Comparison between type 1 diabetes and type 2 diabetes
\r\n\t
",isbn:"978-1-83881-111-2",printIsbn:"978-1-83880-992-8",pdfIsbn:"978-1-83881-112-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"acb2875b3bfc189c9881a9b44b6a5184",bookSignature:"Dr. Abdo Abou Jaoudé",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11865.jpg",keywords:"Linear Operators, Normal Operators, Spectral Theorem, Applications, Differential Operators, Integral Operators, Functional Calculus, Complex Variables, Complex Analysis, Theory, Recent Advances, Latest Trends",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 13th 2022",dateEndSecondStepPublish:"June 21st 2022",dateEndThirdStepPublish:"August 20th 2022",dateEndFourthStepPublish:"November 8th 2022",dateEndFifthStepPublish:"January 7th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Abdo Abou Jaoudé is a pioneering Associate Professor of Mathematics and Statistics at Notre Dame University-Louaizé. He holds two PhDs in Mathematics and Prognostics from the Lebanese University and Aix-Marseille University. His research interests are in the field of mathematics.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé",profilePictureURL:"https://mts.intechopen.com/storage/users/248271/images/system/248271.jpg",biography:"Abdo Abou Jaoudé has been teaching for many years and has a passion for researching and teaching mathematics. He is currently an Associate Professor of Mathematics and Statistics at Notre Dame University-Louaizé (NDU), Lebanon. He holds a BSc and an MSc in Computer Science from NDU, and three PhDs in Applied Mathematics, Computer Science, and Applied Statistics and Probability, all from Bircham International University through a distance learning program. He also holds two PhDs in Mathematics and Prognostics from the Lebanese University, Lebanon, and Aix-Marseille University, France. Dr. Abou Jaoudé's broad research interests are in the field of applied mathematics. He has published twenty-three international journal articles and six contributions to conference proceedings, in addition to seven books on prognostics, pure and applied mathematics, and computer science.",institutionString:"Notre Dame University - Louaize",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Notre Dame University – Louaize",institutionURL:null,country:{name:"Lebanon"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"15",title:"Mathematics",slug:"mathematics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Type 2 diabetes, hypertension frequently associated with type 2 diabetes, and obesity are important risk factors for cardiovascular morbidity and mortality and cardiac- and renal complications. Hyperglycemia as well as hyperinsulinemia in type 2 diabetes is a cardiovascular risk by itself [3]. Type 2 diabetes, hypertension and obesity are characterized by stimulation of the renin-angiotensin-aldosterone system (RAAS), elevated sympathetic activity and insulin resistance. Importantly, these characteristics, themselves, are one of the cardiovascular risks. Therefore, pharmacological and non-pharmacological treatments for type 2 diabetes should be selected from favourable effects on stimulated RAAS, elevated sympathetic nervous system activity, insulin resistance and leptin resistance.
Overweight/obesity has increased in both genders in Australia [Source. Australian Bureau of Statics. 4819.0. Released at 11:30 AM (CANBERRA TIME) 25/01/2008] Reference [
Prevalence of diabetes has increased in parallel with obesity. [Reference 2]
Prevalence of type 2 diabetes, cardiovascular disease and hyperlipidemia (hyper cholesterolemia) increased with obesity in both genders. [Source: National Health Survey, Australia, 2004-2005] Reference [
Weight loss is recommended to delay and prevent type 2 diabetes in obesity, and for the treatment. Lifestyle modification such as a caloric restricted diet, reducing sedentary behaviour and an increase in exercise form the basis of all therapy. Weight loss treated with lifestyle modification including calorie restriction and/or exercise causes normalization of stimulated RAAS, sympathetic activation, insulin resistance, and hyperleptinemia, which are usually observed in type 2 diabetes and obesity. Recently, Straznicky
In addition, most hypertensive patients with diabetes and obesity are very resistant to controlling hypertension and frequently require two or more types of medications to achieve blood pressure goals. Similarly, diabetic patients, especially type 2 diabetic patients with obesity, need higher dose of anti-diabetic medications such as metformin or insulin. However, pharmacological treatments for hypertension and diabetes with weight loss could reduce pharmacological treatment [7, 8].
The purpose of this review is to provide,
Prevalence of diabetes has increased markedly over the last 20 years in parallel with obesity (Figures 2 and 3) [1, 2]. As of 2010 there are approximately 285 million people with the disease compared to around 30 million in 1985. Long-term complications from high blood sugar can include heart disease, strokes, renal failure, diabetic retinopathy, and diabetic neuropathy. Diabetes mellitus includes type 2 diabetes (formerly noninsulin dependent diabetes), type 1 diabetes (formerly insulin dependent diabetes), and gestational diabetes. These 3 types of diabetes have different characteristics and progress [9]. Ninety percent of diabetic patients are type 2 diabetes and the other 10% are due primarily to diabetes mellitus type 1 and gestational diabetes.
Type 2 diabetes is the most common form of diabetes, affecting 90% of all patients with diabetes. This type of diabetes is characterised by metabolic disorder with insulin resistance and relative insulin deficiency [10]. This is in contrast to type 1 diabetes, in which there is an absolute insulin deficiency due to destruction of islet cells in the pancreas [9, 11]. Obesity is thought to be the primary cause of type 2 diabetes in people who are genetically predisposed to the disease, and obesity has been found to contribute to approximately 55% of case of type 2 diabetes [12].
The disease is strongly genetic in origin but lifestyle factors such as excess weight, inactivity, high blood pressure and poor diet are major risk factors for its development. Symptoms may not show for many years and, by the time they appear, significant problems may have developed. People with type 2 diabetes are twice as likely to suffer cardiovascular disease. The classic symptoms are excess thirst, frequent urination, and constant hunger.
Type 2 diabetes is initially managed by increasing exercise and dietary modification. If blood glucose levels are not adequately lowered by these measures, medications such as metformin or insulin may be needed. In those on insulin, there is typically the requirement to routinely check blood sugar levels.
Type 1 diabetes is an auto-immune disease targeting on the insulin-producing beta cells in the pancreas. This type of diabetes, also known as juvenile-onset diabetes, accounts for approximately 10% of all people with the disease. In the majority of cases this type of diabetes appears before the patient is 40 years old, triggered by environmental factors such as viruses, diet or chemicals in people genetically predisposed. Patients with type 1 diabetes will require insulin therapy regularly, and should follow a careful diet and exercise plan.
Gestational diabetes, or glucose intolerance, is first diagnosed during pregnancy through an oral glucose tolerance test. Between 5.5 and 8.8% of pregnant women develop gestational diabetes in Australia [13], and 2 to 10 percent of all pregnancies in USA [14]. The hormones produced during pregnancy increase the amount of insulin needed to control blood glucose levels. If the body can’t meet this increased need for insulin, women can develop gestational diabetes during the late stages of pregnancy.
While the glucose intolerance usually returns to normal after the birth, the mother has a significant risk of developing permanent diabetes while the baby is more likely to develop obesity and impaired glucose tolerance and/or diabetes later in life [15]. Risk factors for gestational diabetes include a family history of diabetes, increasing maternal age, obesity, lack of sleep [16], and being a member of a community or ethnic group with a high risk of developing type 2 diabetes. Self-care and dietary changes are essential in treatment.
