\r\n\tReservoir characterization is defined as the model that characterises the reservoirs based on their ability to store and produce hydrocarbons. They are used to indicate the reservoir fluids' behaviour under different circumstances and to find the optimal production techniques that can maximise production. Reservoir modeling is the process of creating a three-dimensional representation of a given reservoir based on its petrophysical, geological, and geophysical properties. These properties are defined during reservoir characterization where geoscientists and engineers gather all physical and chemical data to extrapolate those values throughout the reservoir. They can then create a three-dimensional model to be used for reservoir simulation. From a practical point of view, the integrated reservoir modeling represents now the most valuable technical approach for estimating the oil/gas reserves and computing the future production profiles, reducing the uncertainties always associated with the static and dynamic reservoir descriptions. Reservoir engineering is the formulation of development and production plans that will result in maximum recovery for a given set of economic, environmental, and technical constraints which is not a one-time activity but needs continual updating throughout the production life of a reservoir. Reservoir management is often defined as the allocation of resources to optimize hydrocarbon recovery from a reservoir while minimizing capital investments and operating expenses.
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1. Introduction
Tomato (Solanum lycopersicum) is a flowering plant belonging to the Solanaceae family, also known as Nightshade. It is one of the most popular vegetable crops grown in the world due to its fruit quality—taste, color, flavor and nutritional content [1]. Tomato fruits can be consumed in different forms; either fresh, partially cooked or processed. Tomatoes provide carotenoids, flavonoids, phytosterols, vitamins, and minerals which are essential in human nutrition. Carotenoids are the most abundant in tomatoes with the most common one being lycopene, followed by beta-carotene, gamma-carotene, lutein, phytoene, and a few other minor carotenoids [2, 3] which have anti-cancer properties [4, 5]. It is also a great source of carbohydrates, fiber and a small amount of vitamin A, vitamin B complex (thiamin, riboflavin, and niacin) and vitamin C [6] and is also rich in iron, copper, phosphorus, manganese and potassium [7].
According to the statistical agency of the Food and Agriculture Organization of the United Nations (FAOSTAT) (2020), the world’s total tomato production is estimated at 186,821 million tonnes with a cultivated area of about 5,051,983 hectares. In comparison, there has been a 3.35% increase in production from 180,766 million tonnes in 2019 to 186,821 million tonnes produced in 2020. China is the leading producer of tomatoes in the world accounting for about 34.67%. Egypt ranked fifth in global tomato production contributing 3.6% whiles leading the tomato production in Africa estimated at 6731.22 million tonnes cultivated on an area of 170.862 hectares. In addition to Egypt, other North African countries with both tropical and temperate conditions including Algeria, Tunisia and Morocco accounted for about 2.39% of the world’s tomato production. Among the West African countries, the leading producers, Nigeria and Cameroun produced 3693.72 million and 1.246.65 million, respectively, whiles Kenya produced 1056.18 million to lead tomato production in East Africa [8]. In Ghana, according to the Ministry of Food and Agriculture (MoFA), tomato production is estimated at 420,000 tonnes in 2019 cultivated on 47,000 hectares [9, 10].
The rapid increase in tomato consumption in the tropics is one of the factors influencing emerging production practices and strategies to meet local and export demands. Thus, many tropical countries have expanded their tomato acreage to meet local needs and, in some cases, to generate foreign exchange due to the increased importance of tomatoes in food and nutrition security. Several different production systems have been used successfully in different parts of the world to produce tomatoes. For instance, in the tropics, particularly in Africa, the open field cultivation system is mostly adopted whereas, in the developed countries, there is a massive shift to controlled environment systems [11]. Tomato cultivars with a determinate or semi-determinate growth habit are typically grown in open fields which are usually for fresh consumption. This system is also distinguished by the use of either direct sowing or transplanting where a nursery is established. Currently, transplanting is commonly practiced since it ensures good stand establishment, uniformity, reduced weed competition, and improved survival rate and yield compared to direct sowing [12]. Nonetheless, open-field tomato seedlings tend to be weaker and have a lower rate of transplant survival, resulting in low yields [13]. Other constraints such as biotic (high incidence of pests and diseases) and abiotic stresses (such as drought and high temperature) pose serious threats to open-field tomato production [14]. Root-knot nematodes (including Meloidogyne incognita, M. javanica and M. arenaria) are soil-borne pathogens that cause yield losses of about 30% in tomatoes in the tropics [5]. Thus, they cause stunted growth making the tomato plants more susceptible to soil-borne fungal (such as Fusarium wilt caused by Fusarium oxysporum) and bacterial diseases (such as bacterial wilt caused by Ralstonia solanacearum) [5]. Several studies on grafting techniques to combat these soil-borne root-knot nematodes and fungal diseases have resulted in the identification of potential rootstocks such as Solanum torvum, Solanum macrocarpon, and Solanum aethiopicum [15] that confer tolerance to these soil-borne problems. However, due to the high cost of producing grafted seedlings in large quantities, grafting is not widely used in large-scale production in the tropics [16]. Furthermore, open-field tomato cultivation exposes the plants to a variety of stinging and sucking insects, such as whitefly, thrips, and aphids, which cause moderate to severe physical damage as well as contribute to the transmission of viruses [5]. High temperatures observed in open-field tomato production in the tropics cause heat stress [17]. Tomato is an extremely sensitive crop to heat stress, which can lead to total yield loss [18]. A slight increase in night temperature especially can decrease pollen viability and female fertility thereby impairing fruit set and consequently yield reduction [19].
Increased tomato consumption [20] combined with unfavorable climatic conditions necessitates the development of urgent strategies to boost production whiles improving fruit quality in the tropics. Open field tomato production is hampered by climate change-related factors such as high temperatures, drought and high incidence of pests and diseases. In recent years, greenhouse tomato farming has proven to be the most efficient method of producing high-quality fresh tomatoes for both domestic and international markets [1]. In addition, it provides the opportunity for year-round production. Indeterminate tomato cultivars are usually used in this system, allowing the harvesting period to be extended, thereby, increasing the tomato productivity and revenue as well as improving the livelihood of farmers. This chapter discusses greenhouse structures and systems, agronomic practices, postharvest handling, prospects and challenges of greenhouse tomato production in the tropics and the use of axillary stem cuttings as an alternative method of producing true-to-type tomato seedlings for cultivation.
