Technical specifications of the Goldwind 1.5 MW PMDD WT [17].
\r\n\tThe book will be useful to students, postdocs and researchers interested in dealing with several interesting aspects of discrete behaviours in geometry and dynamics.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"d776a31d1f62e07a4c3600ab1ad6e374",bookSignature:"Dr. Dumitru Baleanu",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9327.jpg",keywords:"Discrete, Lagrangian, Hamiltonian, Dynamics, Fractional calculus, Euler-Lagrange equations, Computer graphics, Computational, Mechanics, Smooth, Curvatures, Numerical methods",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 11th 2019",dateEndSecondStepPublish:"October 2nd 2019",dateEndThirdStepPublish:"December 1st 2019",dateEndFourthStepPublish:"February 19th 2020",dateEndFifthStepPublish:"April 19th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"105623",title:"Dr.",name:"Dumitru",middleName:null,surname:"Baleanu",slug:"dumitru-baleanu",fullName:"Dumitru Baleanu",profilePictureURL:"https://mts.intechopen.com/storage/users/105623/images/system/105623.jpg",biography:"Dumitru Baleanu received a B.Sc. degree in Physics from the University of Craiova, Romania, in 1988, an M.Sc. degree from the University of Bucharest, Romania, in 1989, and a Ph.D. degree from the Institute of Atomic Physics, Romania, in 1996. He is Professor at the Institute of Space Sciences, Romania, and since 2000 he is visiting staff member at Cankaya University, Turkey. He published 500 papers in journals indexed in SCI. He is a co-editor of five books published by Springer. He is coauthor of three books published by Elsevier and World Scientific. He is an editorial board member of six ISI journals and is on the 2015 Highly Cited Researcher list in mathematics. His Hirsch index is 33.",institutionString:"Cankaya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Çankaya University",institutionURL:null,country:{name:"Turkey"}}}],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:"304289",firstName:"Rebekah",lastName:"Pribetic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/304289/images/13255_n.png",email:"rebekah@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"2278",title:"Advances in Wavelet Theory and Their Applications in Engineering, Physics and 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Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40013",title:"Traditional and Modern Medicine Harmonizing the Two Approaches in the Treatment of Neurodegeneration (Alzheimer’s Disease – AD)",doi:"10.5772/48558",slug:"traditional-and-modern-medicine-harmonizing-the-two-approaches-in-the-treatment-of-neurodegeneration",body:'Neurodegenerative disorders, Primarily, are multifactorial diseases characterized by chronic and progressive loss of neurons in discrete areas of the brain, causing debilitating symptoms and globally decreasingcognitivefunction such as dementia, loss of memory, loss of sensory or motor capability, decreased overall quality of life and well-being, disability, and eventually, premature death. For most neurodegenerative diseases, there is little or no treatment; at best, treatments are symptomatic in nature and do not prevent or slow the progression of disease. Clearly, an understanding of pathological progression can help to identify points of intervention and lead to promising therapeutic approaches. A fundamental approach for reducing the burden of neurodegenerative diseases is thus to slow or halt progression, and ultimately, to prevent the onset of the disease process. Strategies for neurorescue, neurorepair, neuroprotection or treatment are being actively pursued by the basic, translational, and clinical research communities. As our population ages, the already enormous impact of neurodegeneration on society will become even larger without better prevention and treatment.
“Dementia” is an umbrella term describing a variety of diseases and conditions that develop when nerve cells in the brain die or no longer function normally. The death or malfunction of these nerve cells, called neurons, causes changes in one’s memory, behavior and Ability to think clearly. In Alzheimer’s disease (AD), these brain changes eventually impair an individual’s Ability to carry out such basic bodily functions as walking and swallowing. As aged population dramatically increases in these decades, efforts should be made on the intervention for curing age-associated neurodegenerative diseases such as AD.
AD is considered to be the most widespread variety of dementia (57%-65%) or a condition typified by continuous decline of mental aptitudes (1,2).
AD affects about 5.4 million people in the United States alone, and that number is projected to reach 12-16 million by the year 2050 (3). Economically, AD is a major public health problem. In the United States in 2011, the cost of health care, long-term care, and hospice services for people aged 65 years and older with AD and other dementias was expected to be $183 billion, and this figure does not include the contributions of unpaid caregivers(3).
Currently, an autopsy or brain biopsy is the only way to make a definitive diagnosis of AD. In clinical practice, the diagnosis is usually made on the basis of the history and findings on Mental Status Examination.
Symptomatic therapies are the only treatments available for AD. The standard medical treatments include cholinesterase inhibitors and a partial N -methyl-D-aspartate (NMDA) antagonist. Psychotropic medications are often used to treat secondary symptoms of AD, such as depression, agitation, and sleep disorders.
In 1901, a German psychiatrist named Alois Alzheimer observed a patient at the Frankfurt Asylum named Mrs. Auguste D. This 51-year-old woman suffered from a loss of short-term memory, among other behavioral symptoms that puzzled Dr. Alzheimer. Five years later, in April 1906, the patient died, and Dr. Alzheimer sent her brain and her medical records to Munich, where he was working in the lab of Dr. Emil Kraeplin. By staining sections of her brain in the laboratory, he was able to identify amyloid plaques and neurofibrillary tangles (4).
A speech given by Dr. Alzheimer on November 3, 1906, was the first time the pathology and the clinical symptoms of the disorder, which at the time was termed presenile dementia, were presented together. Alzheimer published his findings in 1907(5).
In the past 15-20 years, dramatic progress has been made in understanding the neurogenetics and pathophysiology of AD. Four different genes have been definitively associated with AD, and others that have a probable role have been identified. The mechanisms by which altered amyloid and tau protein metabolism, inflammation, oxidative stress, and hormonal changes may produce neuronal degeneration in AD are being elucidated, and rational pharmacologic interventions based on these discoveries are being developed.
Rapid progress towards understanding the molecular underpinnings of neurodegenerative disorders such as AD is revolutionizing drug discovery for these conditions. Furthermore, the development of models for these disorders is accelerating efforts to translate insights related to neurodegenerative mechanisms into disease-modifying therapies.
AD or Alzheimer\'s-type dementia is a progressive degeneration of brain tissue that primarily strikes people over age 65. It is the most common cause of dementia and is marked by a devastating mental decline. Intellectual functions such as memory, comprehension, and speech deteriorate. Attention tends to stray, simple calculations become impossible, and ordinary daily activities grow increasingly difficult, accompanied by bewilderment and frustration.
AD characterized clinically by progressive memory deficits, impaired cognitive function, and altered and inappropriate behavior. AD places a considerable and increasing burden on patients, caregivers, and society. Aging represents the most important risk factor and dementia has become one of the major challenges in our societies due to the universal phenomenon of population aging in the world. Brain regions involved in learning and memory processes, including the temporal and frontal lobes as well as the hippocampus, are reduced in size in AD patients as the result of degeneration of synapses and death of neurons. AD is considered as a protein aggregation disorder, based on two key neuropathological hallmarks, namely the hyperphosphorylation of the tau protein resulting in the formation of neurofibrillary tangles (NFTs), and the increased formation and aggregation of amyloid-beta peptide (Aβ) derived from amyloid precursor protein (APP) (6).
Although the exact underlying cause initiating the onset of AD is still unclear, an imbalance in oxidative and nitrosative stress, intimately linked to mitochondrial dysfunction, characterizes already early stages of AD pathology.
The cause of AD is not entirely known, but is thought to include both genetic and environmental factors (Multifactorial). A diagnosis of AD is made when certain symptoms are present, and by making sure other causes of dementia are not present (DSM-IV criteria).
The only way to know for certain that someone has AD is to examine a sample of their brain tissue after death. The following changes are more common in the brain tissue of people with AD:
"Neurofibrillary tangles" (twisted fragments of protein within nerve cells that clog up the cell)
"Neuritic plaques" (Abnormal clusters of dead and dying nerve cells, other brain cells, and protein)
"Senile plaques" (areas where products of dying nerve cells have accumulated around protein).
When nerve cells (neurons) are destroyed, there is a decrease in the neurotransmitters. As a result, areas of the brain that normally work together become disconnected.
Healthy neurons have an internal support structure partly made up of structures called microtubules. These microtubules act like tracks, guiding nutrients and molecules from the body of the cell down to the ends of the axon and back. A special kind of protein, tau, binds to the microtubules and stabilizes them.
In AD, tau is changed chemically. It begins to pair with other threads of tau, which become tangled together. When this happens, the microtubules disintegrate, collapsing the neuron’s transport system (see the image below). The formation of these neurofibrillary tangles (NFTs) may result first in malfunctions in communication between neurons and later in the death of the cells.
In addition to NFTs, the anatomic pathology of AD includes senile plaques (SPs; also known as beta-amyloid plaques) at the microscopic level and cerebrocortical atrophy at the macroscopic level (see the image below). The hippocampus and medial temporal lobe are the initial sites of tangle deposition and atrophy(7).This can be seen on brain magnetic resonance imaging early in AD and helps support a clinical diagnosis.
SPs and NFTs were described by Alois Alzheimer in his original report on the disorder in 1907(5).They are now universally accepted as the pathological hallmark of the disease.
A continuum exists between the pathophysiology of normal aging and that of AD(8).Pathologic hallmarks of AD have been identified; however, these features also occur in the brains of cognitively intact persons. For example, in a study in which neuropathologists were blinded to clinical data, they identified 76% of brains of cognitively intact elderly patients as demonstrating AD(9).
AD affects the 3 processes that keep neurons healthy: communication, metabolism, and repair. Certain nerve cells in the brain stop working, lose connections with other nerve cells, and finally die. The destruction and death of these nerve cells causes the memory failure, personality changes, problems in carrying out daily activities, and other features of the disease.
The accumulation of SPs primarily precedes the clinical onset of AD. NFTs, loss of neurons, and loss of synapses accompany the progression of cognitive decline(8).
Considerable attention has been devoted to elucidating the composition of SPs and NFTs to find clues about the molecular pathogenesis and biochemistry of AD. The main constituent of NFTs is the microtubule-associated protein tau (see Anatomy). In AD, hyperphosphorylated tau accumulates in the perikarya of large and medium pyramidal neurons. Somewhat surprisingly, mutations of the tau gene result not in AD but in some familial cases of frontotemporal dementia.
Since the time of Alois Alzheimer, SPs have been known to include a starchlike (or amyloid) substance, usually in the center of these lesions. The amyloid substance is surrounded by a halo or layer of degenerating (dystrophic) neurites and reactive glia (both astrocytes and microglia).
One of the most important advances in recent decades has been the chemical characterization of this amyloid protein, the sequencing of its amino acid chain, and the cloning of the gene encoding its precursor protein (on chromosome 21). These advances have provided a wealth of information about the mechanisms underlying amyloid deposition in the brain, including information about the familial forms of AD.
