ACR classification of EGPA [37].
\r\n\tThe successful management of these patients needs a multidisciplinary team composed of gastroenterologists, surgeons, interventional radiologists and specialists in critical care medicine, infectious disease and nutrition. The purpose of this book is to provide the reader with an overview of the multidisciplinary treatment of pancreatitis, both in its acute and chronic form.
",isbn:"978-1-80356-159-2",printIsbn:"978-1-80356-158-5",pdfIsbn:"978-1-80356-160-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,hash:"4e78047fa5099332e086bc621777e71f",bookSignature:"Dr. Marco Massani and Dr. Tommaso Stecca",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11719.jpg",keywords:"Acute and Chronic Pancreatitis, Diagnosis, Pain Management, Nutrition, MRI, CT, Fluid Resuscitation, ERCP, WON, Pancreatic Pseudocyst, Microbiota, Complications",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 2nd 2022",dateEndSecondStepPublish:"March 2nd 2022",dateEndThirdStepPublish:"May 1st 2022",dateEndFourthStepPublish:"July 20th 2022",dateEndFifthStepPublish:"September 18th 2022",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Chief of Department of Surgery at Treviso Regional Hospital, Italy who has served as Adjunct Professor at University of Padua, Italy since 2012. Member of the Italian Surgical Association (SIC), Member of the Italian Hospital Surgical Association (ACOI), Member of ENETS (European Neuroendocrine Tumor Society), and Member of the Executive Board of the Italian Chapter of International Hepato-Biliary-Pancreatic Association (IHPBA).",coeditorOneBiosketch:"Resident General Surgeon at HPB referral center in Treviso General Hospital, Italy with a background in research laboratory focused on liver disease and cholangiocarcinoma. Member of the multidisciplinary team of colorectal cancer and chronic inflammatory bowel disease.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"183231",title:"Dr.",name:"Marco",middleName:null,surname:"Massani",slug:"marco-massani",fullName:"Marco Massani",profilePictureURL:"https://mts.intechopen.com/storage/users/183231/images/system/183231.jpg",biography:"Dr Marco Massani is currently chief of Department of Surgery at Treviso Regional Hospital. He obtained his medical degree at University of Padova and after graduating in 19992 he started working at the regional reference center for hepato-pancreatico-biliary (HPB) surgery, directed by Professor Nicolò Bassi. Also Dr Massani obtained the specialization in general surgery at the University of Padua. Since 2012 he also serves as Adjunct Professor at University of Padua, Italy. \r\n\r\nThroughout his career. Dr Marco Massani has worked at Treviso Regional Hospital but at the same time he has visited many important international institutes. He was visiting surgeon and attended courses at many well known institutes like Memorial Sloan Kattering Cancer Center and Mount Sinai Medical Center in New York, the Thomas Starzl Transplant Institute in Pittsburg, Imperial Hammersmith Hospital School of Medicine College in London, Emory University in Atlanta, Hospital Beujion in Paris etc.\r\n\r\nDr. Massani was a member of the Bioethics Committee and is the author of numerous scientific publications. He is also Member of the Italian Surgical Association (SIC), Member of the Italian Hospital Surgical Association (ACOI), Member of ENETS (European Neuroendocrine Tumor Society) and Member of the Executive board of the Italian Chapter of International Hepato-Bilary-Pancreatic Association (IHPBA).",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Padua",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:{id:"297971",title:"Dr.",name:"Tommaso",middleName:null,surname:"Stecca",slug:"tommaso-stecca",fullName:"Tommaso Stecca",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002jyweAQAQ/Profile_Picture_1639482119258",biography:"Dr. Tommaso Stecca is a Resident General Surgeon at HPB referral center in Treviso General Hospital. He has a background in research laboratory focused on liver disease and cholangiocarcinoma. 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They are characterized by cell inflammatory infiltration and necrosis of small vessels [1]. They are classified as microscopic polyangiitis (MPA) granulomatosis with polyangiitis (GPA, previously known as “Wegener’s granulomatosis”) and eosinophilic granulomatosis with polyangiitis (EGPA, formerly known as “Churg-Strauss syndrome”) [2]. The systemic inflammation seen in these vasculitis result in organ- and life-threatening diseases with a polymorphous clinical presentation. AAV can affect the peripheral nervous system and that could be difficult to diagnose and treat. In cases of pre-existing systemic vasculitis, the diagnosis is easier to make, but when the vasculitis neuropathy is the initial or unique manifestation of the vasculitis, it requires careful clinical, neurophysiological, laboratory and sometimes histopathological investigation. The frequency of vasculitis-related neuropathy is variable and depends on the type of vasculitis [3]. In this chapter, we will discuss the pathogenesis, diagnosis, treatment and prognosis of neuropathies in AAV including the MPA, GPA and EGPA.
Peripheral neuropathy in AAV is caused by thrombosis and ischemic damage of the vasa nervorum. Different etiological agents may induce vascular inflammation [4]. Vascular injury is associated with neoantigens (usually infectious) on the endothelium and neutrophils. Eosinophils contribute to vessel inflammation, as seen in GEPA [5]. Immune complexes with certain immunochemical characteristics activate a complement cascade that induces neutrophil-mediated damage to the vessel wall. The presence of granulocytes is associated with fibrinoid necrosis as they release toxic enzymes during inflammation. Antineutrophil cytoplasmic antibodies identify constituents of neutrophil cytoplasm including proteinase 3 (PR3), myeloperoxidase (MPO) and elastase. The release of these cytoplasmic components induces the release of inflammatory mediators such as TNFα [6]. Inflammation of vasa nervorum leads to ischemia with axonal degeneration that mainly presents as mononeuritis multiplex [7].
Peripheral neuropathy is common in ANCA-associated vasculitis and can be the first manifestation of the disease. The prevalence of PN is variable depending on the type of AAV. It is particularly higher in EGPA (60–80%) than in MPA (40–50%) and GPA (20–25%) [8, 9, 10, 11, 12, 13, 14]. Vasculitis-related neuropathies are also seen in other systemic diseases such as cryoglobulinemic vasculitis associated with chronic hepatitis C virus (HCV) with a prevalence of 60% and in primary Sjogren syndrome [14] and rarely in large-vessel vasculitis [14] and other connective tissue diseases such as systemic lupus [15].