2. Greenhouse structures
Greenhouse farming systems have been adopted in some African countries, especially in Northern Africa (Algeria, Egypt, Morocco, and Tunisia), Eastern Africa (Kenya, Ethiopia, Uganda, and Rwanda), Western Africa (Ghana) and South Africa. In Northern Africa, the greenhouse system is mainly used for vegetable production whiles that of Eastern Africa (for e.g., Kenya), is for flower production. Furthermore, in Rwanda, South Africa and Ghana greenhouse system is mainly used for tomato production [21]. In all these countries, the greenhouse specifications are dependent on the availability of construction inputs, local climatic conditions and socio-economic status [11]. Generally, the initial investment cost of greenhouse construction is very high. Galvanized metals including steel or aluminum are the preferred construction material as they are durable and require less amount of material for construction thereby increasing light transmission (Figure 1). Wood such as bamboo is an alternative material (Figure 2). Though it is less expensive, more wooden materials are required to ensure a solid and firm structure. This, however, reduces light transmission. Also, the cost of maintenance in using bamboo is relatively higher compared to those constructed from metals [21].
Figure 1.
Greenhouse of West Africa Center for Crop Improvement (WACCI), University of Ghana built from galvanized metals including steel or aluminum.
Figure 2.
Greenhouse of Institute of Applied Science and Technology (IAST), University of Ghana built from bamboo.
High sidewalls in greenhouse construction are critical for maximizing the effectiveness of natural ventilation in greenhouses with roof venting. The direct/diffuse ratio in incident light, as well as the diffusion properties of covering materials [22, 23], greenhouse design, time of day, season, and location, all influence light transmission and spatial uniformity of light intensity inside the greenhouse [11]. To promote plant growth and development, an ideal greenhouse ensures that light is evenly distributed. Again, to ensure optimal light transmission in the greenhouse, the type of covering material should be considered. These include; (1) a non-waterproof net which provides partial shade and protection against insect permeability; (2) a plastic film for protection against insects and rains and (3) a glass which is more durable and effective than plastic films. Glass is mostly used for high-tech greenhouses [21]. In most greenhouses in Africa, side nets are fixed to provide natural ventilation (Figure 3). Circulation fans (chimney) (Figure 4), misting/fogging and hosing (Figure 5) can also be used to regulate/manage the climatic conditions in the greenhouse. In addition, shade screens/nets are also used to reduce the intensity of solar radiation in the greenhouse (Figure 5) [21].
Figure 3.
Fixing of side nets (indicated with the arrow) to provide natural ventilation.
Figure 4.
Circulation fans (chimney) are fixed on greenhouses of IAST to regulate the climatic conditions in the greenhouse.
Figure 5.
Misting/fogging and hosing (blue arrow) are used to regulate the climatic conditions as well a shade net (red arrow) is used to reduce the intensity of solar radiations in the greenhouse.
2.1 Greenhouse agronomic practices
Good greenhouse crop management practices serve as a gateway for ensuring sustainable production, increasing yield and high fruit quality, concomitant with increased income generation. Before plant establishment; raising vigorous and healthy seedlings, greenhouse fumigation media selection and sterilization, fertigation and irrigation, etc. need to be considered. In addition, other recommended greenhouse cultural practices such as plant spacing, pruning, topping, training/trellising and hormone application and pollination should be performed.
2.2 Tomato varieties and propagation
The cultivation of tomatoes in the tropics is solely by using seeds; either open-pollinated (OPV) or hybrids. Hybrid seeds of tomatoes are the most suitable planting materials because of their vigor and high yielding potential [24]. Since greenhouse cultivation is done in a limited area, indeterminate hybrid tomato varieties are cultivated [11]. For instance, in Ghana, hybrid tomatoes such as Cobra, Anna F1, Lebombo, Kwando, Jaguar, Gamharr, Jarrah, Eva, Ranja, and Sodaja are being introduced by seed companies for greenhouse cultivation. Several greenhouse screenings and evaluations of exotic tomato lines are being carried out to identify adaptable high yielding types with excellent fruit quality. However, cultivating these hybrid tomatoes in the tropics could be very expensive and as such, vegetative propagation of tomatoes could be a viable option for producing true-to-type tomato hybrid planting materials [25] to ensure sustainable production.
A study was conducted to identify a suitable soilless medium for regenerating tomato seedlings from axillary stem cuttings and to assess the agronomic performance of the regenerated seedlings under greenhouse condition. Cuttings (12–15 cm long) from mature tomato plants were taken and raised using 100% rice husk biochar, 100% rice husk, 100% cocopeat, 50% biochar + 50% cocopeat, 50% cocopeat + 50% rice husk. A 2 × 2 factorial experiment arranged in a Completely Randomized Design (CRD) with four (4) replications was used. Treatments consisted of two factors; two tomato hybrid varieties (Lebombo and Anna) and planting materials (cuttings and seeds). Seedlings were also raised using 100% rice husk biochar. Seedlings and rooted cuttings were sown and transplanted 28 days respectively into pots (22 × 25 cm) half filled with 100% cocopeat. The study identified rice husk biochar (Table 1) as a suitable medium for generating vigorous and healthy tomato stem cuttings obtained from pruned axillary shoots of tomato varieties, Lebombo and Anna F1 (Figure 6). Further evaluation using tomato plants generated from seeds and stem cuttings indicated that there were no significant differences in yield (Table 2) and fruit quality (Table 3). Hence, vegetative propagation via axillary stem cuttings could be used as an alternative method of raising tomato seedlings in the tropics. Seed companies and tomato nursery production operators can collaborate to leverage this method to supply tomato seedlings at affordable rates to ensure sustainable greenhouse tomato production in the tropics.
Substrate
Root length (cm)
Survival (%)
Root volume (cm3)
Shoot dry weight (g)
Root dry weight (g)
Total dry weight (g)
Rice husk biochar/Lebombo
16.6 b
95.8 de
1.71 b
1.74 bc
0.26 ab
1.44 b
Cocopeat/Lebombo
10.4 a
29.2 a
1.89 b
1.41 b
0.14 a
1.55 b
Biochar + Cocopeat/Lebombo
10.1 a
40.6 ab
1.66 b
0.96 a
0.15 a
1.11 a
Cocopeat + Rice husk/Lebombo
13.0 ab
45.8 ab
1.55 b
1.35 ab
0.20 a
1.52 b
Rice husk biochar/Anna
17.4 b
100.0 e
1.89 b
2.13 c
0.38 b
2.54 c
Cocopeat/Anna
10.4 a
50.0 abc
1.71 b
1.40 b
0.17 a
1.56 b
Rice husk biochar + Cocopeat/ Anna
10.7 a
83.3 cde
0.97 a
1.37 ab
0.14 a
1.51 b
Cocopeat + Rice husk/Anna
10.6 a
72.9 bcd
1.58 b
1.45 b
0.20 a
1.62 b
Table 1.
Mean Root length, Survival plants per replication, Root volume, shoot dry weight, root dry weight and Total dry weight. Means followed by the same letters within a column are not significantly different according to Fisher’s Protected LSD at 5%.
