**6.3 Flexural strength**

*Sandy Materials in Civil Engineering - Usage and Management*

silica content present in the used foundry sand.

Torres et al. [33] is shown in **Figure 6**.

**6.2 Split tensile strength**

regular concrete.

used foundry sand as 24.8 and 26.5 MPa, respectively, and for 25% used foundry sand, the compressive strength was below the compressive strength of control mix. In the majority of the research findings, the concrete containing used foundry sand has higher compressive strength than conventional concrete. As per Siddique et al. [54], the increase in compressive strength of the concrete made with used foundry sand may be due to the higher fineness of the used foundry sand than the regular sand, which resulted in the formulation of a denser concrete matrix along with the

In some cases, the compressive strength of used foundry sand incorporated concrete is more or less the same as that of the control mix up to a certain percentage of used foundry sand content, and after that, the compressive strength decreases significantly. Prabhu et al. [53] stated that the concrete mix containing foundry sand up to 20% replacement of fine aggregate with foundry sand, the compressive strength observed was moderately close to the strength of the control mix, but beyond 20% replacement, the concrete mix showed lower strength than control mix. Some researchers pointed out specific reasons for the reduction of compressive strength of concrete made with used foundry sand beyond certain replacement levels of fine aggregate with used foundry sand. Singh and Siddique [31, 56] and Siddique et al. [57] pointed out that the compressive strength of concrete containing used foundry sand above a particular percentage gets reduced probably due to the increase in surface area of fine particles, which lead to the reduction of water-cement gel in the concrete matrix, and hence, the binding process of the coarse and fine aggregates does not take place properly. The graph of the compressive strength of ultra-high-strength concrete made with natural sand replaced by foundry sand at 0, 10, 20, and 30% at 7, 14, and 28 days as reported by

Depending on the source of used foundry sand, the concrete incorporating used foundry sand shows inferior or at par or superior split tensile strengths than the

In some cases, the split tensile strength of concrete made with used foundry sand

increases with the percentage increase in used foundry sand in the concrete mix up to a certain level and decreases afterward. Sohail et al. [30] described that up to 40% replacement of river sand with waste foundry sand from a gray iron foundry, the split tensile strength of concrete at 28th day increases, and further, it reduces consistently up to 100% replacement. Patil et al. [58] confirmed that the split

**18**

**Figure 6.**

*Compressive strength vs. % foundry sand.*

The flexural strength of the concrete containing used foundry sand shows marginal variations with the addition of used foundry sand. The flexural strength of concrete incorporating used foundry sand usually increases marginally to that of normal concrete. In the research on the properties of concrete with used foundry

**Figure 7.** *Split tensile strength vs. % waste foundry sand.*

sand, Siddique et al. [54] observed that the flexural strength of 28.5 MPa characteristic compressive strength concrete consecutively increased with the percentage increase of used foundry sand up to 30% replacement level in which the flexural strength of control mix was 3.41 MPa and the flexural strength at 30% replacement was 4.18 MPa.

In some cases, the flexure strength seems to decrease with the increase in percentage addition of used foundry sand. Seshadri and Salim [12] reported that the high-performance concrete having 60 MPa characteristics compressive strength showed a decrease in flexure strength on the increase in replacement of fine aggregate with used foundry sand, in which the control mix has a flexural strength of 10.05 MPa, and at 40% used foundry sand content, the flexural strength decreased to 7.05 MPa. As per Torres et al. [33], at 10% replacement of fine aggregate with foundry sand, the ultra-high-strength concrete showed an increase in flexure strength, and further, it showed a consecutive decrement in flexure strength for 20 and 30% replacement of fine aggregates with foundry sand. Prabhu et al. [53] observed that the flexural strength of concrete with foundry sand content up to 20% of fine aggregates has similar results as that of the control mix; further, the flexural strength decreases after 20% replacement level. The flexural strength variation of concrete having 36.5 and 46 MPa compressive strength at 28 days made with regular sand replaced at 0, 10, 30, 50, 70, and 100% to chemically bonded foundry sand with water to cement ratio 0.55 and 0.45 as reported by Mavroulidou and Lawrence [41] is shown in **Figure 8**.

### **6.4 Modulus of elasticity**

Generally, the modulus elasticity of concrete containing used foundry sand increases up to certain percentage content of used foundry sand and then tends to decrease with further increase in the used foundry sand content. Manoharan et al. [28] observed that the modulus of elasticity of concrete increased with percentage replacement of natural river sand with used foundry sand from 0 to 20%, and further addition of used foundry sand decreased the modulus of elasticity, the modulus of elasticity of control concrete was 23.60 GPa, whereas at 20% replacement of river sand with used foundry sand, the elastic modulus increased to 25.40 GPa. As per the research findings of Prabhu et al. [53], the replacement of fine aggregate

**21**

*A Review on the Usage of Recycled Sand in the Construction Industry*

with used foundry sand slightly improved the modulus of elasticity of concrete mix. Some researchers observed marginal reduction of modulus of elasticity by the addition of used foundry sand. Basar and Aksoy [34] stated that the waste foundry sand content in the ready-mixed concrete reduces the modulus of elasticity. The variation of modulus of elasticity of ultra-high-strength concrete made with foundry sand at 7, 14, and 28 days for foundry sand percentages of 0, 10, 20, and 30% as reported in

**7. Absorption and permeability characteristics of concrete made with**

The absorption and permeability characteristics of concrete include water absorption, rapid chloride permeability, sorptivity, and carbonation. The absorption and permeability characteristics of concrete incorporating used foundry sand

The concrete made with used foundry sand is generally more permeable than the normal concrete. However, some researchers reported that the inclusion of used foundry sand has no impact on the water absorption of the concrete. The water absorption is somewhat related to the compressive strength also. As per Basar and Aksoy [34], the concrete having higher water absorption has lower strengths. The water absorption of the hardened concrete has a significant effect on the durability characteristics of concrete. Khatib et al. [26] reported that water absorption of the concrete mix containing used foundry sand, the control mix showed the least and increased for 20, 40, 60, 80, and 100% replacement of fine aggregates with foundry sand. It is further confirmed that the water absorption of 56 days cured concrete samples also followed the same trend. Ready-mixed concrete with used foundry sand also showed similar behavior on water absorption. Basar and Aksoy [34] stated that the water absorption of ready-mixed concrete containing waste foundry sand increased with the increase in percentage replacement of fine aggregate with waste foundry sand. Some researchers observed a marginal decrease in water absorption of the concrete containing used foundry sand over the normal concrete. Salokhe and Desai [59] reported that the foundry waste sand had no apparent impact on

the research findings of Torres et al. [33] is shown in **Figure 9**.

are discussed in detail in the following paragraphs.

**used foundry sand**

**Figure 9.**

**7.1 Water absorption**

*DOI: http://dx.doi.org/10.5772/intechopen.92790*

*Elastic modulus variation of foundry sand concrete.*

**Figure 8.** *Flexural strength vs. % foundry sand.*

*A Review on the Usage of Recycled Sand in the Construction Industry DOI: http://dx.doi.org/10.5772/intechopen.92790*

**Figure 9.** *Elastic modulus variation of foundry sand concrete.*

*Sandy Materials in Civil Engineering - Usage and Management*

and Lawrence [41] is shown in **Figure 8**.

**6.4 Modulus of elasticity**

was 4.18 MPa.

sand, Siddique et al. [54] observed that the flexural strength of 28.5 MPa characteristic compressive strength concrete consecutively increased with the percentage increase of used foundry sand up to 30% replacement level in which the flexural strength of control mix was 3.41 MPa and the flexural strength at 30% replacement

In some cases, the flexure strength seems to decrease with the increase in percentage addition of used foundry sand. Seshadri and Salim [12] reported that the high-performance concrete having 60 MPa characteristics compressive strength showed a decrease in flexure strength on the increase in replacement of fine aggregate with used foundry sand, in which the control mix has a flexural strength of 10.05 MPa, and at 40% used foundry sand content, the flexural strength decreased to 7.05 MPa. As per Torres et al. [33], at 10% replacement of fine aggregate with foundry sand, the ultra-high-strength concrete showed an increase in flexure strength, and further, it showed a consecutive decrement in flexure strength for 20 and 30% replacement of fine aggregates with foundry sand. Prabhu et al. [53] observed that the flexural strength of concrete with foundry sand content up to 20% of fine aggregates has similar results as that of the control mix; further, the flexural strength decreases after 20% replacement level. The flexural strength variation of concrete having 36.5 and 46 MPa compressive strength at 28 days made with regular sand replaced at 0, 10, 30, 50, 70, and 100% to chemically bonded foundry sand with water to cement ratio 0.55 and 0.45 as reported by Mavroulidou

Generally, the modulus elasticity of concrete containing used foundry sand increases up to certain percentage content of used foundry sand and then tends to decrease with further increase in the used foundry sand content. Manoharan et al. [28] observed that the modulus of elasticity of concrete increased with percentage replacement of natural river sand with used foundry sand from 0 to 20%, and further addition of used foundry sand decreased the modulus of elasticity, the modulus of elasticity of control concrete was 23.60 GPa, whereas at 20% replacement of river sand with used foundry sand, the elastic modulus increased to 25.40 GPa. As per the research findings of Prabhu et al. [53], the replacement of fine aggregate

**20**

**Figure 8.**

*Flexural strength vs. % foundry sand.*

with used foundry sand slightly improved the modulus of elasticity of concrete mix. Some researchers observed marginal reduction of modulus of elasticity by the addition of used foundry sand. Basar and Aksoy [34] stated that the waste foundry sand content in the ready-mixed concrete reduces the modulus of elasticity. The variation of modulus of elasticity of ultra-high-strength concrete made with foundry sand at 7, 14, and 28 days for foundry sand percentages of 0, 10, 20, and 30% as reported in the research findings of Torres et al. [33] is shown in **Figure 9**.