PN is usually the first clinical presentation of systemic vasculitis especially in EGPA and MPA. In other cases, the PN is associated with systemic symptoms of the disease such as asthenia, weight loss, fever, arthralgia or arthritis and vascular purpura. PN is characterized by an acute onset of pain, weakness and sensory loss that predominantly affects the distal portion of the extremity. Initially, the PN may present as a mononeuritis evolving over weeks or months later into multifocal neuropathy or mononeuritis multiplex [16]. The pain is described as throbbing and aching rather than burning. The lower limbs are usually affected and the most common involved nerve is the deep peroneal nerve [11, 12, 13, 14, 17, 18, 19, 20, 21, 22]. In the upper limb, the ulnar nerve is the most common affected nerve [17]. The mononeuritis multiplex pattern evolves into an asymmetrical or symmetrical polyneuropathy pattern, which can progress into a generalized sensorimotor neuropathy [17]. Muscle weakness and atrophy is also variable inially mild but subsequently prominent [23]. Uncommon presentations of PN in AAV are symmetrical polyneuropathy from onset and pure motorneuropathy [17, 24].
The diagnosis of vasculitis neuropathy in AAV is usually easier in patients already presenting with multiorgan involvement and mononeuropathy multiplex. However, the diagnosis may be more cumbersome in less typical presentations of AAV or when peripheral neuropathy is the unique manifestation of the disease. In these situations, the diagnosis is helped by focusing on the medical history, physical examination, electrodiagnostic study and nerve biopsy. Electrodiagnostic testing reveal an axonal neuropathy with reduced sensory and motor nerve action potential amplitudes [25, 26, 27, 28] with better preservation of the nerve conduction velocities and distal latencies. These findings are more often in the lower limbs [28]. The nerve biopsy should be guided by the nerve conduction studies and include the nerve and neighboring muscle, such as sural nerve and neighboring gastrocnemius or superficial peroneal nerve biopsy and peroneus brevis muscles [17, 22, 29, 30, 31]. Muscle biopsy may increase the diagnostic sensitivity when concomitantly performed with the nerve biopsy [32]. Nerve biopsy results supportive of vasculitic neuropathy include the presence of vessel wall inflammation with vascular damage; vascular deposits of immunoglobulin M, C3, or fibrinogen, hemosiderin deposits on direct immunofluorescence, asymmetric nerve fiber loss, prominent active axonal degeneration, and myofiber necrosis, regeneration, or infarcts in the peroneus brevis muscle biopsy [23, 32].
Eosinophilic granulomatosis with polyangiitis formerly named Churg–Strauss syndrome is a systemic small-vessel vasculitis associated with asthma and eosinophilia. It was first described in 1951 by Churg and Strauss [33] who remarked the association between asthma, eosinophilia, systemic symptoms and the presence of necrotizing and granulomatosis vasculitis in different organs especially in the peripheral nerves [33]. EGPA is a rare disease, with an annual incidence of 0.5–4.2 cases per million inhabitants [34]. It affects people aged between 40 and 60 years with no gender predominance or ethnic predisposition [35, 36]. In 1990, the American College of Rheumatology (ACR) defined the classification criteria for EGPA to include asthma, eosinophilia >10%, neuropathy, non-fixed lung infiltrates, paranasal sinus abnormalities and extravascular eosinophils on biopsy (Table 1) [37].
|
ACR classification of EGPA [37].
A histologic diagnosis was required in the Chapel Hill classification in 1994 and 2012 [2, 37, 38]. EGPA is a necrotizing vasculitis with an eosinophilic-rich, granulomatous inflammation affecting small- to medium-sized blood vessels in the respiratory tract.
EGPA should be suspected in a patient with an adult-onset asthma in association with multiple systemic symptoms and a subacute asymmetric neuropathy. Asthma of variable severity is noted in 95–100% of patients and could precede the systemic manifestations by many years. Allergic rhinitis, recurrent sinusitis and nasal polyposis are also seen in the prodromic EGPA phase [39, 40, 41]. Eosinophilic cell infiltrates are found in the lung, heart and gastrointestinal tract. The lung parenchyma is affected in up to two-thirds of EGPA patients [41]. Chest X-ray abnormalities generally consist of mainly peripheral, patchy and migratory infiltrates. On high-resolution CT, they appear as ground-glass opacities or poorly defined areas of consolidation, which often coexist with abnormalities due to lower airway involvement, such as tree-in-bud signs, bronchial wall thickening and small centrilobular nodules [41]. The second type of lung involvement is alveolar hemorrhage, which affects 3–8% of the patients [13, 41]. Heart involvement is a poor prognostic factor of the disease and correlated with the level of eosinophilia. Endomyocardial infiltration is the dominant feature, but coronary vasculitis, pericarditis and valvular defects may also occur [42]. Venous thrombo-embolic events, such as deep venous thrombosis and/or pulmonary embolism, are associated with eosinophilia [43]. Renal involvement can also be seen ranging from isolated urinary abnormalities (i.e., microscopic hematuria, proteinuria) to rapidly progressive glomerulonephritis. Skin lesion such as purpura, nodules, urticaria, livedo, and skin ulcers could also be reported mainly in the lower limbs [41].
PN is considered a cardinal feature of the vasculitic phase with a prevalence of 70% [41, 44, 45]. PN is often associated with generalized signs and symptoms of fever, weight loss, and weakness. It usually presents as a mononeuritis multiplex, often complicated by asymmetric foot or wrist drop, but it may also evolve into a symmetric or asymmetric polyneuropathy [41]; sensory deficits and neuropathic pain are frequent [19, 26]. PN is more frequent in ANCA-positive patients than in patients without ANCA antibodies [41, 44].
Laboratory findings in EGPA include a marked peripheral eosinophilia (usually >1500 cells/μL), which correlates with disease activity [46]. C-reactive protein and erythrocyte sedimentation rate are also high in the active phase [41]. ANCA with perinuclear immunofluorescence is noted in 74–90% [41]. Histologic confirmation is the key diagnosis with leukocytoclastic vasculitis with eosinophilic granulomas in biopsy sites such as the lung or kidney. Granulomas are rarely found in peripheral nerves [41].