Treatments
Days to 50% flowering
Days to 50% fruiting
Total number of fruits
Fruits per plant
Fruit weight per Plant (g)
Yield (kg/ha)
Shelf life (days)
Variety
Anna
25
32 a
24 b
5 b
96.5
6431.0
5
Lebombo
27
34 b
21 a
4 a
97.6
6506.0
5
P ≤ 0.05
0.143
<0.001
0.043
0.043
0.895
0.895
0.199
Propagule
Seeds
32 b
37 b
23
5
97.6
6503.0
5
Cuttings
21 a
28 a
22
4
96.5
6434.0
5
p ≤ 0.05
<0.001
<0.001
0.689
0.689
0.902
0.902
0.019
Variety * Propagule
NS
0.021
NS
NS
NS
NS
NS
Table 2.
Days to 50% flowering and fruiting, the total number of fruits, number of fruits per plant, fruit weight per plant, yield and shelf life of tomato plants. Means followed by the same letters within a column are not significantly different according to Fisher’s Protected LSD at 5%.
Treatments
Fruit girth (mm)
Fruit length (mm)
Brix (%)
Firmness (kg/lb)
Pericarp thickness (mm)
Juice volume (cm3)
pH
Titratable acidity
Variety
Anna
34.66 a
44.16
6.64
7.06
4.43 a
26.8
4.12
0.56 a
Lebombo
38.05 b
46.24
6.45
6.66
5.11 b
27.4
4.12
0.73 b
p ≤ 0.05
<0.001
0.036
0.567
0.438
0.050
0.874
0.947
0.028
Propagule
Seeds
36.20
44.50
6.47
6.73
4.49
27.8
4.13
0.58
Cuttings
36.51
45.90
6.62
6.99
5.05
26.4
4.10
0.71
p ≤ 0.05
0.587
0.137
0.653
0.622
0.100
0.684
0.217
0.083
Variety * Propagule
Anna * seeds
34.59
43.63
7.02 b
7.51 b
3.75 a
26.4
4.12
0.71 b
Anna * cuttings
34.73
44.69
6.27 ab
6.61 ab
5.11 b
27.3
4.11
0.41 a
Lebombo * seeds
37.82
45.37
5.93 a
5.95 a
5.23 b
29.2
4.09
0.71 b
Lebombo * cuttings
38.28
47.10
6.98 b
7.36 ab
4.99 b
25.6
4.14
0.75 b
p ≤ 0.05
0.776
0.706
0.017
0.042
0.026
0.510
0.112
0.022
Table 3.
Fruit girth, Fruit length, Brix, Firmness, Pericarp thickness, Juice volume, pH and Titratable acidity of tomato fruits. Means followed by the same letters within a column are not significantly different according to Fisher’s Protected LSD at 5%.
Figure 6.
Lebombo (A) and Anna (B) tomato seedlings raised from stem cuttings.
2.3 Substrate and sterilization
Plant roots are contained within a porous rooting medium called a ‘substrate’ or ‘growing medium.’ A suitable growing medium is required to provide root anchorage and a favorable environment for healthy root development, [26]. Growing media for greenhouse cultivation in the tropics comes in two basic types: soil- and organic-based. Field soil is the main component of the soil-based media and is the most simple and cheapest. However, it is associated with a high risk of soil-borne diseases such as bacterial wilt [21]. On the other hand, organic materials such as composted waste, peat, coconut peat/coir, sawdust, wood and bark are used to prepare the organic-based media [27]. Peat moss, vermiculite, and perlite which are premixed blends of organic and inorganic materials are commercially available. These products, however, are costly and difficult to obtain locally in the tropics, especially in Africa. Agricultural and municipal wastes, which are locally available, affordable, and environmentally sustainable, should be investigated as alternatives to commercial products in the tropics. A good soil-free substrate should have excellent chemical, biological and physical characteristics with low nutrient content, low pH, a unique combination of high-water retention capacity, high air space, lightweight, pest, and disease-free [28]. Cocopeat, a waste product obtained from the mesocarp of coconut (Cocos nucifera) fruit is most widely used in Africa and Asian countries such as the Philippines, Indonesia, India and Sri Lanka, where lots of coconuts are produced [28]. It can be combined with rice husk biochar and oyster shells. Although cocopeat is a better substitute for peat moss, high levels of natural soluble salts, sodium, and chloride are present and could cause osmotic stress to plants. As a result, to make these materials suitable for crop production, they are buffered or flushed out to remove excessive salts [29]. Sterilization of growing media is required before use, especially the locally prepared ones to prevent the introduction of pathogens and weeds in the greenhouse. Heat sterilization is the most common method (Figure 7). Although the most popular and cheapest method is solar sterilization, other improvised systems have been developed. Regardless of the system, it is critical to ensure that the entire media is exposed to uniform and adequate heat for efficient and effective sterilization [27].
Figure 7.
Dry heat from a flame used for the sterilization of growing media.
2.4 Plant spacing and density
Due to the high cost of greenhouse infrastructure, increasing plant density is one strategy for maximizing the limited space [30]. However, it is also important to plant in rows at a recommended spacing (Figure 8) to achieve an optimum yield. The required spacing between tomato plants will ensure an even distribution of resources such as water, nutrients, light, and air [31]. For example, there is more competition for light due to the overlapping and shading of leaves when plants are closely spaced [32]. The amount of light intercepted by the basal leaves could be drastically reduced, lowering the plants’ photosynthetic efficiency. Consequently, the plants may be forced to trade off their energy for stem elongation and reduced assimilate transport to developing fruits [31], thereby, causing yield reduction and poor fruit quality [33]. There have been reports of great increases in tomato yield and yield components when recommended plant spacing was used [33, 34, 35]. A recent study by Nkansah et al. [36] suggested plant spacing of 0.2 × 1.3 m for greenhouse tomato production.
Figure 8.
Tomato plants planted in rows at a recommended spacing.
2.5 Irrigation and fertigation
Adequate water supply to plants is essential for various metabolic and physiological processes such as photosynthesis, nutrient transport, and cell expansion and development [27]. In the tropics, water for greenhouse production can be obtained from rivers, ponds or reservoirs, rain, groundwater (boreholes), and municipal sources (tap water). Unfortunately, water quantity, quality and seasonal availability are not guaranteed in most tropical environments. A good water should be free from pests (such as pathogenic bacteria, fungi, weeds and pesticide contamination) and high concentrations of dissolved salts and toxic ions (heavy metals) [27]. As a result, a thorough biological and chemical analysis of water for greenhouse tomato production is required as this can affect plant health, growth and development. The chemical property, for instance, is useful for the formulation of nutrient solutions.
