The proposed analytical models to estimate AIS operation quality [18, 19].
\r\n\t
",isbn:"978-1-83969-591-9",printIsbn:"978-1-83969-590-2",pdfIsbn:"978-1-83969-592-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"e39a567d9b6d2a45d0a1d927362c9005",bookSignature:"Dr. Umar Zakir Abdul Hamid and Associate Prof. Ahmad 'Athif Mohd Faudzi",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10778.jpg",keywords:"Model-Based Control, Optimal Control, Industrial Automation, Linear Actuator, Nonlinear Actuator, System Identification, Soft Robotics, Service Robots, Unmanned Aerial Vehicle, Autonomous Vehicle, Process Engineering, Chemical Engineering",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 25th 2021",dateEndSecondStepPublish:"March 25th 2021",dateEndThirdStepPublish:"May 24th 2021",dateEndFourthStepPublish:"August 12th 2021",dateEndFifthStepPublish:"October 11th 2021",remainingDaysToSecondStep:"22 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Umar Zakir Abdul Hamid, Ph.D. is an autonomous vehicle expert, and with more than 30 scientific publications under his belt, Umar actively participates in global automotive standardization efforts and is a Secretary for a Society of Automotive Engineers (SAE) Committee.",coeditorOneBiosketch:"Associate Professor Dr. Ahmad 'Athif Mohd Faudzi has more than 100 scientific publications as of 2021 and is currently leading a team of 18 researchers in UTM doing research works on control, automation, and actuators.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"268173",title:"Dr.",name:"Umar Zakir Abdul",middleName:null,surname:"Hamid",slug:"umar-zakir-abdul-hamid",fullName:"Umar Zakir Abdul Hamid",profilePictureURL:"https://mts.intechopen.com/storage/users/268173/images/system/268173.jpg",biography:"Umar Zakir Abdul Hamid, PhD has been working in the autonomous vehicle field since 2014 with various teams in different countries (Malaysia, Singapore, Japan, Finland). He is now leading a team of 12 engineers working in the Autonomous Vehicle Software Product Development with Sensible 4, Finland. Umar is one of the recipients for the Finnish Engineering Award 2020 for his contributions to the development of all-weather autonomous driving solutions with the said firm. He is an aspiring automotive thought leader and often invited as a guest and keynote speaker to industrial and technical events. With more than 30 scientific and technical publications as author and editor under his belt, Umar actively participates in global automotive standardization efforts where he is a Secretary for a Society Automotive Engineers (SAE) Committee.",institutionString:"Sensible 4 Oy",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:{id:"204176",title:"Associate Prof.",name:"Ahmad 'Athif Mohd",middleName:null,surname:"Faudzi",slug:"ahmad-'athif-mohd-faudzi",fullName:"Ahmad 'Athif Mohd Faudzi",profilePictureURL:"https://mts.intechopen.com/storage/users/204176/images/system/204176.png",biography:"Assoc. Prof. Ir. Dr. Ahmad `Athif Bin Mohd Faudzi received the B. Eng. in Computer Engineering, the M. Eng. in Mechatronics and Automatic Control from Universiti Teknologi Malaysia, Malaysia and the Dr. Eng. in System Integration from Okayama University, Japan in 2004, 2006, and 2010 respectively. He was a Visiting Research Fellow at the Tokyo Institute of Technology from 2015 to 2017. From March 2019 to date, he is the Director of the Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Malaysia. He is mainly engaged in the research fields of actuators (pneumatic, soft mechanism, hydraulic, and motorized actuators) concentrate his work in field robotics, bioinspired robotics and biomedical applications. He is a Professional Engineer (PEng), a Charted Engineer (CEng), a member of the IEEE Robotics and Automation Society (RAS) and a member of two Akademi Sains Malaysia Special Interest Group (ASM SIG) of Biodiversity and Robotics. He is also the recipient of Top Research Scientist Malaysia (TRSM) 2020 in the area of Robotics. 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Blood transfusion has become more performable in small and large animal practice. By donor selection and the availability of blood component substitutes, usage of the blood products improved. The use of blood component therapy safely needed knowledge of blood groups, antibody prevalence and the impact of blood groups on veterinary transfusion medicine. Animal blood transfusions antibodies against blood group antigens also play a role. In addition knowledge of the means to decrease the risk of adverse reactions by using proper donors and screening assays that simplify detection of serological incompatibility is important. The clinical significance of blood group antigens in veterinary medicine is generally in the areas of transfusion reactions and neonatal isoerythrolysis (NI). This chapter includes an update on canine and feline, horse, donkey, cattle, sheep, gaot, pig, llama and alpaca blood groups and known blood incompatibilities, donor selection and blood collection, storage of blood components, available equine blood products and indications for transfusion, whole blood (WB) and blood product transfusion in ruminants and camelids, blood component and blood substitute therapy, administration, and adverse reactions in small and large animal blood transfusion.
Blood types are classified according to specific antigens on the surface of erythrocytes. Platelets, leukocytes, and body tissues and fluids may also consists of erytrocyte antigens. [1]. In immunogenicity and clinical significance these antigens can differ. They can serve as markers of disease in some cases and taking part in recognition of self. The clinical significance of blood group antigens is generally noted in transfusion reactions and neonatal isoerythrolysis (NI) in veterinary medicine [2]. These antigens can characteristically trigger a reaction caused by circulating anti-erythrocyte antibodies in the opposite host or donor. These antibodies can occur naturally. Also they can be induced by a previous transfusion. Interaction leads to the destruction by hemolysis of red blood cells (RBCs). This is one of the severe and potentially life-threatening situation. [3].
The dog erytrocyte antigen types or blood types are categorized by the DEA (Dog Erythrocyte Antigen) system. DEA 1.1, 1.2, and 1.3 are termed A system. There are also DEA 3, DEA 4, DEA 5, DEA 6, DEA 7 and DEA 8. [2]. In the United States the incidence of DEA 1.1 is approximately 45% and DEA 1.2 is 20% [4]. DEA 1.3 is common in German shepherd dogs and has been reported only in Australia [5]. Frequency of DEA 1.1 in Kangal Dog was found as 61.1% in Turkey [6]. In Croatia where the closest data studied the rate was 66.7% [7]. The rate was also 56.9% in Portugal [8] and 55% in Japan [9]. Approximately 60 % of the canine population is in DEAs 1.1 and 1.2 group. DEA 1.1 is the strongest antigen in the dog. Two membrane proteins of 50 and 200 kD has been identified by a monoclonal antibody to DEA 1.1 using immunoprecipitation techniques. [10]. Presenting in a single band DEA 1.2 has been found to be an 85-kD protein [11].