GPA is a systemic ANCA-associated granulomatous vasculitis whose lesions primarily affect the respiratory tract and kidneys [47]. Its annual incidence is 5–10/million with a prevalence of 24–157 cases per million. It occurs in both sexes at 65–74 years of age [48, 49]. GPA can affect the central and peripheral nervous system. Centrally, it can be responsible for strokes, brain masses, seizures, and meningitis. Peripherally, in the systemic form of the disease, it can present with a sensorimotor neuropathy or as a mononeuritis multiplex. Nasosinus involvement is observed in 70–100% of patients and present with epistaxis, nasal ulcers, nasal septum perforation and deformation (Figure 1) [50, 51]. The lungs are the second most common affected organ in 50–90% of patients and present with lung nodules, cavitations, infiltrates, pleuritis, pleural effusions, or alveolar capillary hemorrhages. Renal involvement affects 40–100% of patients with hematuria, proteinuria and renal failure due to segmental necrotizing and pauci-immune glomerulonephritis. Skin manifestations include vascular purpura, ulcers and nodules. The systemic symptoms include myalgia, arthralgia, anorexia, weight loss, ocular scleritis, episcleritis, uveitis, retinal alterations, retinal, thrombosis, orbital masses granulomatosis, myopericarditis, intestinal perforation and mesenteric vasculitis [50]. To make the diagnosis of PN related to GPA, it is necessary to consider all the clinical manifestations suggestive of systemic vasculitis like C-ANCA (anti-PR3) determination and histological evidence of necrotizing vasculitis, necrotizing glomerulonephritis or granulomatous inflammation from a relevant organ biopsy. In 1990, the American College of Rheumatology established criteria to help the diagnosis of GPA (Table 2) [52].
Saddle nose deformity caused by bony destruction of the nasal cavity in a patient with Wegener’s granulomatosis [
|
ACR classification of GPA [52].
Microscopic polyangiitis is an uncommon systemic vasculitis associated with perinuclear antineutrophil cytoplasmic (p-ANCA) or anti-myeloperoxidase (MPO). It was formerly considered as polyarteritis nodosa and in 1950, Wainwright and Davson used the phrase “microscopic polyarteritis” to describe this phenotype [53]. Microscopic polyangiitis predominates in men with an average age at onset between 50 and 60 years. Clinical manifestations include general symptoms of fever and weight loss in 70% of patients. Renal involvement is the main feature of MPA. It is characterized by a rapidly progressive glomerulonephritis in 80–100% of patients. It is shown by proteinuria in the nephrotic range in up to 50% of patients, microscopic hematuria, and urinary granular or red blood cell casts. Renal biopsy reveals focal segmental necrotizing glomerulonephritis in up to 100% of patients [54]. The second major organ being affected is the lung in 55% of patients. Clinical manifestations include hemoptysis and alveolar hemorrhage, infiltrates, pleural effusion, pulmonary edema, pleuritis and interstitial fibrosis. These symptoms are related to diffuse alveolar hemorrhage [55]. Computed tomography is necessary to confirm alveolar hemorrhage demonstrating the ground-glass attenuation (seen in >90% of patients) interstitial chronic inflammation of the alveolar septa and capillaritis (Figure 2) [55]. Skin lesions occur in 30–60% of patients being vascular purpura the main presentation. Other skin manifestations include livedo reticularis, nodules, urticaria and skin ulcers with necrosis. Skin manifestations are usually accompanied with arthralgia [54]. Neurologic involvement is common and affects between 37 and 72% of the patients. PN is a predominant feature that presents with a mononeuritis multiplex and distal symmetrical polyneuropathy [53]. Other clinical symptoms are gastrointestinal bleeding, intestinal ischemia, and liver dysfunction [54]. ANCA is the laboratory test that facilitate the diagnosis and is positive in 50–75% of patients with MPA, but its absence does not exclude its diagnosis. Biologic markers of inflammation are elevated such as erythrocyte sedimentation rate and C-reactive protein [56]. The diagnosis of the disease is based on clinical symptoms and biopsy of the affected organs.
Coronal chest CT scan image with diffuse bilateral ground glass infiltrates and focal areas of consolidation [
The treatment is based on induction therapy and maintenance therapy. Unfortunately, there is not an universal protocol (dose or duration) for each form of therapy. Induction therapy is based in the combination of corticosteroids and cyclophosphamideor rituximab. Standard initial therapy consist of high-dose corticosteroids (prednisone 1 mg/kg/day) or IV methylprednisolone (1 g every day for three days and then once a week for three months) followed by a taper. Pulses of methylprednisolone are used in severe cases (i.e., mononeuritis multiplex and organ-threatening disease). Pulse IV cyclophosphamide (1 g/m2 per month for six months; or 15 mg/kg every two weeks for three doses and then every three weeks for three to six months) is simultaneously started with corticosteroids, especially in more severe cases. Cyclophosphamide is adjusted by age (>60 years) and to renal function and leukocyte counts. IV Rituximab at 375 mg/m2 per week for four weeks every six months in combination with corticosteroids could be also used as induction therapy [57, 58]. Rituximab is often used in less severe cases with insufficient data in more severe neurological manifestations, however, some case studies are promising [7]. The five factor score (FFS) (Table 3) could be used to assess the prognosis and mortality of vasculitis in the next few years and then to guide when a more aggressive therapy is required, usually when FFS > 1 [59].
Proteinuria >1 g/24 h Creatinemia>140 μmol/L Specific gastrointestinal involvement Specific cardiomyopathy Specific CNS involvement One point for each of these five items when present. |
Five-factor score in AAV.
After the induction therapy, a maintenance therapy follows with oral immunosuppressants drugs such as azathioprine (1 mg/kg/day to 2 mg/kg/day), methotrexate (7.5 mg to 25 mg weekly), mycophenolate mofetil (1 g to 1.5 g, 2 times per day) or IV Rituximab pulsations every six months [7, 60]. Oral cyclophosphamide is not recommended because of the risk of serious complications [60, 61] such as hemorrhagic cystitis, alopecia, leukopenia, myelodysplasia, neoplasm, etc.