**1.7 Health behavior change is a critical component of mhealth apps**

The purpose of providing patients with an mhealth app should be to improve their experiences of health care and their health outcomes [39]. If health behaviors are optimal, then adding a mhealth app may bring no additional benefit, especially if the disease has a lifestyle or behavioral component. Patients with chronic diseases which progress over time may also benefit by identifying trends earlier. mHealth apps do not have any intrinsic health properties. They, by themselves, do not lower blood pressure, treat pain or solve clinical problems. They add their value by helping patients track their relevant clinical problems, visualize their current behaviors, experiment with new behaviors and see the impact of their new behaviors on their health, including medication taking behaviors. Thus, the current short-fall in health-related behaviors (whether lifestyle or medication-related) that leads to poor health experience needs to be identified and the app needs to directly address the highest priority short-falls and those at highest risk for exhibiting poor health behaviors. It is likely that if the app is successful in addressing healthcare needs of the individual and their HCP, then the health of the user will improve, and they will no longer need the app. To maintain on-going and continuous use, app developers will need to continuously evaluate the impact of their app on patient health and add new functions to the mhealth app in future iterations.

If the app does not respond to evolving clinical needs, changes in therapy, changes in knowledge about behavior change techniques that work or changes in guidelines, the app is likely to become obsolete and lose users who increasingly experience barriers to the use of the app from their HCPs [36]. mHealth app development is a dynamic industry.

In addition to relevance and timeliness, mhealth apps need also to be built on a well-researched behavior theory. There are as many as 89 different behavior theory models that have been developed over the last several decades [15], too many to list here. However, having a theory of behavior change to underpin app features is important for several reasons: 1) you cannot change what you do not know; it's important to know current behaviors so they can be measured and tracked through the change process. 2) Understanding the mediators of behavior change allows developers to tease out whether a lack of response was due to poor design of the intervention or whether the intervention worked properly but had no impact on the relevant behavior. 3) Theories of behavior build upon a large body of knowledge and are more likely to work than a poorly informed and poorly conceptualized intervention.

Carey et al. provide a website where app developers and change agents can find links between behavior change theories and mechanisms of action [40]. Although this is an excellent service, using behavior change theories that have already been proven for a particular disease may be a much better approach than trying to pick theories based on conjecture or putative mechanisms of action [41, 42].

### *1.7.1 Literature review and research trump end-user engagement*

Every disease is different, and every patient experiences their disease in a unique way. For example, type II diabetes is caused by a myriad of social, environmental and physiological factors related to physical activity and nutrition. In contrast, our case study disease, myasthenia gravis is an autoimmune disease that can be

triggered or worsened by social, environmental and physiological factors, but is not caused by them. These differences have profound influences on what the most effective mhealth interventions are likely to be [43–48].

It is important to understand the range of behaviors that are detrimental for someone with a particular disease. Poor nutritional and exercise habits are important barriers for good health in diabetes [49], while poor medication adherence and exposure to exacerbating conditions are important considerations in myasthenia gravis, for example [50]. This type of information may be available from end-users during end-user engagement sessions, but typically requires systematic investigation by researchers and experts rather than by app developers who may miss important information because they lack expertise in a particular clinical topic [15]. Literature review and synthesis is a key requirement of mhealth app development.


**Transformation of the Personnel Training System for Oil and Gas Projects in the Russian Arctic**

#### **Ekaterina Samylovskaya 1,\*, Regina-Elizaveta Kudryavtseva 2, Dmitriy Medvedev 3, Sergey Grinyaev 3 and Alfred Nordmann 4**

Received: 19 October 2020; Accepted: 19 November 2020; Published: 22 November 2020

**Abstract:** This paper analyses the process of transforming specialist training systems for oil and gas projects in the Arctic, which has been taking place within the structure of education in Russia over the past decade. Using classical methods of analysis, synthesis, and classification, the authors studied the main global trends in training personnel for the Arctic and the manifestations of these trends in the system of training Russian specialists. To identify the qualitative characteristics of the educational system development, the authors applied the survey method and composed a list of leading universities in training personnel for the Russian Arctic, as well as the "Arctic professions of the future". As a result of the study, the authors came to the conclusion that global trends in training "Arctic personnel" show the need to develop an interdisciplinary approach, to form basic knowledge in Natural Sciences, to study the socio-cultural specifics of the region, to develop new educational standards, to implement the concept of 'Life Long Learning', to widely introduce digital technologies and to internationalize education. In general, the Russian personnel training system is adapting to changing conditions, in particular, some progress has been made in the formation of "digital" competencies and skills to work in a developed IT infrastructure. The introduction of "digital fields" has led to an increase in the demand for IT specialists in the Arctic oil and gas sector. With the help of an expert survey, it was revealed that in the future, the most popular professions, along with "drillers" and transport specialists, will be IT specialists who ensure the "digital fields" functioning. The leading Russian universities that train specialists for modern oil and gas projects in the Arctic have been identified. It is noted that not all leading industry universities in Russia are participating in international educational projects and organizations. There is skepticism about the internationalization of education.

**Keywords:** oil and gas education; personnel training; Arctic education system; oil and gas projects; international cooperation


*3.3. Preparative Experiments*

A Schlenk tube was charged with the respective alkyne (1.0 mmol) and acyl chloride (1.5 mmol; only 1.0 mmol of the acyl chloride was used in the reaction of ethynylferrocene with benzoyl chloride to avoid decomposition). After flushing the reaction vessel with argon, catalyst **5** (0.5 mol.% Pd) was introduced, followed by dry acetonitrile (5 mL) and anhydrous triethylamine (0.7 mL, ca. 5 mmol). The reaction mixture was stirred at 50 ◦C for 2 h, diluted with ethyl acetate (10 mL) and cooled on ice. The cold reaction mixture was filtered, and the solid residue was washed with ethyl acetate. The combined organic washings were evaporated under vacuum, leaving a crude reaction product, which was taken up with 1,4-dioxane. Solid NaHCO3 was added (≈0.1 g), and the resulting mixture was stirred at room temperature for 1–7 days to hydrolyze unreacted acyl chloride. The hydrolyzed reaction mixture was evaporated, and the residue was extracted with ethyl acetate. Organic washings were dried over anhydrous MgSO4 and evaporated. Analytically pure coupling products were isolated by column chromatography over silica gel using ethyl acetate-hexane (1:10 or 1:20) as the eluent (dichloromethane was used in the case of **11ag**).


**3.1 Single-nucleotide polymorphism (SNP)**

Single nucleotide polymorphisms (SNPs) (pronounced: snip) are an alteration in a lone DNA order structure building block unit: (A, T, C, or G) which termed a nucleotide, **Figure 2** [56]. It is the simplest formula of genetic difference among persons. SNPs are the most frequent occurrence from all genetic variants, which happen usually in a person's DNA. It is a ratio of occurrence near 90% of human genomic variants [57, 58].

They may be occurring one time in each 300 nucleotides on usual, that is, average is about 10 million SNPs in the individual's genome. Greatest frequently, those SNPs are set between genes or within genes. They may perform as living signs and/or hereditary indicators, aiding experts find sequence, which are linked with disease. As soon as SNPs happen inside a gene or in an adjusting area nearby a gene, they might show an additional strong impact in disease via stirring the gene's role. However, the SNPs generally have no influence on the general state of health. Moreover, investigators have instituted that SNPs might assist and guess a person's reaction to definite medications. Additionally, they are utilized for a pathway of genetic factors of malady inside relatives [59–62].


*Edited by Marcos Soto-Hernandez,*

*Mariana Palma-Tenango*

*and Maria del Rosario Garcia-Mateos*

Phenolic compounds comprise a broad class of natural products formed mainly by plants, but also microorganisms and marine organisms that have the capacity to form them. Nowadays the interest in these compounds has increased mainly due to their diverse chemical structure and wide biological activity valuable in the prevention of some chronic or degenerative diseases. The functional foods are a rich source of these phytochemicals, and this is the starting point for this book, which shows the state of the art of the phenolic compounds and their biological activity. This book integrates eleven chapters that show the state of the art of diverse biological activity of the phenolic compounds, present in some crops or fruits.

ISBN 978-953-51-2959-2

Phenolic Compounds - Biological Activity

Photo by mythja / iStock

## Phenolic Compounds Biological Activity

*Edited by Marcos Soto-Hernandez, Mariana Palma-Tenango and Maria del Rosario Garcia-Mateos*


**2.4 Biological terms commonly used in diabetes**

**Insulin:** An anabolic hormone, produced by the beta cells of the islets of Langerhans of pancreas in response of elevated blood sugar level in the body. It helps to control the blood sugar level in the desirable range.

**Glucose:** Glucose is a simple sugar present in everyone's body. It is an essential nutrient that provides energy for the proper functioning of the body cells. After meals, food is digested in the stomach and intestines. The glucose in digested food is absorbed by the intestinal cells into the blood stream and is carried by the blood to all the cells in the body. Glucose needs insulin to enter into the body as it can not get into the cells alone.

**Glucagon:** Glucagon is a hormone synthesized and secreted from alpha cells of the pancreatic islets used for carbohydrate metabolism. Its secretion increases rapidly when the sugar level is too low in the body. It maintains the level of glucose in the blood by binding to specific receptors on hepatocytes causing the liver to release its intracellular stores of glucose. As these stores become depleted, glucagon then encourages the liver to synthesize glucose by gluconeogenesis which will be released to prevent the development of hypoglycemia, low sugar level.

**Insulin Resistance**: Sometimes the cells throughout the body become resistant to the insulin produced by the pancreas due to which it becomes difficult for the sugar to enter the cells. This condition is known as insulin resistance.

**Diabetic Ketoacidosis:** It is a condition in which the cells of muscle, liver and other body parts are unable to take up glucose for producing energy due to the absence of insulin. It is a

Therapeutic Modelling of Type 1 Diabetes 467

The insulin input I(t) will be given through injection at subcutaneous level at periodic intervals, which leaks its contents into the system over a period of time. Therefore, I(t) may

> 0 *t It b t t*

> > *I t*

Food input source term, F(t), is the source for food input to the plasma glucose level, the contents of which are reduced in a simple exponential manner. Therefore, F(t) may be

0

For 0 *t t* , in non – diabetic case, *F t*() 0 and *I t*() 0 and for diabetic case, *F t*() 0 , *I t*() 0 . A mathematical model for the dynamics of glucose concentration in patients with type 1 diabetes using CSII [15] therapy as an external source of insulin has been developed by us. We attempt to model the effect of an external source of insulin release, as a prescribed function of time, on glucose levels. The model is then used to assess the optimal insulin release profile, and the threshold amount required to bring the level of glucose to within a

To model the pump's delivery of insulin, we take into account three major factors: (i) the total amount of insulin released over a specific period; (ii) the time profile of insulin release, f(t); and (iii) the glucose threshold concentration Gc, below which the pump stops releasing insulin. The amount of insulin (TDD) is proportional to the total amount of glucose, whose concentration is assessed by the sensor in the pump's controller. This amount is released by the pump in a dual wave shaped insulin bolus which allows the patient to combine both normal and square wave techniques. The body characteristics of the patient determine how much insulin is needed to maintain the glucose level within the normal physiological range after each meal. The dual wave shape also provides a rapid increase in insulin plasma concentration, and sustained high circulating insulin levels while a meal is being consumed. Here, we extend the minimal model to incorporate the above factors, which leads to the

*dG XG l G G*

,

0

*t t*

0 *t t*

, - quantity of injection, t0 – time of injection, *t* -time lag to

0 0

*t t*

(4)

(5)

*t* (3)

0

0 *t t*

where, S - quantity constant of meal, - delay parameter.