Although the amyloid cascade hypothesis has gathered the most research financing, other interesting hypotheses have been proposed. Among these are the mitochondrial cascade hypotheses(10).
In addition to NFTs and SPs, many other lesions of AD have been recognized since Alzheimer’s original papers were published. These include the granulovacuolar degeneration of Shimkowicz; the neuropil threads of Braak et al(11); and neuronal loss and synaptic degeneration, which are thought to ultimately mediate the cognitive and behavioral manifestations of the disorder.
Plaques are dense, mostly insoluble deposits of protein and cellular material outside and around the neurons. Plaques are made of beta-amyloid (Aβ), a protein fragment snipped from a larger protein called amyloid precursor protein (APP). These fragments clump together and are mixed with other molecules, neurons, and non-nerve cells.
Amyloid plaques.
Amyloid precursor protein (APP) is the precursor to amyloid plaque.
In AD, plaques develop in the hippocampus, a structure deep in the brain that helps to encode memories, and in other areas of the cerebral cortex that are used in thinking and making decisions. Plaques may begin to develop as early as the fifth decade of life(12).Whether Aβ plaques themselves cause AD or whether they are a by-product of the AD process is still unknown. It is known that changes in APP structure can cause a rare, inherited form of AD.
Tangles are insoluble twisted fibers that build up inside the nerve cell. Although many older people develop some plaques and tangles, the brains of people with AD have them to a greater extent, especially in certain regions of the brain that are important in memory. There are likely to be significant age-related differences in the extent to which the presence of plaques and tangles are indicative of the presence of dementia.
NFTs are initially and most densely distributed in the medial aspect and in the pole of the temporal lobe; they affect the entorhinal cortex and the hippocampus most severely (however, Braak et al found that in sporadic AD, tauopathy may appear first in the lower brainstem rather than in the transentorhinal region(12). As AD progresses, NFTs accumulate in many other cortical regions, beginning in high-order association regions and less frequently in the primary motor and sensory regions.
SPs also accumulate primarily in association cortices and in the hippocampus. Plaques and tangles have relatively discrete and stereotypical patterns of laminar distribution in the cerebral cortex, which indicate predominant involvement of corticocortical connections.
Although NFTs and SPs are characteristic of AD, they are not pathognomonic. NFTs are found in several other neurodegenerative disorders, includingprogressive supranuclear palsyand dementia pugilistica (chronic traumatic encephalopathy). SPs may occur in normal aging.
Therefore, the mere presence of these lesions is not sufficient to support the diagnosis of AD. These lesions must be present in sufficient numbers and in a characteristic topographic distribution to fulfill the current histopathologic criteria for AD. There is consensus that the presence of even low numbers of NFTs in the cerebral neocortex with concomitant SPs is characteristic of AD.
Some authorities believed that NFTs, when present in low densities and essentially confined to the hippocampus, were part of normal aging. However, the histologic stages for AD that Braak et al formulated include an early stage in which NFTs are present at a low density in the entorhinal and perirhinal (ie, transentorhinal) cortices(12).Therefore, even small numbers of NFTs in these areas of the medial temporal lobe may be abnormal.
A central but controversial issue in the pathogenesis of AD is the relationship between amyloid deposition and NFT formation. Evidence shows that abnormal amyloid metabolism plays a key pathogenic role. At high concentrations, the fibrillar form of Aβ has been shown to be neurotoxic to cultured neurons.
Cultured cortical and hippocampal neurons treated with Aβ protein exhibit changes characteristic of apoptosis (self-regulated cell destruction), including nuclear chromatin condensation, plasma membrane blebbing, and internucleosomal DNA fragmentation. The fibrillar form of Aβ has also been shown to alter the phosphorylation state of tau protein.
The identification of several point mutations within theAPPgene in some patients with early-onset familial AD and the development of transgenic mice exhibiting cognitive changes and SPs also incriminate Aβ in AD. The apolipoprotein E (APOE) E4 allele, which has been linked with significantly increased risk for developing AD, may promote inability to suppress production of amyloid, increased production of amyloid, or impaired clearance of amyloid with collection outside of the neuron.
Autopsies have shown that patients with 1 or 2 copies of the APOE E4 allele tend to have more amyloid. Additional evidence comes from recent experimental data supporting the role of presenilins in Aβ metabolism, as well as findings of abnormal production of Aβ protein in presenilin-mutation familial AD.
Although very popular, the amyloid hypothesis is not uniformly accepted. On post-mortem analysis, amyloid plaques may be undetectable in the brains of patients who had severe AD but may be present in the brains of elderly patients who did not have dementia(13).
Dementia severity correlates better with the number of neocortical NFTs than with SPs. The tau protein stabilizes neuronal microtubules. Destabilization of the microtubular system is speculated to disrupt the Golgi apparatus, in turn inducing abnormal protein processing and increasing production of Aβ. In addition, this destabilization may decrease axoplasmic flow, generating dystrophic neurites and contributing to synaptic loss.
Granulovacuolar degeneration occurs almost exclusively in the hippocampus. Neuropil threads are an array of dystrophic neurites diffusely distributed in the cortical neuropil, more or less independently of plaques and tangles. This lesion suggests neuropil alterations beyond those merely due to NFTs and SPs and indicates an even more widespread insult to the cortical circuitry than that visualized by studying only plaques and tangles.
The cholinergic system is involved in memory function, and cholinergic deficiency has been implicated in the cognitive decline and behavioral changes of AD. Activity of the synthetic enzyme choline acetyltransferase (CAT) and the catabolic enzyme acetylcholinesterase are significantly reduced in the cerebral cortex, hippocampus, and amygdala in patients with AD.
The nucleus basalis of Meynert and diagonal band of Broca provide the main cholinergic input to the hippocampus, amygdala, and neocortex, which are lost in patients with AD. Loss of cortical CAT and decline in acetylcholine synthesis in biopsy specimens have been found to correlate with cognitive impairment and reaction-time performance. Because cholinergic dysfunction may contribute to the symptoms of patients with AD, enhancing cholinergic neurotransmission constitutes a rational basis for symptomatic treatment.
Oxidative damage occurs in AD. Studies have demonstrated that an increase in oxidative damage selectively occurs within the brain regions involved in regulating cognitive performance(14).
Oxidative damage potentially serves as an early event that then initiates the development of cognitive disturbances and pathological features observed in AD. A decline in protein synthesis capabilities occurs in the same brain regions that exhibit increased levels of oxidative damage in patients with mild cognitive impairment (MCI) and AD. Protein synthesis may be one of the earliest cellular processes disrupted by oxidative damage in AD(15).
Oxidative stress is believed to be a critical factor in normal aging and in neurodegenerative diseases such asParkinson disease,amyotrophic lateral sclerosis, and AD.
The apoptotic pattern of cellular death seen in oxidative stress is similar to that produced by Aβ peptide exposure, and Aβ neurotoxicity is attenuated by antioxidants such as vitamin E. Aβ may induce toxicity by engaging several binding sites on the membrane surface.
Several investigators now believe that converging environmental and genetic risk factors trigger a pathophysiologic cascade that, over decades, leads to Alzheimer pathology and dementia.
The following risk factors for Alzheimer-type dementia have been identified: Advancing age;Family history; APOE 4 genotype; Obesity; Insulin resistance; Vascular factors; Dyslipidemia; Hypertension; Inflammatory markers; Down syndrome and Traumatic brain injury(16-19).
In addition, epidemiologic studies have suggested some possible risk factors (eg, aluminum(20,21),previous depression) and some protective factors (eg, education(22, 23),long-term use of nonsteroidal anti-inflammatory drugs(24).
Although most cases of AD are sporadic (ie, not inherited), familial forms of AD do exist. Autosomal dominant AD, which accounts for less than 5% of cases, is almost exclusively early onset AD; cases occur in at least 3 individuals in 2 or more generations, with 2 of the individuals being first-degree relatives(25).
Familial clustering represents approximately 15–25% of late-onset AD cases and most often involves late-onset AD. In familial clustering, at least 2 of the affected individuals are third-degree relatives or closer(25).
Mutations in the following genes unequivocally cause early-onset autosomal dominant AD:
The amyloid precursor protein (APP) gene on chromosome 21
The presenilin-1 (PS1) gene on chromosome 14
The presenilin-2 (PS2) gene on chromosome 1
All 3 of these genes lead to a relative excess in the production of the stickier 42-amino acid form of the Aβ peptide over the less sticky 40-amino-acid form.
This beta-pleated peptide is postulated to have neurotoxic properties and to lead to a cascade of events (as yet incompletely understood) that results in neuronal death, synapse loss, and the formation of NFTs and SPs, among other lesions. Nonetheless, the mutations that have been found to date account for less than half of all cases of early-onset AD.
Other than the apolipoprotein E epsilon 4 (APOE E4) genotype, no polymorphisms in other genes have been consistently found to be associated with late-onset AD. However, genome-wide association studies have identified the following additional susceptibility loci(26).
The observation that patients with Down syndrome (trisomy 21) develop cognitive deterioration and typical pathological features of AD by middle age led to the discovery of the APP gene on chromosome 21. Simultaneously, a locus segregating with a minority of early-onset familial AD kindreds was mapped to this chromosome, in the same region as the APP gene.
Subsequently, several missense mutations within the APP gene that resulted in amino acid substitutions in APP were identified in these familial AD kindreds. Such mutations appear to alter the previously described proteolytic processing of APP, generating amyloidogenic forms of Aβ.
Skin fibroblasts from individuals carrying APP mutations produce increased Aβ 42/43. Increased plasma concentration of Aβ 42/43 is also seen in these patients, regardless of age, sex, or clinical status. Interestingly, some patients with sporadic AD may exhibit similar elevations of plasma Aβ 42/43.
Approximately 50-70% of early-onset autosomal-dominant AD cases appear to be associated with a locus (AD3) mapped by genetic linkage to the long arm of chromosome 14 (14q24.3). Numerous missense mutations have been identified on a strong candidate gene, called PS1.
At the same time, another autosomal dominant locus responsible for early-onset AD was localized to chromosome 1. Two mutations were identified on the candidate gene, designated PS2. The physiological role of presenilins and the pathogenic effects of their mutations are not yet well understood.
The gene encoding the cholesterol-carrying apolipoprotein E (APOE) on chromosome 19 has been linked to increased risk for AD, principally late-onset but also some early-onset cases. The gene is inherited as an autosomal codominant trait with 3 alleles. The APOE E2 allele, the least prevalent of the 3 common APOE alleles, is associated with the lowest risk of developing AD(27),with a lower rate of annual hippocampal atrophy and higher cerebrospinal fluid Aβ and lower phosphotau, suggesting less AD pathology (28).
The E3 allele confers intermediate risk of developing AD, with less risk than the E4 allele. The E3 allele, which is more common than the E2 allele, may protect tau from hyperphosphorylation, and the E2 allele’s effect on tau phosphorylation is complex.
