},{id:"59317",title:"Effect of Alcohol on Brain Development",slug:"effect-of-alcohol-on-brain-development",totalDownloads:1208,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In the world, 3.3 million deaths occur every year due to harmful use of alcohol; this represents 5.9% of all deaths. Ethanol metabolites’ production and their post-translation modification are one of the proposed mechanisms that lead to neuronal toxicity. The projected neurochemical changes in chronic alcohol drinkers may be due to an imbalance between excitatory and inhibitory neurotransmitters. Interaction of alcohol with GABA and glutamate receptors (NMDA and AMPA) resulted in diverse adaptive changes in gene expression through neuronal pathways leading to alcohol toxicity. Alcohol consumption in an individual leads to biochemical changes that are correlated with complex inflammatory signaling pathways such as phosphorylation of proteins, synthesis of nitric oxide (NO), NF-kappaB and MAP kinase pathways in certain regions of the brain. Ethanol exposure activates neurons and microglial cells that lead to release of neuroimmune factors like high-mobility group box 1 (HMGB1), toll-like receptor 4 (TLR4) and certain cytokines involved in immune responses leading to neuroimmune signaling in the brain. Epigenetic modification of DNA and histones may lead to neuronal gene expression, thus regulating ethanol toxicity. Researchers attempt to modulate therapies that can help to foil alcohol toxicity and support the development of original neuronal cells that have been injured or degenerated by alcohol exposure.",book:{id:"6404",slug:"drug-addiction",title:"Drug Addiction",fullTitle:"Drug Addiction"},signatures:"Farhin Patel and Palash Mandal",authors:[{id:"217215",title:"Dr.",name:"Palash",middleName:null,surname:"Mandal",slug:"palash-mandal",fullName:"Palash Mandal"},{id:"219333",title:"Ms.",name:"Farhin",middleName:null,surname:"Patel",slug:"farhin-patel",fullName:"Farhin Patel"}]},{id:"61035",title:"Induced Pluripotent Stem Cell-Derived Human Glutamatergic Neurons as a Platform for Mechanistic Assessment of Inducible Excitotoxicity in Drug Discovery",slug:"induced-pluripotent-stem-cell-derived-human-glutamatergic-neurons-as-a-platform-for-mechanistic-asse",totalDownloads:1251,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Since the guiding principles of Replace, Reduce, and Refine were published, wider context-of-use for alternatives to animal testing have emerged. Induced pluripotent stem cell-derived human glutamatergic-enriched cortical neurons can be leveraged as 2- and 3-dimensional platforms to enable candidate drug screening. Uniquely so, 2-dimensional models are useful considering that they exhibit spontaneous firing, while, 3-dimensional models show spontaneous synchronized calcium transient oscillations. Here, the limitations of selected induced acute seizure models as well as the early utilization of fully differentiated glutamatergic neuron models for interrogation of inducible excitotoxicity following exposure to neuromodulators will be described. 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The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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