1( ), *b*

*dt*

*t b t t*

0

0, *t t Se t t F t*

be defined as

At t = t0, I(t) = 0

where, 0

maximum.

modeled as

0 *t t t*

,

normal physiological range.

following differential equations:

state of absolute or relative **insulin deficiency** aggravated by hyperglycemia, dehydration, and acidosis-producing derangements in intermediary metabolism. To avoid starvation the body begins to break down fat for energy. Fatty acids and ketone bodies are released due to the break down of fat causing chemical imbalance (metabolic imbalance) called Diabetic Ketoacidosis. Moderate or large amounts of **ketones** in urine are dangerous. They upset the chemical balance of the blood.

**Chronic hyperglycemia:** Chronic hyperglycemia means elevated blood sugar level in the blood.

#### **2.5 Treatment therapies for diabetes**

Type 1 Diabetes is very serious, with a sudden and dramatic onset, usually in youth. Type 1 diabetes is an autoimmune condition, where the body attacks its own insulin producing cells. The body's immune cells, or white blood cells, include B cells and T cells. B cells make antibodies and present 'antigens' to T cells, allowing them to recognize, and kill invaders. People with Type 1 diabetes must maintain an insulin-monitoring and insulin-injecting regimen for the rest of their lives as the islets of Langerhans are destroyed in this type of diabetes. Treatment for type 1 diabetes includes taking insulin shots or insulin pump to deliver insulin in the body, making wise food choices, exercising regularly and controlling blood pressure and cholesterol.

Type 2 diabetes can be treated successfully with diet, physical activity and medication, if necessary.[23] Physical activity can help to control blood sugar levels and increases body's sensitivity to insulin.[6] Also, it helps delays or stop heart diseases, a leading complication of diabetes. Diet plays an extremely important role in controlling this type of diabetes. Being overweight can increase the chances of developing type 2 diabetes. Usually GDM in pregnant women disappears itself after delivery.

#### **2.6 Mathematical model**

The first approach to measure the insulin sensitivity *in vivo* was introduced by Himsworth and Ker [24] and the first mathematical model to estimate the glucose disappearance and insulin sensitivity was proposed by Bolie. In this model, he assumed that glucose disappearance is a linear function of both glucose and insulin. The insulin secretion and disappearance is proportional to glucose and plasma insulin concentration respectively.

The main objective here is to prescribe a more accurate, but less simple, method of arranging the palatable composition of a diabetic diet.

The modified coupled differential equations for the plasma glucose and insulin concentration [1-14, 16-22], when the normal fasting level of plasma glucose is 70 - 120 mg/dl, are given as follows

$$\frac{d\mathbf{g}}{dt} = -l\_1 h \overline{\mathbf{g}} + l\_2 \left(\mathbf{g}\_0 - \mathbf{g}\right) \mathcal{U}\left(\mathbf{g}\_0 - \mathbf{g}\right) + l\_3 F\left(t\right) \tag{1}$$

$$\frac{d\mathbf{h}}{dt} = l\_4 \left(\mathbf{g} - \mathbf{g}\_0\right) \mathcal{U} \left(\mathbf{g} - \mathbf{g}\_0\right) - l\_5 h\_0 + l\_6 I \left(t\right) \tag{2}$$

where, g(t) - plasma glucose concentration, h(t) - insulin concentration, li - sensitivity constants, i = 1,2,3,4,5,6, F(t) - food source input for plasma glucose, I(t) – insulin input and U(g0 - g) is unit step function.

The insulin input I(t) will be given through injection at subcutaneous level at periodic intervals, which leaks its contents into the system over a period of time. Therefore, I(t) may be defined as

$$I(t) = \frac{\rho t}{\overline{t} - t\_0} + b$$

At t = t0, I(t) = 0

466 Type 1 Diabetes – Complications, Pathogenesis, and Alternative Treatments

state of absolute or relative **insulin deficiency** aggravated by hyperglycemia, dehydration, and acidosis-producing derangements in intermediary metabolism. To avoid starvation the body begins to break down fat for energy. Fatty acids and ketone bodies are released due to the break down of fat causing chemical imbalance (metabolic imbalance) called Diabetic Ketoacidosis. Moderate or large amounts of **ketones** in urine are dangerous. They upset the

**Chronic hyperglycemia:** Chronic hyperglycemia means elevated blood sugar level in the

Type 1 Diabetes is very serious, with a sudden and dramatic onset, usually in youth. Type 1 diabetes is an autoimmune condition, where the body attacks its own insulin producing cells. The body's immune cells, or white blood cells, include B cells and T cells. B cells make antibodies and present 'antigens' to T cells, allowing them to recognize, and kill invaders. People with Type 1 diabetes must maintain an insulin-monitoring and insulin-injecting regimen for the rest of their lives as the islets of Langerhans are destroyed in this type of diabetes. Treatment for type 1 diabetes includes taking insulin shots or insulin pump to deliver insulin in the body, making wise food choices, exercising regularly and controlling

Type 2 diabetes can be treated successfully with diet, physical activity and medication, if necessary.[23] Physical activity can help to control blood sugar levels and increases body's sensitivity to insulin.[6] Also, it helps delays or stop heart diseases, a leading complication of diabetes. Diet plays an extremely important role in controlling this type of diabetes. Being overweight can increase the chances of developing type 2 diabetes. Usually GDM in

The first approach to measure the insulin sensitivity *in vivo* was introduced by Himsworth and Ker [24] and the first mathematical model to estimate the glucose disappearance and insulin sensitivity was proposed by Bolie. In this model, he assumed that glucose disappearance is a linear function of both glucose and insulin. The insulin secretion and disappearance is proportional to glucose and plasma insulin concentration respectively. The main objective here is to prescribe a more accurate, but less simple, method of arranging

The modified coupled differential equations for the plasma glucose and insulin concentration [1-14, 16-22], when the normal fasting level of plasma glucose is 70 - 120

1 20 0 3 *dg l hg l g g U g g l F t dt*

4 0 0 50 6 *dh l g g U g g lh lI t*

where, g(t) - plasma glucose concentration, h(t) - insulin concentration, li - sensitivity constants, i = 1,2,3,4,5,6, F(t) - food source input for plasma glucose, I(t) – insulin input and

(1)

(2)

chemical balance of the blood.

blood pressure and cholesterol.

**2.6 Mathematical model**

mg/dl, are given as follows

U(g0 - g) is unit step function.

pregnant women disappears itself after delivery.

the palatable composition of a diabetic diet.

*dt*

**2.5 Treatment therapies for diabetes**

blood.

$$\implies b = -\frac{\rho \cdot t\_0}{\overline{t} - t\_0} \qquad\qquad\therefore \; I(t) = \frac{\rho \left(t - t\_0\right)}{\overline{t} - t\_0} \qquad\qquad\Rightarrow \lambda + \mu \text{ t}\tag{3}$$

where, 0 0 *t t t* , 0 *t t* , - quantity of injection, t0 – time of injection, *t* -time lag to

maximum.

Food input source term, F(t), is the source for food input to the plasma glucose level, the contents of which are reduced in a simple exponential manner. Therefore, F(t) may be modeled as

$$F(t) = \begin{cases} \mathcal{S}e^{-\alpha \left(t - t\_0\right)}, & t > t\_0 \\ 0, & t \le t\_0 \end{cases} \tag{4}$$

where, S - quantity constant of meal, - delay parameter.

For 0 *t t* , in non – diabetic case, *F t*() 0 and *I t*() 0 and for diabetic case, *F t*() 0 , *I t*() 0 .

A mathematical model for the dynamics of glucose concentration in patients with type 1 diabetes using CSII [15] therapy as an external source of insulin has been developed by us. We attempt to model the effect of an external source of insulin release, as a prescribed function of time, on glucose levels. The model is then used to assess the optimal insulin release profile, and the threshold amount required to bring the level of glucose to within a normal physiological range.

To model the pump's delivery of insulin, we take into account three major factors: (i) the total amount of insulin released over a specific period; (ii) the time profile of insulin release, f(t); and (iii) the glucose threshold concentration Gc, below which the pump stops releasing insulin. The amount of insulin (TDD) is proportional to the total amount of glucose, whose concentration is assessed by the sensor in the pump's controller. This amount is released by the pump in a dual wave shaped insulin bolus which allows the patient to combine both normal and square wave techniques. The body characteristics of the patient determine how much insulin is needed to maintain the glucose level within the normal physiological range after each meal. The dual wave shape also provides a rapid increase in insulin plasma concentration, and sustained high circulating insulin levels while a meal is being consumed. Here, we extend the minimal model to incorporate the above factors, which leads to the following differential equations:

$$\frac{d\mathbf{G}}{dt} = -\mathbf{X}\,\mathbf{G} + l\_1 \{\mathbf{G}\_b - \mathbf{G}\}^+,\tag{5}$$

$$\frac{dX}{dt} = -p\_1 X + p\_2 (I - I\_b)\_{\prime} \tag{6}$$

Therapeutic Modelling of Type 1 Diabetes 469

plasma 118 mg dl-1

plasma 7 µU ml-1

uptake 10 Min-1

uptake 0.0107 min-1

0.007 min-2 µU ml-1

**S No Parameter Description Value Unit**

2 Gc Glucose threshold concentration in plasma 100-107 mg dl-1

5 l2 Scaling factor determining TDD of insulin Variable min-1 µU mg-1 6 l3`` The rate of decay for insulin in plasma 0.264 min-1

Base line value of glucose concentration in

Baseline value of insulin concentration in

The insulin dependent rate of tissue glucose

The rate of spontaneous decrease of glucose

The rate of insulin – dependent increase in tissue glucose uptake due to insulin concentration excess over its baseline

Table 1. Description and values of the model parameters obtained from the published

This particular work is published in Applied Mathematics and computation, 2007, pages 1476 – 1483 and has been cited by Kato, R, Munkhjargal, M and Takahashi, D "An autonomous drug release system based on chemo- mechanical energy conversion "Organic Engine" for feedback control of blood glucose", Biosensors and Bioelecetronics in 2010 Vol

More advanced mathematical models can be formulated to explain the effects of obesity on diabetes, effects of exercise on management of type 2 diabetes. Parameters involving glucose sensors can be added to the insulin pump model for a better programmed insulin delivery

[1] D. Araujo-Vilar, C.A. Rega-Liste, D.A. Garcia-Estevez, F. Sarmiento-Escalona, V.