\n\t\t\t | \n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
\n\t\t\t\t | \n\t\t\tGenetic, environmental and auto-immune factors, idiopathic | \n\t\t\tGenetic, obesity (central adipose), physical inactivity, high/low birth weight, GDM, poor placental growth, metabolic syndrome | \n\t\t
\n\t\t\t\t | \n\t\t\tIncreased thirst & urination, constant hunger, weight loss, blurred vision and extreme tiredness, glycouria | \n\t\t\tFeeling tired or ill, frequent urination (especially at night), unusual thirst, weight loss, blurred vision, frequent infections and slow wound healing, asymptomatic | \n\t\t
\n\t\t\t\t | \n\t\t\tChildren/teens | \n\t\t\tAdults, elderly, ethnic groups | \n\t\t
\n\t\t\t\t | \n\t\t\tAll | \n\t\t\tmore common in African American, Latino/Hispanic, indigenous, Asian or Pacific Islander | \n\t\t
\n\t\t\t\t | \n\t\t\tBelieved to be triggered autoimmune destruction of the beta cells; autoimmune attack may occur following a viral infection such as mumps, rubella cytomegalovirus | \n\t\t\tAppears to be related to aging, sedentary life-style, genetic influence, but mostly obesity | \n\t\t
\n\t\t\t\t | \n\t\t\tMostly Normal or Thin | \n\t\t\tMostly Overweight or Obese | \n\t\t
\n\t\t\t\t | \n\t\t\tYour body makes too little or no insulin. | \n\t\t\tYour body either cannot produce insulin or does not use it properly. | \n\t\t
\n\t\t\t\t | \n\t\t\t5% -10% of the 171 million of people affected by diabetes in 2000 | \n\t\t\t90% - 95%-of total cases. Although the projected number of Americans that will have type II diabetes in the year 2030 will double from 171 million to 366 million cases | \n\t\t
\n\t\t\t\t | \n\t\t\tBetween 5 - 25 (maximum numbers in this age group; Type 1 can affect at any age) | \n\t\t\tUntil recently, the only type of diabetes that was common in children was Type 1 diabetes, most children who have Type 2 diabetes have a family history of diabetes, are overweight, and are not very physically active. Usually develops around puberty | \n\t\t
\n\t\t\t\t | \n\t\t\tOpen and absorb glucose into cell to be utilized by processes after the induction of insulin | \n\t\t\tAre unable to open and absorb glucose, therefore glucose cannot be utilized by processes; as a result the glucose stays in the blood stream | \n\t\t
\n\t\t\t\t | \n\t\t\tNone | \n\t\t\tPhysical exercise, healthy loss of weight & diet control | \n\t\t
\n\t\t\t\t | \n\t\t\tInsulin Injections, dietary plan, regular check up of blood sugar levels, daily exercise Goals: optimal glucose, prevent/treat chronic complications, enhance health with food/PA, individual nutrition needs | \n\t\t\tDiet, exercise, weight loss, and in many cases medication. Insulin Injections may also be used, SMBG | \n\t\t
\n\t\t\t\t | \n\t\t\tInsulin-dependent | \n\t\t\tNot insulin-dependent | \n\t\t
\n\t\t\t\t | \n\t\t\tRapid (weeks) | \n\t\t\tSlow (years) | \n\t\t
Comparison between type 1 diabetes and type 2 diabetes
[Reference, American Diabetes Association-Executive Summary-2012, A-348]
The prevalence of type 2 diabetes has dramatically increased in parallel in rising prevalence of obesity (Figures 1 and 2), and it increases with obesity (Figure 3). Rates of diabetes in 1985 in worldwide were estimated at 30 million, increasing to 135 million in 1995 and 217 million in 2005 [17]. This increase is believed to be primarily due to the global population aging, a decrease in exercise, and increasing rates of obesity [18].
The prevalence of diabetes is recognized as a global epidemic by the World Health Organization (WHO) [19]. The World Health Organization (WHO) has reported 346 million people worldwide had diabetes in 2004. Globally as of 2010 it was estimated that there were 285 million people with type 2 diabetes, and this is equivalent to about 6% of the world’s adult population [18]. In 2010, diabetic patients were estimated as 316 million people worldwide, and this is equivalent to about 6% of the world’s adult population. Importantly, the National Diabetes Fact Sheet 2011 by Centers for Disease Control and Prevention (CDC) pointed out 7.0 million American people (27% of those with diabetes) were not diagnosed and an estimated 79 million Americans have pre-diabetes, indication that much more of the population were affected by diabetes [20, 21]. An estimated 3.4 million people died from consequences of diabetes in 2004, which more than 80% of diabetes deaths occurs in low- and middle-income countries, and diabetes deaths will increase by double between 2005 and 2030 [22]. The five countries with the greatest number of people with diabetes as of 2000 are India, China, the United States, Indonesia, and Japan. Diabetes is common both in the developed and the developing world, but not in the underdeveloped world. Women seem to be at a greater risk as do certain ethnic groups such as South Asians, Pacific Islanders, Latinos, and Native Americans [18]. This may be due to enhanced sensitivity to a Western lifestyle in certain ethnic groups [23].
Many epidemiological studies showed a strong association between obesity and type 2 diabetes, however it is also true that not all obese individuals have type 2 diabetes [20]. Majority of the onset and development of type 2 diabetes is caused by a combination of lifestyle and genetics. Other confounders are also reported to relate to the onset and development of type 2 diabetes:
A number of lifestyle factors are known to be important to the development of type 2 diabetes, including obesity, lack of physical activity (sedentary life style) [12], poor diet, stress, and urbanization.
Excess body fat is associated with 30% of cases in type 2 diabetes of Chinese and Japanese descent, 60-80% of cases in those of European and African descent, and 100% of Pima Indians and Pacific Islanders [19, 23]. Interestingly, Pima Indians and Pacific Islanders have relatively higher waist-to-hip ratio even if they are not obese, suggesting that abdominal obesity and visceral fat is more important to cause type 2 diabetes.
Dietary factors also influence the risk of developing type 2 diabetes. Consumption of sugar-sweetened drinks in excess is associated with an increased risk. It has been demonstrated saturated fat and trans-fatty acids increase LDL cholesterol, the risk of type 2 diabetes, and cardiovascular risk. Poly-unsaturated, and monounsaturated fat decreasing the risk, but It is recommended to take both in a limited quantity. The American Heart Association has recommended that Americans should limit their intake of saturated fats to 7% of their total calories in a day, while unsaturated fats can form 30% of the calorie intake.
Most cases of diabetes involve many genes, with each being a small contributor to an increased probability of becoming a type 2 diabetic. If one identical twin has diabetes, the chance of the other developing diabetes within his lifetime is greater than 90% while the rate for non-identical siblings is 25-50%. As of 2011, more than 36 genes have been found that contribute to the risk of type 2 diabetes. All of these genes together still only account for 10% of the total heritable component of the disease. The TCF7L2 allele, for example, increases the risk of developing diabetes by 1.5 times and is the greatest risk of the common genetic variants. Most of the genes linked to diabetes are involved in beta cell functions in pancreas.
There are a number of rare cases of diabetes that arise due to an abnormality in a single gene (known as monogenic forms of diabetes or "other specific types of diabetes"). These include maturity onset diabetes of the young (MODY), Donohue syndrome, and Rabson-Mendenhall syndrome, among others. Maturity onset diabetes of the young constitutes 1–5% of all cases of diabetes in young people.