In the tropics, the manual irrigation system is the cheapest but does not give precision in terms of the quantity of water and nutrients applied. Gravitational fertigation in combination with drip irrigation is the commonly adopted method. The water tank is elevated (Figure 9) to allow water and nutrients to flow naturally [37]. Water and nutrients can be reused by using a recirculation system [11]. Water recirculation, on the other hand, increases the risk of spreading soil-borne diseases, necessitating the use of a disinfection unit (UV or heat treatment) [38] which can be costly. Another means of supplying water and nutrients is using a computerized system with sensors and a pre-programmed fertigation regime (Figure 10). This system, however, is reliant on a constant supply of electricity, which is a major challenge in the tropics [21].
Figure 9.
Water tanks are elevated above the level of the field to allow for the natural flow of water and nutrients.
Figure 10.
Water and nutrients are applied using a computerized system with sensors and a pre-programmed fertigation regime.
2.6 Pruning, topping and training/trellising
Tomato cultivars are divided into two categories based on their growth habits: determinate and indeterminate. Determinate tomatoes grow in a bush-like manner, reaching a fixed mature size characterized by synchronized flower formation and fruit production. On the other hand, indeterminate tomatoes grow in a vine-like manner, continuing to grow throughout the growing season and thus, having continuous flower and fruit formation [39]. The indeterminate tomato cultivars are used in greenhouse tomato cultivation [11]. Tomato vines are pruned by removing the stem suckers (Figure 11). These are stem branches or side shoots that emerge from the leaf axils which are the junctions between the main stem and the true leaf. If not pruned, these suckers will grow into full shoots with leaves, flowers, and fruits, and even regenerate new suckers. When suckers are young and small, they can be pinched or cut using pruners such as knives, scissors and secateurs. In any of these pruning approaches, it is better to ensure decontamination either by using an alcohol-based sanitizer or washing with soap to prevent the spread of pathogens [40]. Pruning can be done on weekly basis to improve or ensure efficient air circulation/aeration [41]. In addition, pruning helps to prevent the diversion of assimilates from the developing fruits thereby, improving tomato fruit quality [40, 42].
Figure 11.
Pruning of tomato vines by removing the stem suckers.
Another important greenhouse technique is topping (Figure 12), which involves cutting or pinching off the terminal bud to break the apical dominance [43]. This technique is critical because tomato cultivars for greenhouse cultivation are indeterminate types characterized by indefinite growth. Topping has been shown to improve fruit quality and yield by causing assimilates to be redistributed to developing fruits [44, 45]. In the Solanaceae family, topping improved yield and yield components in eggplant [46], pepper [47] and tomato [36]. According to Nkansah et al. [36], tomato yields were increased by topping at truss 2.
Figure 12.
Topping tomato plants by cutting or pinching off the terminal bud.
The main stem of tomato plants is positioned upright immediately after transplanting to keep the leaves and fruits from touching the ground [48], facilitate pollination, maximize light interception of the younger leaves, and increase labor efficiency in pruning and harvesting [11]. This method known as stem training/trellising (Figure 13) is necessary for indeterminate tomato cultivars. It entails securing the main stem with a twine/rope suspended from a horizontal wire about 2.5–3.2 m above the ground [11, 49]. Non-slip loops or clips are used to secure the twine’s tip to the stem’s base. The twine is then neatly wound in two or three spirals around the stem for each truss without damaging the stem [11].
Figure 13.
Trellising or training of tomato plants by securing the main stem with a twine/rope suspended above the ground.
2.7 Hormone application and pollination
Heat stress is a major problem hampering tomato production in the tropics [50]. Poor fruit set occurs in greenhouse systems where the microenvironment is not fully controlled or automated. Tomato is an extremely sensitive crop to heat stress, which can lead to total yield loss. The optimal day and night temperatures for tomato production are 21–29.5°C and 18.5–21°C, respectively. However, a slight increase in night temperature especially can decrease pollen viability and female fertility thereby impairing fruit set and consequently yield reduction [19]. Pollination and fertilization must both be completed before the fruit set can occur (Figure 14) [51]. Under heat stress, however, these processes are disrupted, resulting in flower abortion and flower drop [50]. Unfortunately, the molecular mechanisms underlying tomato fruit set are unknown, despite the fact that exogenous application of auxin and gibberellin to the tomato stigma improved tomato fruit set. Bypassing pollination and fertilization, auxin or gibberellin can stimulate tomato fruit development (cell division and expansion) [51]. As a result, using these hormones can help increase greenhouse tomato production by increasing fruit set and yield [52]. The coordinated mechanism of auxin, gibberellin, and cytokinin has been investigated for the development of parthenocarpic tomato fruits [53], which improves fruit quality. Although this may be labor intensive, the high returns from increased productivity and improved fruit quality can compensate for this.
Figure 14.
Pollination and fertilization of tomato flowers before fruit set.
2.8 Greenhouse pest and disease management
One of the reasons for the rise in greenhouse tomato production in the tropics is the benefit of reducing pest and disease outbreaks, which can affect plant growth and development, resulting in lower yields and poor fruit quality. To control pest or disease outbreaks, an integrated pest management approach including cultural, biological and chemical measures (Figure 15) is used. Because prevention is the best approach, ensuring good environmental practices is an important first step [54]. Regular cleaning and washing of the greenhouse and its equipment with disinfectant (such as bleach) and fumigation prior to the start of the production cycle are examples of best practices. Another strategy is to keep a close eye on the crops in the greenhouse in case of a pest or disease outbreak [55]. Pheromone traps and sticky cards (Figure 16), for example, are used to trap, detect, and determine pest population thresholds of pests such as leaf miners, whiteflies aphids and thrips [8, 55]. A comprehensive pest management guide for tomato production is available [8]. Pruning, trellising, and proper plant density and spacing ensure good aeration. Avoidance of wet floors by preventing irrigation water spillage helps to reduce the creation of a microclimate that promotes disease outbreaks [55].
Figure 15.
Chemical application for the management of pest and disease in greenhouse vegetable production.
Figure 16.
Pheromone traps (A) and sticky cards (B) are used to trap, detect, and determine pest population thresholds in greenhouses.
2.9 Harvesting and postharvest handling
Harvesting of greenhouse tomatoes is usually done at the breaker of color or when the fruit is orange-red, by handpicking. Thus, greenhouse tomatoes are typically harvested riper than fresh market field-grown fruit, making them more susceptible to mechanical injuries due to their softer nature and shorter shelf life than mature-green fruit. Greenhouse-grown fruit harvesting is done twice or three times per week as it reaches the appropriate stage of fruit development [11]. Prior to temporary storage, tomato fruits are sorted and graded. Grading allows a grower to serve different qualities at different prices to different markets, such as a supermarket and a wet market. As such, good packaging is required to reduce losses during transportation [21]. Harvested tomato fruits are chilling sensitive. Breaker fruits can be stored at 10–12.5°C for a week whiles orange-red at 7–10°C for 3–5 days [11]. Even though greenhouse tomatoes are more expensive than field-grown fruits, they are primarily produced for local consumption in the tropics. On the other hand, Northern African countries (such as Egypt and Morocco) and South Africa, produce greenhouse tomatoes for export to Europe [21].