[2] R.N. Bergman, L.S. Phillips, C. Cobelli, Physiologic evaluation of factors controlling

[3] B.W. Bode, R.D. Steed, P.C. Davidson, Reduction in severe hypoglycemia with long term

[4] A. Boutayeb, E.H. Twizell, K. Achouayb, A. Chetouani, A mathematical model for the

[5] A. De Gaetano, O. Arino, Mathematical modelling of the intravenous glucose tolerance

subject, Diabetes Res. Clin. Pract. 39 (1998) 129–141.

glucose tolerance in man, J. Clin. Invest. 68 (1981) 1456–1467.

Mosquera-Tallon, J. Cabezas-Cerrato, Minimal model of glucose metabolism: modified equations and its application in the study of insulin sensitivity in obese

continuous subcutaneous insulin infusion in type 1 diabetes, Diabetes Care 19

burden of diabetes and its complications, BioMed. Eng. (2004), doi:10.1186/1475-

1 Gb

3 Ib

4 l1

7 p1

8 p2

26(4), pages 1455 - 1459.

**2.7 Future work**

by insulin pump.

**3. References**

(1996) 324–327.

925X-3-20, Online.

test, J. Math. Biol. 40 (2000) 136–168.

literature

$$\frac{d\mathcal{I}}{dt} = -l\_2(\mathcal{G} - \mathcal{G}\_c)^+ f(t) - l\_3(I - I\_b)\_\prime \tag{7}$$

where G is the blood glucose concentration, X is an auxiliary function representing remote insulin action, and l is the insulin plasma concentration. A description of the model parameters and their values are given in Table 1.

The important part of this extension is the first term of (7) which models all three factors mentioned above. This term contributes to the insulin plasma when the glucose concentration exceeds the threshold Gc, and is defined as

$$\mathbf{l}\_2(\mathbf{G} - \mathbf{G}\_c)^+ = \begin{cases} \mathbf{l}\_2(\mathbf{G}(t) - \mathbf{G}\_c) \, f(t) & \text{if } \mathbf{G}(t) > \mathbf{G}\_c \\ 0 & \text{if } \mathbf{G}(t) < \mathbf{G}\_c \end{cases} \tag{8}$$

The function models the profile of insulin release from the pump, and the coefficient represents a scaling factor determining TDD of insulin released by the pump. In the next section, we discuss different profiles of insulin release and compare their effects on the optimal control of glucose concentration. The newer generation of pumps can be programmed to release insulin using three different bolus techniques.

A normal bolus can be used if small amounts of carbohydrates are consumed or if a correction to the blood glucose level outside the physiological range needs to be made. A square wave profile is helpful when eating foods that are high in both fat/protein and carbohydrate (fat and protein delay the absorption of carbohydrates). If a normal bolus is given for a meal high in protein and fat concentrations, circulating insulin levels rise rapidly and may peak before the carbohydrates are absorbed. This mismatch in insulin and blood glucose levels can result in postprandial hypoglycemia. Therefore, a dual wave bolus, as a combination of the normal and square wave bolus techniques, can be introduced. Using this technique, half of the insulin dose is given (over a short period of time) at the onset of the meal, and the remainder over a 2–4 h period. The profile of a dual wave bolus is modeled as a function of time, f(t), in Eq. (4) over a period of 3 h (Fig. 1(a)–(c)).

Fig. 1. Profile of insulin release by the pump *f*(*t*), for 3h: HLL release; (b)LHL release; (c) LLH release, where *H* stands for high amount release of insulin and *L* stands for its low amount per hour *f*(*t*) is normalized so that *H*=*L*

*p X pI I dt*

*dI l G G ft l I I dt*

where G is the blood glucose concentration, X is an auxiliary function representing remote insulin action, and l is the insulin plasma concentration. A description of the model

The important part of this extension is the first term of (7) which models all three factors mentioned above. This term contributes to the insulin plasma when the glucose

2

( () ) () () ( ) 0 ( )

*l G t G f t if G t G lG G*

The function models the profile of insulin release from the pump, and the coefficient represents a scaling factor determining TDD of insulin released by the pump. In the next section, we discuss different profiles of insulin release and compare their effects on the optimal control of glucose concentration. The newer generation of pumps can be

A normal bolus can be used if small amounts of carbohydrates are consumed or if a correction to the blood glucose level outside the physiological range needs to be made. A square wave profile is helpful when eating foods that are high in both fat/protein and carbohydrate (fat and protein delay the absorption of carbohydrates). If a normal bolus is given for a meal high in protein and fat concentrations, circulating insulin levels rise rapidly and may peak before the carbohydrates are absorbed. This mismatch in insulin and blood glucose levels can result in postprandial hypoglycemia. Therefore, a dual wave bolus, as a combination of the normal and square wave bolus techniques, can be introduced. Using this technique, half of the insulin dose is given (over a short period of time) at the onset of the meal, and the remainder over a 2–4 h period. The profile of a dual wave bolus is modeled as

Fig. 1. Profile of insulin release by the pump *f*(*t*), for 3h: HLL release; (b)LHL release; (c) LLH release, where *H* stands for high amount release of insulin and *L* stands for its low

*dX*

parameters and their values are given in Table 1.

2

concentration exceeds the threshold Gc, and is defined as

*c*

programmed to release insulin using three different bolus techniques.

a function of time, f(t), in Eq. (4) over a period of 3 h (Fig. 1(a)–(c)).

amount per hour *f*(*t*) is normalized so that *H*=*L*

1 2 ( ), *b*

2 3 ( ) ( ) ( ), *c b*

(6)

*c*

(8)

(7)

*c c*

*if G t G*

Table 1. Description and values of the model parameters obtained from the published literature

This particular work is published in Applied Mathematics and computation, 2007, pages 1476 – 1483 and has been cited by Kato, R, Munkhjargal, M and Takahashi, D "An autonomous drug release system based on chemo- mechanical energy conversion "Organic Engine" for feedback control of blood glucose", Biosensors and Bioelecetronics in 2010 Vol 26(4), pages 1455 - 1459.


**Author details**

*Green Energy Advances*

**Site Se,C**

**Table 5.**

**(g)**

*configurations evaluated for LS limit state.*

**Se,D (g)**

Y directions, respectively, while for DLLS, intensity values are 0.170 and 0.215 g for X and Y directions, respectively. Consequently, a full seismic rehabilitation (i.e., αLS ratio greater than 1) has been achieved also for Palermo, as reported in **Table 5**.

*C3 configuration: in plan layout of the retrofitted building (a) and 3D view of the model (b).*

**C1 ("as-built") C2 C3**

**Se,C (g)**

*Comparison between seismic capacity values and hazard demand relevant to as-built and postintervention*

Milan 0.110 0.042 2.60 0.168 0.057 2.95 0.430 0.063 6.82 Palermo 0.135 0.81 0.180 0.93 0.200 2.15

**Se,D (g)**

**α = Se,C/ Se,C**

**Se,C (g)**

**Se,D (g)**

**α = Se,C/ Se,C**

**α = Se,C/ Se,D**

In the present work, an application of integrated rehabilitation intervention on reinforced concrete (RC) existing buildings has been presented. A case study located in two different cities, Milano and Palermo, having different climatic condi-

The building under study has been analyzed considering three different configurations, with different arrangement and features of the infill panels, in order to highlight their role on both thermal and seismic performances. Quasi-static and dynamic methodologies have been used for the calculation of the energy demand, highlighting the importance of accounting for thermal bridges in the investigations. As for seismic performance, the results of the incremental nonlinear dynamic analyses show that infill panels having greater thermal and mechanical properties increase the seismic capacity. Nevertheless, a full seismic rehabilitation for the site with the highest seismic hazard (i.e., Palermo) has been achieved only by strength-

Future work, besides including additional case studies (building types, different seismic and climate sites,…), will be devoted to set up and propose a methodology

tions and seismic hazard values, has been investigated.

ening the RC structure with additional RC frames.

**68**

**4. Conclusions**

**Figure 9.**

Antonio D'Angola\*, Vincenzo Manfredi, Angelo Masi and Marianna Mecca Scuola di Ingegneria, Università della Basilicata, Potenza, Italy

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**3. Results**

The simulated transmitted electric field distributions at a transverse plane 250 mm away from the metasurface are depicted in Figure 7. Figure 7a–d,e–h show the transmitted electric field distributions with incidences propagating along −*z* direction with a *y*-polarization and along *z* direction with a *x*-polarization, respectively. Figure 7a,c,e,g show the normalized transmitted electric field amplitude distributions at 5.14 GHz with design schemes in Figure 5a,b, respectively. Amplitude nulls can be observed due to the phase singularity at the center of OAM carrying beams, and along with the donut-shaped field distribution verified the characteristic of the vortex beams.

The transmitted phase distributions are shown in Figure 7b,d,f,h. The phase accumulations along a full circular path around the beam null in Figure 7b,d are 2 *π* and −2*π*, which indicates OAM orders of +1 and −1, respectively. Figure 7f,h depict 4 *π* and −4*π* phase accumulations along a full circular path and therefore indicate OAM orders of +2 and −2. Thus, by using the proposed structure, the designed metasurfaces can simultaneously convert the polarization of the incident wave and generate vortex beams carrying OAM of four different orders, which has great potentials for radar imaging applications.