There are a number of medications, including glucocorticoids, thiazides, beta blockers, atypical antipsychotics, and statins that can predispose to diabetes [25].
Statins have beneficial effects on reductions of cardiovascular risks through lipids control, and mortality and morbidity in patients with high cardiac risk such as diabetes, coronary heart disease, ischemic heart disease, chronic kidney disease, chronic heart failure, and peripheral vascular disease [26, 27]. National and international clinical guidelines in the management of these cardiovascular disease conditions all advocate for the utilization of statins therapy in appropriate patients. The meta-analysis including 80,771 participants with low cardiac risk showed that all-cause mortality was significantly lower among patients receiving a statin than among controls with a 10-year risk of cardiovascular disease < 20% [28]. Patients in the statin group were also significantly less likely than controls to have nonfatal myocardial infarction, and nonfatal stroke, but the effects did not depend on high- and low-potency statins, or larger reductions in cholesterol. The JUPITER trial [27] and Atherosclerosis Risk in Communities (ARIC) Study [29] demonstrated that suppression of low-grade inflammation by statins improves these clinical outcomes.
Recently, concerns were raised regarding the onset and development of diabetes in statin-treated patients [30]. The meta-analysis studied by Coleman
Combined bezafibrate/statin therapy is theoretically believed more effective in achieving a comprehensive lipid control and residual cardiovascular risk reduction [36, 37]. The ACCORD Study [38], however, showed that the combination of fenofibrate and simvastatin did not reduce the rate of fatal cardiovascular events, nonfatal myocardial infarction, or nonfatal stroke, as compared with simvastatin alone. Based on the beneficial effects of pan-PPAR agonist bezafibrate on glucose metabolism and prevention of new-onset diabetes, one could expect a neutralization of the adverse pro-diabetic effect of statins using the strategy of a combined statin/fibrate therapy [39, 40].
Several epidemiological studies are available to understand that diabetes is a strong cardiovascular disease risks. A population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study [41], which consisted of 23,455 participants (9,729 men and 15,438 women) followed up from 1994-1998 to 2006, showed that participants with a high risk of the development of diabetes had significantly higher risks of myocardial infarction and stroke than those with a low risk of diabetes development. Subjects at a high risk of diabetes development were also at considerably higher risks of developing cardiovascular complications in general.
The Framingham Heart Study [42] observed that both cardiovascular disease and non-cardiovascular disease mortality rates among individuals with diabetes mellitus were approximately 2-fold higher compared with individuals without diabetes. Non-cardiovascular disease mortality declined among women without diabetes mellitus, while no change in non-cardiovascular disease mortality was observed among women and men with diabetes between the “1950 to 1975” and “1976 to 2001” period. Importantly, individuals with diabetes were at a higher risk of all-cause mortality, especially cardiovascular disease mortality, in both the periods compared to those without diabetes. Another study has shown that diabetes is associated with a substantial increase in all cause and coronary heart disease mortality [43]. Regarding the gender differences in mortality and morbidity of cardiovascular complications, Kanaya
Recently, the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study [45] was performed in 1,123 patients with type 2 diabetes and no symptoms of coronary heart disease using adenosine-stress radionuclide myocardial perfusion imaging from 2000 to 2007. The cumulative cardiac event rate in DIAD was 2.9% over a mean follow-up of 4.8 years for an average of 0.6% per year, which is higher compared to the general population.
The World Health Organization Multinational Study of Vascular Disease in diabetes [46, 47] examined the relationship between excess mortality and proteinuria/hypertension in a stratified random sample of 4,714 diabetic patients aged 35-55 years from 1975 to 1987. Even in the absence of proteinuria and hypertension, standardized mortality rates were significant higher in patients with both type 1 and type 2 diabetes compared to the general population. Standardized mortality was higher in those with type 1 diabetes compared with type 2 diabetes, and the standard mortality rate increased with increasing diabetes duration. In addition, both hypertension and proteinuria had a strikingly high mortality risk by 11-fold for men with type 1 diabetes, and 5 fold for men with type 2 diabetes, indicating that diabetes accompanying cardiovascular disease leads to even higher mortality risk.
Hypertension is twice as frequent in diabetic patients in the general population, and its prevalence is higher in type 2 diabetes than in type 1 diabetes. In type 2 diabetes, the onset of hypertension often precedes the diagnosis of diabetes, whereas in type 1 diabetes it is strictly related to the presence of nephropathy [48].
Further, many studies have shown the strong associations of myocardial infarction [49] and atherosclerosis [50-52]. A number of epidemiological studies provide evidence that diabetes mellitus is a significant risk factor for cardiovascular disease mortality and morbidity [53]. Longer duration of diabetes is a stronger predictor of mortality among diabetic patients. Therefore, people who have diabetes mellitus or strong lifestyle or dietary factors to predict the development of type 2 diabetes [54] should avoid the cardiovascular complications.
It is widely recognized that insulin resistance is a major mechanism of the onset of type 2 diabetes. Insulin resistance in children could predict future glucose intolerance and type 2 diabetes in 10 years [46, 55]. Reduced energy expenditure and resting metabolic rate are predictive of weight gain (obesity). The sympathetic nervous system participates in regulating energy balance through thermogenesis. Many epidemiological and clinical studies have shown a close relationship between sympathetic nervous system activity and insulin levels in obesity and in weight gain [56, 57]. Elevations of sympathetic nervous system activity and insulin levels during weight gain [58-60] and reductions of sympathetic activity and insulin levels during weight loss [61-64] are typically observed. In addition, The response of the sympathetic nervous system to changes in plasma insulin levels after oral glucose loading (oral glucose tolerance test) are different between subjects with and without insulin resistance [65], between nonobese and obese subjects [66], and between subjects with and without metabolic syndrome [67]. Those observations provide evidence of a strong linkage between the activity of the sympathetic nervous system and insulin levels over glucose metabolisms. Straznicky
Acute hyperglycemia caused sympathetic activation and peripheral vasodilation. Moreover, both acute and chronic hyperglycemia and hyperinsulinemia may enhance adrenergic vasoconstriction and decrease vasodilation in animal models (pithed rats) [69, 70]. Insulin causes forearm vasoconstriction in obese, insulin-resistant hypertensive humans [71]. On the other hand, van Veen
Huggett
Moreover, stimulation of the renin-angiotensin-aldosterone system (RAAS) is frequently demonstrated in type 2 diabetes [73], and may be related to insulin resistance either via direct or indirect mechanisms [74].
Weight loss is recommended as the first line of treatment for type 2 diabetes and hypertension associated with obesity, because obesity is the primary cause for insulin resistance, metabolic syndrome and type 2 diabetes. Indeed, lifestyle modification including a low caloric diet, reducing sedentary behaviour and exercise form the foundation of all therapy. For the subjects who are more severely obese or unable to undertake an exercise program, bariatric surgery is recommended.
Weight loss is recommended as the first-line treatment for obesity-related type 2 diabetes and hypertension. The objective of treatment for obesity, type 2 diabetes and hypertension is both to reduce the high risk of cardiovascular events and to prevent or delay the onset of type 2 diabetes and complications. Lifestyle intervention with diet and exercise leading to weight loss prevents and delays the onset of type 2 diabetes or glucose intolerance [75]. Weight loss may also prevent cardiovascular- and renal-complications [76-79], and renal function and left ventricular hypertrophy as a marker for future cardiac events in obese individuals with metabolic syndrome and hypertension [77, 80]. The US Diabetes Prevention Program [81] and the Oslo Diet and Exercise Study [82] have shown marked clinical benefits with lifestyle intervention, and modest weight loss, on the resolution of the metabolic syndrome and type 2 diabetes. A limited number of epidemiological studies have shown that intentional weight loss may be associated with increased mortality and fat loss may reduce the all-cause mortality rate [83].