3. Prospects and challenges of greenhouse tomato production in the tropics
3.1 Prospects
In the tropics, greenhouse tomato production has the potential to create attractive jobs for youth and women in particular [56]. Greenhouse training programs have been introduced in West Africa, particularly in Ghana, to target entrepreneurs and young graduates to learn how to grow vegetables in greenhouses [57].
The increased demand for greenhouse tomatoes, owing to their superior fruit quality, benefits growers by earning appreciable income to improve their livelihoods [58]. People in urban and peri-urban cities have gradually accepted and are willing to pay more for greenhouse tomatoes, despite the fact they are more expensive than those grown in the field [59].
Greenhouse tomato production supplements local tomato production, which is primarily a field-grown system that is affected by biotic and abiotic factors. Thus, the introduction of greenhouses in the topics has helped to ensure year-round tomato production and supply of high-quality fruits, ensuring sustainable food and nutrition security [60]. Also, there will be a constant supply of tomatoes to the processing industries for various industrial activities.
In addition, the greenhouse tomato production system contributes to the economic maximization of limited land and other resources [61]. This system, for example, ensures efficient water and nutrient supply to the plants while reducing losses such as leaching, which is common in field-grown systems. Also, unproductive lands, rooftops and concreted areas can be utilized for greenhouse tomato cultivation [62].
Another advantage of greenhouse tomato production is the complete control over indiscriminate agrochemical (pesticides, fungicides and weedicides) application. Strict adherence to greenhouse agronomic practices and integrated pest management systems eliminates traces of these agrochemicals on tomato fruits, which are harmful to human health [58]. This could promote the use of traceability systems to encourage the export of greenhouse tomato fruits in order to generate foreign exchange to boost tropical economies [63].
The introduction of greenhouses has opened up new areas in the tropics for academic and research work. To improve greenhouse tomato cultivation in the tropics, researchers should look into areas such as greenhouse agronomic practices, breeding for tropics-adapted greenhouse tomatoes, commercial adoption of grafting techniques for soil-based greenhouse cultivation, development of tropical soilless media and nutrient solutions, assessment and availability of raw materials for greenhouse constructions and so on.
3.2 Challenges
The initial cost of constructing a greenhouse is high which deters average income entrepreneurs to venture into greenhouse tomato production [64]. In addition to this, accessibility to credit facilities is difficult [65]. Lack of greenhouse technical know-how has also hindered the adoption of greenhouse tomato production in most tropical countries. In some areas, there are no greenhouse training centers for hands-on training to fully equip trainees in greenhouse design, construction, repair and maintenance and cultivation [66].
The unavailability of adaptable greenhouse tomato cultivation possess a major challenge. There is a high influx of imported tomato hybrids into various countries, however, some of these tomato hybrids are not adequately evaluated or screened to identify the promising candidates for further evaluations and official release. In addition, the available tomato hybrids are generally expensive for the local growers and may have fruit quality characteristics which are not preferred by the local market [45].
There is also a lack of greenhouse cultivation inputs and important resources. For instance, poor water quality and quantity prevent seasonal and year-round greenhouse tomato cultivation. Also, the unavailability of quality soilless substrates is a major challenge [58].
4. Conclusions
In conclusion, greenhouse tomato production is a promising technology that can ensure sustainable food and nutrition security in Africa. The selection of the proper greenhouse structure and system as well as the adoption of the appropriate agronomic practices and postharvest handling techniques would ensure enhanced tomato production under greenhouse condition in the tropics. Our research findings point to tomato cuttings as a viable source for raising planting material for tomato cultivation in the developing countries. The yields and fruit quality obtained from the use of seedlings versus stem cuttings were comparable.
It is therefore essential to encourage scientific research about greenhouse production in Africa to foster its adoption. Greenhouse tomato production has the potential of creating jobs and increasing income generation thereby improving the livelihood of the people in the greenhouse tomato value chain.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"greenhouse, tomato production, food and nutrition security, tropics",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82772.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82772.xml",downloadPdfUrl:"/chapter/pdf-download/82772",previewPdfUrl:"/chapter/pdf-preview/82772",totalDownloads:48,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 29th 2022",dateReviewed:"June 14th 2022",datePrePublished:"July 22nd 2022",datePublished:null,dateFinished:"July 22nd 2022",readingETA:"0",abstract:"Greenhouse vegetable cultivation offers one of the optimistic approaches to ensuring sustainable food and nutrition security in the tropics. Although greenhouse vegetable production is known to be costly, this system of production is gaining popularity and contributes to sustainable tomato production with improved fruit quality and productivity, which results in higher economic returns. Among vegetable crops, tomato is the most cultivated under this system. A study was conducted to identify suitable soilless media for regenerating tomato cuttings from axillary stem of tomato plants and to assess the agronomic performance of the regenerated cuttings under greenhouse condition. The tomato cuttings were raised using 100% rice husk biochar, 100% rice husk, 100% cocopeat, 50% biochar +50% cocopeat, 50% cocopeat +50% rice husk. Two tomato hybrid varieties (Lebombo and Anna) were used. Cuttings from axillary stems were compared with those raised from seed. A 2 × 2 factorial experiment was arranged in a Completely Randomized Design (CRD) with four replications. From the study, 100% rice husk biochar was found to induce root development in stem cuttings of tomato. However, no significant differences in yield and fruit quality were found between plants raised from seed and those from stem cuttings.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82772",risUrl:"/chapter/ris/82772",signatures:"Peter Amoako Ofori, Stella Owusu-Nketia, Frank Opoku-Agyemang, Desmond Agbleke and Jacqueline Naalamle Amissah",book:{id:"11620",type:"book",title:"Tomato - From Cultivation to Processing Technology",subtitle:null,fullTitle:"Tomato - From Cultivation to Processing Technology",slug:null,publishedDate:null,bookSignature:"Prof. Pranas Viskelis, Dr. Dalia Urbonavičienė and Dr. Jonas Viskelis",coverURL:"https://cdn.intechopen.com/books/images_new/11620.