The symptomatic management of neuropathic pain consist of tricyclic antidepressants (i.e., amitriptyline, imipramine, nortriptyline, etc.), serotonin-norepinephrine reuptake inhibitors (i.e., duloxetine, venlafaxine) or antiepileptic drugs such as gabapentin and pregabalin, which are preferred because their better bioavailability [58]. Kinesitherapy should be included in the management of motor disability. PN in AAV requires regular medical visits due to the relapse risk.
PN is one of the possible neurologic manifestations encountered by physicians in AAV. Therefore, it is important to take a detailed medical history and examination and adequate investigations to assess for an underlying systemic vasculitis that may be associated with the neuropathy. Mononeuritis multiplex is the most common features of PN in the AAV. The electrodiagnostic studies and nerve biopsy may help in the diagnosis of the disease and PN. When the PN precedes the diagnosis of vasculitis, the medical history and biologic test, especially ANCA test, are vital for diagnosis, but its absence does not exclude the disease. PN in AAV carries a prognostic factor because of the potential risk for motor complications. Therefore, rapid treatment with corticosteroids and immunosuppressant agents is almost warranted in all patients, especially in severe cases. Continuous follow-up of PN in AAV is essential because of frequent relapses.
Authors disclose no conflict of interest.
In recent years significant quantity of research work have been done using Artificial Intelligence (AI) and Expert Systems (ES) to predict and estimate the faults in the system in order to investigate the complete health status of the system [1]. Soft computing technique is an intelligent computing approach which integrates the human reasoning ability with learning capability of uncertainty and imprecision of the system. The prime importance of the soft computing technique is the non-requirement of the well-defined mathematical model of the system. Therefore, soft computing is becoming more popular in the fault diagnosis and prognosis applications. The powerful machine learning techniques used for the current work are Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference System (ANFIS). A brief description and implementation of these techniques for fuel system health management is presented in this chapter. The general steps to determine the system health status are described as below:
Detecting the symptoms of faults by monitoring and analyzing
Identification of the cause of faults
Manual insertion of faults within the model of the system
Diagnosing the effect of the fault on the system and
Predicting the functional behavior of the system.
Figure 1 illustrates the health management architecture of the fuel system and its diagnostic and prognostic functioning process. This architecture comprises three main functions, such as monitoring, diagnosis and prognosis. The operation of the first function is to monitor the fuel flow rate and status of its co-operating components like pumps, the quantity of fuel in each tank, valve functioning, the rate of fuel consumption by the engine. Based on the initial fuel flow rate, the rate of fuel consumed by the engine is estimated by the monitoring function. Further, the required fuel flow rate from each tank is calculated by the prediction model. An artificial intelligence approach is implemented for the simulation of a typical small aircraft fuel system for fault analysis.
Fuel system health management architecture.
In the health management process initially, a comparison is done between the actual flow rate and the required fuel flow rate. If the flow rate is not in the desired range, an analysis is conducted to detect and determine the reason for this disagreement, which activates the fault analysis process. Further, it is used as the criterion of failure that leads to the diagnostic function. The prognostic function in the assumed fuel system is to forecast the fuel flow rate from each of the tanks. Thus, the monitoring function helps to predict the variations in the fuel flow rate and helps to assess the overall health status of the fuel system.
Many different approaches of fault diagnosis and prognosis are demonstrated in the literature with their pros and cons and it is difficult to say which is a better approach. It depends on the type of the complex system considered to model, available expert knowledge and data. AI techniques are very effective in pattern classification, decision making, pattern recognition and problem solving
The combination of AI techniques with model-based approaches has excellent efficiency for fault diagnosis. ANFIS takes advantage of the learning ability of ANN and knowledge representation of Fuzzy logic systems. Implementation of ANFIS as fault diagnosis tool was reported by [2, 3]. A method for faults diagnosis of induction motors using ANFIS and regression techniques was described by [4, 5] used recurrent NN and ANFIS to forecast the crack propagation in rotating machine and provided the comparison results of both predictors. Application of prognosis in aerospace industries is an evolving arena with very a smaller number of real-time implementations. Thus, implementation of a model-based methodology with the integration of AI techniques is considered for providing better representation of the complex system like aircraft fuel system.
The fuel level monitoring function is primarily used to manage and monitor the quantity of fuel in each tank. A platform for the study of the fuel system sequencing, a simulation of fuel management system was modeled by [6]. Fuel level sensors installed in each tank provides the quantity of fuel and also, transfer the signal to the fuel management system. The outer model of a typical small aircraft fuel system is shown in Figure 2. The Simulink model developed includes a tank model, tank parameters and fuel pump model as sub-models. The following are some assumptions made during the simulation using Simulink.
In the aircraft fuel tank, the fuel and gas temperature are homogeneous
The acceleration and deacceleration effect on the fuel is not considered while analyzing. Thus the sloshing is not interpreted for the simulation.
Tank geometry of cubic meter is considered without any fixed reference frame
The fuel properties are not affected by the changing ambient pressure and temperature
Snapshot of outer model of the typical aircraft fuel system.
The assumed typical small aircraft fuel system contains four fuel tanks and a total of eight fuel pumps as depicted in Figure 3. It consists of four main pumps of which two pumps are used for backup and remaining two pumps for delivery of fuel between wings at emergency conditions [7]. The flow of fuel in this aircraft is managed and monitored by the designed adaptive intelligent controller. The primary objective is to manage the fuel flow to the engine without fail and to reach the required engine fuel consumption rate. If any fault arises in any of the fuel tanks, the controller model detects it and reacts as per the fuel requirement of the aircraft’s engine. The typical fuel system consists of four tanks with a total fuel capacity of 2800 kg with each tank capacity of 700 kg respectively. The tanks are symmetrically located in both left and right wings.
Structure of a typical small aircraft fuel system.
