Worldwide prevalence of Behçet’s disease [24].
\r\n\tTo sum up, there are numerous engineering applications of diamond which are yet to be realized and this book will address some of the mentioned and hopefully open some new topics.
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Behçet’s disease (BD) is described as a severely debilitating chronic, recurrent, multisystem vascular inflammatory disorder characterised by the triad of oral and genital ulceration and ocular lesions. This exemplary presentation would have been probably first identified by Hippocrates [1, 2]. Even though Behçet’s disease was first identified by the father of medicine in the fifth century BC, there were no literature representations for this entity till 1930, and then a Greek ophthalmologist Benediktos Adamantiades presented one of his case of relapsing eye lesions associated with genital ulceration and arthritis in a 20-year-old male; since then entity has been added with a synonym “Adamantiades-Behçet’s disease” [3, 4, 5, 6]. Further to its classic expression of orogenital ulceration and ocular lesions, the conditions seem to involve the musculoskeletal system, nervous system, gastrointestinal tract, vascular beds, urogenital tract and cardiopulmonary system, so Behçet’s disease have a high morbidity and mortality particularly in males with early age onset [7, 8]. The self-limiting inflammation and the relapsing episodes of clinical manifestations are the hallmark of Behçet’s disease [9]. Frequency and span of regression are uncertain and show no distinguishable pattern of onset.
\nAlthough Behçet’s disease has a worldwide distribution, it is more rampant in Turkish population, where the prevalence is about 421 cases per 100,000 and 13 to 20 cases per 100,000 population in China, Korea, Japan and the Middle East [10, 11]. Behçet’s disease is most commonly diagnosed in the second and third decades of life especially during the reproductive years and shows a female predilection among Europeans. Behçet’s disease rarely occurs prior to puberty and after the fifth decade of life. Behçet’s disease developing in early age has shown to have severe clinical manifestations and mortality. Familial occurrences have been reported in various literatures but are unusual, and the aetiology of Behçet’s disease remains cryptic [12, 13, 14, 15, 16, 17, 18]. The widely accepted hypothesis is that of an inappropriate inflammatory response provoked by an infectious agent in a genetically liable host [19].
\nDiagnosis of Behçet’s disease is based on the criteria developed by the International study group (ISG) in 1990 and International Criteria for Behçet’s Disease (ICBD) [20]. The ICBD was developed with the intention of bettering the ISG criteria that did not consist of nervous system, vascular and gastrointestinal involvement that are severe but rather infrequent complications of Behçet’s disease. A recent study conducted in the United Kingdom has compared the efficacy of ISG and ICBD diagnostic criteria among their patients with Behçet’s disease and concluded that the sensitivity was higher for ICBD than ISG criteria, but the specificity of ICBD was much lower than the ISG criteria [20, 21, 22].
\nManagement is aimed to alleviate symptoms, resolve inflammation, restrain tissue damage, dwindle frequency and severity and avoid lethal complications. A multitude of treatment modalities have been proposed for managing Behçet’s disease ranging from anti-inflammatory to immunomodulators and biologic monoclonal antibodies. Detrimental ramifications are a main concern since it requires long-term medication [8].
\nBehçet’s disease is observed to have a worldwide distribution. However, it is seen commonly among the inhabitants of the historic Silk Road, which was supposed to be an old trading route that linked Japan and China in the Far East to the Mediterranean Sea, including countries such as Turkey and Iran. Therefore, Behçet’s disease is also known by the synonym “Silk Road disease” [23]. Behçet’s disease has a highest prevalence rate in Turkey, where the estimated prevalence is about 421 per 100,000 population followed by Iran, Israel and Japan [24]. It is rarely reported in African countries and the USA [23] as well as Australia, where the prevalence remains unknown but with an annual incidence of 0.6% (Table 1) [25].
\nPrevalence per one lakh population | \nCountry | \n
---|---|
Turkey | \n421 | \n
Iran | \n80 | \n
Saudi Arabia | \n20 | \n
Iraq | \n17 | \n
Israel | \n15.2 | \n
Japan | \n13.5 | \n
France | \n7.1 | \n
USA | \n5.2 | \n
Sweden | \n4.9 | \n
Germany | \n2.26 | \n
Portugal | \n1.53 | \n
UK | \n0.64 | \n
Worldwide prevalence of Behçet’s disease [24].
Two studies conducted in Korea and Turkey in 2016 and 2017 reported the prevalence of Behçet’s disease. A study conducted in Korea estimated the prevalence of Behçet’s disease, and change in prevalence rate over time from the Korean healthcare big data that consists of medical institution claims data concerning diagnosis, prescription medicines and surgical treatment and represents the total population. The database was reviewed for patients diagnosed to have Behçet’s disease during the year 2011 and 2015. The diagnosis was based on the Korean Standard Classification of Diseases (KCD). The estimated prevalence was about 35.0 per 100,000 population which was a little more than the previously reported prevalence of 30.2 per 100,000 in Korea. The change in prevalence rate was also estimated during the time period of 2011 and 2015 and concluded that there is a creeping escalation in the annual prevalence rate between 2011 and 2015. The prevalence rate was 32.8 per 100,000 in 2011, 34.0 per 100,000 in 2012, 34.6 per 100,000 in 2013, 35.5 per 100,000 in 2014 and 35.7 per 100,000 in 2015. The prevalence would have been more since patients with milder form of the disease have not come for treatment. Females had a higher prevalence, with a female-to-male ratio of 1:0.54 in 2011 and 1:0.56 in 2015 [26].
\nAnother study conducted in Northern Turkey showed a prevalence of Behçet’s disease in 600/100,000 population who have crossed their second decade of life and is considered as the highest prevalence reported in the literature so far [27]. Nonetheless this conclusion seems to be biased since there are methodological errors. Patients were randomly selected from the 52 urban and 33 rural general practices of a city and are invited for an examination and interview by three dermatologists. As per the authors, the invited sample would well represent the whole population since all individuals are recorded in general practices. Eye examination and pathergy test were performed in suspected individuals. Individuals compatible to ISG criteria were diagnosed as Behçet’s disease. However, the total number of individuals who were invited for interview was not mentioned. Authors reported that 2325 of 2428 individuals interviewed were included in the study, of which 1290 were females and 1035 were males. Symptomatic individuals may have responded to the invitation, which might have led to a higher prevalence estimate. Authors have also reported increased prevalence of Behçet’s disease in females (860/100,000) compared to males (140/100,000) (p = 0.022) which does not correlate with the previous studies conducted in Turkey, since they concluded no significant gender predilections [27].
\nA recent meta-analysis that included 45 prevalence studies suggested that even though there was clearly a wide variation across countries, the overall prevalence of Behçet’s disease were 10.3/100,000. The estimated prevalence was 119.8/100,000 for Turkey, 31.8/100,000 for the Middle East, 4.5/100,000 for Asia and 3.3/100,000 for Europe [28]. A meta-regression analysis showed that classification criteria, study period or reference type had hardly any effect and study design was the deciding factor for Behçet’s disease prevalence in these studies. The authors have also suggested that true geographic variations and different study methods are the possible factors responsible for the large variation in Behçet’s disease prevalence across countries.