APOE E4 gene “dose” is correlated with increased risk and earlier onset of AD(29).Persons with 2 copies of the APOE E4 allele (4/4 genotype) have a significantly greater risk of developing AD than persons with other APOE subtypes. Mean age at onset is significantly lower in the presence of 2 APOE E4 copies. A collaborative study has suggested that APOE E4 exerts its maximal effect before the age of 70 years.
Many APOE E4 carriers do not develop AD, and many patients with AD do not have this allele. Therefore, the presence of an APOE E4 allele does not secure the diagnosis of AD, but instead, the APOE E4 allele acts as a biological risk factor for the disease, especially in those younger than 70 years.
The worldwide population and especially the wesearn and U.S. population is getting older, and as it ages, AD is becoming an increasingly bigger concern. Within the next 50 years, the incidence of Alzheimer\'sis expected to quadruple, affecting one in 45 Americans.
Today, there is still no cure for Alzheimer\'s. People with the disease progressively lose memory and the ability to function as Alzheimer\'s advances.
Several different types of medications are used to treat the memory loss, behavior changes, sleep problems, and other symptoms of AD. These medications won\'t stop the disease, but they can slow down the symptoms for a few months or even years. All of these medications can have side effects, which can be even more pronounced in the elderly.
Early diagnosis and treatment allows AD patients to maintain the highest levels of cognitive and functional ability possible.
Today main pharmacological treatment of AD is Cholinesterase inhibitors (ChEIs), andmental exercises are used in an attempt to prevent or delay the deterioration of cognition in patients with AD. Here, we will try to shed lights on the important available pharmacological treatments and complementary therapies such as Herbal medicine utilized on the treatment of AD:
Numerous lines of evidence suggest that cholinergic systems that modulate information processing in the hippocampus and neocortex are impaired early in the course of AD. These observations have suggested that some of the clinical manifestations of AD are due to loss of cholinergic innervation to the cerebral cortex.
Centrally acting ChEIs prevent the breakdown of acetylcholine. Four such agents have been approved by the FDA for the treatment of AD, as follows:
Tacrine
Donepezil (Aricept, Aricept ODT)
Rivastigmine (Exelon, Exelon Patch)
Galantamine (Razadyne, Razadyne ER)
Of note, tacrine has potential hepatotoxicity and hence requires frequent blood monitoring. Since the other ChEIs have become available, tacrine has rarely been prescribed.
All ChEIs have shown modest benefit on measures of cognitive function and activities of daily living. Patients on ChEIs have shown slower declines on cognitive and functional measures than patients on placebo. However, ChEIs do not address the underlying cause of the degeneration of cholinergic neurons, which continues during the disease. The ChEIs may also alleviate the noncognitive manifestations of AD, such as agitation, wandering, and socially inappropriate behavior(30).
Although the usefulness of ChEIs was originally expected to be limited to the early and intermediate stages of AD (because the cholinergic deficit becomes more severe later in disease and because fewer intact cholinergic synapses are present), they are also helpful in advanced disease(31).ChEIs are also helpful in patients with AD with concomitant infarcts and in patients with dementia with Lewy bodies. Frequently, AD and dementia with Lewy bodies occur in the same patient; this is sometimes called the Lewy body variant of AD.
The ChEIs share a common profile of adverse effects, the most frequent of which are nausea, vomiting, diarrhea, and dizziness. These are typically dose related and can be mitigated with slow up-titration to the desired maintenance dose. In addition, gastrointestinal side effects may be reduced by using the transdermal patch rather than the oral form of the drug. As antimuscarinic drugs are used for the treatment of incontinence, logically, ChEIs might exacerbate incontinence. One brief report has supported this hypothesis(32).
ChEIs prescribed to treat dementia can provoke symptomatic bradycardia and syncope and precipitate fall-related injuries, including hip fracture. In a study of older adults with dementia who were taking cholinesterase inhibitors, hospital visits for syncope were found to be more frequent in patients receiving ChEIs than in control patients (31.5 vs 18.6 events per 1000 person-years(33).Other syncope-related events, including hospital visits for bradycardia, permanent pacemaker insertion, and hip fracture, were also found to be more common in patients receiving cholinesterase inhibitors. ChEI use in older adults with dementia is associated with increased risk of syncope-related events; these risks must be weighed against the benefits of taking ChEIs(33).
Anecdotal reports exist of acute cognitive and behavioral decline associated with the abrupt termination of ChEIs. In several of these cases, restarting the ChEI did not lead to substantial improvement. These reports have implications concerning the best practice when switching a patient from one ChEI to another in this class. Reasons for switching might include undesirable side effects or an apparent lack of efficacy. Nonetheless, no published data are available to help clinicians know when it would be helpful to switch to another ChEI.
The common practice of tapering a patient off one CNS-active medication before starting a new one should not be followed when changing ChEIs. For example, a patient who was taking 10 mg of donepezil should be started the next day on galantamine at a dose of at least 8 mg/day and possibly 16 mg/day. No current evidence supports the use of more than 1 ChEI at a time. Centrally acting anticholinergic medications should be avoided.
It is not uncommon for patients to receive both ChEIs and anticholinergic agents, which counteract each other. Medications with anticholinergic effects, such as diphenhydramine, tricyclic antidepressants (eg, amitriptyline, nortriptyline), and oxybutynin (commonly used for bladder spasticity), can cause cognitive dysfunction. Therefore, a careful listing of the patient’s medications is important so that the physician can reduce the doses of, or ideally eliminate, all centrally acting anticholinergic agents.
Cholinesterase inhibitors (ChEIs) are used to palliate cholinergic deficiency. All 4 currently approved ChEIs (ie, tacrine, donepezil, rivastigmine, galantamine) inhibit acetylcholinesterase (AChE) at the synapse (specific cholinesterase). Tacrine was the first agent that was approved for AD, but because of its potential to cause hepatotoxicity, it is now rarely used.
Tacrine and rivastigmine also inhibit butyrylcholinesterase (BuChE). Although BuChE levels may be increased in AD, it is not clear that rivastigmine and tacrine have greater clinical efficacy than donepezil and galantamine.
Galantamine has a different second mechanism of action; it is also a presynaptic nicotinic modulator. No data exist to indicate that this second mechanism is of clinical importance.
Donepezil is indicated for the treatment of dementia of the Alzheimer type. Donepezil has shown efficacy in patients with mild to moderate AD, as well as moderate to severe AD. It selectively inhibits acetylcholinesterase, the enzyme responsible for the destruction of acetylcholine, and improves the availability of acetylcholine. Donepezil\'s long half-life provides a long duration of drug availability for binding at the receptor sites. There is no evidence to suggest that the underlying disease process of dementia is affected by administration of donepezil.
Dosing recommendations for mild to moderate AD are 5-10 mg given once daily. Patients with moderate to severe AD can be given 10 or 23 mg once daily.
Rivastigmine is indicated for the treatment of mild to moderate dementia of the Alzheimer type. Initial dosing recommendations are 1.5 mg given twice daily, with a maximum dose of 12 mg/day. Rivastigmine is a potent, selective inhibitor of brain AChE and BChE. Rivastigmine is considered a pseudo-irreversible inhibitor of AChE.
While the precise mechanism of rivastigmine\'s action is unknown, it is postulated to exert its therapeutic effect by enhancing cholinergic function. This is accomplished by increasing the concentration of acetylcholine through reversible inhibition of its hydrolysis by cholinesterase.
Galantamine is indicated for the treatment of mild to moderate dementia of the Alzheimer type. It enhances central cholinergic function and likely inhibits AChE. There is no evidence that galantamine alters the course of the underlying dementing process. The dosing recommendation for the immediate-release formulation is 4 mg twice daily. The extended-release formulation is given at a dose of 8 mg once daily. The maintenance dose after dose titration is 16-24 mg/day.
Tacrine was the first agent approved for AD. It is indicated in patients with mild to moderate dementia. It is associated with hepatotoxicity and is no longer commonly used. Tacrine inhibits AChE, the enzyme responsible for the destruction of acetylcholine, and improves the availability of acetylcholine. Tacrine inhibits both AChE and BChE; however, it is more selective for AChE.
The only drug in the N -methyl-D-aspartate (NMDA) antagonist class that is approved by the US Food and Drug Administration is memantine. This agent may be used alone or in combination with AChE inhibitors.
Namenda is approved for the treatment of moderate to severe dementia in patients with AD. The initial dose for the immediate-release formulation is 5 mg once daily, and it can be titrated to a maximum dose of 20 mg/day. The initial dose for the extended-release formulation is 7 mg once daily, and it can be titrated to a maximum dose of 28 mg/day. Side effects include dizziness, confusion, headache, constipation, nausea, and agitation.
The partial N -methyl-D-aspartate (NMDA) antagonist memantine (Namenda, Namenda XR) is believed to work by improving the signal-to-noise ratio of glutamatergic transmission at the NMDA receptor. Blockade of NMDA receptors by memantine is thought to slow the intracellular calcium accumulation and thereby help prevent further nerve damage. This agent is approved by the FDA for treating moderate and severe AD.
Several studies have demonstrated that memantine can be safely used in combination with ChEIs. The combination of memantine with a ChEI has been shown to significantly delay institutionalization in AD patients(34).Studies suggest that the use of memantine with donepezil affects cognition in moderate to severe AD(35) but not in mild to moderate AD(36, 37).Dizziness, headache, and confusion are some of the most common side effects of memantine.
A variety of behavioral and pharmacologic interventions can alleviate clinical manifestations of AD, such as anxiety, agitation, depression, and psychotic behavior. The effectiveness of such interventions ranges from modest and temporary to excellent and prolonged. No specific agent or dose of individual agents is unanimously accepted for the wide array of clinical manifestations. At present, the FDA has not approved any psychotropic agent for the treatment of AD.
Antidepressants, such as citalopram (Celexa), fluoxetine (Prozac), paroxetine (Paxil), and sertraline (Zoloft) treat irritability and mood.
Anxiolytics, such as lorazepam (Ativan) and oxazepam (Serax) treat anxiety and restlessness.
Antipsychotic medications, such as aripiprazole (abilify), clozapine (Clozaril), haloperidol (Haldol), and olanzapine (Zyprexa) treat hallucinations, delusions, agitation, and aggression.
Antidepressants have an important role in the treatment of mood disorders in patients with AD. Depression is observed in more than 30% of patients with AD, and it frequently begins before AD is clinically diagnosed. Therefore, palliation of this frequent comorbid condition may improve cognitive and noncognitive performance.
Nyth found citalopram to be beneficial in mood and other neuropsychiatric symptoms in patients in the moderate stage of AD(38).Because citalopram can cause dose-dependent increases in the QT interval, the FDA recommends using a maximum of 40 mg a day and considering 20 mg a day in the elderly(39).