Cohort studies with lifestyle intervention [84] and case control studies with bariatric surgeries [85, 86] also provide some evidence that intentional weight loss has long-term benefits on all cause mortality in overweight adults. In a cohort of patients enrolled in a cardiac rehabilitation program, weight loss was associated with favourable long-term outcomes on the composite end-point of mortality and acute cardiovascular events (fatal and nonfatal myocardial infarction, fatal and nonfatal stroke, emergent revascularization for unstable angina pectoris, and congestive heart failure) [87].
Many clinical studies have demonstrated that weight loss associated with life-style modification adds to the first line treatment for diabetes mellitus and the efficacy of antihypertensive pharmacological treatments [8], however, maintaining weight loss is often the greatest challenge [5, 6, 88].
The American Diabetes Association (ADA) has recommended for the maintenance of a healthy weight to prevent and control diabetes as following; (i) more than 2.5 hours of exercise per week, (ii) having a modest fat intake (approximately 30% of energy supply), and (iii) rating sufficient fiber [89]. Recently, several investigations [4, 5, 90] compared the effects on weight loss between calorie restriction (diet) and exercise. They showed that combined intervention with diet and exercise proved to be effective in weight reduction than diet alone or exercise alone. Masuo
A low caloric diet and exercise exert different effects on insulin resistance, the RAAS, and sympathetic nervous activity in obese hypertensive subjects, even though similar weight loss was observed [4, 5]. Low caloric diet may be prominent for normalization of sympathetic nervous activity and exercise may be related more to normalization of insulin resistance [5, 88] (Figure 4). They previously observed that baseline plasma norepinephrine levels could predict future weight gain and weight gain-induced blood pressure elevation over 5 years in a longitudinal study [91], resistant weight loss by weight loss intervention with combination of calorie restriction and exercise intervention over 2 years. [6, 88] Similarly, Straznicky
When significant chages were observed comparisons between a calorie restricted diet vs. mild exercise alone vs. combination with diet + exercise over 24 weeks
Santarpia
Exercise training is important for weight loss and to prevent rebound weight gain after significant weight loss. Public health interventions promoting walking are likely to be the most successful. Indeed, walking is unique because of its safety, accessibility, and popularity. It is noteworthy that there is a clear dissociation between the adaptation of cardiopulmonary fitness and the improvements in the metabolic risk profile such as insulin resistance and sympathetic activation, which can be induced by endurance training programs. Dumortier
Recently, several large cohort studies have shown that saturated fat, which comes mainly from animal sources of food, raises LDL cholesterol and links strongly to cardiovascular risk [99, 100]. Saturated fats are needed for the production of hormones, the stabilization of cellular membranes, the padding around organs, and for energy. A deficiency in the consumption of saturated fats can lead to age-related declines in white blood cell function, along with dysfunction of the immune system and cancer [101]; however, a high content of saturated fat can leads to coronary heart disease [102], ischemic heart disease, and atherosclerosis and increase the chances of stroke.
Consistent evidence from prospective observational studies of habitual trans fatty acids (TFA) consumption and retrospective observational studies using TFA biomarkers indicates that TFA consumption increases risk of clinical coronary heart disease, and other disease outcomes such as cancer [102].
Unsaturated fats are known to increase the levels of High Density Lipoprotein (HDL cholesterol) and hence decrease LDL and VLDL cholesterol. Both types of unsaturated fat- mono-unsaturated and poly-unsaturated fats can replace saturated fats in the diet. Substituting saturated fats with unsaturated fats help to lower levels of total cholesterol and LDL cholesterol in the blood. However, intake of unsaturated fats in very high amounts can also increase the risk of coronary heart diseases.
It is recommended to take both in a limited quantity. The American Heart Association has recommended that Americans should limit their intake of saturated fats to 7% of their total calories in a day, while unsaturated fats can form 30% of the calorie intake to reduced cardiovascular risks.
Low-carbohydrate diets are dietary programs that restrict carbohydrate consumption usually for weight control or for the treatment of obesity. The term "low-carbohydrate diet" is generally applied to diets that restrict carbohydrates to less than 20% of caloric intake, but can also refer to diets that simply restrict or limit carbohydrates. Recently, the low carbohydrate diets has been spotlighted due to strong effects on weight loss, but many investigations have also shown no benefits on the reductions on cardiovascular risk as the major aim of weight loss.
A study of more than 100,000 people over more than 20 years within “the Nurses\' Health Study” observationally concluded that a low-carbohydrate diet high in vegetables, with a large proportion of proteins and oils coming from plant sources, decreases mortality with a hazard ratio of 0.8. In contrast, a low-carbohydrate diet with largely animal sources of protein and fat increases mortality, with a hazard ratio of 1.1, although there were criticisms on the methods [107]. A 2003 meta-analysis that included randomized controlled trials found that "low-carbohydrate, non-energy-restricted diets, appear to be at least as effective as low-fat, energy-restricted diets in inducing weight loss for up to 1 year [108]. Gardner
Previously, in routine practice a reduced-carbohydrate, higher protein diet was recommended approach to reducing the risk of cardiovascular disease and type 2 diabetes [111]. In 2004, the American Diabetes Association (ADA) affirmed its acceptance of carbohydrate-controlled diets as an effective treatment for short-term (up to one year) weight loss among obese people suffering from type 2 diabetes [112]. And the American Diabetes Association (ADA) revised their “
Recently, the effectiveness of low-fat high- protein and low-fat high-carbohydrate dietary advice on weight loss were compared using group-based interventions, among overweight people with type 2 diabetes. However, in a \'real-world\' setting, prescription of an energy-reduced low-fat diet, with either increased protein or carbohydrate, results in similar modest losses in weight, waist circumference and metabolic benefits over 2 years [116].
Ebbeling
The concept of the glycemic index was developed about 1981 by Dr. David Jenkins to account for variances in speed of digestion of carbohydrates. This concept classifies foods according to the rapidity of their effect on blood sugar levels – with fast digesting simple carbohydrates causing a sharper increase and slower digesting complex carbohydrates such as whole grains a slower one. The concept has been extended to include amount of carbohydrate actually absorbed as well, despite differences in glycemic index [118].
If the individuals failed to improve glucose levels or HbA1c, pharmacological therapy is required. The first-line oral agents should minimize the degree of insulin resistance and suppress hepatic glucose production rather than increase plasma insulin concentrations. The decision to include thiazolidinediones (TZDs) and metformin as first-line therapy draws from the algorithm proposed by Wyne
The goal for glucose control is shown in Table 2 [11, 121]. Stimulating insulin secretion and minimizing insulin resistance both have the potential to bring a patient to goal, but it is theorized that bringing a patient to goal by reducing insulin resistance is more likely to reduce the macro-vascular complications and cardiovascular risks.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Blood pressure control | \n\t\t\t< 130/80 mmHg | \n\t\t\tEvery visit | \n\t\t
Lipid control LDL | \n\t\t\t< 70 mg/dl† | \n\t\t\tMeasure yearly or | \n\t\t
Triglycerides | \n\t\t\t< 150 mg/dl | \n\t\t\tmore frequently | \n\t\t
HDL | \n\t\t\t> 45 mg/dl if | \n\tgoals are not met | \n
Urine protein Microalbuminuria | \n\t<30 mg/24 hours | \n\tMeasure yearly | \n
Diabetes Control Goals (by American Diabetes Association) Goals Endpoints Assessment
*American Diabetes Association Standards of medical care for patients with diabetesmellitus.