\nTwo studies conducted in Korea reported on the clinical manifestations of Behçet’s disease patients according to gender [29, 30]. The first study was carried out by a rheumatology department of a university hospital in Turkey, which assessed the prevalence between male and female with Behçet’s disease in a retrospective chart review of 329 patients [29]. The study included patients who fulfilled the ISG criteria. Data on demographic features of the patients, family history, age of onset, manifestations of Behçet’s, positive pathergy test and HLA-B51 were retrieved from patient charts. Among the 329 patients, 199 (60.4%) were males. A family history of Behçet’s disease was reported by 9.7% of the patients and a family history of oral ulcers by 22.5%. Early onset was observed in patients with a positive family history; this phenomenon is termed as genetic anticipation which had already been seen in previous studies [31]. Female patients had an early onset compared to males (23.0 vs. 25.2 years, p < 0.05). Eye lesions were more common among males (42.2 vs. 29.2%, p = 0.014). Vascular involvement was also more common among in males (44.7 vs. 15.3%, p < 0.001), whereas joint involvement and headache were common among women (70.0 vs. 49.2%, p < 0.001 for joint involvement and 60.0 vs. 36.2%, p < 0.001 for headache). Frequency of genital ulcers, nodular lesions, papulopustular lesions and nervous system involvement was similar among males and females. Pathergy test was positive in 48.7% of the patients (52.4% among male and 43.4% among female, p = 0.316). HLA-B51 was positive in 44.1% of Behçet’s disease patients, and the frequency was lower among men, but the difference was not that significant (38.8 vs. 50.6%, p = 0.106). The second study was conducted in Korea, which was a retrospective chart review of 193 patients, which analysed gender, age of onset of Behçet’s disease and HLA-B51 [30]. The study showed that there is an increased prevalence in females (67%), whereas other parameters like the mean age at onset, the mean disease duration, positive pathergy test and positive HLA-B51 were similar in both males and females. Genital ulcers, peripheral joint involvement and inflammatory back pain were more common in females, while skin lesions were more frequent in males. In total, major organ involvement was present in 39% of the patients (uveitis, 27%; nervous system involvement, 7%; vascular involvement, 4% and gastrointestinal involvement, 11%), with similar frequency in both the genders. When clinical findings were compared based on onset of disease, patients with late onset (>40 years) had more nervous system involvement than those with an early onset (15.9 vs. 4.2%, p = 0.007).
\nLike the previous study, HLA-B51 positive patients had a history of early disease onset, but less nervous system (17.2 vs. 2.5%, p = 0.02) and gastrointestinal system (20.7 vs. 2.5%, p = 0.01) involvement than patients with negative HLA-B51 [32, 33].
\nA Japanese study conducted in 2015 analysed the change in disease phenotype over time among Behçet’s disease patients treated in seven hospitals in a district in mid-Japan between 1991 and 2015. Patients fulfilled the 1987 revised diagnostic criteria of the Behçet’s disease research committee; the Ministry of Health, Labour and Welfare of Japan was included in the study [34, 35]. This criterion classified the manifestations of Behçet’s disease into major and minor symptoms. Major symptoms include recurrent aphthous oral ulcers, skin lesions, ocular inflammation and genital ulcers, and minor symptoms include arthritis, intestinal ulcers, epididymitis, vascular lesions and neuropsychiatric disease.
\nThose who have all four major symptoms during the clinical course are classified as complete, whereas those who have three major symptoms, two major and two minor symptoms, typical recurrent ocular inflammation and one or more major symptoms or typical recurrent ocular inflammation and two minor symptoms are classified as incomplete Behçet’s disease. Patients were divided into three groups as group A, diagnosed before 2000; group B, diagnosed between 2000 and 2007; and group C, diagnosed after 2008. Authors observed a decrease in the number of patients with complete disease over time, suggesting a trend for milder disease in more recent years which could be due to usage of newer medicaments and awareness about the disease. The male-to-female ratio and the age of onset of disease were not changed over time, whereas the frequency of genital ulcers and HLA-B51 positivity decreased and gastrointestinal involvement frequency increased [34, 35].
\nAdditionally, individuals migrated from countries with high prevalence of Behçet’s disease to countries with less prevalence rate were shown to have less risk of developing disease [36, 37]. Prevalence of Behçet’s disease among Turkish individuals in Germany is about 21/100,000, which, although higher than that of the German population, remains inferior to prevalence in Turkey [38].
\nAlthough Behçet’s disease is rare in Africans, two series have been published recently based on studies conducted in Nigeria and Dakar from the year 2007 to 2011. Based on the observation by Italian authors, the prevalence of Behçet’s disease was found to be higher among immigrants when they analysed its prevalence among patients of Italian and non-Italian origin living in these areas.
\nA study conducted in the Netherlands estimated the prevalence of Behçet’s disease in patients with diverse ethnic origins residing in the Rotterdam area showed a prevalence of 1/100,000 population among Dutch Caucasians, 39/100,000 among Moroccans and 71/100,000 among Turks, suggesting that the prevalence among Moroccans is the same as in their countries of origin [39, 40].
\nPeculiar geographical distribution, familial aggregation, polymorphisms in genes that control immune responses and correlation with HLA-B51 class I antigen are the four factors that are considered to contribute to Behçet’s disease susceptibility. Ohno in 1973 described the association of BD with HLA-B5 [39]. They demonstrated that HLA-B5 includes HLA-B51 and HLA-B52 as it has a heterogeneous composition. In major histocompatibility locus, HLA-B51 and HLA-B5701 were associated with the pathogenesis of the disease, mainly among populace alongside of ancient Silk Road. Although associations with HLA-A and HLA-C have been described, they are non-specific and require confirmation. Other MHC genes such as TNF and MHC class I genes (MICA) are under study; the exact mechanism has not been fully understood. The association with HLA-B51 appears to be important in neutrophil activation. However, the presence of HLA-B51 alone is not ample to explain the manifestations of Behçet’s disease. A case series of Iranian patients showed association with HLA-B35, HLA-B51, HLA-B52 and HLA-Bw4. Supplementary studies suggest associations with HLA-B15, HLA-B27, HLA-B57 and HLA-A26 [40].
\nResearches focused on single-nucleotide polymorphisms (SNPs) revealed that SNP which is in the HLA-B region between HLA-B and MICA genes is responsible for the relationship between HLA-B51 and Behçet’s disease. The epistatic interactions with endoplasmic reticulum-associated aminopeptidase 1 (ERAP-1) has also been studied, and the authors suggested that the interaction of ERAP-1 and HLA-B has also been consistent, and these cytokines were apparently high in the peripheral blood mononuclear cells of patients with Behçet’s disease compared to the control group. Another conclusion was the unbalanced suppressor of cytokine signalling expression in patients with Behçet’s disease compared to controls [41].
\nA Chinese study stated that the polymorphisms of NOS3/rs1799983, a nitric oxide synthase gene, were found to be associated with the disease and the involvement of CD16 and CD11c in Behçet’s disease susceptibility. Mutations in the familial Mediterranean fever (MEFV) gene were suggested by a meta-analysis of 8 studies which analysed 2538 patients and 2792 healthy controls. Researches also suggested the associations of Toll-like receptors 7 (TLR7) and other nucleic acid-sensing genes of innate immunity-like inflammatory pathways such as IFI16 (a dsDNA cytosolic sensor and mediator of the AIM2-dependent inflammatory pathway) found to influence Behçet’s disease susceptibility [39]. Epigenetic studies with inverted repeat sequences (IRS) demonstrated that methylation level of IRS elements may influence the pathogenesis of the Behçet’s disease. Researches were done on genes related to apoptosis, but the results were not conclusive [41].
\nA multitude of factors that indicate the interaction between environmental, innate and adaptive immunity in the development of the disease has been already discussed in the Genetics section. Various researches have studied about the role of bacterial and viral agents in the development of Behçet’s disease, but the results were inconclusive. Streptococcus sanguinis and Herpesviridae were suspected as contributing extrinsic factors for the oral presentations in Behçet’s disease; the immune response to these microorganisms and bacterial plaque ecology may be affected in Behçet’s disease, leading to changes seen in the oral mucosa. However good prognosis was noticed in patients with good oral health status [41]. Recent studies in Behçet’s disease patients have demonstrated peculiar dysbiosis of the gut microbial flora with a notable drop in the production of butyrate. Butyrate can initiate the differentiation of regulatory T cells (T-reg), and thus, its decrease would lead to the reduction of regulatory T-cell response and activation of immunopathological T-cell effector responses. Predisposition to insulin resistance and metabolic syndrome in patients and reduction of angiopoietin 1 especially in patients with vascular involvement has been observed in other studies [42]. In an attempt to find out specific Behçet’s disease antigen, a Chinese research group demonstrated high IgG reactivity to an endothelial cell autoantigen [41].