Weintraub et al(40)and Petracca et al(41)found sertraline and fluoxetine to have no short- or long-term benefit in mood over placebo. Similarly, Banerjee et al found that treatment of depression with sertraline or mirtazapine provided no benefit compared with placebo and increased the risk of adverse events(42).
Other mood modulators, such as valproic acid, can be helpful for the treatment of disruptive behaviors and outbursts of anger, which patients with moderately advanced or advanced stages of AD may have.
Results of several studies indicate that anticonvulsants (eg, gabapentin, valproic acid) may have a role in the treatment of behavioral problems in patients with Alzheimer disease. However, a trial of 313 patients with moderate AD found that 24 months of treatment with valproate did not delay emergence of agitation or psychosis, did not slow cognitive or functional decline, and was associated A variety of experimental therapies have been proposed for AD. These include antiamyloid therapy, reversal of excess tau phosphorylation, estrogen therapy, vitamin E therapy, and free-radical scavenger therapy. Studies of these therapies have yielded mostly disappointing results.
In the past 10 years, numerous antiamyloid therapy studies have been conducted to decrease toxic amyloid fragments in the brain, including studies of the following:
Vaccination with amyloid species
Administration of monoclonal antiamyloid antibodies
Administration of intravenous immune globulin that may contain amyloid-binding antibodies
Selective amyloid-lowering agents
Chelating agents to prevent amyloid polymerization
Brain shunting to improve removal of amyloid
Beta-secretase inhibitors to prevent generation of the A-beta amyloid fragment
To date, no phase III study of antiamyloid therapies has shown a combination of acceptable efficacy and side effects.
Growing awareness that tau is a central player in AD pathogenesis has suggested that this protein may offer an avenue for therapeutic intervention. Studies are ongoing with agents that may prevent or reverse excess tau phosphorylation and thereby diminish formation of neurofibrillary tangles(43).
Free-radical scavenger therapy has also attracted attention, because excess levels of free radicals in the brain are neurotoxic. Nonetheless, no study has demonstrated efficacy of free-radical scavengers in the treatment of the cognitive symptoms of AD.
Various studies indicate that oxidative stress may be a part of the pathogenesis of AD. In the AD, high-dose vitamin E (2000 units per day of alpha-tocopherol) for 2 years slowed the progression of disease in patients with moderate AD(44).This benefit presumably resulted from the antioxidant effects of vitamin E.
Subsequent studies, however, have suggested that vitamin E supplementation may increase risk of adverse cardiovascular outcomes. Therefore, use of vitamin E is not currently recommended.
Transcranial magnetic stimulation (TMS) has been used to identify therapeutic targets in AD and to monitor the effects of pharmacologic agents, and both TMS and transcranial direct current stimulation are being explored for a possible therapeutic role in AD. However, evidence of therapeutic benefit from these modalities is highly preliminary(45).
In all cultures, the origins of herbal medicine are lost in the mists of time. There is little doubt that humans used herbs for healing well before anything could be written about them. At some point in an advancing culture, written documents become the repository for knowledge that had been passed on from one generation to the next. Among the earliest such documents are those describing the religious beliefs of the people and those describing the medical practices. Medical foods are dietary supplements intended to compensate specific nutritional problems caused by a disease or condition.
Effective pharmacological drugs for treating AD are still to be discovered. Current western pharmacological approaches against neurodegeneration in dementia develop symptom-relieving and disease-modifying drugs. Current integrative and holistic approaches of Chinese medicine to discovering drugs for neurodegeneration in dementia include (1) single molecules from the herbs, (2) standardized extracts from a single herb, and (3) herbal formula with definite composition. At present, acetylcholinesterase inhibitors (AChEI) are the first group of drugs approved by the FDA to treat mild to moderate Alzheimer\'s disease. Most of these drugs such as huperzine and galanthamine are originally isolated from plants. However, AChE inhibitors have limited success as they only improve memory in mild dementia but cannot stop the process of neurodegeneration; while memantine possesses neuroprotective effects only with a little ability in memory enhancement. There has been a major rush among neuroscience research institutions and pharmaceutical firms worldwide to search for safer and more effective therapeutic agents for AD.
However, mounting evidence obtained in vitro and in vivo, suggests that various traditionally used plants significantly affect key metabolic alterations culminating in AD-typical neurodegeneration.
Beside synthetic drugs, a variety of AD related medicine originates from traditionally used plants. In this respect, Ginkgo biloba and galantamine represent the most famous cases.
Indeed, the majority of recent reports on plants with traditional uses and activities relevant for AD originate from the traditional Chinese and Oriental Medicine, as well as from Kampo Ayurveda and Mediterranean traditional knowledge.
Originally, Ginkgo biloba (Coniferae) has been traditionally used for respiratory disorders in China and to improve memory loss associated with blood circulation abnormalities in Iran. This herb has been subjected to numerous investigations regarding its potential in cognitive disorders. Standardized extracts, particularly EGb 761, derived from the plants’ leaves are successfully used as herbal drug for the improvement of cognitive and memory impairment. EGb 761 represents a prototype of plant extracts for attenuating Central Nervous System disorders, due to the fact that both flavonoids and terpenic lactones, which are partly also present in numerous other plant extracts, have been identified as the active principles in Ginkgo extracts as well as the ample experimental evidence on EGb 761’s protective efficiency in vitro and in vivo. The potential of EGb 761 to attenuate the cytotoxic effects of Alzheimer\'s related neurotoxic amyloid peptides when added to the culture medium was demonstrated not only in neuronal-like cell lines but also primary neurons, though with different efficiency. The impact of Ginkgo extract has been largely attributed to its antioxidant activity. The effects of oxidative stress were reduced in lymphocytes and brain cells derived of EGb 761-treated AD-transgenic and non-transgenic mice. Recent data, however, indicate that Ginkgo biloba extract-761 (Gbe-761) also affects the production of neurotoxic beta-amyloid peptides (Aβ), for example, by up-regulating α-secretase activity both in cells and animals.
We speculate that metabolic alterations, mediated by vasodilatory and tropic effects of EGb 761, might be responsible for this finding.
Ginkgo biloba extract (Gbe) and two ingredients, bilobalide and ginkgolide B, are presented to the CSWG as part of a review of botanicals being used as dietary supplements in the United States. (1 of 3 adults in the United States is now taking dietary supplements). Sweeping deregulation of botanicals now permits GBE to be sold as a dietary supplement to a willing public eager to "improve brain functioning" or "promote radical scavenging activity.".
Gbe is a well defined product, and it or its active ingredients, the ginkgolides, especially ginkgolide B, and bilobalide, has clearly demonstrated biological activity. It can be consumed in rather large doses for an extended period of time. Under the Dietary Supplement Health and Education Act of 1994, Gbe can be sold legally if it is not labeled or accompanied by any therapeutic or health claims. Herbal remedies can be labeled with descriptions of their role in affecting physiological structure or function, but must be labeled with a disclaimer that the product has not been evaluated by the FDA for cure, prevention, or treatment of a disease.
The extract utilized in medicine is standardized in a multi-step procedure designed to concentrate the desired active principles from the plant. These extracts contain approximately 24% flavone glycosides (primarily composed of quercetin, kaempferol, and isorhamnetin) and 6% terpene lactones (2.8-3.4% ginkgolides A, B, and C, and 2.6-3.2% bilobalide). Ginkgolide B accounts for Aβout 0.8% of the total extract and bilobalide accounts for Aβout 3% of the extract. Other constituents include proanthocyanadins, glucose, rhamnose, organic acids, D-glucaric acid and ginkgolic acid (at most 5 ppm ginkgolic acids). Much of the curative properties of Gbe are due to the activities of these flavonoids.
Human Exposure: There is potential for ingestion of Gbe to a widespread consumer population, since this product is readily available without prescription at a cost highly competitive with prescription medications. The recommended dose ofGbe is 120 to 160 mg daily for persons with intermittent claudication and 240 mg daily for cerebrovascular insufficiency, early stage Alzheimer\'s disease, resistant depression, and impotence.
Galantamine is an alkaloid known form several members of the Amaryllis family (Amaryllidaceae), and the idea for developing a medical product for AD from these species seems to be based on the local use of one of these species in a remote part of Europe. It has become an important therapeutic options used to slow down the process of neurological degeneration in AD. Its development from little known observational studies in the Caucasus Mountains (Southern Russia), to the use of this drug in Eastern European countries (esp. Bulgaria) in the treatment of poliomyelitis and ultimately to the recent introduction onto Western markets in the treatment of AD. Galantamine was first isolated from snowdrop (Galanthus spp.) but today it is obtained from Narcissus spp. and Leucojum spp. as well as synthetically. According to unconfirmed reports, in the 1950s, a Bulgarian pharmacologist noticed the use of the common snowdrop growing in the wild by people who were rubbing it on their foreheads to ease nerve pain. Also, some of the earlier publications indicate the extensive use of snowdrop in Eastern Europe, such as Romania, Ukraine, the Balkan Peninsula, and in the Eastern Mediterranean countries. However, Mashkovsky and Kruglikova-Lvov published the first work that establishes the acetylcholine esterase inhibiting properties of galantamine isolated from Galanthusworonowii. Poliomyelitis was one of the first indications for galantamine, especially in the Eastern and Central European, since the compound enhances nerve impulse transmission at the synapse. Studies indicating blood–brain barrier penetration of the alkaloid pioneer the development of CNS-related indications. Based on the knowledge of galantamine in both the peripheral and central nervous system, many countries in Eastern Europe used it as an acknowledged treatment in Myasthenia gravis and muscular dystrophy, residual poliomyelitis paralysis symptoms, trigeminal neurologica, and other forms of neuritis. A crucial step for the success of galantamine as a medicine against AD was based on the synthesis developed in the mid-1990s. The scientific rationale for using cholinesterase inhibitors in the management of AD is based on the cholinergic hypothesis. Impairment of the central cholinergic system represents one hallmark of AD, which is characterized by loss of cholinergic neurons in the forebrain and a marked decrease in the activity of choline acetyltransferase. Overall, galantamine represents an example for the successful ethnobotany-driven development of a natural product into a clinically important drug.
In the last years, focus on AD drug discovery is shifting away from AChE inhibitors and a large number of other targets are currently being explored.
Ginseng products are popularly referred to as “adaptogen,” which connotes that these products purportedly increase to physical, chemical, and biological stress and builds up general vitality, including physical and mental capacity for work. Panax ginseng roots are traditionally taken orally as adaptogens, aphrodisiacs, nourishing stimulants, and in the treatment of sexual dysfunction in men. The fresh root, can be directly chewed, or soaked in various wines for a period of time before drinking or chewing. Ginseng is most often availble either in whole or sliced dried form. However, usually ginseng is used at subclinical doses for a short period and as such; it does not produce measurable medicinal effects. Panax notoginseng is widely used in traditional Chinese medicine (TCM) to improve learning and memory. Moreover, protective actions against cerebral ischemia, beneficial effects on the cardiovascular system, and haemostatic, antioxidant, hypolipidemic, hepatoprotective, renoprotective, and estrogen-like activities.