†National Cholesterol Education Program: Implications of clinical trials for the ATP
Based on several long-term, prospective studies which showed the significant reductions in cardiovascular risks associated with diabetes, the American Diabetes Association and American Association of Clinical Endocrinologists set forth standards and guidelines for the medical management of diabetes [11]. The recommendations clearly outline a multifactorial plan for managing diabetes and reducing complications [11], but they do not provide specific recommendations for selection and titration of pharmacological treatment. Pharmacological treatment for glucose control aims to reduce cardiovascular risk and to delay diabetic complications.
Pharmacological treatment for the management of obesity is primarily aimed at weight loss, weight loss maintenance and cardiovascular risk reduction. Anti-obesity agents decrease appetite, reduce absorption of fat or increase energy expenditure. Recently, anti-obesity drugs such as orlistat, sibtramine and rimonabant have been developed and placed on markets, however, the latter two were withdrawn from markets in the United States, Europe and Australia due to serious adverse events including psychiatric and cardiovascular related concerns. Recently, contrave, a combination of two approved drugs of bupropion and naltrexone, completed Phase III trials with significant weight loss and was approved by FDA in 2010, but subsequently the FDA declined to approve contrave due to serious cardiovascular adverse events in 2011 [122]. A contrave cardiovascular outcome trial, called “Light Study”, is ongoing and is expected to be completed by the first quarter of 2013. Importantly, obesity is, at least, in part, determined by genetic backgrounds [123], suggesting that a genetic approach to limiting obesity may find a place in the future.
Gastric bypass and adjustable gastric banding are the two most commonly performed bariatric procedures for the treatment of morbid obesity or obesity which is resistant to lifestyle modification such as a low caloric diet plus exercise. Multiple mechanisms contribute to the improved glucose metabolism seen after bariatric surgery, including caloric restriction, changes in the enteroinsular axis, alterations in the adipoinsular axis, release of nutrient-stimulated hormones from endocrine organs, stimulation from the nervous system, and psychosocial aspects including a dramatic improvement in quality of life [124]. Dixon
All forms of weight loss surgery lead to calorie restriction, weight loss, decrease in fat mass, and improvement in insulin resistance, type 2 diabetes mellitus, obesity and obesity-related hypertension [127]. Left ventricular relaxation impairment, assessed by tissue Doppler imaging, normalized 9 months after surgery [129]. Laparoscopic gastric bypass and gastric banding are both safe and effective approaches for the treatment of morbid obesity, but gastric bypass surgery seems to exert a better early weight loss and more rapid ameliorative effects on insulin resistance and adipocytokines, muscle metabolism and left ventricular function.
The prevalence of diabetes, especially type 2 diabetes and hypertension are significantly increased due, at least in part, to the increased prevalence of obesity. Type 2 diabetes is frequently associated with obesity, and is an important risk factor for cardiovascular morbidity and mortality and cardiac- and renal complications. Type 2 diabetes, hypertension and obesity are characterized by stimulation of the renin-angiotensin-aldosterone system (RAAS), elevated sympathetic activity and insulin resistance. Importantly, these characteristics, themselves, confer cardiovascular risk. Therefore, treatments for type 2 diabetes should be selected from favourable effects on stimulated RAAS, elevated sympathetic nervous system activity, insulin and leptin resistance.
Weight loss is recommended as the first line of treatment for type 2 diabetes and hypertension associated with type 2 diabetes in obesity. Lifestyle modification such as a caloric restricted diet, reducing sedentary behaviour and increases in exercise form the basis of all therapy. Weight loss treated with lifestyle modification including calorie restriction and/or exercise causes normalization of stimulated RAAS, sympathetic activation, insulin resistance, and hyperleptinemia. Recently, Masuo
In addition, special diets such as a low carbohydrate diet were reported as beneficial on weight loss previously, but it might cause an increase in cardiac risk. The official statement from American Heart Association reported that high-protein diet and low carbohydrate diet are not recommended diets due to increases in cardiovascular risk.
Gastric bypass and adjustable gastric banding are the two most commonly performed bariatric procedures for the treatment of morbid obesity or obesity which is resistant to lifestyle modification such as a low caloric diet plus exercise. Weight loss by bariatric surgery leads to improvement or normalization of glucose metabolisms from multiple mechanisms including caloric restriction, changes in the enteroinsular axis, alterations in the adipoinsular axis, release of nutrient-stimulated hormones from endocrine organs, stimulation from the nervous system, and psychosocial aspects including a dramatic improvement in quality of life.
Understanding the mechanisms underlying type 2 diabetes in obesity may help to achieve weight loss and maintenance of weight loss and resultant better control on type 2 diabetes, and delay and prevent the onset of type 2 diabetes or reduce complications.
This review provides information regarding,
In the field of renewable energy sources, wind energy is gaining much importance. The increase in the level of generation has two main restrictions, one is due to the limitations of the switching frequency of the power devices with respect to the power drive of the Doubly Fed Induction Machine (DFIM) and second one is the requirement of good dynamic torque performance, these restrictions are addressed by the proposed new Direct Torque Control (DTC) approach.
\nThe foremost torque control methods proposed were classified into Field Oriented Control (FOC) techniques, Blaschke [1] and the direct control techniques. Then after, the concept of DTC, Takahashi and Ohmori [2] and Direct Self Control (DSC), Depenbrock [3] were introduced to achieve good steady-state and transient torque control conditions. Moreover, direct control techniques do not require current regulators, nor coordinate transformations or specific modulations like Pulse Width Modulation (PWM) or Space Vector Modulation (SVM) for pulse generation. The disadvantages are the lack of direct current control, torque control difficulties at very low speeds and especially variable switching frequency behavior.
\nThe last important drawback put forward several authors in recent years, thus, methods like Direct Mean Torque Control (DMTC), [4] and Direct Torque Control based on Discrete Space Vector Modulation (DTC–DSVM), [5], have already achieved constant switching frequency. Furthermore, the methods proposed in Kang and Sul [6, 7] have extended the solution to reduce torque ripple at the same time imposing the switching frequency, as well as for different voltage-source multilevel topologies.
\nFurther the concept of DTC have been applied to the brushless doubly fed induction machine, [8], or the equivalent Direct Power Control (DPC) strategy for several grid connected converter applications, [9].
\nIn this paper, the analysis on the Doubly Fed Induction Machine (DFIM), which is a common solution for variable speed wind turbines, is discussed. The control methods like FOC have been performed by many authors, for example [10]. DTC and DPC methods without switching frequency imposition have also been carried out in Gomez & Amenedo [11], Datta & Ranganathan [12], while the DPC at constant switching frequency has also been developed in [13].