**Figure 7.** Simulated cross-polarized electric field distributions of the transmitted OAM carrying beams at a transverse plane 250 mm away from the metasurface: (**a**) Amplitude and (**b**) phase distributions for OAM order of *l* = +1. (**c**) Amplitude and (**d**) phase distributions for OAM order of *l* = +2. (**e**) Amplitude and (**f**) phase distributions for OAM order of *l* = −1. (**g**) Amplitude and (**h**) phase distributions for OAM order of *l* = −2.

The proposed metasurface was fabricated using PCB processing as shown in Figure 8. The overall size of the fabricated sample is 326.72 mm × 326.72 mm with a thickness of 4.35 mm. Vias connecting the middle layer to the top and bottom layers are fabricated by back drilling leaving two holes on the top and bottom layers of each unit cell. The back drill holes and prepregs have been considered in the simulations and have little influences on the metasurfaces performance.

The fabricated metasurfaces were measured using a vector network analyzer Agilent E8363b (Keysight Technologies, California, United States) and a two-dimensional near field scanning measurement system. The metasurface was placed between a lens horn antenna (used as the excitation) and a WR-229 open-ended rectangular waveguide probe (used for receiving the OAM carrying beams). The measurement was conducted in the anechoic chamber. The response calibration was used to eliminate the effect of external noise during the measurement. The polarization conversion was confirmed by receiving and analyzing the co-polarization and cross-polarization components of the EM waves, which was realized by rotating the open-ended rectangular waveguide probe. A schema of the measurement devices and settings is depicted in Figure 9.

**Figure 8.** Photos of the fabricated metasurface: (**a**) Front view. (**b**) Back view.

**Figure 9.** Schema depicting the measurement devices and settings.

The measured amplitude and phase distributions of the cross-polarized transmitted electric field at a transverse plane 250 mm away from the metasurface are shown in Figure 10. Figure 10a–d show the amplitudes and phases of the *x*-polarized transmitted wave with a *y*-polarized excitation propagating along −*z* direction. Figure 10a,c depict an amplitude null at the field center. Figure 10b,d show phase accumulations of 2*π* and 4*π* along a full circular path, indicating OAM orders of +1 and +2, respectively. Figure 10e–h show the amplitudes and phases of the *y*-polarized transmitted wave with an *x*-polarized excitation propagating along *z* direction. The amplitude distributions shown in Figure 10e,g show amplitude nulls level at 0.01 compared to the maximum value. Due to the deviations in fabrication and measurement, the perfect offset of amplitude at the center may be compromised. Still, −20 dB nulls level is satisfying [29]. The phase distributions in Figure 10f,h show −2*π* and −4*π* phase accumulations along a full circular path, indicating OAM order of −1 and −2, respectively.

**Figure 10.** Measured cross-polarized electric field distributions of the transmitted OAM carrying beams at a transverse plane 250 mm away from the metasurface: (**a**) Amplitude and (**b**) phase distributions for OAM order of *l* = +1. (**c**) Amplitude and (**d**) phase distributions for OAM order of *l* = +2. (**e**) Amplitude and (**f**) phase distributions for OAM order of *l* = −1. (**g**) Amplitude and (**h**) phase distributions for OAM order of *l* = −2.

The simulated and measured amplitudes of the co-polarized transmitted wave are shown in Figure 11a,b, respectively. For each condition, the co-polarized transmissions are randomly distributed with a low amplitude. In the simulation results, the co-polarized electric fields amplitude level is lower than 0.06, while the measured results show co-polarization level lower than 0.15. The discrepancy between simulation and measurement comes from fabrication deviations and the slight amount of diffracted EM waves. However, compared with the cross-polarization level, the co-polarization level is low and does not affect the generated cross-polarized vortex beams. The co-polarization level can be enhanced if absorbers are placed around the metasurface.

**Figure 11.** Simulated co-polarized electric fields amplitude distributions for different OAM orders: (**a**) *l* = +1. (**b**) *l* = +2. (**c**) *l* = −1. (**d**) *l* = −2. Measured co-polarized electric fields amplitude distributions for different OAM orders: (**e**) *l* = +1. (**f**) *l* = +2. (**g**) *l* = −1. (**h**) *l* = −2.


**2.1 Rejected parts per million (PPM) level**

PPMeq is a pointer for inward part quality by monitoring the performance of inward parts during on-going production, once parts is handed over by Component Development Materials Management (CDMM) to Supplier Quality Assurance (SQA) and procured by Supply Chain Management (SCM). This guideline sets the procedure of calculating the PPM Equivalent (PPMeq) and declaring non-conforming parts in the supplier deliveries. The suppliers' performance has assessed by the index PPM which has based only on rejection of parts at receiving stage and on line. But this index did not reflect the performance of the suppliers whose parts has mostly reworked on lines, the parts for which deviations/concessions were sought. Also much effort has been put in for segregating parts if any non-conformity has found in a lot. Some supplier parts also get rejected at the final assembly stage due to which the whole assembly faces rejection. To capture the effect of all the above conditions, a new index PPMeq has been formed.


**Aneta Manda-Handzlik 1,2,\* and Urszula Demkow 1**

Received: 30 October 2019; Accepted: 18 November 2019; Published: 21 November 2019

**Abstract:** Under normal conditions, neutrophils are restricted from trafficking into the brain parenchyma and cerebrospinal fluid by the presence of the brain–blood barrier (BBB). Yet, infiltration of the central nervous system (CNS) by neutrophils is a well-known phenomenon in the course of different pathological conditions, e.g., infection, trauma or neurodegeneration. Different studies have shown that neutrophil products, i.e., free oxygen radicals and proteolytic enzymes, play an important role in the pathogenesis of BBB damage. It was recently observed that accumulating granulocytes may release neutrophil extracellular traps (NETs), which damage the BBB and directly injure surrounding neurons. In this review, we discuss the emerging role of NETs in various pathological conditions affecting the CNS.

**Keywords:** neutrophil extracellular traps (NETs); Alzheimer's disease; multiple sclerosis; ischemic stroke; meningitis; central nervous system; brain; neurons; brain–blood barrier; neutrophils


*2.1.1. Analytical protein microarrays or antibody microarrays*

In these arrays, antibodies are printed onto array surface and used multiplexed affinity reagents to detect and quantify proteins in complex biological samples [8, 11].

It is based on the antibody antigen interaction. Antibodies have the ability to bind to a very specific protein, so particular or rare analytes can be detected in highly heterogeneous mixtures. They are usually used to identify biomarkers which predict a biological condition such as healthy or pathologic.

So many studies have shown that only a fraction of antibodies may properly work and this may be due to the loss of antibody activity by degradation or denaturation on storage or during the printing process, or due to inappropriate antibody orientation onto the array surface [7].


4. Distribution of axial velocity, temperature and mass fraction profiles along the vertical tube

to describe the heat and mass transfer. Note that NI is the total grid points in the axial direction, NJ is the total grid points in the radial direction at the gas region, and NL is the total

Figure 3. Comparison with numerical study of Hassaninejadfarahani et al. [17] for (a) dimensionless mass fraction at the

grid points in the radial direction at the liquid region.

tube exit, and (b) dimensionless mixture temperature.

64 Desalination and Water Treatment

This chapter investigates the process of the liquid film condensation from the water vapour and non-condensable gas mixtures inside a vertical tube. The results of this study have been obtained for the case of inlet gas temperature T0=70C, inlet pressure P0=1 atm and inlet Reynolds number is fixed at Re0 = 2000. The range of each parameter for this study is listed in Table 1. At first, the air is used as non-condensable gas.

Figures 5–7 Illustrate the profiles of velocity, temperature and the mass fraction of water vapour at different axial locations of the tube. From the distribution of velocity in Figure 5, it is observed that the variation of the velocity in the gas mixture is higher than that in the liquid region. Moreover, as the gas flow progresses along the tube, the velocity in the mixture decreases, while the velocity in the liquid film slightly rises. This behaviour is due to the mass transfer from the mixture to the liquid film. In fact, when the gas mixture loses the mass, it loses velocity too, however, the liquid film gaining mass as well as acceleration. Figure 6 Presents the evolution of the temperature profiles in both mixture and liquid phases at different tube sections. It can be seen that in the liquid phase, the temperature profiles are close to the temperature of the wall and nearly linear. This indicates that the interface temperature

Figure 6. Distributions of axial temperature profile in both the liquid and vapour phases at different tube sections.

Computational Study of Liquid Film Condensation with the Presence of Non-Condensable Gas in a Vertical Tube

http://dx.doi.org/10.5772/intechopen.76753

67

Figure 7. Distributions of axial vapour mass fraction profile in gas phases at different tube sections.

Figure 5. Distributions of axial velocity profile in both the liquid and vapour phases at different tube sections.

Computational Study of Liquid Film Condensation with the Presence of Non-Condensable Gas in a Vertical Tube http://dx.doi.org/10.5772/intechopen.76753 67

obtained for the case of inlet gas temperature T0=70C, inlet pressure P0=1 atm and inlet Reynolds number is fixed at Re0 = 2000. The range of each parameter for this study is listed in

Figures 5–7 Illustrate the profiles of velocity, temperature and the mass fraction of water vapour at different axial locations of the tube. From the distribution of velocity in Figure 5, it is observed that the variation of the velocity in the gas mixture is higher than that in the liquid region. Moreover, as the gas flow progresses along the tube, the velocity in the mixture decreases, while the velocity in the liquid film slightly rises. This behaviour is due to the mass transfer from the mixture to the liquid film. In fact, when the gas mixture loses the mass, it loses velocity too, however, the liquid film gaining mass as well as acceleration. Figure 6 Presents the evolution of the temperature profiles in both mixture and liquid phases at different tube sections. It can be seen that in the liquid phase, the temperature profiles are close to the temperature of the wall and nearly linear. This indicates that the interface temperature

Tube length (L (m)) 3.0, 4.5, 6.0 Tube radius (R(m)) 0.008, 0.01, 0.012 Inlet vapour mass fraction (w0) 0.05, 0.125, 0.2

Figure 5. Distributions of axial velocity profile in both the liquid and vapour phases at different tube sections.

C)) 5, 20, 35

Non-condensable gas Oxygen, air, nitrogen

Table 1. At first, the air is used as non-condensable gas.

wall temperature (TW(

66 Desalination and Water Treatment

Table 1. The ranges of the physical parameters.