\nMatzinger suggested the danger model, which occurs due to a continuous autoimmune cascade resulting from signals emitted by the affected cells of the host which will override the external stimulus. T cells and other antigen-presenting cells (APC) would command the process, which would be perpetuated on a favourable genetic terrain. Damage by a non-self-entity would trigger permanent aggression by activating an uncontrolled adaptive response. Because of their similarity to other pathogenic proteins, heat shock proteins (HSP60) could be involved. This adaptive reaction to external stimuli would persist in the permanent pathogenic presence via autoantigens that would activate dendritic T cells and B cells [40]. Thus, overexpression of proinflammatory cytokines especially Th1 and Th17 and probably association with genetic susceptibility may be the contributing factor for the increased inflammatory reaction in Behçet’s disease. Stimulated lymphocytes would activate neutrophils and endothelial cells, and HSPs would probably trigger innate and adaptive immune responses. Hypersensitivity to Streptococcus sanguinis is observed through the innate immune system. Microbial flora along with stress proteins in oral and periodontal tissues could cross-react with host tissues and stimulate the proliferation of autoreactive T-cell clones. HSPs would transfer antigenic peptides to APCs that could be identified by TLRs, triggering an endogenous signal of danger that would lead to the activation of innate and adaptive immune systems. They could also, directly or indirectly, cause increased expression of vascular endothelial growth factor by T cells, causing both endothelial cell damage and vasculitis.
\nMany studies have demonstrated the presence of IFN-gamma and IL-12 in peripheral blood of Behçet’s disease patients suggesting involvement of TH1 cells. Elevated IL-1, IL-6, IL-18, TNF-alpha and chemokines would reflect the activation of innate and adaptive immune systems. Neutrophils are hyperactivated with increased phagocytosis, chemotaxis, superoxide and lysosomal enzyme production. Lymphocytes have also shown abnormal functions such as clonal expansion of autoreactive T cells specific for heat shock protein 60 peptides. In addition, gamma-delta T cells are abundant in the blood and mucosal lesions of the affected individuals. Researchers have concluded that the interaction between T cells, neutrophils and antigen-presenting cells probably contributes to the pathogenesis of Behçet’s disease [40].
\nBehçet’s disease is a form of systemic vasculitis which affect almost all vascularized systems (Table 2) and is characterised by episodes of relapses and remissions [9, 43, 44].
\nManifestation | \nPrevalence | \nPrognosis | \nComments | \n
---|---|---|---|
Oral ulcers | \n47–83% | \nFavourable | \nAppear in all patients during the disease course | \n
Genital ulcers | \n57–93% | \nFavourable | \nLesion will leave a scar | \n
Ocular lesions | \n30–70% | \nPoor | \nMore frequent in males. Has high morbidity | \n
Cutaneous lesions | \n38–99% | \nFavourable | \nErythema nodosum more frequent in females | \n
Joint involvement | \n45–60% | \nFavourable | \nNondeforming and nonerosive | \n
Cardiovascular involvement | \n7–49% | \nPoor | \nMore frequent in males, high morbidity and mortality | \n
Neurological involvement | \n5–10% | \nPoor | \nMore frequent in males, high morbidity and mortality | \n
Gastrointestinal involvement | \n3–26% | \nPoor | \nMore frequent among Japanese | \n
Manifestations of Behçet’s disease.
The triad of oral aphthae, genital ulcers and ocular lesions specifies the disease pattern even though Behçet’s disease has other multisystem involvement and several clinical manifestations.
\nChildren exhibit more frequent perianal ulceration and more severe course of chorioretinitis and less frequent genital ulcers and vascular involvement [44, 45].
\nA meta-analysis of 18 articles which was performed by Horie et al. to assess the association between HLA-B51 and the ocular manifestations of Behçet’s disease among various ethnic group studies from Middle and Far East suggested that there was a strong association between HLA-B51 expression and ocular involvement, whereas studies from North Africa and Europe showed no association [46].
\nAccorinti et al. investigated demographic and clinical trends of 385 Behçet’s disease patients with uveitis seen over 44 years in a referral unit at Sapienza University in Rome, Italy. The cohort was divided into cohort 1, which included the patients seen from 1968 to 1992, and cohort 2 included the patients seen during 1993 and 2011. Compared to the cohort 1, the cohort 2 has more female patients, more patients with milder disease (more frequent isolated anterior uveitis, less frequent hypopyon) and more immunosuppressive use. Ocular complications such as optic atrophy, maculopathy and retinal neovascularisation and retinal detachment were significantly more common in cohort 1. The visual acuity at final evaluation was better in the second cohort. When considered both the cohorts, more severe findings were observed in males. From the above findings, the authors concluded that timely and aggressive management reduces ocular complications and facilitates better prognosis [47].
\nEven though uveitis is the common ocular pathology in Behçet’s disease, it is not a common cause of uveitis. The highest incidence of uveitis is reported by Mishima S from Japan [48], where it constitutes about 25%, but it is much rarer in other countries such as Brazil, where the incidence is 2% [49], and India, where the incidence is 1% reported by Sen DK from a study conducted in 94 Indian children [50]. The classical feature of Behçet’s disease is recurrent, acute anterior uveitis associated with hypopyon. Other ocular manifestations seen in Behçet’s disease include necrotizing vascular lesions involving the retina and the optic nerve. These manifestations may be unnoticed because of severity of the anterior segment reaction and commonly include macular oedema, retinal periphlebitis and periarteritis, thrombosis of the vessels and retinal or vitreous haemorrhages which are frequently observed in males with Behçet’s disease [51, 52].
\nRecurrent oral ulcers are one of the earliest manifestations of Behçet’s disease in around 47–86% of patients and are seen in almost all patients during their clinical course [53]. Other symptoms may at times take years to appear after the onset of disease. Oral ulcerative lesions usually manifest as a round- or oval-shaped ulcer with discrete erythematous border with a greyish-white pseudomembrane or a central yellowish fibrinous floor and grow rapidly from a flat ulcer to a deep sore [9]. They may occur as single ulcers or as numerous [9, 53]. Oral ulcers most commonly affect the gingival and buccal mucosa, tongue and lips yet may also appear in the soft and hard palates, pharynx and tonsils [9]. Minor ulcers which are less than 1 cm in diameter heal without scarring over a period of 1 to 2 weeks, whereas major ulcers which are more than 1 cm in diameter are more painful and heal within 2–6 weeks. Herpetiform ulcers occurring in recurrent crops of small ulcers that are 0.2–0.3 cm in diameter are painful and may coalesce to form large ulcers. Treatment is usually symptomatic, and prognosis of oral ulcerations is favourable [18, 53].
\nThe most commonly seen cutaneous manifestations are erythema nodosum and pseudofolliculitis, in which erythema nodosum is manifested as painful multiple subcutaneous nodules that vary in size and colour, whereas pseudofolliculitis (pustulosis) appears as a dome-shaped sterile pustule on a round reddish edematous base that looks like acne vulgaris [54]. Although these are commonly seen on the lower extremities, it can be seen throughout the body [55]. The pathergy reaction is included in the criteria for the diagnosis of Behçet’s disease. Although more than 50% of patients from Turkey and Japan had a positive pathergy test, it is rarely observed in patients from Northern Europe, the USA and Australia [56, 57]. It used to be an important diagnostic criterion for Behçet’s disease; however, the frequency of the pathergy phenomenon was reported to decrease during the recent years [58]. Subcutaneous thrombophlebitis is often confused with lesions of erythema nodosum; the former is manifested as tender erythematous nodules with a linear arrangement in most case. The lesions may relocate depending on the vascular segment involved [59].