\nThese mentioned direct control techniques that achieve constant switching frequency behavior are based on predictive control with a prediction horizon equal to one sample period. In this paper, predictive DTC technique for the DFIM will be employed based on [13] and the performance is compared with the DTC strategy, at constant switching frequency and with reduced torque and flux ripples criteria. This control technique is based on a prediction of the torque and the flux evolution of the DFIM. Hence, the new Predictive DTC (PDTC) strategy presented in this paper is based on a direct control of the electromagnetic torque and the rotor flux of the machine. Simulation results are presented and discussed, and at last, the results show that the predictive DTC technique presents good dynamic response compared to classical DTC concept.
\nIn this Chapter, predictive DTC technique for the DFIM has been implemented and its performance is compared with the classical DTC of DFIM based on certain parameters like constant switching frequency, torque ripple and flux ripple. This control technique is based on a prediction of the torque and the flux evolution of the DFIM. To validate the proposed control scheme, results are presented. From these results, it is observed that the predictive DTC technique gives good dynamic response compared to classical DTC concept.
\nThe control strategy even reduces the switching losses of the converter and reduces the electromagnetic torque and flux ripples at low switching frequency even under variable speed operating conditions.
\nIn Section 1, the introduction of the Chapter is given.
\nIn Section 2, Contributions and Novelty of this Chapter is explained.
\nIn Section 3, Modeling of the DFIM is given.
\nIn Section 4, the basic control principle of predictive DTC is explained and also implementation of the proposed predictive DTC strategy of DFIM along with selection of rotor voltage vectors for constant switching frequency and reduction of switching power losses are described.
\nIn Section 5, Results are presented to validate the proposed control strategy.
\nIn Section 6, the Conclusions of the Chapter are described.
\nIn Section 7, the summary of the Chapter is given.
\nThe main contributions of this Chapter are as follows:
Implementation of a new predictive Direct Torque Control (DTC) strategy of the Doubly Fed Induction Machine (DFIM) is presented which is designed to operate at a low constant switching frequency.
The proposed DTC method effectively reduces the torque and flux ripples at low switching frequency, even under variable speed operation conditions.
Results are presented to validate the proposed control strategy.
The novelty of this Chapter is that the predictive DTC have been developed to control the different parameters of DFIM, to improve its performance during transient, steady state, tracking behavior and operation near synchronous speed. All these cases are thoroughly investigated.
\nThe DFIG model adopted is the qd0 rotating reference frame. It is because the model of DFIM is quite suitable with this frame of reference during transients. The transient solution of the DFIM model is possible because of the transformation from abc to qd0 by which the differential equations with time-varying inductances is converted into differential equations with constant inductances,
\n(or) simply (1) can be written as
\nSimilarly, the q and d-axis rotor voltages referred to the stator are given by,
\n(or) simply (3) can be written as
\nThe stator and rotor fluxes can be calculated by using (5) and (6) can be used to calculate the magnitudes.
\nThe electromagnetic torque of DFIM is given in (7).
\nThe active and reactive powers are given by:
\nThe main drawback of classical DTC scheme is nonconstant switching behavior; it is avoided by the proposed predictive Direct Torque Control strategy. The constant switching behavior is achieved by increasing slightly the complexity of control strategy.
\nA sequence of three voltage vectors will be introduced at a constant switching period of which two are active vectors always followed by a zero vector, in order to reduce the ripples of both directly controlled variables compared to selection of four rotor voltage vectors depending on the position of rotor flux in classical DTC scheme. For that purpose, ripple reduction criteria based on a prediction of the electromagnetic torque and rotor flux evolution over time is implemented which is derived from (7) and (10) [13] and illustrated in \nFigure 1(a)\n.
\n(a) Simplified prediction scheme of torque and rotor flux of DFIM. (b) Variation of electromagnetic torque with zero vectors at constant torque and rotor flux.
The basic principles of the predictive DTC scheme are firstly, constant switching period ‘ts’ is defined. In predictive DTC, the control procedure is discretized unlike in DTC scheme, which is based on time domain. Secondly, at steady state condition, by taking the electromagnetic torque and flux amplitude errors to be minimized, the three different rotor voltage vectors are injected during ts. Note that according to the chosen three vector sequence together with the specified time intervals for each vector, the electromagnetic torque and flux evolutions within the switching period can be different. Thirdly, this procedure is repeated at constant period ts.
\nThe Doubly Fed Induction Machine is modeled using (2), (4)–(6). The torque is calculated by using (7) in terms of complex conjugate of rotor flux and stator flux.
\nUsing (2), (4)–(7), the predictive expressions for torque and rotor flux are given by [2, 13]
\nThe below space vector representations are used in order to analyze the expressions (9) and (10).
\nwhere
where
\n\nEq. (15) comprises of a cosine term and two constant terms, the cosine term with depends on DC bus voltage and it indicates that the cosine term of rotor flux variation is constant for zero vectors and only depends on active vectors.
\n\nEq. (10) can be simplified considering the stator flux vector module which is nearly constant.
\n\nEq. (17) is similar to rotor flux derivation with only one constant and one sine term.
\nEquations (15) and (17) are used practically as shown in \nFigure 1(a)\n, instead of (9) and (10). As shown in \nFigure 1(a)\n, the torque and flux derivatives depends on only four terms when considering the DC bus voltage, stator voltage and flux as constant magnitudes. In the expression (17), the constant term depends on Tem and, that means it depends on machine operating condition, which can be positive or negative as shown in \nFigure 1(b)\n. Because of this reason only, the phase shift order of each active vector varies, whereas, it is not like that for rotor flux derivative. The key point is knowing the slopes or the derivatives of torque and flux for each of the rotor voltage vector based on \nFigure 1(a)\n and \nTable 1\n and considering these slopes to be constants within the specific time in the given switching period, ts, the torque and flux ripples can be maintained within the limits.
\n\n | Error of electromagnetic torque | \n||
---|---|---|---|
\n | 1 | \n−1 | \n|
Error value of rotor flux | \n1 | \nV(n − 1)\n | \nV(n + 1)\n | \n
−1 | \nV(n − 2)\n | \nV(n + 2)\n | \n
With constant values
\nFrom the \nFigure 1(b)\n, it can be seen that the slope of torque derivative varies proportionally to the slip speed provided at fixed torque and rotor flux operating conditions and also near synchronous speed, the slope of the zero vector becomes smaller obeying the fact that from the expression (17) the zero vector produce small torque variation. This fact implies that near synchronous speed the amplitude of rotor voltage vector is small demanding the condition of zero vectors. As shown in \nFigure 1(b)\n, this transition from positive to negative torque slope is different to the synchronous speed.
\nThe first vector is selected depending on the errors of torque and flux and the sector where the rotor flux lies, the look up table for vector selection is shown in \nTable 1\n. From the \nTable 1\n, it is noticed that the required rotor voltage vector should produce either positive or negative slope variation depending on the output of the torque or flux hysteresis comparators that is either 1 or −1, respectively.
\nThe first vector is actually selected based on the classical DTC; the second vector is selected such that it is always followed by zero vectors in order to reduce the torque and flux ripples based on the predictive DTC strategy.
\nFrom \nFigure 2\n, it can be seen that from the derivative calculations block the required values of torque and flux evolutions depending on the each rotor voltage selected are fed to the ripple reduction criteria block, in which based on the slopes calculations by (15) and (17), the required rotor voltage vectors which are selected are active for the time period of this constant slopes. This constant time period of the constant slopes is fed to the switching table, where in the switching operation of rotor voltages are chosen in such a way that it reduces the switching losses.