Figure 6. Distributions of axial temperature profile in both the liquid and vapour phases at different tube sections.

Figure 7. Distributions of axial vapour mass fraction profile in gas phases at different tube sections.

decreases from the inlet to the tube exit, resulting in the reduction of the heat transfer across the condensate film. Also, the slopes of the temperature of the mixture are decreasing along the tube owing to the absorbed energy transferred from the gas flow to the liquid film. The distributions of the mass fraction of water vapour in the gas region are illustrated in Figure 7. It is interesting to observe that the vapour mass fraction w0 decreases from the entrance to the tube exit, which implies that the condensation rate is decreasing along the tube. Consequently, w0 is reduced from the Centre line (η = 1) to the liquid-vapour interface (η = 0).

#### 5. Effect of the tube geometry (length L and radius R)

In the desalination units, the tube geometry of which the water vapour condenses (whether it is the length or the radius) contributes positively to the improvement of the condensation process if they are well dimensioned.

To reveal their impacts, we first examined the impact of the length of the tube on the liquid film thickness and the condensing mass flux at the interface along the tube. Figure 8 shows the influence of the tube length on the thickness of the film and condensing mass flux at the interface. It is noted that δx increases with increasing the tube length L. The results also indicate that, for a high tube length, the mechanism of the condensation is important when the distance is less than (X = 0.8), especially for L = 4.5 m and 6.0 m, because for a fixed Reynolds gas number (the inlet velocity is fixed too), the condensed vapour decreases with the increase of tube length. This means that near to the tube exit, the condensation process becomes almost unimportant. It is

also observed that the condensing mass flux reduces from the inlet to the outlet, particularly for the two large values of L approaching zero, because the total amount of the water vapour is

Figure 9. Effect of the tube radius on the variation of the liquid film thickness and the condensing mass flux at the

Computational Study of Liquid Film Condensation with the Presence of Non-Condensable Gas in a Vertical Tube

http://dx.doi.org/10.5772/intechopen.76753

69

The effect of changing the tube radius R is shown in Figure 9. From the curves in this figure, it is interesting to observe that for a fixed value of T0, w0 and Re0, a smaller tube radius corresponds to a thicker liquid film thickness. This trend is true for every position X from the inlet to the outlet of the tube. These results are directly related to the velocity of the gas mixture at the inlet. Obviously, for a fixed Reynolds number, δx increases according to the velocity increases with a weak tube radius. This implies that a higher inlet velocity tends to move the air away from the interface and thus maintains its lower fraction, leading to the increase of the heat transfer coefficient, which improves the condensation process. It is also found that the condensing mass flux increases with the tube radius only near the inlet when X < 4 due to a high interfacial shear

In the majority of thermal desalination units, the water vapour that does not condense at the first effect, with all the non-condensable gases content, is transferred to the second effect, and this produces gas accumulation up to inadmissible concentrations. These gases cause a reduc-

transferred to liquid film and remains only the air at the vapour-liquid interface.

stress. This tendency is reversed as the gas mixture progresses along the tube.

6. Effect of water vapour mass fraction w0

tion in the performance of the system.

interface.

Figure 8. Effect of the tube length on the variation of the liquid film thickness and the condensing mass flux at the interface along the tube.

Computational Study of Liquid Film Condensation with the Presence of Non-Condensable Gas in a Vertical Tube http://dx.doi.org/10.5772/intechopen.76753 69

Figure 9. Effect of the tube radius on the variation of the liquid film thickness and the condensing mass flux at the interface.

also observed that the condensing mass flux reduces from the inlet to the outlet, particularly for the two large values of L approaching zero, because the total amount of the water vapour is transferred to liquid film and remains only the air at the vapour-liquid interface.

The effect of changing the tube radius R is shown in Figure 9. From the curves in this figure, it is interesting to observe that for a fixed value of T0, w0 and Re0, a smaller tube radius corresponds to a thicker liquid film thickness. This trend is true for every position X from the inlet to the outlet of the tube. These results are directly related to the velocity of the gas mixture at the inlet. Obviously, for a fixed Reynolds number, δx increases according to the velocity increases with a weak tube radius. This implies that a higher inlet velocity tends to move the air away from the interface and thus maintains its lower fraction, leading to the increase of the heat transfer coefficient, which improves the condensation process. It is also found that the condensing mass flux increases with the tube radius only near the inlet when X < 4 due to a high interfacial shear stress. This tendency is reversed as the gas mixture progresses along the tube.


**7. Plant disease resistance mechanism**

Plants, humans and animals give instant response to the pathogen. In animals, this effect is seen as antibody production, while in plants, it is seen in the form of secretion of various proteins, such as defense-related enzymes and pathogenesis-related proteins [45]. Defense-related enzymes are of great importance in the plant disease resistance mechanism. Immunized plants have rich defense-related enzymes that prevent them from suffering large losses.

If a plant is stimulated by a pathogen, early local defense reactions (a local programmed cell death) are followed by systemic responses (signal is transmitted from infected tissue to the whole plant).  Consequently, signal perception is essential for plants to combat pathogens [46, 47].

Numerous studies have been done on the transporter genes of plants for improved resistance to *Fusarium* spp. A sucrose transporter gene (IbSWEET10) of the SWEET gene family obtained from the sweet potato line ND98 was tested for this purpose. This overexpression of the gene has been shown to reduce sugar levels and has a potential use to lower carbohydrate levels and increase the resistance of the plant [48].


**1. Introduction**

The skin is the largest organ of the body, accounting for about 15% of the total adult body weight. It is made up of three layers: the epidermis, dermis, and the hypodermis (Figure 1, [1]). Bioengineered skin substitutes, in the form of either cellularized engineered skin grafts or acellular dermal regeneration templates (DRT), have been developed to address two main issues still affecting the repair of extended deep wounds [2–10]: firstly, limiting the amount of healthy skin removed from the patient needed for closure; and secondly, acting as promoter for the restoration of the physiologic conditions of the skin avoiding the formation of severe scars. Skin acts not only as a barrier between the organism and the environment preventing invasion of pathogens and fending off chemical and physical assaults [11], it also plays a crucial role in the regulation of body temperature, moisture, and trafficking of water and solutes [12]. In addition, the sensory system of the skin allows the sensing of pain, temperature, light touch, discriminative touch, vibration and pressure. Finally, other important adnexal structures, such as sweat glands and hair follicles, contribute to the functionality of the healthy skin. In addition to the epidermis, severe damage due to burns, chronic ulcers and reconstructive surgeries, induce the destruction of the dermis. Unlike the epidermis, the dermis is characterized by an impaired healing process in which the final assembly of the extracellular matrix (ECM) is far from physiologic conditions. This mismatch compromises the reestablishment of the aforementioned regulatory functions of the whole organ [13–15]. The components of the ECM of the dermis (collagen elastin, hyaluronic acid, fibronectin, perlacan, water and other molecules), possess specific three-dimensional arrangements of sequences orchestrating the cross-talk among the different cell populations comprising the skin. Ultimately, such cross-talk affects the attachment, migration, differentiation and morphogenetic phenomena. For instance, the ECM promotes 'appropriate' communications between keratinocytes and the fibroblasts, and it is responsible for the formation and maintenance of the adnexal structures such as hair follicles, sweat glands and innervations [16–18]. Furthermore, when such adnexal structures become compromised, the self-regeneration of the epidermis cannot occur, and the wound becomes hard to heal. The repair of a deep wound can be divided into four subsequent phases [19,20]: (i) the coagulation and homeostasis phase (immediately after injury); (ii) the inflammatory phase (shortly after injury to tissue), during which swelling takes place; (iii) the proliferation period, where new tissues and blood vessels are formed; and (iv) the maturation phase, in which remodeling of new tissues takes place. How the maturation phase takes place determines the difference between repair and regeneration. The former is a "mere" closure process where fibroblasts bridge the wound gap by organizing their ECM differently from the healthy status. The latter restores the organization of the ECM that will appear indistinguishable from the healthy status [21]. The impaired ECM organization featuring the repair process depresses the regulatory and repository role of the extracellular space that ultimately forms an extended scar characterized by the loss of biological functionalities, inducing the insurgence of severe aesthetics and mobility-associated concerns. A classical approach to skin grafting and repairing is depicted in Figure 2. After debridement of the wound bed, a DRT is applied. Fibroblasts and endothelial cells from the recipient take at least one month to invade the DRT. After this time, it is possible to apply a split thickness skin graft (STSG): an epidermis with a layer of dermis removed from healthy sites of the patient. Both vascularization and fibroblast-secreted ECM molecules affect the take of the STSG that serves to trigger the regeneration of the epidermis due to the lack of adnexa and basal lamina [22–26]. As shown in Figure 2, with a period of two years, the remodeling of the neodermis occurs. To date, even though progress in biomaterials science and tissue engineering has led to the realization of different classes of skin substitutes (either cellularized or not), their healing potential is still limited in triggering a repair process instead of regeneration. In addition to economics, safety and regulatory (in case of allogenic or xenogeneic materials) concerns, in this work the currently available skin substitutes will be reviewed in the light of the composition of the dermis compartment and how this can affect the regeneration process. DRT can be fabricated starting from connective tissues of either allogenic or xenogeneic origin after removal of the cellular component [27,28]. The decellularization processes remove the associated risk of transmission of pathogens, preserving the composition of the ECM. On the other hand, the functionality of molecules of the ECM resulting from the decellularization processes compromise the correct signal presentation to the cells [16–18]. Pre-cellularization with endothelial cells, fibroblasts and keratinocytes seems to improve the biological performance of reconstructed three-dimensional matrices of both natural or synthetic origins [2,27,29–31], by speeding up the vascularization, the synthesis of neodermis, the take of the STSG and the closure of the wounds [32–34]. Nevertheless, the reconstructed three-dimensional matrices used to accommodate living cells prior to the implant are composed of exogenous biomatrices that possess composition, stiffness and three-dimensional arrangements that are quite different from the native dermis. For this reason, some doubts on their effectiveness in triggering a regeneration process have been raised [17]. Finally, a tissue engineering strategy that use patients' own cells to build up in vitro a human-like vascularized ECM featured by the absence of any exogenous material, is presented as an alternative to guide the wound toward a physiological regeneration process [35–37].