DEA 1.1 is the most antigenic group in respect to transfusion medicine. Little is investigated about DEA 3, 4, 5 and 7 in comparison to DEA 1.1. In literature, the frequency of DEA 3 is lower in comparison to DEA 1.1 blood type. In the United States it is determined that approximately 6% of the general dog population is DEA 3 positive [12]. This rate is reported as 13% in Brazil [13]. In Turkey, DEA 3 is most found blood type in the Kangal Dog [6]. In the canine blood groups DEA 4 is the most common type. In USA, it is indicated that overall 98% of the general dog population have DEA 4 blood [12]. In Brazil, all dogs blood type were positive for DEA 4 [13]. The molecular weight of DEA 4 present in a single band has been found to be 32 to 40 kD using immunoprecipitation techniques [11].
In the United States typing sera can be commercially obtained only for DEA 1.1, 1.2, 3, 4, 5, and 7 [4]. In Brazil a report studied on German shepherd dogs determined that 14% of the dogs were positive for DEA 5 and 8% were positive for DEA 7 [13]. The frequency of DEA 5 and 7 positive dogs was 55.5% and 71.7% respectively in Turkey [6]. Also, DEA 7 may cause an antibody response in dogs that lack it. A system of nomenclature about antigen Tr has described. The Tr antigen system is a 3-phenotype, 6-genotype system [14]. The molecular weight of DEA 7 present in 3 distinct bands has been found to be 53, 58, and 63 kD by using immunoprecipitation techniques [11].
An exact definition of a canine universal donor is not agreed among veterinary transfusion experts. Well excepted description of the universal donor is that a dog negative for DEA 1.1, 1.2, DEA 3, DEA 5, DEA 7, and positive for DEA 4. It is difficult to find DEA 4 negative dog because 98% of all dogs are positive for DEA 4. Thus there is a very little chance to influence donor selection. If the dog is DEA 7 positive, some other experts do not exclude it from the donor pool [15]. In most populations the incidence of DEA 4 blood type is more than 98% [16]. Because of this in transfusion medicine these dogs are the best candidate for being a donor. If other donors are known to be compatible with the recipient they can also be utilized [17]. DEA 3, 5 and 7 negative dogs have naturally occurring antibodies to DEA 3, 5 and 7 positive red cells. However during the first transfusion these blood groups do not possess a major transfusion reaction [4]. In Turkey, the most common blood types were DEA 1.1, 4 and 7. Because all Kangal dogs have DEA 4 positivity it does not seem to be important in respect to transfusion medicine. The prevalence and antigenic properties of DEA 1.1 and 7 are significantly important. If unmatched transfusion is performed in Turkish Kangal dogs they can constitute acute hemolytic transfusion reactions [6]. Dogs with DEA 1.1 or 1.2 are called group A positive. Adversely, dogs do not have DEA 1.1 or 1.2 are called group A negative [1].
A blood group system described as N-acetylneuraminic acid and N-glycolylneuraminic acid present on gangliosides (hematosides) of the RBC membrane in Japan [18]. It is referred as the D system. This system is consist of two antigens, D1 and D2, with phenotypes, D1, D2, and D1D2. The D1 and D2 antigens are codominanat factors. Anti-D1 is identical to anti-DEA3. The importance of this system in transfusion medicine pointed out by transfusion of D2 type blood into a D1 type patient, or of D1 type blood into a D2 type patient consistently cause severe acute transfusion reactions [19, 20]. RBCs of some dogs designated as type C at titre sup to 128 are aglutinated rather than lectin extracted from seeds of Clerodendron tricotomum. Type C is completely negative for other dogs. C system was compared to the DEA system and determined to be different [10, 19, 21]. Specific IgG alloantibodies in previously sensitized Dalmatian dog by blood transfusion is described as the Dal blood type. The frequency is not known. Typing sera for this antigen also is commercially not available [2, 22, 23].
Three blood types are described in the feline AB blood group system and mik group system. In cats a new blood group defined as Mik. It is named after the alloantibody identified in the first blood donor cat, Mike. In three cats that had not previously received transfusions Mik antibodies were detected. They are defined as a cause of incompatibilities between donor and recipient blood that are not related to the AB blood group system [24].
The phenotypes type A, type B, and type AB are occured. A null phenotype is not exist. The most common blood type is Type A. Type B is less common. Type AB is rare [2, 25]. Type B is indicated in Australia (26.3%), and Greece (20.3%) ([26], [27] ). In large studies of both pedigree and non-pedigree cats in the USA distribution of type AB cats is demonstrated to be rare (0.14%) ([28] ). Type AB were 0.4% in Australia (([26]). In Scotland the incidence of AB cats is 4.4% ([29] ).
Type B is indicated in Australia (26.3%), and Greece (20.3%) ([26, 27]. In large studies of both pedigree and non-pedigree cats in the USA distribution of type AB cats is demonstrated to be rare (0.14%) [28]. Type AB were 0.4% in Australia [26]. In Scotland the incidence of AB cats is 4.4% [29].
In Turkey, 60 % of Van cats and 46.4 % of Angora cats are type B [30]. And 220 (73.1%) nonpedigree domestic cats had type A blood, 74 (24.6%) had type B and seven (2.3%) had type AB [31] in Turkey. Except type AB group, cats have naturally occurring alloantibodies. It is known that cats have naturally occurring alloantibodies (isoantibodies) against the blood type they are lacking. Because of this to prevent blood incompatibility reactions in cats feline blood typing is important in clinical practice. Blood type incompatibility can especially result in two fatal reactions. The first is acute haemolytic transfusion reactions, occur particularly in cat transfused with type A blood [32]. Feline neonatal isoerythrolysis (NI) is the second incompatibility reaction. It occurs when type A or AB kittens born to type B queens are nursing. Naturally occurring anti-A alloantibodies result in blood incompatibility reaction in the type B queen’s colostrum and milk [25, 30].