The model of the aircraft fuel system is simulated based on the fuel system design [8], and the fuel system health management is analyzed using the hybrid fuzzy system. The efficiency ANN based controller is analyzed by comparing with ANFIS based intelligent tool. The total usable capacity of 2800 kg of fuel is sufficient for at least 30 minutes of operation of the assumed aircraft at full continuous power. Simulation of fuel pump is done using the properties of the Hyjet-4A fluid as fuel. The assumed temperature and the viscosity value of fuel are 22.72°C and 1, respectively. The fuel line is built and simulated in the same manner as in the practical fuel systems using the metal pipes that are firm and fixed. A pipe of length 500 mm is assumed which connects the internal wing-tip tanks. The length of pipe between the engines and the integral wing tanks is considered to be of 7200 mm. The pipes have an internal diameter of 10 mm, and the shape of pipes is decided with a geometry factor of 64. Figure 4 shows the Simulink model of the geometry of the fuel tank. An integral fuel tank of height 0.65 m, length 4.39 m and width of 2.0771 m is modeled. An axial pump containing an electric motor drive is selected, with an angular velocity of 1770 rpm and the correction factor of 0.8. Figure 5 shows the model of the fuel pump as well as the fuel line for the transfer of fuel between the wing tanks. In other words, the monitoring function helps to provide alert messages to the flight crew and provide data to the prediction model. Table 1 provides the simulated values assumed at normal operating conditions for linear motion of the aircraft.
Geometry of the aircraft fuel tank.
Fuel pump and fuel line model.
Sl. No. | Parameter | Value |
---|---|---|
1 | Mach number | 0.26 |
2 | Temperature of the ambient air | 22.72°C |
3 | Speed of sound | 314.8 m/sec |
4 | Aircraft speed | 81.9 m/sec |
Assumed simulated values of an aircraft.
The flight range of simulated typical aircraft was calculated based on steady linear motion. Substituting assumed values of Table 1 into the equation, for each tank the flight ranges are obtained. For a fuel system with four tanks each of 700 kg of fuel capacity, the flight range can be calculated as:
Where,
Figures 6 and 7 shows the level of fuel of a healthy tank and a faulty tank. The faults are introduced intentionally in the first tank by decreasing the fuel level which may occur due to leakage in the tanks, pipelines, valve stuck or maybe because of the filters blocks or icing within the fuel system. Figure 7 demonstrates the leakage in the fuel system for 5 seconds, which affects the operation of the engine. The decrease in the level of fuel in the first tank is detected and diagnosed effectively by the developed health management methodologies. The level of fuel decreases after 6.5 sec during simulation for a simulation time of 25 seconds as shown in Figure 7.
Level of fuel in healthy tank.
Level of fuel in fault tank.
This section discusses the diagnostic and prognostic function associated with the assumed fuel system. Prognosis is prediction of remaining useful life time of the system. Based on the available fuel quantity, engine fuel consumption and status of the components, the prognosis function generates the control signals to the fuel management system to predict and mitigate the faults within the system. Further, through the diagnosis function the obtained flow rate is matched with the control signals and corrective measures are taken to identify and detect the fault in the fuel system. In this way, using these functions health management of the fuel system can be achieved.
Soft computing techniques such as Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for diagnostic and prognostic analysis of the aircraft fuel system. Health management process using ANN and ANFIS as a controller is illustrated in this chapter.
Backpropagation is considered to be the most efficient training algorithm among different artificial neural network algorithms [9]. Learning process in ANN is achieved by collecting the information and training them accordingly. For the assumed fuel system, ANN-based health management tool uses the fuel flow rate as input data and previous engine fuel consumption rate as the historical information. The ANN model has trained accordingly and generates output signals in relation to working condition of the fuel system. Figure 8 shows the structure of the assumed fuel system with ANN controller. Based on the present and previous instance fuel flow rate, it generates the output signal in the presence of faults. The ANN health management tool continuously monitors the flow rate of fuel to the engines and provides the require d fuel flow to both the engines.
Structure of the assumed fuel system with ANN as controller.
This section exploited the basics of ANN. Proper training of NN helps to perform fault analysis, detection, and diagnosis without the requirement of a complex mathematical model. The interpretation of the NN similar to human thinking and multiple parallel processing features of NN enhances the network performance [10].
Feedforward neural network is implemented which regulates the input parameters of the assumed fuel system to obtain the desired outputs. A simple model of the neural network is shown in Figure 9. The ‘n’ number of input signals are denoted as
A neural network model.
Backpropagation (BP) is a supervised learning algorithm [4]. By gradient descent, the algorithm calculates the error function in relation to the weights of the neural networks. Learning process in ANN is achieved by collecting the information and training accordingly. The following equations describe the implementation of a feed-forward neural network.
where,
For hidden layers according to the BP algorithm, the derivation error of backpropagation is expressed as:
Where,
For the assumed fuel system, ANN-based health management tool uses the fuel flow rate as input data and previous engine fuel consumption rate as the historical information. The ANN model has trained accordingly and generate the output signals in relation to the working state of the fuel system. The predictive output signal of the ANN model is obtained as:
Where,
Training of ANN with backpropagation algorithm calculates the gradient error and regulates the weights by the required flow rate to be consumed by the engines. Continuous updating of ANN model helps to maintain the required flow rate of fuel irrespective of malfunctioning of the any of the component within the fuel system. The disadvantage of the Feed Forward Neural Network with BP algorithm is the necessity of complex mathematical calculation, slow rate of error convergence and more amount of time required for a non-linear system operating condition. A hybrid ANFIS methodology is utilized to overcome this drawback. This methodology provides better performance results for non-linear and changing operating condition for the assumed aircraft fuel system.
ANN controller monitors and manages the flow of fuel without any limitations to meet the required flow rate. Fault occurrence within the fuel tanks are detected, and corrective measures are taken by the ANN. Any change in the data is identified by the unique property of the ANN algorithm that differentiate the data points in noisy conditions. The ANN controller calculates the gradient error, and accordingly the target signals are generated by adjusting the weights of the BP algorithm. Figure 10 shows the flowchart of the updation process of the ANN. Timely maintenance of the fuel tanks and fuel system helps to improve the performance of the aircraft. Faults in the tanks like leakage, filter blockage or pump failure during flight may degrade the operation of the aircraft. Under such circumstances, ANN algorithm continuously updates with the actual data and maintains the required flow rate.
Flowchart of ANN-based prognostic tool for the fuel system.
The two hidden layers of ANN model using Simulink is depicted in Figure 11. In this neural network four input samples and four target samples are used. The hidden layer consists of 100 neurons and output layer of four nodes for the assumed four-tank fuel system. The neural network toolbox of Matlab/Simulink utilizes the data points in three stages. In the first stage, the training data points are used for training the neurons and the gradient error is calculated by using the weights of successive layers. The upadated weights of the network reduces the error for the given value. During second stage, validation fail check is done. Testing process of data points is carried out in the third stage. The performance of the developed NN is then analyzed.