\nCardiac involvement is a rare finding in patients with Behçet’s disease. Behçet’s disease affects arteries, veins as well as the heart. Cardiovascular features have been reported to affect 7–49% of patients, more frequently in males. They occur 3–16 years after the onset of Behçet’s disease. Cardiac involvement in Behçet’s disease patients was first described by Mirone et al. in 1958 as a case of paroxysmal fibrillation and heart block. In 1963, Oshima et al. described a case of myocardial infarction complicated by incomplete right bundle-branch block [60]. Since then different types of cardiovascular lesions, including pericarditis, atrial thrombus, complex ventricular arrhythmia, myocardial infarction, heart block and sudden death, have been reported in association with Behçet’s disease [61]. Dong Soo et al. in 1998 reported a case of superior vena cava syndrome caused by Behçet’s disease in a 40-year-old man with recurrent oral aphthous ulcers and skin rashes on the anterior chest wall [62].
\nNeurological involvement in Behçet’s disease, the neuro-Behçet’s disease, occurs very rarely and only in 5–10% of patients. It is frequently seen in males [63]. It usually occurs around 5 years after the onset of the disease and is associated with long-term morbidity and mortality [9]. Neuro-Behçet’s disease can be parenchymal, nonparenchymal or mixed brain disease, in which the parenchymal brain disease affects the brainstem and/or basal ganglia and is correlated with a poor prognosis, whereas nonparenchymal also known as vasculo-Behçet’s disease or angio-Behçet’s disease is characterised by subsets of cerebral venous thromboses, dural venous sinus thrombosis, arterial vasculitis and aseptic meningitis and comprises the most devastating symptom category of Behçet’s disease with high mortality [63, 64]. A mixed parenchymal and nonparenchymal disease has also been reported but is very rare. Infrequent clinical manifestations like epilepsy, stroke, brain tumour-like neuro-Behçet’s disease, acute meningeal syndrome, movement disorders, optic neuropathy, spinal cord involvement and asymptomatic and subclinical neurological involvement have also been reported [65, 66]. Meningitis, neurological deficits including motor disturbances and brainstem symptoms and psychiatric symptoms including personality changes develop in patients with neuro-Behçet’s disease are considered as a classic finding [18, 63]. These symptoms are associated with disease exacerbations and gradually cause disability and unfortunately are irreversible [18]. At late stages, dementia develops in approximately one-third of patients with neuro-Behçet’s disease.
\nGastrointestinal involvement was observed in 3–26% of patients and varies among different populations [38, 39]. It is much more frequent in Japan than in the Middle East and the Mediterranean region [1, 2, 3]. Mucosal inflammation and ulceration can occur anywhere in the gastrointestinal tract but typically in the ileocecal region. Other rarely involved sites include the oesophagus, ascending colon and transverse colon [18, 19, 67]. Clinical symptoms include anorexia, vomiting, dyspepsia, diarrhoea, melena, abdominal pain and, less frequently, perforation requiring surgical intervention [68]. Due to the similarity in intestinal and extraintestinal symptoms, it is tough to differentiate Behçet’s disease from inflammatory bowel disease; however, the presence of granulomata can be used to confirm inflammatory bowel disease [66].
\nJoint involvement is common in patients with Behçet’s disease seen in almost half of patients. It presents as an initial manifestation seen long before the commencement of other features of BD [55]. Erosive, non-deforming oligoarthritis typically involving the knees, ankles and wrists is seen in most of the Behçet’s disease patients. Rarely, it can present as sacroiliitis or erosive arthritis. Myopathy has also been reported by Arkin in 1980 [69, 70, 71, 72].
\nVasculitis is one of the classic signs in Behçet’s disease, and the typical finding is deep vein thrombosis. Thrombophlebitis occurs generally in the first year after onset of Behçet’s disease, and relapses are frequent. Vasculitis can affect both the veins and arteries and capillaries although venous involvement is more common [55]. Venous thrombosis may occur at any site and can even involve large vessels like the inferior vena cava, the superior vena cava and the pulmonary artery. [55]. Arterial involvement is seen in 3–5% of cases [73]. Aneurysm formation is found to be the foremost manifestation, and the affected patients can be asymptomatic [74]. Vascular surgery is mandatory, but relapse is a frequent concern [55].
\nPulmonary manifestations are rare in patients with Behçet’s disease. Haemoptysis is the only reported manifestation, but it can be massive and fatal [74].
\nThe diagnosis of Behçet’s disease relies purely on its clinical manifestation since it lacks specific biological test, so diagnosis with clinical symptoms is challenging, especially due to non-concomitant symptoms. Hence different diagnostic criteria have been developed for the determination of Behçet’s disease. In 1990, an ISG for Behçet’s disease was formed by a group of researchers [21]. The International Study Group developed a set of classification criteria which is designed for research studies, which in many instances is used for diagnosing. The features were defined as the presence of specific symptoms as seen in Table 3. Oral ulceration along with any other two symptoms is said to be diagnostic [75]. The capability of the ISG criteria as a diagnostic tool has been questioned [56]. An international team which includes 27 countries was formed to evaluate and reassess the ISG criteria, and a new ICBD was developed that include the major criteria consisting of oral aphthosis, genital aphthosis and ocular lesions which were each given 2 points, whereas 1 point was assigned to the skin lesion, neurological manifestation, vascular manifestation and positive pathergy test (which is optional and not included in the primary criteria due to geographic variation and its declining sensitivity and increased specificity) [58, 75]. Higher sensitivity of the ICBD was reported as it aided in earlier diagnosis and hence earlier treatment and thereby better prognosis [75]. However, the delay between the onsets of major manifestations may be decades needs to be considered [76]. Inflammatory markers such erythrocyte sedimentation rate (ESR) and C-reactive protein have no longer been recognised as accurate or specific for the disease activity (Table 4).
\nRecurrent oral ulcerations | \nMinor aphthous, major aphthous or herpetiform ulceration observed by physician or patient, which recurred at least three times in one 12-month period | \n
Plus any two of the following | \n|
Recurrent genital ulceration | \nAphthous ulceration or scarring observed by physician or patient | \n
Eye lesion | \nAnterior uveitis, posterior uveitis or cells in vitreous on slit lamp examination or retinal vasculitis observed by ophthalmologist | \n
Skin lesions | \nErythema nodosum observed by physician or patient, pseudofolliculitis or papulopustular lesions or acneiform nodules observed by physician in post adolescent patients not on corticosteroid treatment | \n
Positive pathergy test | \nRead by a physician at 24–48 hours | \n
The international study group criteria for Behçet’s disease [21].
Symptoms | \nPoints | \n
---|---|
Oral aphthosis | \n2 | \n
Genital aphthosis | \n2 | \n
Ocular lesion | \n2 | \n
Skin lesion | \n1 | \n
Neurological manifestation | \n1 | \n
Vascular manifestation | \n1 | \n
Positive pathergy test | \n1b | \n
International criteria for Behçet’s disease [75].
Investigations for Behçet’s disease include routine total blood count; renal, liver and bone profile; inflammatory markers, including C-reactive protein and erythrocyte sedimentation rate; urine analysis; chest X-ray; coeliac screen; stool sample; autoimmune screen; coagulation profile and antiphospholipid antibodies; mouth and genital ulcer swab and culture; and occasionally eye swab. Other investigations that may be required include oral biopsy and direct immunofluorescence to exclude orofacial granulomatosis and bullous dermatosis. Vulval biopsy is indicated to exclude lichen sclerosis).