Cats constitute non-self antibodies in contrast to dogs. As a result of this non-self antibodies potentially fatal antibody-mediated reactions can occur towards non-self red blood cells. Nearly 20% of type A cats have anti-B antibodies. These antibodies are usually weak. All type B cats have strong anti-A antibodies. In contrast AB cats do not have alloantibodies [32]. In previously unsensitized cats naturally occuring isoantibodies are responsible for transfusion reactions. Nearly all type B cats have highly titered anti-A agglutinins and hemolysins. RBCs can be destructed rapidly in type B cats taking type A blood. In type B cats the high titres of naturally occurring anti-A antibodies cause rapid intravascular destruction of transfused type A red blood cells [33]. This can be mediated by IgM, complement fixation and the release of potent vasoactive compounds. As a result of this shock can develop usually due to possessed antibodies towards the transfused RBCs [3, 34]. This can cause severe transfusion reaction and death even if as little as 1 ml of type A blood is administered to a type B-cat [2, 35]. Because of their endotheliochorial placenta newborn kittens have no alloantibodies. Nevertheless colostral transfer of immunoglobulin (Ig) G and a small amount of IgM occurs. Neonatal isoerythrolysis develops in cats. It is one of the cause of the fading kitten syndrome. Kittens that are type A or AB and those that are born to type B queens are at risk. In affected kittens Clinical sings can range from unapparent, to severe hemolytic anemia with hemoglobinuria, icterus, and death [1, 36, 37, 38].
Packed red blood cells (pRBCs) and fresh frozen plasma (FFP) are components generally provided for canine transfusions. If processed at once, 1-4 each unit (450 mL) of whole blood can be seperated into 1 unit of pRBCs and 1 unit of FFP. It is difficult to prepare components from a small volume of blood. Because of this cat blood transfusions are usually administered as fresh or stored whole blood. If patients requires specific components like pRBCs and FFP, in this case whole blood can be separated into them [39].
In veterinary medicine, red blood cell transfusions are used more frequent recently. They are the integral part of lifesaving. They are used in critically ill as advanced treatment. Situations required transfusions include life-threatening anemia from acute hemorrhage or surgical blood loss, hemolysis from drugs or toxins, immune-mediated diseases, severe nonregenerative conditions, and neonatal isoerythrolysis [40].
Indications of red blood cell transfusions are in the treatment of anemia caused by hemorrhage, hemolysis, or ineffective erythropoiesis. Oxygen is poorly soluble in plasma. Because of this oxygen in blood is mostly carried by hemoglobin (Hgb). In anemic patient, RBC transfusions increase the oxygen-carrying capacity. Therefore inadequate delivery of oxygen to tissues with consequent tissue hypoxia are prevented or treated [41].
The treatment of severe anemia caused by hemorrhage, hemolysis, ineffective erythropoiesis, auto-immune hemolytic anemia, or neoplasia is primary indication for blood transfusion. Lethargy and altered mentation, increased respiratory effort, pale mucous membranes and tachycardia are the clinical signs of anaemia. The body carry out a number of adaptive responses physiologically, to maintain carrying of oxygen to the tissues [42, 43]. The solution of oxygen in plasma is weak. Because of this hemoglobin (Hgb) carries approximately whole oxygen in blood [41]. The decision to conduct a RBC transfusion is generally based on a measurement of the patient\'s packed cell volume (PCV), hematocrit (Hct) or Hgb concentration (Hgb) and especially on clinical evaluation of the patient [41]. Clinically animals should be evaluated individually. Generally when the hematocrit is less than 10%, the treatment of anemia is transfusion. However, animals with acute-onset anemia usually require transfusion before their hematocrit decreases to 15%. This contrasts with the situation in animals with chronic anemia. Other indications for transfusion are hypovolemia, thrombocytopenia, clotting factor deficiency, and hypoproteinemia [1]. Electrocardiographic signs of myocardial ischaemia are similar to those identified in human patients with myocardial infarction. It can ocur with anemia [44].
The usage of administration of FFP are for the treatment of a single or multiple clotting factor deficiency, vitamin K deficiency or antagonism, surgical bleeding or where a massive transfusion is required [45]. Hypoalbuminaemia and coagulopathies especially due to liver disease are the main reported indications for FFP transfusions in cats [46].
Stored blood is more than 8 hours old. The length of storage depends on the anticoagulant/preservative solution used. It varies from 48 hours for 3.8% sodium citrate (no preservative) to 4 weeks for CPD-A1 (citrate, phosphate, dextrose, and adenine). Acid citrate dextrose (ACD), citrate phosphate dextrose (CPD and CP2D), and citrate phosphatedextrose-adenine (CPDA-1) are mostly used as preservatives. The viability of RBCs is provided by the added dextrose, phosphate, and adenine. Due to the preservative used, the storage can extend up to 3 to 5 week ([3, 41, 47].
In patients that are hypothermic or receiving large volumes of blood, refrigerated RBC products should be prewarmed to temperatures between 22 C and 37 C immediately before transfusion. In the routine practice of RBC products to normovolemic anemic patients, refrigerated blood components do not need warming before transfusion. Warming may accelerate the deterioration of stored RBCs and may cause rapid growth of contaminating microorganisms [48].
In clinical practice advances in safety of blood transfusion is important in preventing transfusion-transmitted infections (TTI). The most frequent severe infectious outcome of transfusion has been known as bacterial contamination of platelets, with resultant sepsis in the recipient recently. Using automated or semi-automated blood culture devices, apheresis platelets and prestorage pooled platelets are most often tested [49].
Generally, before a blood transfusion is given to animals, blood typing and/or cross-matching of the recipent and donor should be done to avoid the likelihood of a transfusion reaction. Also, ineffective therapy is caused by shortened survival of transfused mismatched red cells. In order to prevent primary sensitization and risk of developing hemolytic disease in breeding females, cross-matching and/or blood typing is important. In general veterinary practise, blood typing for canine DEA 1.1 and for feline types A and B is applied [1].
To decrease adverse reactions one sould pay attention to blood typing and crossmatching procedures as much as monitoring. There is always risk in blood transfusions. For this reason, they should be performed only when warranted. When taking history, previous transfusion therapy should be asked and in a history of previous transfusion therapy cross-matching is necessary [1, 50].
Depending on availability and indication for transfusion, whole-blood or blood-component therapy may be administered. RBCs, white blood cells (WBCs), platelets, all the coagulation factors, albumin and immunoglobulins constitute whole blood (WB) [51].
In cats, fresh whole blood is the most common product used recently. Stored whole blood, packed red blood cells and fresh frozen plasma (FFP) are also given as transfusions [45].
The heavier cellular elements from the supernatant plasma are sedimented by centrifugation of whole blood sediments. Due to separation of blood collection within 8 hours all protein activity and concentration are maintained in the plasma. The obtained supernatant usually frozen. For subsequent transfusion, it is stored as fresh frozen plasma (FFP). In addition it can also processed to provide cryoprecipitate and cryosupernatant. It can also be transfused immediately as fresh plasma [52, 53]. Fresh frozen plasma have to be stored frozen at -30 C before used. Also it should be identified with the donor blood type, name and collection date. Samples thawed and not used sould discarded or stored in a fridge and used within 12-24 h and should not be refrozen [43].