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-459-3",printIsbn:"978-1-80356-458-6",pdfIsbn:"978-1-80356-460-9",isAvailableForWebshopOrdering:!0,editors:[{id:"83785",title:"Prof.",name:"Pranas",middleName:null,surname:"Viskelis",slug:"pranas-viskelis",fullName:"Pranas Viskelis"}],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. Greenhouse structures",level:"1"},{id:"sec_2_2",title:"2.1 Greenhouse agronomic practices",level:"2"},{id:"sec_3_2",title:"2.2 Tomato varieties and propagation",level:"2"},{id:"sec_4_2",title:"2.3 Substrate and sterilization",level:"2"},{id:"sec_5_2",title:"2.4 Plant spacing and density",level:"2"},{id:"sec_6_2",title:"2.5 Irrigation and fertigation",level:"2"},{id:"sec_7_2",title:"2.6 Pruning, topping and training/trellising",level:"2"},{id:"sec_8_2",title:"2.7 Hormone application and pollination",level:"2"},{id:"sec_9_2",title:"2.8 Greenhouse pest and disease management",level:"2"},{id:"sec_10_2",title:"2.9 Harvesting and postharvest handling",level:"2"},{id:"sec_12",title:"3. Prospects and challenges of greenhouse tomato production in the tropics",level:"1"},{id:"sec_12_2",title:"3.1 Prospects",level:"2"},{id:"sec_13_2",title:"3.2 Challenges",level:"2"},{id:"sec_15",title:"4. Conclusions",level:"1"},{id:"sec_19",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Nicola S, Tibaldi G, Fontana E, Crops A-V, Plants A. Tomato production systems and their application to the tropics. Acta Horticulturae. 2009;821:27-34'},{id:"B2",body:'Beecher GR. Nutrient content of tomatoes and tomato products. Proceedings of the Society for Experimental Biology and Medicine. 1998;218:98-100'},{id:"B3",body:'Chaudhary P, Sharma A, Singh B, Nagpal AK. Bioactivities of phytochemicals present in tomato. Journal of Food Science and Technology. 2018;55:2833-2849'},{id:"B4",body:'Miller EC, Hadley CW, Schwartz SJ, Erdman JW, Boileau TW-M, Clinton SK. Lycopene, tomato products, and prostate cancer prevention. Have we established causality? Pure and Applied Chemistry. 2002;74:1435-1441'},{id:"B5",body:'Naika S, Van Lidt de Jeude J, de Goffau M, Hilmi M, Van Dam B. Cultivation of tomato. Production, processing and marketing. In: Dam BV, editor. 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Acta Agriculturae Slovenica. 2008;91:103-119'},{id:"B35",body:'Castoldi R, Faveri L, Souza J, Braz L, Charlo H. Productivity characteristics of endive as a function of spacing. In XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): International Symposium on Quality-Chain Management of Fresh Vegetables: From Fork to Farm. Vol. 936. ISHS Acta Horticulturae; 2010. pp. 305-309'},{id:"B36",body:'Nkansah GO, Amoatey C, Owusu-Nketia S, Ofori PA, Opoku-Agyemang F. Influence of topping and spacing on growth, yield and fruit quality of tomato (Solanum lycopersicum L.) under greenhouse condition. Frontiers in Sustainable Food Systems. 2021;5:470'},{id:"B37",body:'Raphael O, Amodu M, Okunade D, Elemile OO, Gbadamosi A. Field evaluation of gravity-fed surface drip irrigation systems in a sloped greenhouse. 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Cytokinin-induced parthenocarpic fruit development in tomato is partly dependent on enhanced gibberellin and auxin biosynthesis. PLoS One. 2013;8:e70080'},{id:"B54",body:'Melanson RA. Greenhouse Tomatoes Pest Management in Mississippi. Agricultural Communications, Mississippi State University Extension Service; 2019'},{id:"B55",body:'Centre A-I. Commercial greenhouse tomato production: Pest and disease management. 2018'},{id:"B56",body:'Njenga PK, Mugo F, Opiyo R. Youth and Women Empowerment Through Agriculture in Kenya. Kenya: VSO Jitolee Nairobi; 2011'},{id:"B57",body:'Gyimah NY. Greenhouse vegetable production; Ghana’s bet to reducing vegetable importation. 2021'},{id:"B58",body:'Osei MK, Ofori PA, Adjebeng-Danquah J, Nketia SO, Frimpong-Anin K, Osei-Bonsu I, et al. Harnessing technologies for vegetable cultivation: A panacea for food and nutrition insecurity in Ghana. In: Vegetable Crops-Health Benefits and Cultivation. London, UK: IntechOpen; 2022'},{id:"B59",body:'Ackerman K. Urban agriculture: Opportunities and constraints. In: Metropolitan Sustainability. Sawston, Cambridge: Woodhead Publishing; 2012. pp. 118-146. DOI: 10.1533/9780857096463.2.118'},{id:"B60",body:'O\'Sullivan C, Bonnett G, McIntyre C, Hochman Z, Wasson A. Strategies to improve the productivity, product diversity and profitability of urban agriculture. Agricultural Systems. 2019;174:133-144'},{id:"B61",body:'Zhang Y, Alvarez-Manzo H, Leone J, Schweig S, Zhang Y. Botanical medicines cryptolepis sanguinolenta, artemisia annua, scutellaria baicalensis, polygonum cuspidatum, and alchornea cordifolia demonstrate inhibitory activity against Babesia duncani. Frontiers in Cellular and Infection Microbiology. 2021;11:22'},{id:"B62",body:'Sanyé-Mengual E, Cerón-Palma I, Oliver-Solà J, Montero JI, Rieradevall J. Integrating horticulture into cities: A guide for assessing the implementation potential of rooftop greenhouses (RTGs) in industrial and logistics parks. Journal of Urban Technology. 2015;22:87-111'},{id:"B63",body:'Eerenstein J, Zalmijn A. Production of greenhouse vegetable crops; Principles for humid tropical areas. In: Good Agricultural Practices (GAP) for Greenhouse Vegetable Crops. SURAGRIC – 009 (COMCEC); 2015'},{id:"B64",body:'Asci S, VanSickle JJ, Cantliffe DJ. Risk in investment decision making and greenhouse tomato production expansion in Florida. International Food and Agribusiness Management Review. 2014;17:1-26'},{id:"B65",body:'Forkuor G, Amponsah W, Oteng-Darko P, Osei G. Safeguarding food security through large-scale adoption of agricultural production technologies: The case of greenhouse farming in Ghana. Cleaner Engineering and Technology. 2022;6:100384'},{id:"B66",body:'Ozor N, Nwobodo C, Baiyeri P, Enete A. Controlled environment agriculture in Africa: Benefits, challenges and the political economy. Agriculture Development. 2018;34:38'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Peter Amoako Ofori",address:null,affiliation:'
Institute of Applied Science and Technology College of Basic and Applied Sciences, University of Ghana, Ghana
Department of Crop Science, College of Basic and Applied Sciences, University of Ghana, Ghana
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To achieve such properties, these structures are based on techniques that allow detection and diagnosis of failures in real time. Detection signals faults and diagnostics provide the root cause and location. Fault detection is based on signal and process mathematical models, while fault diagnosis is focused on systems theory and process modeling. Monitoring and supervision complement each other in fault management, thus enabling normal and continuous operation. Its application avoids stopping productive processes by early detection of failures and by applying real-time actions to eliminate them, such as predictive and proactive maintenance based on process conditions. The integration of all these methodologies enables intelligent monitoring and supervision systems, enabling real-time fault detection and diagnosis. Their high performance is associated with statistical decision-making techniques, expert systems, artificial neural networks, fuzzy logic and computational procedures, making them efficient and fully autonomous in making decisions in the real-time operation of a production system.