\nDoppler studies, CT or MRI brain and spinal cord, magnetic resonance venography, computerised tomographic venography, magnetic resonance angiography, computerised tomography angiography to evaluate neurologic and vascular disease; CSF studies, electro encephalogram, electromyography, nerve conduction study, 18 F fluoro2-deoxyglucose positron emission tomography, with CT/MRI localisation (for early inflammation in large vessel vasculitis). Musculoskeletal BD—synovial fluid analysis, X-ray, ultrasound or MRI to evaluate joints. Skin biopsy and immunofluorescence for cutaneous involvement. Cardiac BD—electrocardiogram, echocardiogram in case of cardio vascular involvement chest CT to assess mediastinal diseases, fibrosing mediastinitis, aneurysms, pleural effusions, complications of venous thrombosis and collaterals. Stool sample for faecal calprotectin, endoscopy and biopsy if there is Gastrointestinal involvement. Ocular BD—optical coherence tomography, visual evoked potentials, fluorescein angiogram, Schirmer’s test, intraocular fluid culture to exclude infections in Behçets patients with ocular involvement. Cystoscopy and kidney, urinary bladder CT for urological disease [7].
\nThe multisystem involvement in Behçet’s systems alarms the requirement of a teamwork from different medical specialties such as oral medicine, ophthalmology, dermatology, rheumatology, neurology, cardiology and gastroenterology for its management. The main objective of management of Behçet’s disease is to induce and maintain remission and improve the patient’s quality of life, preventing irreversible damage and exacerbation of mucocutaneous and articular disease [77, 78]. Since Behçet’s disease has plethoric expressions, treatment plan may vary depending on severity, organ affected, age of disease onset, disease duration and frequency of recurrences [59].
\nThe European League Against Rheumatism (EULAR) formed a committee in 2008 to develop an evidence-based recommendation for the treatment modalities of Behçet’s disease [78]. Suggestions regarding ocular and mucocutaneous diseases and arthritis are primarily evidence-based, but recommendations on neurological, vascular and gastrointestinal involvement relied on observational studies, retrospective analyses and expert opinion [78].
\nTreatment of Behçet’s disease is mainly based on the suppression of the inflammatory attack using immunomodulatory agents such as corticosteroids, azathioprine or interferon 𝛼 [78]. The primary line of management of mucocutaneous lesions in Behçet’s disease is colchicine (1 mg/day). NSAID is usually sufficient for joint manifestations [54]. Clinicians should keep in mind the neurotoxicity of cyclosporine A while prescribing it to any patient with neurologic problems for treating vascular involvement and refractory eye disease [54]. Biologic agents are the only option if the disease seems to be resistant to treatments [78]. Suggested management options are summarised in Tables 5 and 6.
\nManifestations | \nTreatments | \n
---|---|
Mild mucocutaneous lesions | \nTopical steroids | \n
Moderate to severe mucocutaneous lesions | \nInitial: systemic steroid, colchicine | \n
Refractory case: azathioprine, interferon𝛼 | \n|
Ocular lesions | \nInitial: systemic steroid + azathioprine | \n
Refractory case: first cyclosporine A + steroid + azathioprine Second interferon 𝛼+/− systemic steroid | \n|
Arthritis | \n(First) colchicine nonsteroidal anti-inflammatory drug, (second) azathioprine, interferon 𝛼 | \n
Vascular: deep venous thrombosis | \nAzathioprine, cyclosporine A, cyclophosphamide (larger vessels) | \n
Vascular: arterial aneurysm | \nCyclophosphamide + systemic steroid | \n
Neurological | \nCyclophosphamide (drug of choice) | \n
Gastrointestinal | \nSystemic steroid, sulfasalazine, azathioprine | \n
Disease-modifying drugs (DMDs) | \nDosage | \n
---|---|
Oral DMDs | \n|
Azathioprine | \n2–3 mg/kg/day | \n
Mycophenolate mofetil | \n2–3 g/day | \n
Methotrexate | \n20–25 mg/week | \n
Tacrolimus | \n4–8 mg/day | \n
Ciclosporin | \n2–5 mg/kg/day | \n
Sulfasalazine | \n2–3 g/day | \n
Dapsone | \n2–3 mg/kg/day | \n
Thalidomide (exceptional use) | \n50–300 mg/day | \n
Colchicine | \n0.5–2 mg/day | \n
Prednisolone | \nVariable dose (depending on indication and stage of treatment) | \n
Parenteral DMDs | \n|
Cyclophosphamide | \n15 mg/kg (vasculitis regimens) | \n
Anti-TNF α inhibitors | \n|
Infliximab | \n5 mg/kg at 0, 2 and 6 weeks then once every 8 weeks | \n
Adalimumab | \n40 mg every 2 weeks | \n
Etanercept | \n50 mg/week | \n
Certolizumab | \n400 mg at 0, 2 and 4 weeks then once every 4 week | \n
Rituximab | \n1 g at 0 and 2 weeks | \n
Interferon α | \nVarious regimens for Roferon A and pegylated interferon α 2b | \n
Alemtuzumab | \n3 mg (day 1), 10 mg (day 3), 30 mg (day 5), 30 mg (day 8), 30 mg (day 10) and 30 mg (day 12) | \n
Disease-modifying drugs used in Behçet’s disease [7].
Behçet’s disease is a chronic, relapsing vasculitis with multisystem involvement with considerable morbidity and mortality. There exists a delay in the diagnosis of Behçet’s disease since it lacks a specific diagnostic test and biomarkers. Another area to be focussed on is to develop a more effective disease activity score. Researches are on the go to explore more about the disease’s pathophysiology which may aid in early diagnosing and effective treatment planning.
\nWe thank Prof Dr. Sobha Kuriakose, Dr. Sarika S. Kamal, Dr. Sumal V. Raj, Dr. Nithin V. S and Dr. Deena C. Thomas, Dr. Alaka Subodh and Adithi S. A for critically analysing this manuscript.
\nThe Cognitive Dynamic System (CDS) is an organized physical model and research tool that is based on certain features of the brain. Following its first introduction in [1], it was later expanded in [2] leading to its first applications in cognitive radio [3] and cognitive radar [4]. Since then, CDS has progressed enormously to give rise to Cognitive Control (CC) [5] and Cognitive Risk Control (CRC) [6] as two of its particular functions. Using those principles, the CDS was first merged in [7] with the Smart Grid (SG) to form a new structure, based on the DC state estimation model, that shows tremendous potential for handling the possible problems that the SG will be facing in the near future. Furthermore, in [8], the construct presented in [7] was expanded to include a more complex CRC that is closer to the brain. In that paper, it was proven how this new approach can to be used to mitigate the problem of cyber-attack in the SG. From a neuroscience perspective, the CDS is founded on Fuster’s paradigm of cognition comprising of the following five principles: perception-action cycle (PAC), memory, attention, intelligence and language [9]. In its simplest form, the CDS is built on two main components: the perceptor, on one side, and the executive on the other with the feedback channel uniting them together. In [7], it was shown that the integration of the over-arching function of CDS, CC, with the SG, is well adapted for slowly progressing cyber-physical systems. In this chapter, the construct presented in [7], where the DC-estimation model was involved, will be re-engineered to be able to carry out AC state estimation optimally and also be able to detect cyber-attacks. In order to do so, the perceptor of the CDS will incorporate a generative model that will allow it to sense and control the environment indirectly. Moreover, in order to bring forward the cognitive ability of the CDS and make it compatible with the current nonlinear state estimation in SG, the steps involved in the state estimation process will be re-engineered in a novel way. It will also be shown how the entropic state, which is the objective function of the CDS, will be instrumental in implementing a control-sensing mechanism that is capable of identifying and handling bad measurements. We will also show how this entropic state serves as the basis for detecting False Data Injection attacks (FDI) in SG.