Recently an ultra-purified polymerised bovine haemoglobin solution is the only commercially available alternative to red cell transfusion (Oxyglobin). It is not licensed in cats but it has been used in treatment of anaemia in cats and also in therapy of carbon monoxide poisoning [54, 55].
Hemostatic protein deficiencies lead to hemorrhagic disorders and the treatment is done principally by plasma components [56]. In animals with von Willebrand disease (vWD) and hereditary coagulation factor deficiencies active hemorrhage is controlled by plasma components. Plasma components are also used for preoperative prophylaxis in these diseases [53].
For preparation of plasma components sterile plastic bags are used. After that they are stored and transferred as frozen in individual boxes. Products have to be stored at -20 C or lower. Just before transfusion they warmed to 37 C in a water bath or incubator. Preferred route of administration is the intravenous transfusion of plasma components. If attempts at vascular access have failed, intraosseous transfusion can be used in emergency situations. When acut allergic reactions occur transfusion is stopped and antihistamines and/or short-acting steroids are given [53, 57].
Cats have antibodies to non-self blood types within the plasma. Because of this only type-specific plasma should be administered to cats in contrast to dogs. Using one of the commercially available systems whole blood can be separated into FFP and packed red cells if it is taken aseptically. The blood spun at 3800 rpm at 10˚C in a refrigerated centrifuge for 12 mins. Using a plasma extractor the plasma is extracted and stored at –20 C [57].
In hypoalbuminemic dogs and cats, human serum albumin has been used for therapeutic use [58].
Correction of coagulation by fresh platelets are shown by in vitro coagulation studies. Freshly collected platelets correct thrombocytopenia, control associated hemorrhage, and prevent death from bleeding. Hemorrhagic diathesis are prevented by platelet replacement for thrombocytopenia [59].
Severe thrombocytopenia or thrombopathia result in bleeding. Platelet transfusion is used for the control of this bleeding. In veterinary medicine platelet transfusion has been used rarely compared to red blood cell (RBC) and plasma transfusion. In dogs, reports related to platelet transfusion are generally associated with experimental hematopoietic stem cell transplantation. Platelet-rich blood products consist of fresh whole blood (FWB), platelet-rich plasma (PRP) and platelet concentrate (PC). They are used for aggressive anticancer therapy and treating complex hematologic disorders. Centrifugation of whole blood constitute platelet-rich plasma (PRP) and centrifugation of platelet-rich plasma constitude platelet concentrates (PC). Platelet activation is induced by centrifugation so that the resuspension of the platelet pellet during PC preparation from dogs is difficult. The preparation efficiency of PC from dogs can be improved by addition of PGE1 in PRP before the centrifugation of PRP. Also therapeutic efficacy of the platelets are maintained. In 10-28 kg body weight dogs plateletpheresis has been used successfully. On the canine donor thrombocytopenia and hypocalcemia are the main adverse effects of plateletpheresis [60-62].
At room temperature (RT) (20-24 C), PRP and PC can be stored for 5-7 days with continuous or intermittent agitation. At RT FWB can be stored for up to 8 hours. The interest in freezed (4 C) storage of platelets is increasing because of the increased risk of bacterial proliferation at RT storage. Storage of human PRP and PC are limited to 5 days because of prevention of bacterial proliferation at room temperature [60- 63].
Platelet transfusions as with RBC and plasma components should be performed with 170 µm filters standard blood administration sets. Transfusion sets which can bind platelets should be exempt from latex [60].
The most common reaction to PC are febrile reactions. The frequency is decreased by pre-storage leukoreduction. In immunocompetent dogs receiving multiple transfusions, alloimmunization to platelet antigens occurs. Leukocyte reduction and ultraviolet B irradiation are recently accepted methods for preventing the development of platelet alloimmunization [64-66].
Recently platelet cryopreservation are used to provide long-term storage and immediate availability of platelet products for transfusion. When fresh platelets are unavailable cryopreserved platelets can be activated in vitro and provide therapeutic benefit [63].
Granulocyte transfusion can be used as supportive therapy. It is used in patients with life-threatening neutropenia caused by bone marrow failure or in patients with neutrophil dysfunction. Granulocyte transfusions is shown to be useful in treatment of infections in patients after treatment with high-dose chemotherapy. It is helpful especially in the chemotherapy associated with conditioning for hematopoietic stem cell transplant. By using granulocyte colony-stimulated factors higher doses of granulocytes for transfusion are produced. Thus recently the use of therapeutic granulocyte transfusion has been increased. The outcome of transfusion are effected by the type of infection being treated, the likelihood of recipient marrow recovery, and recipient alloimmunization [67].
In small animals therapeutic granulocyte transfusions have been used especially in experimental models of myelosuppression and neonatal sepsis. In clinical veterinary medicine they have been used rarely. Granulocytes can be used to identify the site of inflammation. Beside leukapheresis, centrifugation of FWB, with or without colloid-facilitated sedimentation, may be used to isolate canine and feline buffy coats. Only sedimentation may also be used in the cat. At RT granulocytes are stored immobil for 24 hours. The dose for beginning is 1 x 1011 granulocytes/kg in a volume of 15mL/kg. It is used once to twice in a day [68-70].
To select permanent blood donors, blood typing have to be performed. Donors should be healthy young adults. They undergo routine physical check up and hematology and clinical chemistry evaluations are done. They should never taken a blood transfusion and should be free of blood parasites and other infectious diseases [1].
Nulliparous and spayed female dog and cat donors have to be chosen. Blood have be collected via jugular venipuncture aseptically. Acepromazine interferes with platelet function. Because of this donors should not be sedated with it [1].
Every 3 to 4 weeks, dogs can donate between 13 and 17 ml of blood per kilogram of body weight. Features of donors sould include well nourished, supplemented with oral iron, bled less than once per month to prevent iron deficiency, greater than 25 kg, and negative for antigens for DEAs 1.1, 1.2, 3, 5, and 7. Donors should not have heartworm disease, babesiosis, brucellosis, ehrlichiosis, and Rocky Mountain spotted fever. Donors have appropriate neck skin that allows easy entrance to the jugular vein, have a packed cell volume that is at least 0.40 L/L, have demonstrated a good temperament and be in good physical condition, have no past time history of transfusion or pregnancy, and have got sufficient levels of von Willebrand factor (vWF) [1, 3].