The delicate white flower and the Bulb of Galanthus woronowii
About this species
A snowdrop native to Turkey, Russia and Georgia, Galanthus woronowii was named in honour of the Russian botanist and plant collector Georg Woronow (1874–1931). It is popular in cultivation in Europe, and valued for its wide, green, shiny leaves, whicht provide good ground-cover and contrast with the leaves of the commonly grown snowdrop G. nivalis.Galanthus woronowii occurs from northeastern Turkey to the western and central Caucasus (Georgia and Russia). It is primarily found around the eastern Black Sea coast in the ancient provinces of Colchis and Lazistan (the Euxine Province). It occurs at 70–1,400 metres above sea level, in stony and rocky spots (on calcareous rocks, in gorges, on stony slopes and on scree), on river banks, in scrub and at forest margins, and sometimes as an epiphyte or on fallen tree trunks, rooting in moss(46-48).
The scientific literature indicates that cannabinoid therapy may provide symptomatic relief to patients afflicted with AD while also moderating the progression of the disease.
The intracerebroventricular administration of the synthetic cannabinoid prevented cognitive impairment and decreased neurotoxicity in rats injected with amyloid-beta peptide(49).
Additional synthetic cannabinoids were also found to reduce the inflammation associated with AD in human brain tissue in culture. In the results of Ramirez et al. 2005 indicate that cannabinoids succeed in preventing the neurodegenerative process occurring in the AD,"(50). Follow up studies by investigators demonstrated that the administration of the nonpsychotropic plant cannabinoid cannabidiol (CBD) also mitigated memory loss in a mouse model of the disease(51).Investigators at the Scripps Research Institute in California in 2006 reported that THC (Tetrahydrocannabinol (THC) is the active chemical in cannabis and is one of the oldest hallucinogenic drugs known) inhibits the enzyme responsible for the aggregation of amyloid plaque — the primary marker for AD — in a manner "considerably superior" to approved Alzheimer\'s drugs such as donepezil and tacrine. The investigators’s results provide a mechanism whereby the THC molecule can directly impact AD pathology," researchers concluded. "THC and its analogues may provide an improved therapeutic option for AD by simultaneously treating both the symptoms and the progression of the disease(52).
More recently, investigators at Ohio State University, reported that older rats administered daily doses of synthetic cannabinoid [WIN 55,212-2 (C27H26N2O3.CH3SO3H)] for a period of three weeks performed significantly better than non-treated controls on a water-maze memory test. Marchalant et al. 2007, reported that rats treated with the compound experienced a 50 percent improvement in memory and a 40 to 50 percent reduction in inflammation compared to controls(53).
Previous preclinical studies have demonstrated that cannabinoids can prevent cell death by anti-oxidation. Some experts believe that cannabinoids\' neuroprotective properties could also play a role in moderating AD (54). Campbell and Gowran. 2007, reported that "Cannabinoids offer a multi-faceted approach for the treatment of AD by providing neuroprotection and reducing neuroinflammation, whilst simultaneously supporting the brain\'s intrinsic repair mechanisms by augmenting neurotrophin expression and enhancing neurogenesis. Manipulation of the cannabinoid pathway offers a pharmacological approach for the treatment of AD that may be efficacious than current treatment regimens (55).
In addition to potentially modifying the progression of AD, clinical trials also indicate that cannabinoid therapy can reduce agitation and stimulate weight gain in patients with the disease. Most recently, investigators at Berlin University (2006), reported that the daily administration of 2.5 mg of synthetic THC over a two-week period reduced nocturnal motor activity and agitation in AD patients in an open-label pilot study(56).
Clinical data presented at the 2003 annual meeting of the International Psychogeriatric Association previously reported that the oral administration of up to 10 mg of synthetic THC reduced agitation and stimulated weight gain in late-stage Alzheimer\'s patients in an open-label clinical trial(57).Improved weight gain and a decrease in negative feelings among AD patients administered cannabinoids were previously reported by Volicer et al. 1997 (58).
Curcumin (diferuloylmethane), a polyphenol compound responsible for the bright yellow color of turmeric, is believed to be the principal pharmacological agent. It is prepared from the roots of Curcuma longa (59). In addition to curcumin, turmeric contains the curcuminoids atlantone, bisdemethoxycurcumin, demethoxycurcumin, diaryl heptanoids, and tumerone. Turmeric also contains sesquiterpenoids and the constituent ar-tumerone(60). Other constituents include sugars, resins, proteins, vitamins, and minerals (including iron and potassium).
Due to various effects of curcumin, such as decreased Beta-amyloid plaques, delayed degradation of neurons, metal-chelation, anti-inflammatory, antioxidant and decreased microglia formation, the overall memory in patients with AD has improved(61).
Researchers found that curcumin may help the macrophages to clear the amyloid plaques found in Alzheimer\'s disease. Macrophages play an important role in the immune system. They help the body to fight against foreign proteins and then effectively clear them. Curcumin was treated with macrophages in blood taken from nine volunteers: six AD patients and three healthy controls. Beta amyloid was then introduced. The AD patients, whose macrophages were treated with curcumin, when compared with patients whose macrophages were not treated with curcumin, showed an improved uptake and ingestion of the plaques. Thus, curcumin may support the immune system to clear the amyloid protein(61,62).
Curcumin (diferuloylmethane).
In addition, Curcumin has powerful antioxidant and anti-inflammatory properties; according to the scientists, these properties believe help ease Alzheimer\'s symptoms caused by oxidation and inflammation(63).It is well known that Aβinduced oxidative stress which is a well-established pathway of neuronal cell death in AD (64).Three curcuminoids from turmeric (Curcuma longa L.), including curcumin, demethoxycurcumin, and bisdemethoxycurcumin, were found to protect PC12 rat pheochromocytoma and normal human umbilical vein endothelial (HUVEC) cells from beta A(1-42) insult. These compounds may protect the cells from beta A(1-42) insult through antioxidant pathways. Other animal studies of AD also suggest that curcumin may reduce levels of amyloid and oxidized proteins and prevent cognitive deficits (65). One alternative mechanism of action for these effects suggested by Baum et al (65), is metal chelation, which may reduce amyloid aggregation or oxidative neurotoxicity. Since curcumin more readily binds the redox-active metals and than the redox-inactive, curcumin might exert a net protective effect against beta toxicity or might suppress inflammatory damage by preventing metal induction of NF-kappaB. Mouse studies that evaluated the effects of dietary curcumin on inflammation, oxidative damage, and plaque pathology demonstrated that both low and high doses of curcumin significantly lowered oxidized proteins and interleukin-1beta, which is a proinflammatory cytokine elevated in the brains of these mice (66). Low-dose but not high-dose curcumin treatment has been shown to reduce the astrocytic marker GFAP and significantly decrease insoluble (Aβ), soluble Abeta, and plaque burden by 43-50%. However, levels of amyloid precursor (APP) in the membrane fraction were not reduced.
Huperzine A, shows promise for enhancing memory and protecting cognitive functions and may improve cognition in AD.
Huperzine-Ais a new supplement derived from an ancient traditional Chinese herbal medicine that offers hope to those suffering from AD and other age-related mental conditions(67).
Existing evidence suggests that patients with AD who have takenHuperzine Ahave improved general cognitive function, global clinical status, functional performance and reduced behavioural disturbance compared to patients taking placebos.In addition to benefiting patients suffering from Alzheimer’s, Huperzine’s memory-enhancing properties suggest that it may be an effective agent for improving memory and learning in healthy humans as well.Huperzine A is a natural compound derived from an ancient Chinese remedy, Qian Ceng Ta. This traditional herbal medicine was prepared fromHuperzia serrata, a clubmoss that grows on the ground in damp forests and rock crevices. Brewed as an herbal tea, Qian Ceng Ta has been used in China to treat fever, inflammation, and irregular menstruation, and has been used as a diuretic.
In the late 1980’s, researchers in China discovered that a purified alkaloid extracted from Huperzia, Huperzine A, was a potent, reversible inhibitor of acetylcholinesterase (AChE). Huperzine A readily crosses the blood-brain barrier to prevent acetylcholinesterase (AchE) from destroying acetylcholine. Indeed, part of the damage involved in AD is a loss of acetylcholine-containing neurons in the basal forebrain. This suggests that drugs that could inhibit cholinesterase, which breaks down acetylcholine, could increase the ability of remaining cholinergic neurons. Scientists know that Huperzine A can block acetyl cholinesterase and that it can work both in the peripheral and central nervous systems (68).
Scientists had previously learned that AchE inhibitors such as tacrine and donepezil worked by sliding into the AChE molecule to "jam up" its molecular machinery and impair its ability to degrade acetylcholine(69,70).
In summary, Huperzine A is rapidly absorbed when taken orally, and possesses a very slow rate of dissociation from the enzyme and a longer duration of action. Studies in rodents show that AChE remains inhibited by 33% after 6 hours. Huperzine A has a strong specificity for AChE, and is exceptionally well-suited to its new role, fitting into the active sites of acetylcholinesterase much like a key slipping into a lock. "Hup-A appears to bind more tightly and specifically to acetylcholinesterase than the other AChE inhibitors". This makes it a promising agent for treating various forms of dementia including AD(71-73).
Aromatherapy uses essential oils from plants, either applied in a lotion and absorbed by the skin or inhaled and absorbed into the lungs and nasal passages, to improve physical and mental health. Aromatic oils from plants have been used for over 5,000 years: To protect against stroke and other neurodegenerative diseases such as Alzheimer’s and Lou Gehrig’s disease. Rosemary contains carnosic acid, a powerful antioxidant, which helps to fight off free radicals in the brain. Carnosic acid stimulates the synthesis of Nerve Growth Factor (NGF) which may help prevent nerve cell deterioration in Alzheimer’s (74).Rosmarinus officinalis is a woody evergreen native to the Mediterranean and a universal symbol of remembrance used to honor those who have passed on. The tradition of laying sprigs of rosemary across the coffin or upon a tombstone dates back to ancient Egypt. This custom continued well into the medieval period and beyond. For instance, Shakespeare’s Juliet was bestowed with rosemary upon her untimely death. In Australia, where Anzac Day is celebrated in remembrance of one’s family ancestors, it is still customary to wear sprigs of rosemary today. Rosemary is also associated with enhancing memory and recall. Shakespeare\'s Ophelia petitions Hamlet with, "There\'s rosemary, that\'s for remembrance, pray you love, remember." Scholars of ancient Greece wore wreaths of rosemary about the brow to help improve recall while taking exams. This reputation has earned the herb a place among traditional wedding herbs used to grace the bride’s bouquet, headpiece, and dress. Wedding guests are also given sprigs of rosemary to wear to help them remember the occasion. It was also once common to add rosemary to the couple’s wine to help them remember their sacred vows to each other. At one time, it was customary for the bride and groom to plant rosemary near the marital threshold on their day of matrimony. However, the old saying "where rosemary flourished, the woman ruled," prompted some husbands to pluck the plant from the ground lest anyone should think he wasn’t fit to rule the roost. Perhaps this is why the practice fell out of favor by the late 15th century. Rosemary takes its name from the Latin ros maris, which means “dew of the sea.” This is likely in reference to the herb’s preference for growing along the seashore of its indigenous domain. The Spanish began to call the plant Romero because they believed that another Mediterranean native took refuge beneath a large rosemary bush to shelter herself and her young son as they fled to Egypt to escape Herod. In honor of this brave, young woman, the plant came to be known as Rose of Mary, which was eventually shortened to the modern name familiar to us today.