\nThe next generation of engineering systems consisting of the Internet of Things (IoT) and Cyber-physical systems (CPSs) are currently paving the way towards the fourth industrial revolution [10]. As those systems are gradually occupying a more prominent role in our daily lives, through applications in critical infrastructures such as electrical power grids or transportation systems, the cyber-security aspects of those systems will also grow in importance [11]. In the context of this chapter, emphasis will be laid upon the SG and its most dangerous threat known as False Data Injection (FDI) attacks. More specifically, compared to our previous research where the DC model for state estimation was investigated [7], focus will be laid upon on the AC model, which is more a realistic representation of the smart grid, and the introduction the CDS for a new way of control and FDI attack detection.
\nMaking use of all the new generation of sensing, monitoring and control strategies, the SG is forecasted to be a more powerful entity than the traditional power grid in many facets such as reliability and efficiency [12, 13]. In the SG, the Supervisory Control and Data Acquisition systems (SCADA) is responsible for monitoring and processing the main control actions by collecting meter measurements from remote terminal units (RTUs) consisting of different field devices or sensors. Through a process known as state estimation, those measurements are then processed and analyzed for errors and inconsistencies after being transmitted to a control center [14, 15]. The state variables that are calculated by this process usually consist of the voltage magnitudes and angles of the different busses in the system [16]. The measurements used for state estimation are the currents, real and reactive power flows, power injections and voltage magnitudes and angles. In the DC model, the state variables are the bus angles only while in the more complex AC model, the voltage magnitudes and angles of the different busses in the network are estimated. Weighted Least Squares (WLS), introduced by Schweppe [14], is the technique used for the power system state estimation using those measurements. In order to enhance the accuracy of the estimated states, another process, known as Bad Data Identification, is carried out to remove bad measurements. Bad measurements are erroneous measurement readings that will impact state estimation negatively. The most commonly applied bad data identification techniques are the Chi-Squared Tests and Largest Normalized Residual Test [15, 17]. Those statistical tests rely on the residuals between the estimated states and the measurement residuals to identify the bad data. In the case of an FDI attack, bad data, which can bypass the previously mentioned tests, is introduced into the system such that the estimated states can be modified stealthily. Those bad data are maliciously crafted offsets to measurements that are injected to the sensor readings so as the state estimation process is influenced in a particular way. Consequently, with the incorrect calculated states, bad control decisions will be applied.
\nAlthough FDI attacks have been a popular topic of research over the past years [18], most of the works, e.g., in [10, 11, 12, 13, 19], investigated the FDI attacks on the DC model. Few works have been published on the AC model and those attacks [18, 20, 21]. Nevertheless, the DC model is just a simplified representation of the nonlinear AC state estimation model. There are major differences between the two models that could explain why the AC model has been unpopular. Firstly, in the nonlinear state estimation model, the estimated states are obtained after undergoing iterations, while in the DC model, those states are obtained in closed-form. Moreover, the linear state estimation relies on active power flow analysis [16, 22, 23]. On the other hand, the AC model uses both active and reactive power flow analysis. Furthermore, the state variables in the DC model consist of the voltage angles only while the states in the AC model consist of both the voltage angles and magnitudes. Consequently, these differences raise the complexity and computational expense of nonlinear state estimation as a topic of research when it comes to FDI attacks [24]. In fact, DC based FDI attacks can be detected by AC-based data detection techniques [20]. Hence, since the AC model is commonly applied in power systems, finding a way to detect these attacks and mitigating them under that environment is going to be very important for the coming years.
\nThe main contributions of this chapter can be summarized as follows:
The architectural architecture of the CDS, tailored for AC state estimation and FDI attack detection in the SG, is presented. Compared to our earlier work in [7], which was based on the DC model, we will show how that construct can re-engineered with the goal of nonlinear state estimation and computational efficiency in mind. Consequently, it will be shown how the CDS allows for optimal state estimation with relatively less computations, using the principles of cognition rooted in the brain.
To expand on our previous research, the entropic state will be re-introduced for two purposes namely; (1) it serves as a metric of the grid’s health on a cycle to cycle basis and (2) it is used in the detection of FDI attacks. The optimization of the entropic state is the goal of the cognitive controller residing in the executive of the CDS. The latter does this by selecting the most optimal actions that will maximize the available information from one PAC to the next. Simulations are performed on the IEEE 14-bus network to show the efficiency of this new approach using the CDS. By learning which measurements to prioritize and which ones to neglect, the CDS showcases a new way of control for bad data correction and FDI attack detection with the SG being the topic of application.
The rest of this chapter is organized as follows: In Section 2, the basic concepts of state estimation and data detection for the AC model will be presented and contrasted. The mathematics of FDI attacks for this model will also be demonstrated. Section 3 expands on the structure of the CDS for the SG. Since this research is an extension of [7], the material presented in that paper will be re-engineered for this new application. In the context of the CDS, the SG is considered as the environment with which it interacts. Section 4 gives a discussion on the application and simulation results of this approach on the IEEE 14-bus network. It will be shown how this new structure is able to handle the two problems of bad data detection and FDI attack detection simultaneously. Finally, Section 5 concludes this paper by highlighting the key results and presenting new avenues of research for this novel construct.
\nIn order for the Energy Management System (EMS) to operate properly, it is important for the SCADA to provide the latter with the required measurement data so that correct control decisions can be applied in real-time. However, as those signals are often contaminated with noise, filtering is carried out by both the state estimator and the bad data detector to obtain the most accurate states. However, since power systems comprise of an overdetermined system whereby redundant measurements are taken, the filtering process allows the discarding of those erroneous measurements that will be detrimental for state estimation.
\nThe states of a power system refer to the bus voltages angle \n
In the AC model, the nonlinear power flow equations are fundamental for state estimation since they indicate the link between the measurements and the estimated states. In this model, the active and reactive power for the transmission line between busses k and m are given by
\nAdditionally, for each bus k, it is calculated using the following equations:
\nwhere \n
where
x is the n vector of the true states (voltage magnitudes and angles)
z is the m vector of measurements (active and reactive power flows, active and reactive power injections, voltage magnitudes and angles)
h is the m x n Jacobian matrix (relates measurements to states)
h(x) is the m vector of nonlinear function linking measurements to states
e is the m vector of measurement errors
\nm is the number of measurements
\nn is the number of variables
H in (5), also known as the Jacobian matrix, is a matrix that defines the theoretical calculations that relates the states to the measurement vector z and therefore serves as a mathematical description of the power system. These equations are also referred to as the power flow equations and are described as vectors inside H. While in the DC model, those entries consists of a set of linear functions of the state variables, those functions are nonlinear as far as the AC model is concerned. The determination of the state variables is done according to the following criteria:
\nW in (6), is a diagonal matrix that contains the measurement weights. These are based on the reciprocals of the measurement error variance \n
where Rz is the covariance matrix of the measurement. The performance index J(X) is then differentiated to obtain the first order optimal conditions which can be solved using iterative methods, such as Honest Gauss Newton method, Dishonest Gauss Newton method and Fast Decoupled State Estimator [23]. The first order optimality condition of (6) to be solved is then expressed as:
\nwhere \n
During the state estimation process, faulty measurements have to be detected and identified to be removed as they lead to erroneous calculated states. However, the statistical properties of these errors simplify their detection and identification. In order to determine those errors, the estimated measurements, \n
The individual estimated measurement error is then obtained using:
\nAs these errors follow a zero mean Gaussian distribution [16], techniques such as the Chi-Squares test and normalized residual have been the most common ones applied for their detection [27]. When Chi-squares test is applied, it is assumed that the state variables are mutually independent from each other and the errors follow a normal distribution. The test involves a number of iterative steps that depend on the number of degrees of freedom of the system, sum of squares \n
where k is the appropriate number of degrees of freedom and \n
FDI attacks (also known as Bad Injection attacks) is a special category of attacks targeting the SG, whereby bad measurements are injected such that they are able to bypass the bad data detection methods discussed previously. While FDI attacks can also target other cyber-physical systems, various forms of these attacks and consequences have been investigated in [11, 12, 15, 16, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. In this paper, FDI attacks will be simulated using assumptions from [26], whereby it is assumed that the system parameters and topology (system Jacobian) is known to the attackers, and [18], where a mathematical formulation for simulating the FDI attack in the AC model is provided. Additionally, FDI attacks satisfying the first assumption regarding prior knowledge of the system have been proven to result in more disastrous consequences. Moreover, in [17], the authors demonstrate how an attacker, using that knowledge of the system matrix \n
Hence, this will result in the calculation of an incorrect system state vector \n
For the AC model, it is shown in [18] that the attack vector will remain undetected when it satisfies the condition:
\nIt is then proven as follows:
\nConsequently, in the case of nonlinear state estimation, it is more complicated to implement the FDI attack. Compared to the attack in the DC case [17], where the attacker only required knowledge of the Jacobian matrix, in the AC model, the latter is now additionally required to have some prior knowledge of the current states of the system. While it is more complicated to meet those conditions, it is still shown in [18] that such an attack is possible and the consequences can be disastrous. In both the DC and AC model, the calculation of wrong state variables, caused by this attack, can start a domino effect of incorrect control decisions leading to dire consequences. As this type of attack targets state estimation in the SG predominantly, the vector \n
From a neuroscience perspective, the CDS is the entity that matches Fuster’s paradigm [9] the closest as far as cognition is concerned. Basically, the CDS is made up of four components namely; environment, perceptor, executive and feedback channel. Moreover they are arranged in a very particular way. The feedback channel links the perceptor and executive, which are situated on two opposite sides. The environment finally closes the global feedback channel whereby the entire CDS is contained within it. Since the focus of this chapter is the nonlinear state estimation and FDI attack in the SG, the AC state estimator will be considered as the environment with which the CDS interacts since it is the recipient of the measurements in the network. By acting as the supervisor of the network, the CDS empowers the state estimator, through CC, with the cognitive ability to learn during every PAC which measurements to prioritize for optimal state estimation and which to ones to discard. Figure 1 shows the complex diagram whereby the CDS and AC state estimator are brought together for meeting the goals mentioned previously. In the next subsections, it will be elaborated how the arrangement and the role of each constituent plays a major role for goal-oriented action on the SG.