Developed ANN Simulink model.
The BP algorithm uses the Mean Square Error (MSE) procedure to compute the error in each step or iteration. MSE is the performance index of the BP algorithm. It is the error computed as variance between the network output and target output. The relation given in Eq. (8) computes the MSE in each step for each output,
Where,
During the training process of ANN after 4th iteration the performance of 4.33e-33 obtained which indicates the amount of minimized error. Gradient of 3.16e-12 indicates the variance occurred in the error rate, Mu of 1.00e-07 is the threshold value achieved after each iteration and validation check indicates reduced error after current (4th) iteration as compared to previous (3rd) iteration. The graph illustrated in Figure 12 shows the performance and status of the training process. It is a curve showing the plot of MSE versus four epochs. The blue line plotted represents the training result, green color line denotes the result of validation check and red line indicates the test result. During the ANN training process, as the MSE decreases, consequently the network output reaches to target output. The performance using ANN as fault diagnosis and prognosis process is assessed by computing the MSE based on the Eq. (8). The best validation performance achieved is of 1.6835 at four epochs. The graph with all the three results of training, testing and validation coincide is considered to be the best performance.
Training performance of ANN.
The gradient and validation plot determine a process of assessing the performance of the training and testing of ANN technique. The gradient of error and validation plot for four epochs obtained is as shown in Figure 13. It is observed that a smooth decrease in the gradient error at four epochs and maximum measures of validation check at four iteration fails to zero.
Gradient and validation performance plot of the ANN model.
Another important factor considered while evaluating the performance of the ANN is the correlation coefficient obtained for training, testing and validation. From the Figure 14, it is seen that the overall correlation coefficient is 0.6818. Table 2 provides the details of the ANN training data where intentional faults are inserted into the tanks and trained accordingly. Figure 15 shows the Simulink model of four tank fuel system with ANN as a controller. Due to faults, the input data points vary and such variations are trained consequently by updating the weights of the neural network. The ANN-based health management process is implemented by taking corrective measures in the presence of faults (Table 3).
Training performance plot of the ANN model.
Sl.No | Tank 1 | Tank 2 | Tank 3 | Tank 4 |
---|---|---|---|---|
1 | 1 | 1 | 1 | |
0.7000 | 1 | 1 | 1 | |
0.3000 | 1 | 1 | 1 | |
0 | 1 | 1 | 1 | |
1 | 0.7000 | 1 | 1 | |
1 | 0.3000 | 1 | 1 | |
1 | 0 | 1 | 1 | |
1 | 1 | 0.7000 | 1 | |
1 | 1 | 0.3000 | 1 | |
1 | 1 | 0 | 1 | |
1 | 1 | 1 | 1 | |
1 | 1 | 1 | 1 | |
1 | 1 | 1 | 0 |
ANN training data.
The Simulink model of the aircraft fuel system with ANN as a controller.
Tank 1 | Tank 2 | Tank 3 | Tank 4 |
---|---|---|---|
1 | 1 | 1 | 1 |
0.7000 | 1.3000 | 1 | 1 |
0.3000 | 1.4000 | 1.3000 | 1 |
0 | 1.5000 | 1.5000 | 1 |
1 | 0.7000 | 1.3000 | 1 |
1.4000 | 0.3000 | 1.3000 | 1 |
1.5000 | 0 | 1.5000 | 1 |
1 | 1.3000 | 0.7000 | 1 |
1.3000 | 1.4000 | 0.3000 | 1 |
1.5000 | 1.5000 | 0 | 1 |
1 | 1 | 1.3000 | 0.7000 |
1 | 1.4000 | 1.3000 | 0.3000 |
1.3000 | 1.3000 | 1.4000 | 0 |
Target data of ANN.
The simulated model of the fuel system can operate continuously for 30 minutes with full control and power. Fuel consumption test and fuel management test performed on the fuel system without a controller is shown in Figures 16 and 17 respectively. In the plot blue line indicates the required fuel flow rate and red line indicates the obtained flow rate without controller within the fuel system.
Fuel consumption by the engine of the aircraft fuel system without a controller.
Fuel management test without a controller.
As observed in Figure 17 for a simulation time of 20 seconds, within four seconds engine(s) get the required amount of fuel. The blue line indicates the required flow rate of 2800 kg/hr., which is fulfilled by delivering the fuel from all the four tanks with capacity of 700 kg per tank. It was obseverd that after a time period of 4 seconds, the level of the fuel in the first tank decreases indicating the presence of faults in the fuel tank. Because of faults the level of fuel in first tank decerases. Thus, when the quantity of fuel and level of fuel in the tank varies, it affects the flow rate of fuel to the engine. This may lead to the fuel starvation that causes the failure of the aircraft engine because of insufficient fuel supply to the engine. The existing aircrafts fuel system with automatic and programmed fuel management system may not identify sudden decrease in the level of fuel properly due to which the performance of the system gets degraded.
Figure 18 shows the fuel management test using ANN as a controller which identifies, detects the change in the fuel quantity and level of fuel in the first tank and correct it by fetching the required fuel from the second and third tanks. The ANN as controller predicts the required fuel by fetching the fuel quantity from the other tanks as per the Table 4.
Fuel management test with the ANN model as a controller.
Tank-1 | 70 kg | Required Fuel |
---|---|---|
Tank-2 | 960 kg | 2800 kg |
Tank-3 | 870 kg | |
Tank-4 | 700 kg | |
2600 kg |
Fuel fetched from each tank using ANN as controller.
The weight updating process of the BP algorithm during training process classifies the fault data, and testing process identifies the fault occurrence and mitigate it by predicting and fetching the required fuel from other tanks. Compared to the existing fuel management system, ANN as a controller in the presence of faults manages the fuel quantity and flow rate more efficiently. It detects the time of the fault, diagnoses it and mitigates it providing the complete health management of the fuel system. Minimizing the gradient of error for four iterations, the ANN model could be able to fetch the fuel flow rate of 2600 kg/hr. as shown in Figure 19. Using ANN as contorller during fuel managemnt test able to fetch 2600 kg/hr. of fuel where as the required flow rate by the engine is 2800 kg/hr. This variation in the flow rate is because of the drawbacks such as slow rate of error convergence and necessity of intensive calculation of BP algorithm for assumed operating conditions. Thus, an intelligent control model ANFIS as a health management tool is used to fulfill the engine fuel consumption requirement.