Medicinally, rosemary has a wealth of uses, both old and new. In one of the earliest herbals known to be printed in England, Rycharde Banckes recommended that one gather leaves of rosemary and “…boyle them in fayre water and drinke that water for it is much worthe against all manner of evils in the body." Indeed, rosemary was once thought to be a cure for poor digestion, migraine, joint disorders, and muscle aches. In fact, Queen Elizabeth of Hungary was reputedly cured of semi-paralysis when she sipped a concoction of rosemary to ease her painful joints. Hence, this formula came to be known as the infamous Hungary Water. Today, rosemary is recognized as possessing several medicinal properties. For one thing, the plant contains salicylic acid, the forerunner of aspirin. This may explain why massaging the oil of rosemary into joints effectively eases arthritic or rheumatic pain. It also contains antibacterial and antimicrobial agents, and is used by modern herbalists to treat a variety of skin disorders, including dandruff. Rosemary is also being studied for its potential anti-cancer effects since initial studies indicate that its compounds inhibit carcinogenic chemicals from binding to cellular DNA. Rosemary may also become useful in preventing and treating Alzheimer\'s disease in the near future. Researchers have discovered that certain phytochemicals in the herb prevent the degradation of acetylcholine, an important brain chemical needed for normal neurotransmission. A deficiency of this chemical is commonly seen in Alzheimer\'s patients.
The majority of recent reports on plants with traditional uses and activities relevant for AD originate from the traditional Chinese and Oriental Medicine, as well as from Kampo Ayurveda and Mediterranean traditional knowledge. They are many plants useful for the treatment of Neurodegenerative diseases and they are many athor still to be discovered.
Global installed wind power capacity has been tremendously increased over the last 15 years from 23,900 MW in 2001 to 486,790 MW in 2016 [1]. More than 314,000 WTs are now operating around the world, which accounts for more than 4.3% of 2015 global electricity demand. Yet it is still far from ambitious targets, e.g., increasing wind energy’s contribution to 20% of US electricity supply by 2030 [2]. To approach that, it is of critical importance to accurately evaluate the WT performance considering realistic environmental conditions.
\nThe most common factors that are considered when planning a wind farm include substantial wind resources, landowner and community support, feasible permitting, compatible land use, nearby access to electrical grid, appropriate site conditions for access during construction and operations, aviation compatibility, and favorable electricity market [3]. However, the influences of meteorological variables (e.g., pressure, temperature, and humidity) are often neglected which could cause inaccurate evaluation of WT performance. For example, a dry air assumption (i.e., constant air density) does not really consider the moisture changeability. Baskut et al. discussed the effects of several meteorological variables including air density, pressure difference, humidity, and ambient temperature on exergy efficiency and suggested that neglecting these meteorological variables while planning wind farms could cause important errors in energy calculations [3].
\nThe efficiency performance of a WT can be studied in two aspects, energy and exergy efficiencies. The former is calculated as the ratio of produced electricity to the total wind potential within the swept area of the rotor. Thus, only the kinetic energy of the air flow is considered in the energy efficiency calculation, while other meteorological variables such as pressure and temperature are often neglected. The latter considers the maximum useful work that can be obtained by a system interacting with an environment in thermodynamic equilibrium state [4]. The exergy efficiency along with availability and capacity factor of a small WT (rated power 1.5 kW) has been studied in Izmir, Turkey, to assess the WT system performance [5]. Sahin et al. developed an improved approach for the thermodynamic analysis of wind energy using energy and exergy, which provided a physical basis for understanding, refining, and predicting the wind energy variations [6]. According to [7], exergies are suggested as the most appropriate link between the second law of thermodynamics and the environmental impact, in part because it measures the deviation between the states of the system and the environment.
\nThis brief précis thus illustrates the importance of energy and exergy analyses for wind energy systems considering meteorological variables and provides a motivation for the thermodynamic analysis conducted herein. The chapter presents the methods and results of thermodynamic analysis of a 1.5 MW WT, which is assumed to be deployed in the northeastern United States, experiencing meteorological reanalysis data retrieved from the NASA’s MERRA-2 data set. Matlab scripts are developed to calculate the energy and exergy efficiencies using the MERRA-2 data set. Section 2 provides the fundamental theory of thermodynamic analysis, particularly in derivations of energy and exergy efficiencies. The studied site, meteorological data, and the selected WT are explained in Section 3, which is followed by results and discussion in Section 4. Concluding remarks are provided in Section 5.
\nA WT converts kinetic energy from air flow to electrical energy through subassemblies including rotor blades, drivetrain, generator, and electronic control systems, as well as other auxiliary components. As the kinetic energy is extracted, the air flow that passes through the turbine rotor must slow down. Assuming there is a boundary surface that contains the affected air flow inside, a long stream tube extended far from the upstream and to the downstream with varied cross sections is often used to study the thermodynamics of horizontal-axis WTs [6, 7] (Figure 1). The wind speed, pressure, and temperature at the inlet of the stream tube are represented by V1, P1, and T1, respectively. Their counterparts at the outlet are V2, P2, and T2 and at the rotor are Vave, Pave, and Tave. Here a constant specific humidity ratio is assumed in the stream tube for a short-period time (e.g., 10 minutes or 1 hour). The following sections explain the theory of WT thermodynamics in two aspects, energy analysis and exergy analysis, which both apply the meteorological variables such as wind speed, air density, atmospheric pressure, temperature, and humidity. The use of energy and exergy efficiencies considering a comprehensive set of meteorological variables can enable us to accurately evaluate the efficiency performance of WTs.
\nA schematic plot of WT stream tube for thermodynamic analysis.
The energy analysis of WT systems stems from the air flow’s kinetic energy Ek that is calculated as
where m and V are the mass and speed of the air flow, respectively. The mass m can be further expressed as
where \n
where V1 and V2 are the wind speeds at the inlet and outlet, respectively, of the stream tube (Figure 1). The rate of momentum change is also equal to the resulting thrust force. Thus, the power absorbed by the WT is calculated as
where Vave is the average flow speed at rotor. On the other hand, the rate of kinetic energy change of the flow can be calculated as
\n\n
Based on the conservation of energy, Eqs. (4) and (5) should be equal which results in
\n\n
Hence, the retardation of the wind before the rotor\n
\n
Let \n
In order to obtain the maximum power, equate 0 to the differentiation of Eq. (8) with respect to a resulting in \n
the maximum power coefficient is calculated \n
Despite the simplicity of Eq. (9) when calculating power coefficient, the total input power in the denominator does not take account of the impacts from pressure, temperature, and humidity. Actually the air density changes as the ambient pressure, temperature, and humidity change, which can be expressed as
where ω (−) is the humidity ratio of air, gas constant Ra = 287.1 J/kg K, water vapor constant Rv = 461.5 J/kg K, and T is the absolute temperature (unit: K). In order to distinguish wind power P, the small letter p is used to represent the pressure (unit: Pa) in the humid air hereafter. Combining Eqs. (9) and (10), the power coefficient of a WT considering a comprehensive set of meteorological variables can be expressed as
\n\n
The above derivations provide the fundamentals of the theoretically available energy/power that a WT can extract from the air flow. However, various effects could have influence on the real power output, e.g., vortices shed from the blade tip and hub could significantly affect the rotor lift force and power output [11]. Power losses also occur during the energy transformation through rotor to mechanical shaft and to generator that converts angular kinetic energy to electrical energy. In addition, sustained high wind speeds could cause strong fatigue and extreme loads on WT systems without proper turbine control or safety protection. Thus, wind power is intended to be constrained, when the inflow wind speed is beyond a rated value (i.e., rated wind speed), through different strategies commonly including stall regulation, pitch regulation, and yaw control [12]. As a result, the output power Pout of a WT is corresponding to four operating stages: (1) zero power when the inflow wind speed is smaller than a cut-in wind speed, (2) exponentially increased power as the wind speed increases between the cut-in wind speed and the rated wind speed, (3) rated output power when the wind speed is between the rated wind speed and a cutout wind speed, and (4) zero power when the inflow wind speed is larger than the cutout wind speed (Figure 2).
\nA typical power curve of WTs with four operational stages I–IV.
In thermodynamics, the exergy of a system is defined as the maximum amount of useful work during a process that can bring the system into equilibrium with a reference environment [13]. Based on the second law of thermodynamics, exergy analysis is an alternative useful tool for analysis, evaluation, and design of many power and energy systems, e.g., renewable and traditional energy systems. The significant difference between energy and exergy analyses may be characterized as [6]:
In real irreversible process, exergy is always consumed; thus it is not subjected to a conservation law. In contrast, energy is neither created nor destroyed, but changing from one form to another, during a process. Thus, it is subjected to the conservation of energy law.
Although from a theoretical point of view exergy may be defined without a reference environment, it is often defined as a quantity relative to a specified reference environment and is equal to zero when it is in equilibrium with the reference environment.
The total exergy Ex of a flow with unit mass generally consists of four parts, which can be expressed as
where Exki, Expo, Exph, and Exch represent the kinetic, potential, physical, and chemical exergies, respectively. For thermodynamic analysis of WT systems, the potential exergy and chemical exergy are negligible in the total exergy. Thus, the total exergy for a WT can be reduced as
where the kinetic exergy is defined herein as the maximum possible available kinetic energy that the air flow can produce from a wind speed to a complete stop and the physical exergy includes the enthalpy and entropy changes related to the turbine operation. The physical exergy can be calculated as [6, 7].
where the first term and the second term on the right side of Eq. (14) are the enthalpy and entropy contributions, respectively. cp is the specific heat of the flow; T0, T1, T2, Tave are the reference temperature, inlet temperature, outlet temperate, and average temperature, respectively; P1 and P2 are the inlet pressure and outlet pressure, respectively (see Figure 1); and R is a constant related to the gas and water vapor constants. Ideally, temperature and pressure at both inlet and outlet are needed to calculate the physical exergy. However, it is cumbersome to measure the temperatures and pressures at both inlet and outlet for the WT stream tube in real applications, not to mention the situation when evaluating the wind energy resource and/or WT efficiency performance before deploying WTs. In addition, the meteorological variable humidity is not considered in Eq. (14). To handle this difficulty, other studies have provided another formula to calculate the physical exergy for wind energy [3, 5, 14, 15]:
where cp,a and cp,v are specific heat of air and water vapor, respectively; ω0 and ω are the humidity ratio of air at the reference state and at the current state, respectively; Ra and Rv are the gas constant and the water vapor constant, respectively; T0 and P0 are the reference temperature and atmospheric pressure, respectively; and T and p are measured temperature and pressure in this study.