\nArchitectural structure of CDS for the nonlinear SG.
When the environment is free of uncertainty, the PAC is responsible for updating the CDS with new information from the environment for every cycle. Thus, with the continuous acquisition of new information from this global feedback loop, the information extraction ability of the perceptor is constantly being improved with each successive cycles. Consequently, this sets up an uninterrupted cyclic directed flow of information from the perceptor to the executive to lead the PAC with the most optimal actions to be performed on the environment. As a result, this hypothesis for a goal-focused scenario is then modified with new information gained from the PAC to allow the executive to improve its current ability to achieve the primary goal that it was designed for.
\nSimilar to the concept of Percept in the agent of AI [39], both in the brain and CDS, a perception process is performed on incoming measurements. The perceptor of the CDS extracts useful information from the noisy measurements, which subsequently the executive uses to optimize its actions and improve the information gain for the next cycles. Those actions, performed by the executive under CC, are called cognitive actions. However unlike the role of the percept in AI, the perceptor perceives the environment directly and extracts relevant information from it which in turn the cognitive controller, residing in the executive, uses to sense the environment indirectly. In order to perform its function, the perceptor is made up of the generative model and the Bayesian filter, which are reciprocally coupled to each other.
\nAs defined in [6], the first component of the perceptor for the CDS is conceptually the Bayesian generative model [6], which acts as classifier for the observables received from the environment. However, in [7], it was argued that due to the dynamic nature of the SG, the Bayesian generative model would not be suitable for this specific application. Due to the complexity of the SG and its adoption for almost all applications, it is of upmost importance to detect anomalies or cyber-attacks as soon as possible before they can infect the network further, thereby starting a domino effect of cascaded problems throughout the entire network and end users. Therefore, inspired from quickest detection theory, the generative model proposed for the perceptor was based on cumulative sum (CUSUM) and is written as follows:
\nWhere \n
The second component of the perceptor is the Bayesian filter, which is coupled to the generative model. Although the equations describing the SG for state estimation are nonlinear in nature, we can linearize the state estimates using the Kalman filter and assuming that it is operating under additive white Gaussian noise [42]. Since we are assuming that the power system is quasi-static in nature in this paper [43, 44, 45], we can use the well-known Kalman filter as the Bayesian filter in the perceptor. The Kalman filter is based on the state-space model which operates on a pair of equations known as the Process equation and the Measurement equation respectively. Moreover, under quasi-static assumptions, we can assume that the state variables \n
where \n
and the covariance of matrix \n
As we are assuming that the system is operating under quasi-static conditions, a random walk model can be employed as the process equation as follows:
\nwhere \n
Referring to (18) and (20), the system matrix \n
The predicted estimated states of the generative model and predicted error covariance, \n
When the next cycle starts, those two estimates are then used for the measurement update stages to calculate the Kalman gain, \n
As a result, through the iteration of the time update and measurement update steps, the preceding a posteriori estimates are used to predict new a priori estimates.
\nThe feedback channel has very distinctive roles in the CDS as it completes the PAC by bringing together the perceptor and the executive. It is mainly related to control and cyber-attack detection in the SG. In order for the CDS to supervise the SG, the feedback channel holds the entropic-information processor, which is tasked with calculating the entropic state and internal rewards during reinforcement learning in the executive. This will be elaborated in sub-Section 3.4 (Executive) where it is more relevant to the role of the executive during planning.
\nThe directed cyclic flow of information from the perceptor to the executive is known as the entropic state of the perceptor. The entropic state is built on the principles of the perceptual posterior, which can be viewed as the incoming filtered posterior embodying the essence of the generative model, Kalman filter and entropy, which is derived from Shannon’s information theory [46]. The entropic state at time \n
where \n
When the environment is operating in the absence of uncertainty, \n
When uncertainties are present, \n
From a design perspective, the Executive is the most important entity of the CDS as it is responsible for control of the SG in the absence of uncertainty. With this goal in mind, it consists of Reinforcement Learning (RL) and Cognitive Control (CC), which can be further subdivided into the action space, planner, working memory and policy.