Fuel consumption by the engine using ANN as a controller.
ANFIS is a supervised gradient descent algorithm. In this, fuzzy rules configured upon the NN structure provide a qualitative description for the fault analysis of aircraft fuel system. In the resultant hybrid model, the NN recognize the fault pattern and adapt to the changing atmosphere. On the contrary, the FIS integrates the data and performs inferencing and decision making. The dynamic performance of the system can be represented by modeling the neuro-fuzzy method by extracting the numerical data from the model. Based on this approach, the system modeling serves two purposes. They are: the functional behavior of the assumed system can be predicted from the derived model, and the design of a controller is done using the resultant model. The ANFIS model is built first by initializing the input variables with the rules extracted from the input–output data of the assumed system. Later, the NN is utilized to fine tune the rules of the fuzzy model. The flow chart for ANFIS training procedure is as shown in Figure 20. In this work, ANFIS is used to detect and identify the presence of faults in the aircraft fuel system.
Flowchart for ANFIS training procedure.
The ANFIS structure developed is based on the model developed by [11]. The ANFIS network is mapping of input and output variables in a multi-layer network with a single target output [12]. The operating model of the ANFIS controller is depicted in Figure 21. The ANFIS methodology as a fault diagnosis and prognosis process for aircraft fuel system is briefly described in the following sub-sections. ANFIS is a structural plan that links expert’s knowledge and the knowledge capability of the neural networks. ANFIS builds a FIS whose membership function parameters are obtained by training appropriately. Consider FIS with two inputs ‘
Working model of the ANFIS controller.
The equivalent ANFIS architecture.
ANFIS is a five-layered feed-forward neural network,
where,
where the logical operator “and” is modeled as continuous term an in this case it is stated as a product. The individual o/p of all rules is obtained as a linear combination among parameters of the antecedents of every rule as signified by Eq. (10).
The output of the model
where,
The hybrid ANN signifying this inference is an adaptable network with five layers. All layers indicating the operation of the Fuzzy Inference System of the ANFIS is examined as follows.
In this method, every input layer is represented as an input variable, and it refers to the fuzzification layer. The input parameters
where,
This layer may be identified as the
In this layer each node of this layer is fixed which represents the “if” part of a fuzzy rule. It is process of normalization of the input weights that can complete the fuzzy “and” operation. In this layer, each node computes the ratio of the
where,
This is an adaptive layer that gives output membership function based on predetermined fuzzy rules. The node function is given by Eqs. (18) and (19).
where,
The output layer is symbolizing the THEN part of the fuzzy rule. This consists of one fixed node that computes the total output which is the summation of the input signals given by the following Eq. (20).
Where, f is the total output and the function of ANFIS is verified by considering a higher number of signals. Training the ANFIS model for the given inputs generate the control signals which help to maintain the fuel flow rate within the aircraft fuel system.
The important benefits of ANFIS are improved learning capacity, the ability to incorporate the non-linear structure of the system and rapid adaption capability. ANFIS can achieve exceptionally nonlinear mapping, far better than other techniques. Some of the drawbacks of this technique are: there are no standard methodologies to incorporate the changing human learning or experience into the base of a FIS and also there is a need for agent techniques used for tuning the membership functions to diminish or minimize the error during execution.
This section defines the simulation procedure of health management of aircraft fuel system using ANFIS as a controller. Figure 23 shows the model of aircraft fuel system with ANFIS as controller. It includes fuel tanks, pumps, pipelines that connects the tanks and pumps with the engines. The fuel system function is to distribute clean fuel at the required pressure and fuel flow rate to the engines in different operating conditions. The diagnostic and prognostic process of small aircraft fuel system is regulated by the ANFIS intelligent control model, that provides better fuel flow rate compared to ANN methodology. That is because of ANFIS’s significant features of significant reasoning ability and the low level of computational power during training process. The main purpose of the ANFIS control model is to direct the fuel flow to the engine and to access the essential engine fuel consumption rate. If a fault arises in any of the fuel tanks, the controller model detects the fault and activates the necessary actions as per the fuel requirement of fuel engine.
Structure of the aircraft fuel system with ANFIS as controller.
The ANFIS controller is concentrated on the optimization of parameters of the aircraft fuel system. Similar to the ANN controller, the ANFIS controller is assessed with the previous instance fuel flow and the engine fuel consumption value of the fuel system.
ANFIS’s learning ability carried out through the five-layer structure of the Fuzzy Logic system helps to approximate the non-linear functions which depends on the antecedent and consequent parameters. ANFIS is more robust and has better performance compared to conventional computing methods. These unique properties of the ANFIS such as improved computational power and high reasoning ability, permit it to be used in the fault diagnosis and prognosis of the fuel system to manage fuel flow rate as per engine consumption. The structure of the ANFIS process implemented for the health management of the fuel system is as depicted in Figure 24. The control signals generated are decided based upon the input parameters such as engine fuel consumption and previous instance fuel flow to the engine.
The structure of the proposed controller.
The rule structure of ANFIS is determined by the interpretation of the features of the variables of the fuel system model. ANFIS learn details of the input data points, calculate the membership function that best suits to track the input data and output data. The parameters related to the membership functions varies with the learning process of the fuzzy system which depends on the gradient vector. This gradient provides the measure to check the ability of the FIS for the given set of system parameters. The performance is evaluated by considering the error the difference between the actual and desired outputs. Table 5 shows the fuzzy inference rules framed for the four-tank fuel system. Based on these seven logical rules the learning of the system parameters related to the fuel system with the fault data is carried out. The training data points of input data and output data sets applied to the ANFIS scheme is configured similarly as applied to the ANN.