The ideal feline blood donors should be healthy, indoor-only cats with an agreeable temperament for easy handling and restraint. Owned pet cats should be donate maximum once every 2 months [43]. The features of feline donor sould be as follows; weigh more than 4.5 kg, have a packed cell volume that is at least 0.35 L/L, have demonstrated a good temperament, and be in good physical condition [3]. Donor cats can donate between 10 and 12 ml/kg. Adult healthy cats can donate 50 ml every weeks. Donors have to be type A. Type B donors may be demanded depending on breed prevalence and geography. Feline leukemia virus, feline immunodeficiency virus (FIV), feline infectious peritonitis, heartworm disease, and Hemobartonella sp have to be excluded in donor cats [1].
For appropriate care of donors some processes needed. These are current vaccinations, if there is contact with new animals every 6 mo fecal floatation, monitorization of hemogram every year, analysing clinical chemistry, screening for infectious diseases and in the dog preventative heartworm therapy in areas where it is necessary. When blood collection is taken the donor\'s weight, temperature, and packed cell volume have to be analysed [3, 71]. PCV or Hb are measured by taking a blood sample. Preferentially cats with a PCV of 30–35% are used but cats with low–normal PCVs should not be used [43].
In the cat, blood can be taken by using a 19- to 20 gauge needle or butterfly into a syringe via jugular vein venipuncture. The region over the jugular vein is clipped and prepared aseptically and sedation is administered. It is prefered to use a 1:1 combination of ketamine 100 mg/ml and midazolam 5 mg/ml. It is made up in a small syringe and given intravenously up to a maximum dose of 5 mg/kg ketamine (0.1 ml/kg of combination). Syringe consists of either ACD, CPD, or CPDA- 1 (1 mL/9 mL of blood), or heparin (5 units/mL of blood). Before a preservative solution is used it can be placed in a small blood bag. To access the jugular vein a 19-21G butterfly needle is used. The blood is collected over a total of 10 15 mins. At once a maximum of 10-12 ml/kg blood can be donated. Isotonic crystalloid fluid therapy post-donation at a rate of 60 ml/h for 3 h is given to the cat [3, 43].
Precaution is necessary to prevent damage of the blood product and harm to recipient. Blood typing or crossmatching have to be carried out to provide compatibility before RBC transfusion [41].
Transfusions of red blood cell should be administered through a filter. The filter is arranged to remove clots and particles which are potentially harmful to the patient. Blood infusion sets have in-line filters. These filters trap large cells, cellular debris, and coagulated proteins. The pore size range from 170µm to 260µm. A filter may be used to administer 2-4 units of blood to a patient or for a maximum time limit of 4 hours according to human blood banking standards. High protein concentration at the filter surface and room temperature conditions promote proliferation of any contaminating microorganisms. The rate of flow slowed down by accumulated material. After 5 days or more of refrigerated storage constituted microaggregates composed of degenerating platelets, white blood cells (WBCs), and fibrin strands in blood. They are removed by other blood filters with a pore size of 20-40 Jim. For transfusions of RBCs primarily microaggregate filters are designed. In administering small volumes of blood (<50 mL WB or <25mL pRBCs) to cats and small dogs a pediatric micro-aggregate blood filter (18 um pore size, priming space <lmL) is especially helpful. Because of a progressive decrease in pore size due to increased blood filtered larger volumes of blood administration can result in hemolysis [41].
If plasma is taken from blood preservative solutions can be put in. Blood preservative solutions are dextrose, adenine, mannitol, and the sodium chloride. They are necessary for RBCs to carry on their energy metabolism and viability during storage [3]. Canine pRBCs stored in a RBC preservative can be applied directly. Other pRBC products have to be diluted by putting 10mL of saline feline pRBCs or 100mL of saline to the blood bag so that the viscosity of the donor blood decreased [41].
In the dog, if sedation is needed, butorphanol (0.1 mg/kg BW, IV) is generally used for sedation. But acepromazine should not be used because it may cause platelet function disturbance [72]. In the cat, ketamin may be used 2 to 4 mg/kg BW, IV for sedation. In addition to ketamin is very successful when it is used together with 0.1 to 0.2 mg/kg BW diazepam [3]. Also combinations of ketamine hydrochloride, midazolam and butorphanol tartrate, or mask administration of sevoflurane can be used [73, 74].
Generally, intravenous administration is used for RBC transfusions. In addition intraosseous administration is a perfect alternative. Peripheral veins may be preferred to central veins because of an increased bleeding predisposition [41].
Blood is administered through administration sets containing 0.9% saline intravenously. Contraindications include hypotonic saline, 5% dextrose in water and lactated Ringer\'s solution. Cardiac arrest may be caused by injection of undiluted citrate containing anticoagulants [1].
Using a syringe driver or by hand the transfusion should begin slowly at 0.25 ml/kg/h. If no adverse affects are encountered after the first 30–60 mins of administration the rate can be increased. Due to the urgency of the requirement for whole blood and any underlying concurrent disease the rate of administration can vary [75].
With a PCV of 20%, dogs and cats with chronic anemia can be cardiovascularly stable [76]. Conversely in patients with an acute onset of anemia and continuing blood loss or hemolysis, transfusion to a higher PCV is necessary for stabilization. Generally administration of 2mL/kg of WB or lmL/ kg of pRBCs will increase the patient\'s PCV by 1% if there is no continuing hemorrhage or hemolysis [41].
Patient\'s overall condition determine the rate of blood administration. The maximum rate of transfusion is 10-20mL/ kg/h in normovolemic anemic patients, to avoid circulatory overload [41].
To provide blood volume again fluid therapy with crystalloids or colloids is necessary. If the patient\'s total blood volume do not decrease under 20% this is usually enough for losses. If losses are more than 20% whole blood or packed red cell transfusion is used. Between 20% and 50% of blood volume losses are treated by crystalloids and packed RBCs [3, 77].
Blood components like cryoprecipitate and platelet-rich plasma are used infrequently. Cryoprecipitate contains vWF, factors VIII, XIII, fibrinogen, and fibronectin. In vWF-deficient patients cryoprecipitate is recommended particularly when surgery is planned or patient affected by blood loss. Bleeding hemophilia A patients, or patients having hypo or dysfibrinogenemia are the other indications for choosing it [3, 78].
Sometimes platelet-rich plasma is used in veterinary practice. In small-sized animals it is more useful because in larger dogs it is difficult to gain enough volume and management of platelet count. An alternative to platelet-rich plasma are frozen platelet concentrates [79].