\nThe efficiency for wind energy systems is explained by using energy efficiency η and exergy efficiency ψ. The former is obtained as the ratio of useful energy produced by a WT to the total input wind energy, while the latter is defined as the useful exergy created by a WT to the total exergy of the air flow. These general definitions of energy and exergy efficiencies have been introduced in several literature (e.g., [3, 5, 6, 7, 16]). However, the specific definitions of useful energy/exergy for wind energy systems are often not very clearly explained in the literature. In order to avoid confusion, here we define that both the useful energy and useful exergy are equal to the rate of electricity output Eout that a WT can produce under a wind speed (i.e., Eout equals to actual output power Pout). Thus, the energy efficiency and exergy efficiency are calculated as, respectively,
\n
where Wwind is the total input wind energy equal to the total kinetic energy given in Eq. (1) and Ex is the total exergy given in Eq. (13). By incorporating the meteorological variables and referring Eqs. (9)–(11), the energy efficiency can be expressed as
where Pout is the output power defined by the power curve (see Figure 2). By Eqs. (13), (15), and (17), the exergy efficiency can be reorganized as
\n\n
Eqs. (18) and (19) derive the energy and exergy efficiencies given various meteorological variables, which can offer a straightforward evaluation of WT efficiency performance in a perspective of energy and exergy before deploying WTs. Hence, it will be beneficial in wind resource evaluation, wind farm site selection, and new WT design.
\nUsing the presented thermodynamic analysis methods for wind energy systems, the wind energy potential is evaluated by investigating the energy and exergy efficiencies of a Goldwind 1.5 MW WT (model GW82/1500) [17], which is assumed to be deployed at Ithaca, New York, where 18-year reanalysis meteorological data are obtained from the Modern-Era Retrospective analysis for Research and Application, version 2 (MERRA-2), the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office [18]. This section explains the site; the meteorological data including wind speed, pressure, temperature, and humidity; and the characteristics of the WT used for thermodynamic analysis.
\nThe wind energy potential is evaluated at Ithaca, which has moderately complex terrain in a landscape dominated by patches of forest, crop fields, hills, waterfalls, and lakes in the Upstate New York (at approximately 42.44° N, 76.50° W, Figure 3). Experiencing a moderate continental climate, Ithaca has long, cold, and snowy winters and warm and humid summers with a dominance of westerly wind flows. The meteorological data are obtained from the MERRA-2 (a meteorological reanalysis data set created by NASA), which has a resolution of 0.5° latitude × 0.625° longitude [19]. Although it does not provide measured data in fields, the meteorological reanalysis is thought as a valuable tool to estimate the long-term variables, such as wind speed and temperature, for subsequent meteorological, climatological, energy, and environmental studies. By specifying the latitude and longitude of Ithaca, five types of meteorological data are retrieved from the MERRA-2 including 10-m eastward wind U10M (in ms−1), 10-m northward wind V10M (in ms−1), surface pressure PS (in Pa), 10-m air temperature T10M (in K), 10-m specific humidity QV10M (in kg kg−1), as well as their hourly time stamps from January 2000 to December 2017. The 10-m horizontal wind speed U is calculated as \n
Location of Ithaca, New York, where thermodynamic analysis of a 1.5 WM WT is investigated.
The expected wind energy that can be harvested at a location is highly related to the WT characteristics, e.g., power curve and the available wind resources. Herein a Goldwind 1.5 MW permanent magnet direct-drive (PMDD) WT (GW85/1500) is assumed to be deployed at Ithaca area and used for evaluating the WT’s energy and exergy efficiencies. Table 1 provides a summary of technical specifications of the WT. Since this study investigates the WT efficiency performance before real deployment, measured output power data are not available. It is assumed that the WT is performing perfectly according to its power curve, which consists of four operational stages (Figure 2). The WT starts to produce electricity at its cut-in wind speed of 3 ms−1, and the produced power is increased to the rated one of 1.5 MW at the rated wind speed of 10.3 ms−1. In order to mitigate the fatigue and structural loadings under sustained high wind, WT control systems (e.g., the active blade pitch control) are operated to maintain the aerodynamic loads applied on blades and control the output power to be constant at the rated power. The WT is stopped, when wind speed is larger than the cut-out wind speed of 22 ms−1, to keep the whole turbine safe under extreme wind conditions. In this study, the power curve is represented by a six-order polynomial equation of wind speed during the cut-in and rated speeds, which is expressed as
\n\nIEC wind class | \nIIIA | \n
Rated power (kW) | \n15,000 | \n
Cut-in wind speed (ms−1) | \n3 | \n
Rated wind speed (ms−1) | \n10.3 | \n
Cutout wind speed (ms−1) | \n22 | \n
Swept area (m2) | \n5325 | \n
Number of blades | \n3 | \n
Hub height (m) | \n90 | \n
Power control | \nActive blade pitch control | \n
Generator | \nPMDD synchronous generator | \n
Rated voltage (V) | \n690 | \n
Yaw system | \n3 induction motors with hydraulic brakes | \n
Tower | \nTubular steel tower | \n
Foundation | \nFlat foundation | \n
Converter | \nFull-power convert modular system | \n
Control system | \nMicroprocessor controlled with remote monitoring | \n
Technical specifications of the Goldwind 1.5 MW PMDD WT [17].
With the available meteorological data and the selected WT properties, assumptions are made for calculating the energy and exergy efficiencies: (1) the air pressure, temperature, and humidity are not significantly changed in the swept area of the WT. Thus, the surface pressure data, 10-m air temperature, and 10-m specific humidity obtained from the MERRA-2 data are directly used for the thermodynamic analyses. (2) Due to the wind shear effect in the atmospheric boundary layer, the normal wind profile model with a power law exponent of 0.2 is used to convert the 10-m horizontal wind speed to the hub-height (90 m) wind speed according to the IEC standard [20]. It takes about 0.5 hour to convert six channels (five meteorological channels and one channel for time stamps) from the MERRA-2 netCDF4 data to Matlab data and then to calculate 18 years’ hourly energy and exergy efficiencies using the developed Matlab scripts. Results and discussion are elaborated in three aspects: (1) WT efficiency variation in time domain, (2) meteorological variables impact on the efficiencies, and (3) uncertainty of meteorological variables represented by the best-fit distributions.
\nThe energy and exergy efficiencies of the Goldwind WT are calculated by Eqs. (18) and (19), respectively, using the Ithaca meteorological data (wind speed, pressure, temperature, and humidity) retrieved from the MERRA-2 data set. As demonstrated in Figure 4, the variation of energy and exergy efficiencies is more closely following the variation of wind speed comparing with the other three meteorological variables, as wind power is proportional to the cubic of wind speed. Both efficiencies become 0 when the wind speed is less than the cut-in wind speed due to the WT being in idling status at the very low wind speed. As the WT is stopped when wind speed is larger than the cutout wind speed, the efficiencies are also equal to 0. In addition, the energy efficiency present a higher magnitude than that of exergy efficiency, which is consistent with the theoretical derivations (Eqs. (18) and (19)) and previous findings (e.g., [6, 7]). The difference between the two efficiencies is due to exergy destruction caused by irreversibility [7]. The concurrent low temperature and humidity ratio also demonstrate the cold and dry weather in winter of Ithaca.
\nTime series of hourly concurrent (a) wind speed U, (b) pressure P, (c) temperature T, (d) humidity ratio ω, and (e) energy efficiency η and exergy efficiency ψ during January 1–7, 2017.
Figure 5 shows the mean and standard deviation of energy and exergy efficiencies in different years and months. Annual means of energy and exergy efficiencies are smaller than the corresponding standard deviations, which indicates a significant variation of WT efficiency performance in 1 year as also demonstrated in Figure 4(e). Neither energy efficiency nor exergy efficiency exhibits clear trend from 2000 to 2017, even though relatively small and large means are observed in 2005 and 2014, respectively (Figure 5(a)). However, both mean and standard deviation of energy and exergy efficiencies present smaller values in summer than those in winter (Figure 5(b)). This seasonal change of efficiencies is likely related to the fact that high sustained wind speeds with strong variation more frequently occur in winter than in summer at the Ithaca area.
\nMean and standard deviation of energy and exergy efficiencies in different (a) years and (b) months.
Relationships between the WT efficiencies and meteorological variables offer the trends of WT efficiency performance as meteorological variables change. Figure 6 shows the scatter diagrams of energy and exergy efficiencies versus the four meteorological variables (wind speed, pressure, temperature, and humidity ratio), as well as their relationships represented by different metrics. A bimodal relationship between the efficiencies and wind speed is observed due to the nonlinearity of the efficiency function with respect to wind speed (Figure 6(a)). The mean curves in Figure 6(a) show that the maximum means of energy and exergy efficiencies are 46.2% and 45.2%, respectively, at the high peaks when the wind speed is equal to ∼9.2 ms−1, while the counterparts at the low peaks are 42.7% and 38.1% when the wind speed is equal to ∼5 ms−1. Despite the large variation, the efficiencies are linearly proportional to temperature and to the inverse of pressure (Figure 6(b and c)). Figure 6(d) shows that both the energy and exergy efficiencies are increased by ∼8% as the humidity ratio is increased from 0.001 to 0.015 kg kg−1, which indicates humidity plays an important role in affecting the WT efficiency performance.
\nRelationships between the WT efficiency (energy efficiency η and exergy efficiency ψ) and meteorological variables including (a) wind speed, (b) pressure, (c) temperature, and (d) humidity ratio. All 18-year samples of hourly η and ψ versus wind speed are used in (a). For demonstration, samples in (b), (c), and (d) are conditionally sampled under a wind speed bin of 9 ms−1 (bin width 1 ms−1).