#### **2. Tissue Engineering Strategies for Skin Regeneration**

Tissue engineering aims at developing strategies to allow tissue and organ regeneration [32] by two approaches: (i) In in vitro tissue engineering the patient's human skin is re-built in a laboratory using either endogenous or allogenic cell lines (keratinocytes and fibroblasts); after a period of cultivation in three-dimensional matrices [24] and bioreactors [10,38], the engineered skin is then implanted [32]; (ii) in in vivo tissue engineering a three-dimensional matrix is introduced in the wound bed; such matrices are bio-functionalized in order to attract both cells and growth factors supporting skin regeneration [38]. The use of de novo fabricated skin becomes necessary when skin self-regeneration is hindered by adverse conditions [10]; in particular, when severe burns (second-, third-, and fourth-degree burns), chronic ulcers, surgery or trauma lead to the destruction of the dermis and underlying tissues (fat, muscle or bone) are exposed [39,40]. Bioengineered skin substitutes can be classified according to the following categories.

**Figure 1.** Main components of the human skin. (Image source: brgfx/Freepik).

**Figure 2.** The main steps of a two-step procedure to treat deep and partial wounds with the application of a DRT. (**A**) Healthy skin and wound bed after debridement. (**B**) Application of a DRT possessing an artificial silicone epidermis and covered with gauze. (**C**) Removal of the silicone epidermis and application of the STSG. (**D**) Long-term appearance of the repaired dermis. (**E**) Cellular end extracellular dynamics occurring during the wound healing process after the application of a DRT. W = week; M = month. DRT, dermal regeneration templates; STSG, split thickness skin graft; ECM, extracellular matrix.

#### *2.1. Dermal Regeneration Templates (DRT): Materials and Fabrication Techniques*

Porous and fibrous materials. Regardless of the tissue engineering strategy used (in vitro vs. in vivo approaches), a 3D scaffold supporting cell growth is required [42]. Scaffolds are biomaterials acting as temporary porous structures mimicking the 3D architecture of human tissue. In the case of skin tissue engineering, the tissue that one would like to mimic is the dermis. The scaffolds mimicking the dermis can be made by either natural or synthetic polymers (or a combination of both) and, regardless of their origin, they must have different characteristics: non-immunogenic; biocompatible; be able to resist the activity of proteolytic enzymes; stiff and flexible in order to withstand surgical procedures; be able to control the wound contracture; and to possess a degradation rate synchronous with the neo-dermis ingrowth and assembling. Furthermore, the scaffold should support epidermis attachment, maintenance and stratification, and it should promote the blood vessels' influx when implanted [23,34,42]. Three-dimensional porous structures can be obtained by different fabrication techniques allowing the production of different 3D architectures. Starting from melt polymers or polymer solutions, the use of porogen agents, or phase separation techniques, allows the production of sponge-like structures with interconnected pores and porosity ranging from 50 to 500 μm [44]. When porogens are used, the final mechanical properties of the scaffold can be modulated by varying the polymer concentration, the volume fraction and the dimension of the porogens. Phase separation technique exploits the thermodynamic instability of either polymeric solutions or blends [42]. The instability can be induced physically (i.e., temperature) or chemically (i.e., introduction of a solvent/non-solvent agent). The thermodynamic instability induces a segregation process with the formation of a dispersed and a continuum phase. The dispersed phase forms globular structures and after its removal porosity is created. The parameters affecting the final properties of the scaffold (mechanical properties, porosity, pore diameter and pore interconnection) are the initial composition of the polymer solution and, in the case of thermally induced phase separation, the cooling/heating rate is used to induce the thermodynamic instability. Woven and non-woven assembly of nano-fibers using electrospinning allows the production of porous structures categorized as fibrillar scaffolds [30,45,46]. Nanofibrous materials can be also produced by means of different techniques such as self-assembly, phase separation, fiber bonding and electrospinning. This kind of structure is able to mimic better the fibrous nature of the natural extracellular matrix. In in vitro tissue engineering, such preformed structures (either porous or fibrous) are colonized by the cells after the preparation, since their fabrication techniques represent severe conditions for cell viability. This could represent a limitation because a homogenous cell seeding through the thickness of the scaffolds is difficult to achieve and sophisticated bioreactors need to be used [38,47–49]. Hydrogels represent another class of fibrous scaffolds. These are highly hydrated 3D structures obtained by physical, ionic or covalent cross-linking of different polymers of both natural or synthetic origins [49–52]. Three-dimensional hydrogels can be obtained by the assembly and crosslinking of a liquid monomeric phase. This represents an advantage because the monomeric solution can be mixed with the cell suspension. At the end of the gelling process, cells remain entrapped in the 3D structures obtaining a homogenous cellularization of the final 3D scaffold. Although the final properties can be modulated by different parameters (monomer concentration, temperature, pH, UV radiation, ionic strength) the presence of cells poses the same constraints on the control of the final mechanical properties. For instance, UV radiation, pH, temperature and other methods used to induce cross-link of polymeric networks may affect cell viability. Hydrogel are, in general, considered soft materials. Hydrogels composed of gelatin–chitosan hydrogels [53], fibronectin–hyaluronan, or dextran-based hydrogels, in combination with nano-fibrous poly lactic-co-glycolic acid (PLGA) scaffolds have been extensively used for wound healing and regeneration applications [54–56]. In particular, dextran-based hydrogels demonstrated efficient vascularization. Composite hydrogels formed by glycosaminoglycan (GAG)–collagen showed good wound healing in rabbit models [56]. Self-assembling peptide-based hydrogel scaffolds have been reported to reduce burn wound healing time and skin cell proliferation [56].

Protein-based naturals biomaterials. Biomaterials of protein nature can be realized using (but not limited to) collagen, gelatin, silk and fibrinogen. Collagen is the most abundant structural protein of the human dermis secreted by fibroblasts, and is responsible for tensile stiffness. Collagen in skin tissue engineering is used as both acellular scaffold or cell-populated scaffold [56]. Collagen for tissue engineering application is extracted from animals: bovine, ovine and avian are the mostly exploited sources. Examples of acellular/porous scaffolds are the commercially available dermis substitutes Alloderm® or Integra®, while examples of cell-populated collagen hydrogels are represented by Apligraft® and Transcyte®. In addition, collagen-based biomaterials have been processed in the form of membranes, sponges, composite sponges and electrospun biomaterials with nanometric features [56]. Gelatin is a protein obtained by collagen denaturation possessing higher advantages in terms of cell adhesion and inducing a reduced immunogenic response. In the treatment of wounds and burns, gelatin has been used as electrospun nanofibers [57], membranes [58] and gelatin sponges loaded with growth factors [59]. Silk fibroin in the form of sponges, nanofibers and porous films have shown decreased inflammatory response, and promising results in wound healing and skin regeneration has been reported [60].

Polysaccharide-based biomaterials. Polysaccharide-based biomaterials are mainly used in the form of hydrogels. Those mostly used in skin regeneration and wound healing are dextran [54], cellulose [26], chitosan [61], alginate [62] and hyaluronic acid (HA) [63–65]. Among these, HA has been extensively used in skin regeneration leading to the commercialization of different skin substitutes, such as Hyaff®, Laserskin® and Hyalograft®.

Synthetic and composite biomaterials. Synthetic biomaterials comprise [30] the class of aliphatic polyesters, such as polylactic acid (PLA), polyglycolic (PGA) and polycaprolactone (PCL). They possess controllable mechanical stiffness and high process flexibility and are biocompatible and nontoxic. Moreover, PLA, PGA, PCL, and their blends and copolymers are FDA approved. An example of a commercially available skin substitute made using such materials is Dermagraft®.

Decellularized matrices. To date, no biomaterials yet exist that are able to mimic the composition of the native extracellular matrix as a whole [16–18]. Decellularized matrices of allogenic or xenogeneic origin should bridge such a gap. This is very important in skin regeneration because the lack of a functional extracellular matrix is the main cause affecting the impaired wound healing process. Currently available decellularized biomaterials comprise decellularized mesothelium, intestine, amniotic membrane, dermis and skin flaps [9]. Allogenic dermis can be obtained by treating fresh dermis from a cadaver with Dispase–Triton X-100 or NaCl–sodium dodecyl sulfate (SDS). In this way, collagen bundles and basement membranes retain their structure. The abstained biomaterial is further lyophilized. An FDA-approved decellularized dermis obtained with such a technique is Alloderm® [9]. Its use in combination with split-thickness autologous skin grafts allows complete cellularization of skin defects after 12 weeks post application when applied to full-thickness or partial-thickness burn wounds, thereby reducing subsequent scarring [65]. Other acellular dermal matrices are of porcine origin (e.g., Permacol®) and the decellularization process is similar to that of Alloderm®. Different techniques use a foaming process in order to destroy the cellular component, as well as any immunogenic agent. On the other hand, foaming compromises extracellular matrix structure and functions. In general, this kind of decellularized dermis is similar to porous scaffolds and is used for hemostatic applications. Other kinds of decellularized matrices are derived from the mesothelium, including peritoneum, pleura and pericardium [27,66,67]. Decellularized peritoneum is obtained using detergent agents and the processes are designed to maximize the preservation of the extracellular matrix architecture and composition. Because growth factors have a limited shelf life, such biomaterials are often combined with fibroblast growth factor (FGF) and epidermal growth factor (EGF) to promote wound repair. Decellularized mesothelium of porcine origin is used, for instance, in breast reconstruction. Bovine sources are another font of decellularized mesothelium showing a faster healing process than that observed for other decellularized dermis substitutes such as Alloderm® [27]. The intestine is another source to obtain decellularized extracellular matrices. Interestingly, many cytokines and growth factors (FGF and TGF-β families) are retained after the decellularization process. Different applications have been reported comprising the reconstruction of cornea, urethra, vagina, and lung. In the case of dermis reconstruction, decellularized intestine and, in particular, the decellularized small intestine submucosa, is a very promising scaffold due to its capability in promoting angiogenesis. OaSIS® and SurgySIS® (Cook Surgical, Bloomington, IN, USA) are two decellularized matrices from small intestine submucosa [9,68]. The human amniotic membrane is rich in basement membrane and avascular stromal matrix. Decellularization can be obtained using ethylenediaminetetraacetic acid (EDTA) and aprotinin, SDS, DNAase and RNAase. Nevertheless, such a process induces the reduction of anti-inflammatory and anti-scarring components by reducing their superiority compared to other matrices. Clinical applications can be found in the reconstruction of the ocular surface, while different studies have been performed on skin reconstruction in nude mice models [27].