For expansion of plasma volume, different types of colloids as dextrans and hetastarch are used as alternatives to blood products. Altering hemostasis is one of the problems of dextrans and hetastarch. Oxyglobin is a hemoglobin-based oxygen carrier. It is approved for use in the dog in 1998. In emergency situations it is used instead of blood products when there is limited time for preparing it or performing compatibility testing [3, 80].
In clinical signs of anaemia and as a therapy for carbon monoxide poisoning oxyglobin is used in cats. Because it is a potent colloid (colloid osmotic pressure 43 mmHg), the main risk associated with administration is volume overload. In patients with normovolaemic anaemia conservative administration rates are needed such as as low as 0.2-0.4 ml/kg/h and to a maximum of 1 ml/kg/h. Careful monitorization of patients with paying particular attention to their heart and respiratory rate is recommended [81, 82].
A recent study described the clinical outcome in dogs experiencing massive transfusion. Also this study documented predictable changes in electrolytes and coagulation status. Massive transfusion is different from usual transfusions in terms of volume and rate of blood transfusion and blood components administered. Transfusion of a volume of whole blood or blood components has been described as massive transfusion. The administrated blood is greater than the patient\'s predicted blood volume within a 24-hour period or arranged as replacement of half the patient\'s predicted blood volume in 3 hours. In a study, massive transfusion receiving dogs were investigated and in this study the mean volumes of pRBCs was 66.5mL/kg and FFP was 22.2mL/kg. As a result of this mean plasma, RBC ratio was 1:3. After transfusion clinicopathologic changes consists of electrolytes disturbances, dilutional coagulopathy, ionized hypocalcemia and hypomagnesemia and progressive thrombocytopenia and prolongation of prothrombin and activated partial thromboplastin times [41, 83].
The gold standard approach is that the donor and recipient are cross-matched before administration. Administration is maintained mainly intravascular with the use of peripheral or centrally placed catheter. Also intraosseous catheters can be used to administer all blood products. It is useful in collapsed neonatal patients where vascular access is difficult [43, 75, 84].
In acute hemorrhage, anemia, decreased red cell mass, severe methaemoglobinaemia, paracetamol toxicity, chronic non-regenerative anaemia, coagulation disorders, and thrombocytopenia fresh whole blood is used [1, 45].
The reason of anaemia in cats requiring transfusion are haemorrhage and primary immune-mediated haemolytic anaemia. Hemorrhage is caused as a result of peri- or postoperative bleeding, trauma, gastrointestinal bleeding, abdominal neoplasia, primary immune-mediated thrombocytopenia and coagulopathies [85, 86, 87]. Also in a number of infectious diseases anaemia is reported such as especially feline immuno-deficiency virus (FIV) and feline leukaemia virus (FeLV) infections, and feline infectious peritonitis [88, 89]. Other infectious diseases which cause anemia are Ehrlichia species, Bartonella species, Haemoplasmas (Mycoplasma haemofelis, ‘Candidatus Mycoplasma haemominutum’ and ‘Candidatus Mycoplasma turicensis’), Anaplasma phagocytophilum, Neorickettsia risticii, Cytauxzoon felis and Rickettsia felis have additionally been associated with anaemia [43, 90].
The indication of whole blood is in a patient whom needed several blood components or has acutely lost more than 50% of its total blood volume. When 50% of total blood volume is lost oxygen carrying capacity and oncotic activity should be recovered. In anemia, stored whole blood is used. For anemic animals packed erythrocytes especially those with volume overload are prefered. For tissue reoxygenation the transfusion of packed RBCs are used. They are also useful for normovolemic, anemic patient. Before administration, to dilute any potentially damaging antibodies these erythrocytes can be washed with saline. Refrigerated whole blood should be warmed to room temperature. Before administration it sould be gently agitated to resuspend the red blood cells. Infusion rate is limited by colder blood which has a higher viscosity [3, 41, 91].
The usage of transfusion of fresh-frozen or stored-frozen plasma (FFP) are as follows; lack of coagulation factors associated with hepatic insufficiency, disseminated intravascular coagulation (DIC), vitamin K deficiency, rodenticide toxicosis, liver insufficiency, biliary tract obstruction, sepsis/multiple organ dysfunction syndrome, pancreatitis, hypoalbuminemia, and DIC without associated laboratoryproven coagulopathy, malassimilation syndrome, chronic antibiotic use, a need for plasma volume expansion, or a massive blood loss within a few hours. Other It is also used in congenital or a hereditary deficiency in coagulation factors (i.e hemophilia A, B, or von Willebrand\'s disease and hypoproteinemia), [1, 3, 39]. Plasma (FP or FFP) is used especially in the emergency conditions like excessive protein loss such as enteropathy, nephropathy, exudative dermatitis or inadequate intake. It is not appropriate for using as long-term source of protein in these patients [3, 92]. In cats, reactions have not been reported following transfusions of FFP [46].
The collection and re-transfusion of the cat’s own blood is called autotransfusion. It is a useful technique in an emergency situation. It can be obtained when animals bleed into body cavities. It should not be used if the blood is contaminated with urine, bacteria or bile. Blood is collected from the body cavity in a sterile manner. After that it re-transfused into the patient through an appropriate fitler. To prevent clotting anticoagulant like acid citrate dextrose should be included at a ratio of 1:7 [39, 43].
The indication of transfusion reactions can be immunologic or nonimmunologic. They can be immediate or delayed. Antibodies to surface antigens of transfused erythrocytes cause immune-mediated hemolytic reactions. According to surface antigens canine blood is grouped. For six of these antigens typing is available. Except DEA 4, canine universal donor is negative for all dog erythrocyte antigens (DEAs). Universal donors should be examined. If other donors are known to be compatible with the recipient they can be also used. Acute hypersensitivities mediated by IgE antibodies are one of the possible immunologic reaction. The other can be leukocyte or platelet sensitivity caused by recipient antibodies to the donor\'s white cells or platelets. The mechanisms of nonimmunologic reactions are various. According to the specific reaction the type and severity of clinical signs vary [17] Adverse reaction occurs in 2 types. First one is immediate reaction and following transfusion it occurs within 1 to 2 h. Second is delayed reaction and it may begin within days, months, or years later [17]. Adverse reaction varies from mild (fever) to severe (death). Transfusion reactions can be acute or delayed. In animals receiving incompatible transfusions, acute intravascular hemolysis with hemoglobinemia and hemoglobinuria may be seen. Acute hemolytic reaction is the most serious transfusion reaction that can be prevented. It is an immunological reaction and it happens when circulating natural or acquired antibodies towards donor erythrocytic antigens are given. Hemoglobinuria, vasoconstriction, renal ischemia occur due to intravascular hemolysis. Intravascular hemolysis determine clinical signs. Disseminated intravascular coagulopathy (DIC) can be caused by release of thromboplastic substances. Secondary to the release of vasoactive substances, hypotension and shock can ocur. Also acute renal failure and death can develop. After transfusion a decrease in hematocrit between 2 days and 2 weeks resulted in suspicion of delayed hemolysis. As a result of extravascular hemolysis, hyperbilirubinemia and bilirubinuria may occur. In dogs clinical signs are as follows: fever, tachycardia or bradycardia, hypotension, dyspnea, cyanosis, excessive salivation, tearing, urination, defecation, vomiting, collapse, opisthotonos, cardiac arrest, hemoglobinemia, and hemoglobinuria. When an acute hemolytic reaction occured transfusion sould be interrupted at once and shock should be treated. Also blood product being used sould be checked out and the steps that led to the transfusion sould be examined [1, 3, 17, 93].