Variation of meteorological variables could have significant impact on not only energy and exergy efficiencies as explained in Section 4.2 but also many other aspects, e.g., fatigue and structural reliability. Although Weibull distribution is often used to represent the uncertainty of mean wind speed in long term [21, 22], few previous studies have sought to address which parent distribution best represents other meteorological variables, e.g., pressure, temperature, and humidity for WT analyses. This is an important omission since these meteorological variables could have critical roles, but maybe indirectly, to WT performance. For example, high air humidity, low wind speed, and temperature above ∼10°C are preferred by insects that will increasingly foul the leading edges of WT blades and contaminate the blade surface eventually decreasing the aerodynamic performance [23]. Since both the wind speed and pressure considered herein are zero bounded, four positive-valued distribution types (Weibull, lognormal, gamma, and log-logistic; see Figure 7(a and b)) are fitted to wind speed and pressure using maximum likelihood estimation (MLE). Due to the clear two-peak histograms observed for temperature and humidity ratio, four positive-valued bimodal distributions (bi-Weibull, bi-lognormal, bi-gamma, and bi-log-logistic) are fitted to temperature and humidity ratio (Figure 7(c and d)). The probability density function (PDF) of a bimodal distribution consists of two PDFs with the same distributional type, which is expressed as
Histograms and distribution fits for (a) wind speed, (b) pressure, (c) temperature, and (d) humidity ratio; and (e) empirical cumulative distribution function of energy and exergy efficiencies. In the legends, the log-likelihood values are in parentheses. The bolded distribution with the largest log-likelihood value is selected as the best-fit distribution and is summarized in Table 2. Recall all 18-year samples of hourly meteorological data, and the calculated WT energy and exergy efficiencies are used in Figure 7.
where x represents a meteorological variable; (a1, b1) and (a2, b2) are the parameters of the first and the second constituent PDFs, respectively; and w is the weight for the constituent distributions f(x|a1, b1) in the bimodal distribution form. The candidate distribution with the largest log-likelihood value is selected as the best-fit distribution [24].
\nFigure 7 and Table 2 summarize the distributional fits for the four meteorological variables. It is found that the log-logistic distribution is best fit for wind speed and pressure (Figure 7(a and b)), despite the commonly used Weibull distribution for mean wind speed. The bi-lognormal and bi-gamma distributions are best fit for temperature and humidity ratio, respectively. The existence of bimodal shape of the distributions of temperature is likely related to the very distinguished high and low temperature corresponding to the summer and winter seasons, respectively, in Ithaca. The same reason explains the bimodal shape for humidity. The obtained specific distributions for the meteorological parameters, provided in Table 2, are readily applicable for WT performance analyses, i.e., fatigue, structure, aerodynamics, and thermodynamics, in moderately complex terrain of the northeastern United States. Figure 7(e) presents the empirical cumulative distribution function (CDF) of energy and exergy efficiencies calculated herein. Due to the large amount of 0 energy and exergy efficiencies when wind speed is below the cut-in wind speed, the CDF curves show that there is a probability of ∼43% that the efficiencies are equal to 0. The largest discrepancy between CDF of energy and exergy efficiencies occurs at efficiencies equal to 0.4. The presented CDF could be used to evaluate the reliability of wind power performance considering realistic meteorological uncertainty.
\nMeteorological variables | \nBest-fit distribution type | \nProbability density function | \n
---|---|---|
Wind speed (ms−1) | \nLog-logistic distribution | \n\n\n | \n
Pressure (Pa) | \nLog-logistic distribution | \n\n\n | \n
Temperature (K) | \nBi-lognormal distribution | \n\n\n | \n
Humidity ratio (kg kg−1) | \nBi-gamma distribution | \n\n\n | \n
The best-fit distribution form and distribution parameters for the four meteorological variables.
The distributional fits and empirical histograms are shown in Figure 7.
This chapter presents methods and results for thermodynamic analysis of wind energy systems considering four types of meteorological variables, i.e., wind speed, pressure, temperature, and humidity. An improved understanding of WT efficiencies is critically important and necessary before launching any wind projects. The evaluation of WT efficiencies considering thermodynamics, conducted here for an 1.5 MW WT (Goldwind GW82/1500) potentially deployed at Ithaca, New York, is beneficial to WT design, siting, and operation in moderately complex terrain in the northeastern United States. The key concluding remarks are the following:
The chapter offers the fundamental derivations of energy and exergy efficiencies of WTs considering wind speed, pressure, temperature, and humidity, which lay a foundation for the thermodynamic analysis of wind energy systems.
The WT energy efficiency presents higher magnitude than exergy efficiency based on the theoretical derivation and the calculated time series of efficiencies. There is no clear trend of annual variations of mean and standard deviation of both energy and exergy efficiencies. However, a clear seasonal change is found that energy and exergy efficiencies studied herein have smaller values in summer than those in winter.
Although wind speed has a dominating influence, other meteorological variables (i.e., pressure, temperature, and humidity) do have a considerable impact on the WT efficiency performance. The WT efficiencies are linearly associated with pressure and temperature, while it has highly nonlinear relationships with wind speed and humidity ratio.
Log-logistic distributions are most appropriate for the wind speed and pressure data retrieved from the MERRA-2 data set at Ithaca, New York. A bi-lognormal distribution and a bi-gamma distribution are most appropriate for the temperature and humidity ratio, respectively. The obtained PDFs of meteorological variables and CDFs of energy and exergy efficiencies could be beneficial for evaluating the reliability of wind power performance considering realistic meteorological uncertainty in the northeastern United States.
Naturally the specific findings are based on reanalysis meteorological data and the assumed WT deployment; the methodologies of thermodynamic analysis presented here are applicable for real measured meteorological data and recorded WT performance somewhere else if available. In addition, although the thermodynamic analysis of wind energy systems in this chapter focuses on energy and exergy efficiencies, other variables, e.g., dynamic response, fatigue damage, structural deformation, etc., of the PMDD WT are also potentially affected by the meteorological variables, which could be investigated in the future.
\nSupport from the National Natural Science Foundation of China (grant numbers 51475417, U1608256, and 51521064) is gratefully acknowledged. Weifei Hu would like to appreciate Dr. Qinjian Jin and Dr. Frederick Letson at Department of Earth and Atmospheric Sciences, Cornell University, for the introduction and discussion of the MERRA-2 data.
\nThe authors certify that this work has no conflict of interest with any organization or entity in the subject matter or materials discussed in this chapter.
\n rotor swept area power coefficient specific heat of flow, specific heat of air, specific heat of water vapor kinetic energy exergy kinetic exergy potential exergy physical exergy chemical exergy PDF of meteorological variables mass wind power/wind pressure reference pressure, inlet pressure, outlet pressure, average pressure, and wind pressure in humid air, respectively surface pressure retrieved from the MERRA-2 data set 10-m specific humidity retrieved from the MERRA-2 data set gas constant and water vapor constant, respectively reference temperature, inlet temperature, outlet temperature, and average temperature, respectively 10-m air temperature retrieved from the MERRA-2 data set 10-m horizontal wind speed 10-m eastward wind speed retrieved from the MERRA-2 data set 10-m northward wind speed retrieved from the MERRA-2 data set wind speed total input wind energy energy efficiency exergy efficiency air density [kgm−3] humidity ratio of air at the reference state/at the current state Modern-Era Retrospective Analysis for Research and Applications, Version 2 National Aeronautics and Space Administration permanent magnet direct-drive wind turbines/wind turbine
Authors are listed below with their open access chapters linked via author name:
",metaTitle:"IntechOpen authors on the Global Highly Cited Researchers 2018 list",metaDescription:null,metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"New for 2018 (alphabetically by surname).
\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJocelyn Chanussot (chapter to be published soon...)
\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYuekun Lai
\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPrevious years (alphabetically by surname)
\\n\\nAbdul Latif Ahmad 2016-18
\\n\\nKhalil Amine 2017, 2018
\\n\\nEwan Birney 2015-18
\\n\\nFrede Blaabjerg 2015-18
\\n\\nGang Chen 2016-18
\\n\\nJunhong Chen 2017, 2018
\\n\\nZhigang Chen 2016, 2018
\\n\\nMyung-Haing Cho 2016, 2018
\\n\\nMark Connors 2015-18
\\n\\nCyrus Cooper 2017, 2018
\\n\\nLiming Dai 2015-18
\\n\\nWeihua Deng 2017, 2018
\\n\\nVincenzo Fogliano 2017, 2018
\\n\\nRon de Graaf 2014-18
\\n\\nHarald Haas 2017, 2018
\\n\\nFrancisco Herrera 2017, 2018
\\n\\nJaakko Kangasjärvi 2015-18
\\n\\nHamid Reza Karimi 2016-18
\\n\\nJunji Kido 2014-18
\\n\\nJose Luiszamorano 2015-18
\\n\\nYiqi Luo 2016-18
\\n\\nJoachim Maier 2014-18
\\n\\nAndrea Natale 2017, 2018
\\n\\nAlberto Mantovani 2014-18
\\n\\nMarjan Mernik 2017, 2018
\\n\\nSandra Orchard 2014, 2016-18
\\n\\nMohamed Oukka 2016-18
\\n\\nBiswajeet Pradhan 2016-18
\\n\\nDirk Raes 2017, 2018
\\n\\nUlrike Ravens-Sieberer 2016-18
\\n\\nYexiang Tong 2017, 2018
\\n\\nJim Van Os 2015-18
\\n\\nLong Wang 2017, 2018
\\n\\nFei Wei 2016-18
\\n\\nIoannis Xenarios 2017, 2018
\\n\\nQi Xie 2016-18
\\n\\nXin-She Yang 2017, 2018
\\n\\nYulong Yin 2015, 2017, 2018
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\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYuekun Lai
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\n\nKhalil Amine 2017, 2018
\n\nEwan Birney 2015-18
\n\nFrede Blaabjerg 2015-18
\n\nGang Chen 2016-18
\n\nJunhong Chen 2017, 2018
\n\nZhigang Chen 2016, 2018
\n\nMyung-Haing Cho 2016, 2018
\n\nMark Connors 2015-18
\n\nCyrus Cooper 2017, 2018
\n\nLiming Dai 2015-18
\n\nWeihua Deng 2017, 2018
\n\nVincenzo Fogliano 2017, 2018
\n\nRon de Graaf 2014-18
\n\nHarald Haas 2017, 2018
\n\nFrancisco Herrera 2017, 2018
\n\nJaakko Kangasjärvi 2015-18
\n\nHamid Reza Karimi 2016-18
\n\nJunji Kido 2014-18
\n\nJose Luiszamorano 2015-18
\n\nYiqi Luo 2016-18
\n\nJoachim Maier 2014-18
\n\nAndrea Natale 2017, 2018
\n\nAlberto Mantovani 2014-18
\n\nMarjan Mernik 2017, 2018
\n\nSandra Orchard 2014, 2016-18
\n\nMohamed Oukka 2016-18
\n\nBiswajeet Pradhan 2016-18
\n\nDirk Raes 2017, 2018
\n\nUlrike Ravens-Sieberer 2016-18
\n\nYexiang Tong 2017, 2018
\n\nJim Van Os 2015-18
\n\nLong Wang 2017, 2018
\n\nFei Wei 2016-18
\n\nIoannis Xenarios 2017, 2018
\n\nQi Xie 2016-18
\n\nXin-She Yang 2017, 2018
\n\nYulong Yin 2015, 2017, 2018
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