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Gustavo Pérez Alvarez",authors:[{id:"216192",title:"Dr.",name:"Gustavo",middleName:"Pérez",surname:"Alvarez",slug:"gustavo-alvarez",fullName:"Gustavo Alvarez"}]},{id:"65244",doi:"10.5772/intechopen.83810",title:"Fault Diagnosis Techniques for a Wind Turbine System",slug:"fault-diagnosis-techniques-for-a-wind-turbine-system",totalDownloads:1283,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Silvio Simani and Paolo Castaldi",authors:[{id:"209626",title:"Prof.",name:"Silvio",middleName:null,surname:"Simani",slug:"silvio-simani",fullName:"Silvio Simani"},{id:"209627",title:"Dr.",name:"Paolo",middleName:null,surname:"Castaldi",slug:"paolo-castaldi",fullName:"Paolo Castaldi"}]},{id:"70067",doi:"10.5772/intechopen.90157",title:"Analytic Prognostic in the Linear Damage Case Applied to Buried Petrochemical Pipelines and the Complex Probability Paradigm",slug:"analytic-prognostic-in-the-linear-damage-case-applied-to-buried-petrochemical-pipelines-and-the-comp",totalDownloads:2880,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"In 1933, Andrey Nikolaevich Kolmogorov established the system of five axioms that define the concept of mathematical probability. This system can be developed to include the set of imaginary numbers by adding a supplementary three original axioms. Therefore, any experiment can be performed in the set \n\nC\n\n of complex probabilities which is the summation of the set \n\nR\n\n of real probabilities and the set \n\nM\n\n of imaginary probabilities. The purpose here is to include additional imaginary dimensions to the experiment taking place in the “real” laboratory in \n\nR\n\n and hence to evaluate all the probabilities. Consequently, the probability in the entire set \n\nC\n=\nR\n+\nM\n\n is permanently equal to one no matter what the stochastic distribution of the input random variable in \n\nR\n\n is; therefore the outcome of the probabilistic experiment in \n\nC\n\n can be determined perfectly. This is due to the fact that the probability in \n\nC\n\n is calculated after subtracting from the degree of our knowledge the chaotic factor of the random experiment. Consequently, the purpose in this chapter is to join my complex probability paradigm to the analytic prognostic of buried petrochemical pipelines in the case of linear damage accumulation. Accordingly, after the calculation of the novel prognostic model parameters, we will be able to evaluate the degree of knowledge, the magnitude of the chaotic factor, the complex probability, the probabilities of the system failure and survival, and the probability of the remaining useful lifetime; after that a pressure time t has been applied to the pipeline, which are all functions of the system degradation subject to random and stochastic influences.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Abdo Abou Jaoude",authors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé"}]},{id:"65747",doi:"10.5772/intechopen.82781",title:"Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB",slug:"prognostics-102-efficient-bayesian-based-prognostics-algorithm-in-matlab",totalDownloads:1359,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"An efficient Bayesian-based algorithm is presented for physics-based prognostics, which combines a physical model with observed health monitoring data. Unknown model parameters are estimated using the observed data, from which the remaining useful life (RUL) of the system is predicted. This paper focuses on the Bayesian method for parameter estimation of a damage degradation model where epistemic uncertainty in model parameters is reduced with the observed data. Markov-chain Monte Carlo sampling is used to generate samples from the posterior distribution, which are then propagated through the physical model to estimate the distribution of the RUL. A MATLAB script of 76 lines is included in this paper with detailed explanations. A battery degradation model and crack growth model are used to explain the process of parameter estimation, the evolution of degradation and RUL prediction. The code presented in this paper can easily be altered for different applications. This code may help beginners to understand and use Bayesian method-based prognostics.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Ting Dong, Dawn An and Nam H. Kim",authors:[{id:"278745",title:"Prof.",name:"Nam-Ho",middleName:null,surname:"Kim",slug:"nam-ho-kim",fullName:"Nam-Ho Kim"},{id:"285438",title:"Ms.",name:"Ting",middleName:null,surname:"Dong",slug:"ting-dong",fullName:"Ting Dong"},{id:"285439",title:"Dr.",name:"Dawn",middleName:null,surname:"An",slug:"dawn-an",fullName:"Dawn An"}]},{id:"68233",doi:"10.5772/intechopen.88217",title:"Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques",slug:"fault-detection-of-single-and-interval-valued-data-using-statistical-process-monitoring-techniques",totalDownloads:731,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Principal component analysis (PCA) is a linear data analysis technique widely used for fault detection and isolation, data modeling, and noise filtration. PCA may be combined with statistical hypothesis testing methods, such as the generalized likelihood ratio (GLR) technique in order to detect faults. GLR functions by using the concept of maximum likelihood estimation (MLE) in order to maximize the detection rate for a fixed false alarm rate. The benchmark Tennessee Eastman Process (TEP) is used to examine the performance of the different techniques, and the results show that for processes that experience both shifts in the mean and/or variance, the best performance is achieved by independently monitoring the mean and variance using two separate GLR charts, rather than simultaneously monitoring them using a single chart. Moreover, single-valued data can be aggregated into interval form in order to provide a more robust model with improved fault detection performance using PCA and GLR. The TEP example is used once more in order to demonstrate the effectiveness of using of interval-valued data over single-valued data.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Mohammed Ziyan Sheriff, Nour Basha, Muhammad Nazmul Karim, Hazem Nounou and Mohamed Nounou",authors:[{id:"21281",title:"Prof.",name:"Hazem",middleName:"Numan",surname:"Nounou",slug:"hazem-nounou",fullName:"Hazem Nounou"},{id:"21282",title:"Prof.",name:"Mohamed N.",middleName:null,surname:"Nounou",slug:"mohamed-n.-nounou",fullName:"Mohamed N. Nounou"},{id:"191340",title:"Mr.",name:"M. Ziyan",middleName:null,surname:"Sheriff",slug:"m.-ziyan-sheriff",fullName:"M. Ziyan Sheriff"},{id:"191345",title:"Prof.",name:"M. Nazmul",middleName:null,surname:"Karim",slug:"m.-nazmul-karim",fullName:"M. Nazmul Karim"},{id:"253580",title:"Mr.",name:"Nour",middleName:null,surname:"Basha",slug:"nour-basha",fullName:"Nour Basha"}]}],mostDownloadedChaptersLast30Days:[{id:"70067",title:"Analytic Prognostic in the Linear Damage Case Applied to Buried Petrochemical Pipelines and the Complex Probability Paradigm",slug:"analytic-prognostic-in-the-linear-damage-case-applied-to-buried-petrochemical-pipelines-and-the-comp",totalDownloads:2880,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"In 1933, Andrey Nikolaevich Kolmogorov established the system of five axioms that define the concept of mathematical probability. This system can be developed to include the set of imaginary numbers by adding a supplementary three original axioms. Therefore, any experiment can be performed in the set \n\nC\n\n of complex probabilities which is the summation of the set \n\nR\n\n of real probabilities and the set \n\nM\n\n of imaginary probabilities. The purpose here is to include additional imaginary dimensions to the experiment taking place in the “real” laboratory in \n\nR\n\n and hence to evaluate all the probabilities. Consequently, the probability in the entire set \n\nC\n=\nR\n+\nM\n\n is permanently equal to one no matter what the stochastic distribution of the input random variable in \n\nR\n\n is; therefore the outcome of the probabilistic experiment in \n\nC\n\n can be determined perfectly. This is due to the fact that the probability in \n\nC\n\n is calculated after subtracting from the degree of our knowledge the chaotic factor of the random experiment. Consequently, the purpose in this chapter is to join my complex probability paradigm to the analytic prognostic of buried petrochemical pipelines in the case of linear damage accumulation. Accordingly, after the calculation of the novel prognostic model parameters, we will be able to evaluate the degree of knowledge, the magnitude of the chaotic factor, the complex probability, the probabilities of the system failure and survival, and the probability of the remaining useful lifetime; after that a pressure time t has been applied to the pipeline, which are all functions of the system degradation subject to random and stochastic influences.