To detect transfusion reactions earlier requires careful evaluation of patient\'s behavior, vital signs, and perfusion before, during, and after a RBC transfusion. Pre- and post-transfusion measurement of PCV and total solids for example instantly and at 24 hours are needed. Also evaluation of the plasma and urine for the presence of Hgb is done [41].
In the dog the acute hemolytic reaction is rare because in this species naturally occurring anti-erythrocytic antibodies prevalence is low [3]. Alloantibodies against the common canine erythrocyte antigens 1.1 and 1.2 do not exist in dogs. As a result of this generally first transfusion can be safely given without regard for donor blood type. Thus the recipient can be sensitized to immunogenic antigens (i.e 1.1, 1.2, 7, and others). On first transfusion it can cause shortened survival times of the transfused cells. Subsequent predisposition to severe transfusion reaction can develop. DEA 1.1 which is the strongest antigen in dogs, leads to the most severe transfusion reaction [1]. In the second transfusion especially when DEA-1 type blood is applied twice to a DEA- 1-negative dog there is more risk [3].
In cats receiving typed or crossmatched transfusions low rates of transfusion reactions have been indicated. Transfusions with whole blood or packed red blood cells transfusion reactions were reported [45]. But transfusions with FFP no reactions have been reported in cats [46].
Initial or subsequent AB-mismatched transfusions in cats can cause acute hemolytic incompatibility reactions. Erythrocytes are destroyed immediately in cats because of alloantibodies. On the contrary in dogs, delayed transfusion reactions are more often occur. A type B transfusion to type A cat causes mild signs. In this situation shortened erythrocyte survival can occur. This causes ineffective therapy. Acute hemolytic transfusion reaction with massive intravascular hemolysis with serious clinical signs occurs in type A transfusion to a type B cat. These symptoms may occur even if it is the first transfusion. Type AB or A blood can be received by type AB cats safely [1, 94].
The transfusion should be stopped immediately if a transfusion reaction is suspected. The recipient sould be monitored continually for follow up. The most severe is acute haemolytic transfusion reactions developing as a result of naturally occurring alloantibodies [32].
Clinical signs are restlessness, vocalisation, tachypnoea, bradycardia, tachycardia, hypotension and hypertension. Pyrexia is seen frequently as a result of reactions to donor leukocytes, platelets and plasma proteins. As a result of binding by citrate, there is potential for hypocalcaemia when administering large volumes of blood products. Thus, if the patient is showing clinical signs of hypocalcaemia calcium should be measured [38, 43].
The next hour after transfusion nonhemolytic fever can ocur as adverse reactions. If contaminated blood products applied by mistake, fever may occur in an acute hemolytic reaction in association with septicemia. Vomiting or diarrhea can be seen after plasma administration. Rarely urticaria may cause trouble to patient. It can be treated with antihistamines, with or without glucocorticosteroids. If whole blood is administered with rapid administration of a large volume of blood component to normovolemic cats or small-sized dogs hypervolemia can be observed. Hypervolemia can result in pulmonary edema. Cough, tachypnea, dyspnea, or cyanosis can occur due to hypervolemia. Treatment can be done by stopping the transfusion, administering diuretics (furosemide) to reduce pulmonary edema, and providing oxygen support [3,72, 93].
The recipient should be carefully examined before the procedure. Its heart rate, respiratory rate, mucous membrane colour, capillary refill time and temperature sould be recorded. Also the PCV and total plasma protein should be recorded [43, 51].
Delayed adverse transfusion reactions are consist of delayed hemolytic reaction, transmission of infectious disease, and posttransfusion purpura. Posttransfusion purpura has been reported in the dog. It is characterized by the appearance of severe thrombocytopenia in the week following a second transfusion. [3, 95, 96].
Anemia, regardless of underlying cause, is troublesome for clinicians in respect to stabilising and supporting the patient. The survival rate of all reasons for a transfusion is 84% in the first 24 h. It is 75% for blood loss anaemia and 49.6% for ineffective erythropoeisis at 10 days [43, 97].
The incompatibilities between the donor’s red blood cells and recipient’s plasma are identified by major cross-match. The incompatibilities between the donor’s plasma and recipient’s red blood cells is identified by a minor cross-match [43].
Cross-Matching usually is identified as either ‘‘major’’ or ‘‘minor’’ cross-matches. A major cross-match include putting patient serum into donor cells and determine the presence of agglutinating and/or hemolytic antibodies in the patient aganist the donor antigens. The principle of this test is hemolytic or agglutinating reaction. In this test the reagent or antibody reacts with the RBCs. Serological discordance between a candidate donor and the patient is identified by the crossmatching. It does not determine the blood group [3]. A positive in vitro reaction is caused by the presence of antibodies. In patients that had no antibodies at the time of transfusion, a mild reaction can be seen in 4 to 14 days after mismatched transfusions. When blood is transfused to a patient in which antibodies are already present, a severe reaction occurs. This antibody can be developed by either naturally occurring or as a result of a previous mismatched transfusion. Furthermore, high concentrations of antibodies can be caused by isosensitization from transplacental immunization. In dogs that have received transfusions before, a crossmatch should always be performed. A minor cross-match include putting donor serum into patient erythrocytes. This step is not necessary for the donor whom previously tested negative for antibodies. Transfusing packed or washed erythrocytes rather than whole blood can prevent administration of antibodies in donor blood against patient erythrocytes [1].