*3.5. In Vitro Cell-Based Assays of DPLGA and ADN-DPLGA Nanoparticles* 3.5.1. In Vitro Cell Toxicity (MTT Assay)

A549 (adenocarcinoma human alveolar basal epithelial cells) were selected due to the availability of overexpressed ARs. Cells were obtained from National Centre for Cell Sciences (NCCS, Pune, Maharashtra, India). Cell lines were maintained as prescribed by the ATCC guidelines. For cytotoxicity evaluation, different working dilutions of DTX in sterile phosphate buffer saline were created using a 10 mg/mL stock solution in DMSO. Cytotoxicity of all the formulations and naïve drugs was determined by MTT assay based on reduction of MTT dye (yellow) by the vital mitochondrial enzymes to blue-colored formazan product. A549 cells (1 × 104 cells/well) were seeded in 96-well plates and were allowed to attach overnight by incubating at 37 ◦C. For assessing the cytotoxicity, cells were exposed to different dilutions of DTX formulation and standard DTX and were incubated for 48 h at 37 ◦C in DMEM supplemented with a 10% FBS medium. After incubation, the media were aspirated, and the cells were washed twice with phosphate buffer saline (pH 7.4). The cells were processed for MTT assay [7]. The mean % of cell viability relative to untreated cells was estimated from data from multiple experiments (n = 6). The IC50 value was calculated using the curve fitting method.

#### 3.5.2. Receptor Competition Assay

For competitive receptor assay, A549 cells were treated with free ligand before exposing cells with optimized formulations followed by the MTT assay as described earlier in the previous section [38]. Briefly, 1 × 104 cells were co-incubated with an excess of ADN. After 30 min of incubation, cells were washed twice with phosphate buffer saline (pH 7.4), followed by treatment of the cells with nanoparticle formulations and the pristine drug DTX. After incubation (48 h at 37 ◦C), cells were processed for MTT assay as reported earlier [21]. A comparison was completed between the IC50 values from the receptor competition assay and previously obtained IC50 values.


*Review* **Review of Control and Energy Management Approaches in Micro-Grid Systems**

**Abdellatif Elmouatamid 1,2,\*, Radouane Ouladsine 1, Mohamed Bakhouya 1, Najib El Kamoun 2, Mohammed Khaidar 2 and Khalid Zine-Dine 3**

1 College of Engineering and Architecture, LERMA Lab, International University of Rabat,

11100 Sala ElJadida, Morocco<EMAIL_ADDRESS>(R.O<EMAIL_ADDRESS>(M.B.) 2 STIC Laboratory, Faculty of Sciences, CUR"EnR&SIE", Chouaïb Doukkali University,

**Abstract:** The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

**Keywords:** control approaches; energy management; optimization method; objective function; control constraints


Preface

An oil and gas reservoir is a formation of rock in which oil and natural gas have accumulated. The oil and gas are collected in small, connected pore spaces of rock and are trapped within the reservoir by adjacent and overlying, impermeable layers of rock. Conventional hydrocarbon reservoirs consist of three main parts: the source rock that contains the kerogen that the oil and gas forms from; the reservoir rock, which is the porous, permeable rock layer or layers that hold the oil and gas; and the cap rock, which seals the top and sides so that the hydrocarbons are trapped in the reservoir, while water often seals the bottom. Reservoirs containing only free gas are termed gas reservoirs. Such a reservoir contains a mixture of hydrocarbons, which exists wholly in the gaseous state. With the ongoing energy needs of the world, the importance of oil and natural gas is increasing tremendously in the global market.

There are different types of crude oils with different properties, such as light crude, heavy crude, and extra heavy crude oils, which are categorized according to the American Petroleum Institute (API) degree of the crude. Normally, the heavier the crude, the higher its asphalt content. In addition, crude oils are also termed sweet or sour based on their sulfur content. In a refinery, crude oil processing mainly involves distillation, cracking, hydrocracking, hydrotreating, and blending. Depending on the type of crude used as feedstock, different processes are involved, with the main purpose being to convert as much of the barrel of crude oil into transportation fuels. Petroleum processing technology is rather mature. Nevertheless, it continues to develop in consideration of all types of sustainability-related challenges.

This book provides comprehensive information on the oil and gas industry, in addition to highlighting technological developments in the field. It is a useful resource for geoscientists, petroleum and reservoir engineers, and chemical engineers.

#### **Ali Ismet Kanlı, Ph.D.**

Professor of Geophysics, Faculty of Engineering, Department of Geophysical Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey

#### **Tye Ching Thian, Ph.D.**

Associate Professor, School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, Pulau Pinang, Malaysia

**1**

most cases.

**Chapter 1**

**Abstract**

distribution

**1. Introduction**

Knudsen diffusion [10–13].

Gas Slippage in Tight Formations

In order to address the gas slippage for flow through tight formation, with a very low porosity (less than 10%) and permeability in micro-Darcy range, a series of single-phase gas flow experiments were conducted. Two different gases (N2 and He) were used to carry out many single-phase experiments at different overburden and pressure drops and were compared with carbon dioxide (CO2) flow types. The pore size distribution measurements showed the existence of a wide range of pore size distribution. Also, the single-phase gas flow experiments through the core plug, mostly at low pressure, showed Knudsen diffusion type, which is an indication of gas

**Keywords:** tight formation, slip effect, Knudsen diffusion, non-Darcy flow, pore size

Due to its high compressibility, gas flow behavior can vary greatly as the porous media size varies. Understanding this behavior and the type of flow is vital for the oil and gas industry, especially with the increase in production from unconventional

The flow behavior in pores can be estimated using many different mathematical models and experiments, the majority of which used the Knudsen number definition [1–3]. Many mathematical models were developed to determine the flow regime in nanopores as a function of adsorption [4, 5], rock permeability [6, 7], and molecular dynamics [8, 9]. All of these models used some form of the Knudsen number definition in their model in order to predict gas flow behavior as a function of different parameters. Many researchers also attempted to model Knudsen diffusion in shale gas reservoirs to determine the recovery potential when the dominant flow regime was

One of the main experimental methods to determine gas flow in nanopores relies on understanding the formation properties of the unconventional reservoir. Research has shown that unconventional tight sand gas reservoirs have three distinct features. These include relatively large pores with mineral deposition in the pores that resulted in a reduction in the overall pore diameter, narrow and flat pores that were generated due to alteration of the primary porosity of the rock, and grains supported by ultrafine micro-matrix particles, usually clays [14–16]. All three of these have a common feature, which is an extremely small average pore diameter, reaching nanoscale in

*Sherif Fakher and Abdelaziz Khlaifat*

molecules' slippage at the wall of the pores.

reservoirs with extremely low permeability.

**Chapter 1**


**1. Introduction**

Classical swine fever virus (CSFV) is a single-stranded RNA virus belonging to the genus pestivirus (family, Flaviviridae). Classical swine fever (CSF) is one of the most important viral diseases affecting domestic pigs and wild boars [1]. Wild boars are as susceptible to CSFV as domestic pigs; therefore, eradication of CSF from wild boars is of epidemiologic value because it can prevent spread among domestic animals [2]. In Germany, 59% of CSF cases in domestic pigs from 1993 to 1998 were transmitted by direct or indirect contact with wild boars [3]. Over the last decades, several European Union (EU) member states (including Germany, France, and Slovakia) were confronted with outbreaks among wild boar; these outbreaks had a clear tendency to establish endemicity [4,5]. Following EU legislation, surveillance was implemented to ensure that CSFV is not circulating and spreading within wild-boar populations. At the beginning of a CSF outbreak, the antibody prevalence within a population is far

below 5%, and it can be several months until the threshold of 5% is reached [5,6]. A previous study suggests that a higher incidence of CSF among wild boars in a particular region is closely related to the population density [7]. A high density of wild boars, particularly young wild boars, drives CSF outbreaks [7]. Recently, CSF have been reported in Gifu Prefecture, affecting domestic pigs and wild boars since September 2019 in Japan [8,9]. The current circulating CSFV in Japan was identified moderate pathogenicity and most closely matched in nucleotide identity, with CSFVs recently isolated in China and Mongolia [8]. A space–time permutation analysis in Japan showed virus transmission spread (10.3 and 4.9 days/km) among wild boars in two significant clusters [9]. After overlaying of a map of habitat quality, approximately 82% and 75% of CSF notifications in two clusters were found in the areas with potential contact between pigs and wild boars [9]. Monitoring of CSF in 5620 Korean wild boars captured between 2010 and 2014 identified only seven animals with CSFV and 23 animals with CSFV antibodies [10]. CSFV (YC16CS strain) isolated from an outbreak in breeding pigs in the north of Gyeonggi in 2016 shows a high genetic similarity and the same sub-genotype (2.1d) as the CSFV (CW17WB) strain isolated from wild boars in 2017 [11]. The risk of CSFV transmission from wild boars to breeding pigs is clear [11]. Therefore, we attempted to identify the reasons underlying the rapid spread of CSF infection among wild boars to guide development of prevention measures.


*4.4.6* Proteus

*Proteus* species are Gram-negative opportunistic rod-shaped bacteria known for its swarming motility and contamination of agar plates. Furthermore, on agar plates, the bacteria undergoes a morphological conversion to a filamentous swarmer cell expressing hundreds of flagella, and during infection, histological damage is caused by cytotoxins including hemolysin and a variety of proteases [94]. The organism is reported to have negative and positive advantages. According to Drzewiecka [95], *Proteus* species may be indicators of fecal pollution, which may cause food poisoning when the contaminated water or seafood is consumed, and it could be used for bioremediation activity due to its tolerance and ability to utilize polluting compounds as sources of energy.

Virulence factors may include fimbriae, flagella, outer membrane proteins, lipopolysaccharide, capsule antigen, urease, immunoglobulin A, proteases, hemolysins, and amino acid deaminases [96]. The ability to swarm and survive is facilitated by the upregulation of FlhD(2)C(2) transcription activator, which activates the flagellar regulon [97]. The prevalence of *Proteus* spp. in borehole or sachet water may be aided by its ability to swarm and colonize the production environment.