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Abdo Abou Jaoude",authors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé"}]},{id:"65244",title:"Fault Diagnosis Techniques for a Wind Turbine System",slug:"fault-diagnosis-techniques-for-a-wind-turbine-system",totalDownloads:1283,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Silvio Simani and Paolo Castaldi",authors:[{id:"209626",title:"Prof.",name:"Silvio",middleName:null,surname:"Simani",slug:"silvio-simani",fullName:"Silvio Simani"},{id:"209627",title:"Dr.",name:"Paolo",middleName:null,surname:"Castaldi",slug:"paolo-castaldi",fullName:"Paolo Castaldi"}]},{id:"65747",title:"Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB",slug:"prognostics-102-efficient-bayesian-based-prognostics-algorithm-in-matlab",totalDownloads:1359,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"An efficient Bayesian-based algorithm is presented for physics-based prognostics, which combines a physical model with observed health monitoring data. Unknown model parameters are estimated using the observed data, from which the remaining useful life (RUL) of the system is predicted. This paper focuses on the Bayesian method for parameter estimation of a damage degradation model where epistemic uncertainty in model parameters is reduced with the observed data. Markov-chain Monte Carlo sampling is used to generate samples from the posterior distribution, which are then propagated through the physical model to estimate the distribution of the RUL. A MATLAB script of 76 lines is included in this paper with detailed explanations. A battery degradation model and crack growth model are used to explain the process of parameter estimation, the evolution of degradation and RUL prediction. The code presented in this paper can easily be altered for different applications. This code may help beginners to understand and use Bayesian method-based prognostics.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Ting Dong, Dawn An and Nam H. Kim",authors:[{id:"278745",title:"Prof.",name:"Nam-Ho",middleName:null,surname:"Kim",slug:"nam-ho-kim",fullName:"Nam-Ho Kim"},{id:"285438",title:"Ms.",name:"Ting",middleName:null,surname:"Dong",slug:"ting-dong",fullName:"Ting Dong"},{id:"285439",title:"Dr.",name:"Dawn",middleName:null,surname:"An",slug:"dawn-an",fullName:"Dawn An"}]},{id:"70649",title:"Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems",slug:"real-time-fault-detection-and-diagnosis-using-intelligent-monitoring-and-supervision-systems",totalDownloads:1116,totalCrossrefCites:2,totalDimensionsCites:8,abstract:"In monitoring and supervision schemes, fault detection and diagnosis characterize high efficiency and quality production systems. To achieve such properties, these structures are based on techniques that allow detection and diagnosis of failures in real time. Detection signals faults and diagnostics provide the root cause and location. Fault detection is based on signal and process mathematical models, while fault diagnosis is focused on systems theory and process modeling. Monitoring and supervision complement each other in fault management, thus enabling normal and continuous operation. Its application avoids stopping productive processes by early detection of failures and by applying real-time actions to eliminate them, such as predictive and proactive maintenance based on process conditions. The integration of all these methodologies enables intelligent monitoring and supervision systems, enabling real-time fault detection and diagnosis. Their high performance is associated with statistical decision-making techniques, expert systems, artificial neural networks, fuzzy logic and computational procedures, making them efficient and fully autonomous in making decisions in the real-time operation of a production system.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Gustavo Pérez Alvarez",authors:[{id:"216192",title:"Dr.",name:"Gustavo",middleName:"Pérez",surname:"Alvarez",slug:"gustavo-alvarez",fullName:"Gustavo Alvarez"}]},{id:"69286",title:"Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems",slug:"probabilistic-methods-for-cognitive-solving-of-some-problems-in-artificial-intelligence-systems",totalDownloads:809,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"As a result of the analysis of dispatcher intelligence centers and aerial, land, underground, underwater, universal, and functionally focused artificial intelligence robotics systems, the problems of rational control, due to be performed under specific conditions of uncertainties, are chosen for probabilistic study. The choice covers the problems of planning the possibilities of functions performance on the base of monitored information about events and conditions and the problem of robot route optimization under limitations on risk of “failure” in conditions of uncertainties. These problems are resolved with a use of the proposed probabilistic approach. The proposed methods are based on selected probabilistic models (for “black box” and complex systems), which are implemented effectively in wide application areas. The cognitive solving of problems consists in improvements, accumulation, analysis, and use of appearing knowledge. The described analytical solutions are demonstrated by practical examples.",book:{id:"8656",slug:"probability-combinatorics-and-control",title:"Probability, Combinatorics and Control",fullTitle:"Probability, Combinatorics and Control"},signatures:"Andrey Kostogryzov and Victor Korolev",authors:[{id:"148322",title:"Dr.",name:"Andrey",middleName:null,surname:"Kostogryzov",slug:"andrey-kostogryzov",fullName:"Andrey Kostogryzov"},{id:"298431",title:"Dr.",name:"Victor",middleName:null,surname:"Korolev",slug:"victor-korolev",fullName:"Victor Korolev"}]}],onlineFirstChaptersFilter:{topicId:"968",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580",scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
\r\n
\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:1,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. 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Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. 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Science",value:19,count:5}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:3},{group:"publicationYear",caption:"2021",value:2021,count:3},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:1},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"António",middleName:"J. 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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. 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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. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. 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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