\nBlock diagram of proposed predictive direct torque control strategy of DFIM.
From (15) and (17), it can be inferred that the rotor flux is constant for zero vector and it produces opposite sign for torque variation for first two active vectors. These two active vectors along with zero vectors are useful to control torque and rotor flux. By the two active vectors the rotor flux as one vector produce positive slope, the other vectors produce negative slope because of this the flux ripple is not eliminated completely as compared to torque ripple and further it affects the rotor and stator currents. \nTable 2\n shows the selection of second active vector, after the first active vector is selected. It clearly shows that, one vector cannot be selected, as the flux would have a very big or small variation, which leads to poor quality of flux output.
\n\n | Active vectors | \n
---|---|
Below synchronous speed | \nV(n + 1), V(n + 2)\n | \n
Above synchronous speed | \nV(n − 1), V(n − 2)\n | \n
The right choice of zero vectors V0 and V7 implies that there is reduction in switching power loss of the converter. Two different switching sequences exist for each pair of required active vectors, which allows the commutations of the converter to be reduced. \nTable 3\n shows the correct sequence of vectors, which allows only four commutations per switching period ts. The candidate sequences are such that it reduces the switching power losses by transmitting the smallest current values, which is the main task of the proposed control strategy.
\nActive vectors | \nSector | \nSwitching sequences | \nZero vector | \n
---|---|---|---|
V1-V2\n | \n6(subs) | \n100-110-111 | \nV7\n | \n
3(hypers) | \n110-100-000 | \nV0\n | \n|
V2-V3\n | \n1(subs) | \n110-010-000 | \nV0\n | \n
4(hypers) | \n010-110-111 | \nV7\n | \n|
V3-V4\n | \n2(subs) | \n010-011-111 | \nV7\n | \n
5(hypers) | \n011-010-000 | \nV0\n | \n|
V4-V5\n | \n3(subs) | \n011-001-000 | \nV0\n | \n
6(hypers) | \n001-011-111 | \nV7\n | \n|
V5-V6\n | \n4(subs) | \n001-101-111 | \nV7\n | \n
1(hypers) | \n101-001-000 | \nV0\n | \n|
V6-V1\n | \n5(subs) | \n101-100-000 | \nV0\n | \n
2(hypers) | \n100-101-111 | \nV7\n | \n
Sequence of vector selection for reduction of switching power losses under steady-state [13].
subs – sub synchronous speed operation, hypers – super synchronous speed operation.
The predictive DTC control scheme of DFIM has been implemented using MATLAB/Simulink. The specifications of DFIM are given in \nTable 4\n. The conditions of steady state, transient, tracking behavior, and performance near synchronism of DFIM are examined, which are given in three subsections.
\nDoubly fed induction machine | \n|
---|---|
Specifications | \nRatings | \n
Rated power | \n2.6 MW | \n
Rated voltage | \n690 V | \n
Synchronous speed | \n1500 rpm | \n
Frequency | \n50 Hz | \n
Number of pairs of poles | \n2 | \n
Stator to rotor turns ratio | \n0.34 | \n
Base voltage | \n398.4 V | \n
Base current | \n2175 A | \n
Base angular frequency | \n157 rads−1\n | \n
Base power | \n2.6 MW | \n
Stator resistance | \n0.0108 pu | \n
Stator leakage inductance | \n0.102 pu | \n
Magnetizing inductance | \n3.362 pu | \n
Rotor resistance referred to stator | \n0.0121 pu | \n
Rotor leakage inductance referred to stator | \n0.11 pu | \n
Inertia constant(H) | \n0.5 s | \n
Friction coefficient(F) | \n0.05479 pu | \n
DC-link capacitance | \n5e–3F | \n
DC-link voltage | \n1200 V | \n
\n | \n|
Number of blades | \n3 | \n
Rotor diameter | \n70 m | \n
Hub height | \n84.3 m | \n
Turbine total Inertia | \n4.4532 e5Kgm2\n | \n
Stiffness constant (B) | \n2 pu | \n
Mutual damping | \n1.5 pu | \n
Cut-in wind speed | \n3 ms−1\n | \n
Cut-out wind speed | \n25 ms−1\n | \n
Rated wind speed | \n15 ms−1\n | \n
Specifications of DFIM and wind turbine.
The performance of proposed control strategy of DFIM is analyzed for steady state and transient conditions. For the transient conditions, the step change in electromagnetic torque i.e., from −0.4 pu to +0.4 pu at 0.6 s is considered. That means, from generator mode (negative torque) to motoring mode (positive torque), with constant switching frequency of 1 kHz, with speed reference of 1350 rev/min, and DC-link voltage of 1200 V. The DFIM is under steady state operation up to 0.6 s with torque of −0.4 pu and at 0.6 s the DFIM enters into transient state, and again it reaches its steady state value of 0.4 pu.
\nThe response of stator currents in stationary reference frame are shown in \nFigure 3(a)\n. Therefore, there are two waveforms which refer to α, β components of stator currents. From the \nFigure 3(a)\n, it is observed that there are no over currents in stator, which indicates the effectiveness of the proposed control scheme even at sudden variation in torque demand. This is possible because of selection of proper rotor voltage vectors with their respective time intervals.
\n(a) Response of stator currents of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (b) Torque response of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s, (i) proposed strategy (ii) classical DTC. (c) stator active power of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (d) Stator flux response of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (e) Response of rotor flux of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (f) Rotor speed response of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (g) Response of rotor currents of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s.
\n\nFigure 3(b)\n shows the response of developed torque for the proposed strategy and classical DTC strategy (not expressed in p.u. value), from the figure, it is noticed that the torque response of the DFIM closely followed the torque command and also torque ripple is zero.
\nFrom the \nFigure 3(c)\n, it can be seen that stator active power has good dynamic response when the reference torque is changed suddenly. From the figure, it is observed that the stator active power follows the torque demand to make the DFIM to develop the torque to match its reference value.
\nThe response of the stator flux is shown in \nFigure 3(d)\n, from the figure, it is clearly noticed that the stator flux response remains constant which is not affected by variation in torque command.
\nThe response of the rotor flux is shown in \nFigure 3(e)\n, from the figure, it is observed that the rotor flux response is also not affected by change in reference torque and also the rotor flux response is sinusoidal in nature which is not distorted due to sudden change in torque command.
\n\n\nFigure 3(f)\n shows response of the rotor speed of DFIM. From the \nFigure 3(f)\n, it is observed that there is decrease in rotor speed due to step change in reference torque but decrease in rotor speed is very small.
\nThe response of rotor currents is shown in \nFigure 3(g)\n. From the \nFigure 3(g)\n, it is observed that there are no over currents in rotor, which indicates the effectiveness of the proposed control scheme even at sudden variation in torque demand. This is possible because of selection of proper rotor voltage vectors with their respective time intervals.
\nIn this section, the proposed control scheme of DFIM is investigated for variable speed operation implying wind energy applications, and at the same instant, the torque reference may also vary respectively with speed of DFIM. This kind of behavior of wind energy system is called as tracking behavior. At this condition, the actual values of the DFIM should follow the reference values as closely as possible and this is clearly guaranteed by proposed control strategy which can be seen clearly through the results presented in this section. To explore the tracking behavior of DFIM, sinusoidally varying reference torque with amplitude of 0.4 pu and frequency of 3 Hz is set to the DFIM. By this set reference torque, the DFIM operates in generating and motoring modes. In this mode of operation, the other parameters of DFIM are same as mentioned in Section 5.1.