\nAsides from its role in the calculation of the entropic state during each PAC, the feedback channel is also involved in the calculation of internal rewards during the planning stages of the RL [39] algorithm in the executive. RL in the CDS is based on the current entropic state at each cycle which is subsequently used to optimize an objective function for optimal control in the network. Before we elaborate on the pivotal role of RL with the other components of the executive, Bayes-UCB [47] RL algorithm will be covered briefly in order to give an overview on how it operates. Bayes-UCB represents the current state of the art from a class of multi-armed bandit algorithms called UCB algorithms [48], which are based on the principle of optimism in the face of uncertainty. In this approach to the multi-armed bandit problem, the algorithm updates the estimate of the reward distribution for each action using a Bayesian method. The action that will be applied is then chosen according to the one that will yield the highest reward. Consequently, Bayes-UCB algorithm is an index policy that uses the prior distribution to pick a dynamic quantile of the posterior estimates for the index for each action. Hence, at each discrete time \n
where \n
where \n
CC can be considered in many ways as the heart of the CDS as it brings together all the components, described so far, for goal oriented action on the SG. CC is made up of two important modules namely the planner and the policy. The planner is involved in the extraction of a set of prospective actions from the action-space \n
where \n
The presence of uncertainties in the environment, whether stochastic or probabilistic, will cause a deviation in the output of the generative model of the perceptor from the estimated hidden state of the Kalman filter. Hence, the goal of (31) is to reduce this divergence by finding the best configuration weights for the respective meters. This condition is satisfied whenever the \n
Moving forward with equations that describe the planning steps, the stage is now set to define the relationship between the previous steps and the calculation of the internal rewards during RL. The hypothesized internal rewards, \n
As it can be seen from (32), the objective of RL, when operating under CC, is to minimize the amount of uncertainty in the SG by searching for an improved weight configuration during every PAC that will result in a better entropic state than the previous cycle. In other words, RL attempts to restrict the amount of uncertainty or disturbance during the state estimation process to the range computed by the Kalman filter in the perceptor. Referring back to the steps described so far that led to (32), we can see that the CDS, as defined in this specific architecture, learns from the past and present actions to pick the best actions for the future. To assist in this task, after undergoing the shunt cycles during every PAC, the working memory holds temporarily the actions that have achieved the highest quantile from Bayes-UCB in (29) and applies them to the system before starting the next PAC. Thus, when the next PAC starts and a new set of prospective actions are evaluated according to their quantile values, if any of those actions achieves a higher quantile than the quantile of its respective meter in the working memory, then the higher achieving action will replace that previously considered best action. This way of performing control in the SG can also be viewed from a Bandit perspective, whereby it can be considered as a Contextual Bandit problem where every cycle presents new situations to be faced. According to those conditions, the actions performed on the SG will modify the system configuration to a new set point, from which the RL algorithm will have to adapt. This then continues on until the CDS is brought to rest. The complete algorithm of the methodology presented in this chapter can be found in [51] where it is integrated with a cyber-attack mitigation strategy known as Cognitive Risk Control, which was not discussed in this chapter. In [51], a greater discussion on the parameters and its selection is provided and contrasted with other popular cyber-attack detection methods.
\nIn this section, two different experiments are carried out to show the capability of CC in this new CDS architecture adapted for the smart grid. The first experiment shows CC’s potential for optimal state estimation by using the optimization of the entropic state as objective function. In the second experiment, the capability of the entropic state as an attack detector will be demonstrated in four different scenarios based on the amount of information an attacker has and his access to the sensors. As IEEE bus networks have generally been used as benchmarks for evaluation in the other papers previously referenced and relating to this topic, the IEEE 14-bus network will be used for assessing the architecture proposed in this chapter. Since this particular network comprises of a large number of measurements and states, the results for the two different experiments will focus on certain aspects of the network that are relevant to the actual simulation. For both experiments, the data used to simulate the network configuration comes from the 14-bus case file in MATPOWER [53] which is an Electric Power System Simulation and Optimization Tools for MATLAB and Octave. Moreover, in order to bring about the modification for the AC state estimation algorithm, the doSE function of MATPOWER was modified for the requirements of the architecture. Originally, the algorithm uses Honest Gaussian Newton method with a maximum number of iterations of 100 and error tolerance of \n
In the first experiment, the measurement signals relating to the state values were available from the case data in MATPOWER [52]. For this simulation, a noisy version of those signals was then generated with a signal-to-noise ratio (SNR) of 20 dB to create \n
Referring to Figure 2, it can be seen that CC makes the whole network dynamic, whereby the executive of the CDS is assigning the best weight values for the meters for optimal state estimation on a cycle to cycle basis. Consequently, the cognitive controller shows it ability to learn from the current and past cycles to choose the best actions for future. Moreover, the constant modification of the weight values adds another level of nonlinearity on top of the already very complex and nonlinear AC state estimator. While this may appear to be over-complicated at first, the results show that this is not only feasible but it also makes the SG more powerful. As it can be seen in Figure 2, at the first instance of meter malfunction for meter 2 at \n
Graphs of some affected states, weights and entropic state.
In this section, the dual property of the entropic state for FDI cyber-attack detection will be demonstrated. Previously, it was shown how the latter is an objective function for the normal running of CC under the absence of uncertainty whereby it is always positive. However, when the presence of uncertainties are no longer probabilistic, such as when an attack takes place, the entropic state will also enable early detection of such attacks. In all the cases, it is assumed that the attacker has knowledge of current states of the system. Although many specialized attacks such as replay attack or Distributed Denial of Service (DDoS) attack exist, four broad categories of FDI attacks will be considered as follows:
Case 1: Here we assume that the intruder has perfect knowledge of the network configuration \n
Case 2: In this scenario, the intruder still has full knowledge of \n
Case 3: Here the circumstances of case 2 are flipped around; the intruder has access to all the meters but incomplete knowledge of \n
Case 4: Finally, a rogue attack combining case 2 and 3 is considered. The attacker has both imperfect knowledge of \n
The mentioned attacks in those different situations were simulated on the IEEE 14 bus network as shown in Figures 3–6. In all of the mentioned cases, the hacker’s goal is to deflect the value of two of the voltage magnitudes by −0.3 and 0.4 units respectively and one voltage angle by 0.3 radians. Since attack data is not publicly available, the parameters in the MATPOWER package will be used to simulate the IEEE 14 bus network.
\nCase 1.
Case 2.
Case 3.
Case 4.
In all four attack cases, the attack is started at \n
If the CDS architecture proposed in this paper is applied in a medium or large-scale power system, the computational complexity will be lesser compared to the other current detection methods, such as the ones mentioned earlier. A greater elaboration of this technique compared to the other detection methods can be found in [7]. Moreover, the application of the CDS for an application such as the SG is revolutionary as it is a dual system catering to both the control and attack detection aspects of the SG. The main parameter of interest that needs to be scaled up for a more complex grid will be the number of shunt cycles since more meters will have to be evaluated. Nevertheless, it is recommended to keep the action space small so as to make planned rewards, during planning, distinguishable from each other. Another important hyper-parameter in the system, especially for FDI attack detection, are the values in the \n
As voltage fluctuations are common occurrence disturbances in power systems, the second simulation was designed to provide the reader a greater intuition on how the algorithm is able to distinguish between what constitutes a perturbation and the normal condition. When the states of the AC state estimator is experiencing important fluctuations, this is propagated to the generative model and therefore affects the entropic state as a result. Since \n
This chapter covered the following points:
This is the first time that a CDS structure has been proposed for handling the nonlinear version of the SG. While previous research in this field, which were focused on bringing the CDS and the SG together, were based on the DC model, the AC model is a more realistic approach to the SG. Consequently, the new construct, which was described in the chapter, shows a lot of potential at tackling the future problems that the grid will face in the coming years as it becomes increasingly interconnected with the other aspects IT such as IoT.
While there are some tradeoffs to be made due to the already inherent computational complexity of the AC state estimation algorithms, it was shown that the CC is revolutionary in the sense that it allows the application of multiple actions during every PAC while still maintaining the stability of state estimation.
The CDS tailored for the AC model of the SG, proposed in this chapter, is a unique architecture that is able to make the SG more powerful by providing a new kind of control and cyber-attack detection, that are both based on cognition from the brain’s perspective.
In this chapter, a new CDS based architecture was united with the SG in order to tackle the issues of nonlinear state estimation and cyber-attack detection through CC. Computational experiments were carried out to show the individual benefits of CC for optimal state estimation and FDI attack detection respectively. Moreover, it was also discussed how the algorithm and the parameters can be adjusted so that it can be scaled up to work with bigger networks. In those bigger networks comprising of a large number of meters, a function approximator such as a Neural Network [54] can employed to simplify some of the computations involved. Although this chapter focused on the problems of control on state estimation and cyber-attack in the SG, the architecture covered in this paper, can also be formulated to work for other similar applications where state estimation is critical such as Vehicular Radar Systems. In order, to adapt the CDS for other applications, the mathematics involving the perceptor and the executive will have to be adjusted accordingly depending on the final goal of the different intended systems.
\nThe Edited Volume, also known as the IntechOpen Book, is an IntechOpen pioneered publishing product. Edited Volumes make up the core of our business - and as pioneers and developers of this Open Access book publishing format, we have helped change the way scholars and scientists publish their scientific papers - as scientific chapters.
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