Input | Output | |||||||
---|---|---|---|---|---|---|---|---|
Rules | Tank 1 | Tank 2 | Tank 3 | Tank4 | Tank 1 | Tank 2 | Tank 3 | Tank4 |
1 | Normal | Normal | Normal | Normal | Normal | Normal | Normal | Normal |
2 | Medium | Normal | Normal | Normal | Medium | High | Normal | Normal |
3 | Low | Normal | Normal | Normal | Low | High | High | Normal |
4 | Very low | Normal | Normal | Normal | Very low | Very high | Very high | Normal |
5 | Normal | Medium | Normal | Normal | Medium | High | Normal | Normal |
6 | Normal | Low | Normal | Normal | High | Low | High | Normal |
7 | Normal | Very low | Normal | Normal | Very High | Very low | Very high | Normal |
Fuzzy inference rules.
During the evaluation, the ANFIS structure enables the change in the rules of the FIS. This property of ANFIS helps to optimize itself for the given number of iterations by changing the shape of the membership function, rules and also removes the unnecessary rules during training. A suitable ANFIS Simulink model is designed and developed for the health management purpose of the aircraft fuel system. ANFIS as a controller is designed for the aircraft fuel system with two input parameters and five bell membership functions for each input unit. Figure 25 shows Simulink model of aircraft fuel system with ANFIS controller.
Snapshot of the Simulink model of a fuel system with ANFIS as a controller.
Table 6 gives the details of the target output generated by the ANFIS. Similar to the ANN, ANFIS performance is evaluated based on the MSE value. Figure 26 depicts the curve of convergence of the training data with ANFIS target data indicated as reduction of MSE error. As the number of iterations are increased the MSE reduces indicating the fulfillment of desired target data from the training process of the ANFIS.
Tank 1 | Tank 2 | Tank 3 | Tank 4 |
---|---|---|---|
1 | 1 | 1 | 1 |
0.7500 | 1.3300 | 1 | 1 |
0.3500 | 1.4200 | 1.3300 | 1 |
0 | 1.5500 | 1.5500 | 1 |
1 | 0.7500 | 1.3500 | 1 |
1.4000 | 0.3500 | 1.3500 | 1 |
1.5500 | 0 | 1.5500 | 1 |
1 | 1.3500 | 0.7500 | 1 |
1.3000 | 1.4500 | 0.3500 | 1 |
1.5500 | 1.5500 | 0 | 1 |
1 | 1 | 1.3500 | 0.7000 |
ANFIS target data.
Training process of ANFIS.
Figures 27 and 28 shows the rate of fuel consumption and management test using ANFIS as a controller. The target output generated as control signal identifies the presence of a fault and provide 2700 kg/hr. of fuel flow rate which is almost near to the engine requirement 2800 kg/hr. Figure 27 also illustrates that the learning ability of the ANFIS reproduces accurately the desired output as compared to the ANN process. Thus, the error difference between the actual output value and the obtained output value is very small which is of 100 kg/hr. of flow rate. The ANFIS controller predicts the required fuel by fetching the fuel from the other tanks as per the Table 7.
Fuel consumption by the engine using ANFIS as a controller.
Fuel management test with the ANFIS controller.
Tank-1 | 70 kg | Required Fuel |
---|---|---|
Tank-2 | 990 kg | 2800 kg |
Tank-3 | 940 kg | |
Tank-4 | 700 kg | |
2700 kg |
Fuel fetched from each tank using ANFIS as controller.
The effectiveness of the ANFIS health management scheme is evaluated by comparing with ANN and fuel system without a controller. The details of the ANFIS based fault diagnosis process is presented in work of [15]. Both techniques uses similar fault conditions. In terms of comparison of training process using ANFIS and ANN techniques, it is clear from the Figure 29 that ANFIS provides better results. ANN and ANFIS methods detect the time of the fault, diagnose and predict the required flow rate by injecting the additional fuel from other tanks. However, the weight updation process of ANN is based on the historical dataset, which gives the mismatching results during the testing time. Due to this reason, the fuel flow rate obtained by the ANN method is 2600 kg/hr. and is not the desired requirement of fuel by the engine. Hence, it shows that ANFIS technique manage the health status of the aircraft fuel system by monitoring and managing the accurate fuel flow to the engine.
Comparisons of fuel consumption.
Soft computing methodologies like ANN and ANFIS are described in this chapter. A comparison study is made in [16]. All the simulation done is considered for the laboratory conditions only. Based on the theory of NN and FIS, the concept of hybrid five-layer ANFIS structure is implemented and simulated for the health management of the fuel system. Both the techniques help to monitor and manage the rate of fuel flow as required by the aircraft’s engine by generating the control signals. Further, based on an adaptive algorithm fault analysis is carried by the author in paper [17]. Diagnostic and prognostic process are carried out through managing the previous fuel flow and fuel consumption by the aircraft engine using ANFIS. ANFIS is a hybrid computational tool, which helps to tune and explain past data and predict future behavior of the system. The fuzzy inference rules that are created in ANFIS rely on both the input and the target output. Tuning can be accomplished with the learning ability of NN. To achieve flawless performance possible faults in the fuel system are detected and corrected by generating the appropriate control signals before the occurrence of massive damages in terms of economy and human life. In this scenario, health management tools have been encouraged.
First of all, I would like to thank the Supreme power, the Almighty, being with me and always guiding me to work on the right path of life. Without His grace, this would not have been possible. I would like to express my sincere thanks to my Research Guide Dr. Vanam Upendranath, Senior Principal Scientist, Aerospace Electronics and Systems Division, CSIR-National Aerospace Laboratories (NAL) Bangalore, Karnataka, India and Co-guide Dr. D. S. Jangamshetti, Prof. Dept. of EEE, Basaveshwar Engineering College, Bagalkot, Karnataka. This work would not have been possible without their support, encouragement and able guidance.
Sigmoid activation function
Sigmoid function
Threshold activation function
Error derivation
Derivation of error
Corresponding target
Predicted outputs
Actual input
Output of the NN model
Number of iterations
Outputs of the fuzzy layer
Membership functions
Output of the third layer
Consequent parameters set
Total output
Regular hyperplane
Highest distance value
Geometry distance
Kernel function
Time taken by the fuel tank
Time taken by the fuel tank after fuzzification
Target time of fuel tank
Authors are listed below with their open access chapters linked via author name:
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\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJocelyn Chanussot (chapter to be published soon...)
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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
\\n"}]'},components:[{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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Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. 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In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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