\nThe predictive DTC strategy has good tracking behavior and it is confirmed that the reduction in torque and flux ripples is achieved as there are absolute absence of over currents and reduced ripples in stator currents as shown in \nFigure 4(a)\n. From the \nFigure 4(a)\n, it is noticed that there is continuous increase and decrease in the amplitude of stator currents for maintaining the consistency due to variable behavior of torque command. The stationary reference frame stator currents are clearly noticed in \nFigure 4(a)\n.
\n(a) Response of stator currents of DFIM with variable torque command. (b) Response of developed torque of DFIM with variable torque command (i) proposed strategy (ii) classical DTC. (c) Response of stator flux of DFIM with variable torque command. (d) Response of rotor flux of DFIM with variable torque command. (e) Response of rotor speed of DFIM with variable torque command. (f) Response of rotor currents of DFIM with variable torque command.
The torque produced by the DFIM follows as closely as the reference torque, which indicates good tracking behavior of the proposed control scheme comparative to classical DTC (not expressed in p.u. value), it can be seen in \nFigure 4(b)\n.
\nStator flux and rotor flux responses of DFIM are shown in \nFigure 4(c)\n and \n(d)\n respectively. From the \nFigure 4(c)\n and \n(d)\n, it is observed that the variation in torque command is not having any influence on the stator and rotor fluxes of DFIM.
\n\n\nFigure 4(e)\n shows the rotor speed response of DFIM. From the \nFigure 4(e)\n, it is observed that there is continuous variation in rotor speed; of course, this variation is very small it is because of variable torque command, but practically rotor speed almost constant.
\nThe predictive DTC strategy has good tracking behavior and it is confirmed that the reduction in torque and flux ripples is achieved as there are absolute absence of over currents and reduced ripples in rotor currents as shown in \nFigure 4(f)\n. From the \nFigure 4(f)\n, it is noticed that there is continuous increase and decrease in the amplitude of rotor currents, similar to stator currents as shown in \nFigure 4(a)\n.
\nIn this Section, the performance of the proposed control scheme of DFIM has been investigated near the synchronous speed. This is examined by varying the speed of DFIM from 1580 rpm (hyper synchronous value) to 1340 rpm (sub synchronous value) in terms of sine wave with frequency of 3 rads−1 and phase shift of 90°, with the reference values of torque and rotor flux are set to 0.4 pu and 1 pu respectively.
\nEven when the speed command is varied suddenly from hyper synchronous value to sub synchronous value no much transient peaks occur in stator currents, as shown in \nFigure 5(a)\n, which clearly emphasizes there is reduction in over currents in stator and this is because of proper selection of switching sequence of rotor voltage vectors.
\n(a) Response of stator currents of DFIM with variation in rotor speed from 1580 to 1340 rpm. (b) Torque response of DFIM with variation in rotor speed from 1580 to 1340 rpm (i) proposed strategy and (ii) classical DTC. (c) Response of stator flux of DFIM with variation in rotor speed from 1580 to 1340 rpm. (d) Response of rotor flux of DFIM with variation in rotor speed from 1580 to 1340 rpm. (e) Rotor speed of DFIM with variation in rotor speed from 1580 to 1340 rpm. (f) Response of rotor currents of DFIM with variation in rotor speed from 1580 to 1340 rpm.
As shown in \nFigure 5(b)\n, the torque developed by the machine for proposed strategy and classical DTC and it closely follows the reference torque which means the dynamic performance of the machine is quite satisfactory but when the rotor speed nears the synchronous speed at around 0.5 s, variable torque ripple is produced. This variability in the torque ripple is due to continuous selection of zero voltage vectors at that instant. It indicates, the smaller amplitude of rotor voltage vector is required at the instant of rotor speed nearing the synchronism, which actually causes the reduction in torque ripple and this leads to degradation of quality of control. As shown in \nFigure 5(b)\n, the ripples are reduced in electromagnetic torque response.
\nStator flux responses are shown in \nFigure 5(c)\n. From the figure, it is observed that the stator flux remains constant and is not affected by change in rotor speed and also it is sinusoidal in nature.
\nThe response of rotor flux is shown in \nFigure 5(d)\n, from the figure, it is observed that there is no effect on the rotor flux due to sudden change in the rotor speed and rotor flux is also not affected by the changeover.
\nThe response of rotor speed is as shown in \nFigure 5(e)\n, when reference speed is varied from hyper synchronous value to sub-synchronous value and it is observed that the rotor speed response of DFIM follows the command speed.
\nSimilar to stator currents as shown in \nFigure 5(a)\n, no much transient peaks occur in rotor currents, as shown in \nFigure 5(f)\n, that is, there is reduction in over currents in the rotor, which is because of proper selection of switching sequence of rotor voltage vectors.
\nThe proposed control method makes two general contributions to the predictive control techniques. Firstly, it shows that using instead of two voltage vectors operating three appropriate vectors, allows operating at low constant switching frequency. Secondly, it is crucial to achieve the whole good performance of the DFIM, in terms of torque and current ripples by reducing the ripples of both directly controlled variables instead of only one.
\nFrom the proposed control method, it is possible to reduce the stress of the switching devices of the voltage source converter, in terms of low constant switching frequency behavior and switching power losses reduction, often demanded requirements in high power applications.
\nIt presents good tracking behavior, capable of working at variable speed operation conditions, for both motoring and generating modes at sub- synchronous and hyper synchronous speeds when compared to DTC technique, making this control suitable for applications such as wind power generation.
\nThe new DTC technique allows obtaining quick dynamic responses in respect to DTC method, with absolute absence of non-desired over currents in the machine. It ensures reduced torque and flux ripples, due to the control effect. The simulation results showed the effectiveness of the proposed method, to control the torque and the flux of the DFIM at considerably low constant switching frequency.
\nIn this Chapter, new predictive DTC has been developed for DFIM. The proposed control scheme uses two voltage vectors instead of three voltage vectors and it allows operating at low constant switching frequency and reduces torque and flux ripples, and also capable of working at variable speed operating conditions for both motoring and generating modes at sub-synchronous and hyper synchronous speeds compared to classical DTC technique. The comparison of torque and flux ripple values (difference of maximum to minimum ripple value) and its reduction given by the difference of maximum value and minimum value to average value is given in the \nTable 5\n below.
\nS. No. | \nParameter | \nClassical DTC scheme | \nPredictive DTC scheme | \n
---|---|---|---|
1 | \nTorque ripple | \n0.3 pu | \n0.01 pu | \n
2 | \nReduction in torque ripple | \n2.5% | \n1.25% | \n
3 | \nRotor flux ripple | \n0.018 pu | \n0.01 pu | \n
4 | \nReduction in rotor flux ripple | \n1.8% | \n1.05% | \n
Torque and flux ripple reduction comparison.
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He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,annualVolume:11414,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. 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In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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Theriogenology",editors:[{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Animal Nutrition",value:20,count:2},{group:"subseries",caption:"Animal Reproductive Biology and Technology",value:28,count:4},{group:"subseries",caption:"Animal Science",value:19,count:5}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:3},{group:"publicationYear",caption:"2021",value:2021,count:3},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:1},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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