Before transfusion the reason of analysis with these methods are to prevent acute hemolytic reaction due to transfusion, to provide optimal lifetime of the transfused RBCs, to prevent next discordant blood transfusions and to prevent neonatal isoerythrolysis [3].
Because there are blood types that have not been described and it is not possible to type for Mik it is recommended that cross-matching is performed before any transfusion. If the recipient has received a transfusion before more than 4 days cross-matching should be performed [98].
Horses have eight RBC groups or systems: A, C, D, K, P, Q, U, and T. The first seven systems are recognized by the International Society of Animal Blood Grouping Research. Blood-typing antiserum is not readily available for horses. Because of this to identify suitable donors equine blood-group testing can be performed by only few diagnostic laboratories. Over 30 different factors have been identified within these seven equine systems. Experimentally many more systems have been identified [99, 100]. Red cell antigens Ca, Aa, and Qa are play an important role in transfusion reactions and neonatal isoerythrolysis. There is no universal equine blood donor. Because of this to prevent inadvertent sensitization of brood mares against the two most common alloantigens (Aa and Qa) involved in neonatal isoerythrolysis, the preferred donor should be negative for factors Aa, Qa, and Ca [100, 101]. Aa and Qa alloantigens are most immunogenic, and most neonatal isoerythrolysis cases are associated with anti-Aa or Qa antibodies. The horse is clinically relevant for blood group incompatibilities. It is the only livestock species for this situation. Blood group antibodies can laed to transfusion reactions or NI and can be found in horses either ‘‘naturally’’ or as a result of a blood group incompatible pregnancy [2]. A donkey RBC antigen that has not been found in the horse has been identified, it is unique to the donkey and the mule [1].
In horses, requirement of blood transfusion include correction of anemia arising from acute blood loss secondary to trauma, surgical complications, ruptured uterine artery, guttural pouch mycosis, and neonatal isoerythrolysis [99, 102].
Generally, whole blood transfusions are applied to horses that have acute blood loss caused by trauma, surgery, or some other conditions like splenic rupture or uterine artery hemorrhage. The transfusion recovers blood volume and oxygen-carrying capacity in cases of blood loss. There is no certain indicative variables for the beginning of transfusion so that physical examination and clinicopathologic parameters should be used to make the transfusion decision. In cases of acute hemorrhage one sould remember that the packed cell volume (PCV) may be normal for up to 12 hours because of the time required for fluid redistribution and the effects of splenic contraction. As the horse is rehydrated with intravenous fluids, serial monitoring of PCV and total protein (TP) can estimate the amount of blood loss. The transfusion decision is made by suspection of large volume blood loss, together with tachycardia, tachypnea, pale mucous membranes, lethargy, and decreasing TP. During an acute bleeding episode when the PCV fall under 20%, blood transfusion is probably required. In acute severe cases, transfusion may be required before there is a significant fall in PCV. PVC shows the need for beginning of transfusion in chronic anemia better whereas in acute hemorrhage, with transfusions proposed for horses with demonstration of tissue hypoxia and a PCV less than 10-12% [103, 104].
Blood is collected and stored in glass bottles containing acid–citrate–dextrose (ACD). The method traditionally used for collecting blood from donor horses. Glass bottles containing ACD are easy and suitable for rapid vacuum blood draw. Because of this they are recommended for equine whole-blood collection. For equine whole blood the optimal storage method is commercial citrate–phosphate–dextrose with adenine (CPDA-1) bags [105, 106].
Packed RBCs (pRBCs) are specified for normovolemic anemia (i.e neonatal isoerythrolysis, erythropoietic failure, and chronic blood loss). Markers of tissue oxygenation, for example lactate and oxygen extraction are useful in chronic or hemolytic anemia cases. In horses, disseminated intravascular coagulation, clotting factor deficiency, hypoalbuminemia, decreased colloid oncotic pressure, and failure of transfer of passive immunity (FPT) are treated by plasma [104].
Colloid is usually used in patients with a total protein less than 4.0g/dL or serum albumin concentration less than 2.0g/dL. When there is oncotic pressure less than 14 mmHg, clinical symptoms like ventral edema, and conditions which increase microvascular permeability like sepsis are other indications for colloid usage [104].
According to plasma obtained by plasmapheresis and centrifugation preparations, plasma prepared by gravity sedimentation contains greater numbers of erythrocytes and leucocytes. The risk of a transfusion reaction can be increased by these cells. During storage leukocytes can degranulate and fragment and release pyrogens and proinflammatory substances [107, 108, 112].
Multiple hyperimmune plasma products are avaible with bacterial or viral specific antibodies. For the treatment of equine endotoxemia, the efficacy of E. coli (J5) and Salmonella tiyphiimiriuni hyperimmune plasma has proved to be useful in some reports; in contrast, there are some reports which disapprove the utility of such products. For the protection of R. equi, the use of Rhodococcus equi hyperimmune plasma has also been controversial. For treatment of specific disease additional plasma products like botulism antitoxin, West Nile virus antibody, and Streptococcus equi antibody are usable. In general equine practice plasma is administered to neonates to provide protective immunoglobulins. Protective immunoglobulins are used for treatment of failure of transfer of passive immunity or prophylaxis against Rhodococcus equi. Also, the albumin content of the plasma used as a colloid for circulatory volume support and in the treatment of protein-losing enteropathies. In horses heritable and acquired coagulopathies can occur. Specific coagulation factors are not available for supplementation. Also indications include coagulopathies, protein-losing nephropathy and protein loss through third spacing into a body cavity (occurring with peritonitis or pleuritis) [104, 109-113].
Fresh frozen plasma must be separated and frozen within 8 hours of blood collection. Then it can be colder at -18 C and stored for up to 1 year. Frozen plasma is considered as plasma separated any time after 8 hours of blood storage [112, 114, 115].
Healthy, young gelding weighing at least 500 kg is the ideal equine blood donor. Donor horses should be performed current vaccinations. To prevent from equine infectious anemia donors should be tested each year. RBC antigens Aa and Qa are the most immunogenic antigens. Because of this in the ideal donor, the Aa and Qa alloantigens should be absent. There are breed-specific blood factor frequencies. Thus a donor of the same breed as the recipient, particularly when blood typing is absent may be preferable. Horses that have taken blood or plasma transfusions and mares that have had foals are not appropriate as donors. Because they have a higher risk of carrying RBC alloantibodies. Donkeys have a RBC antigen known as "donkey factor". Horses do not have this antigen. Thus donkeys or mules should not be used as donors for horses because horses can develop anti-donkey factor antibodies if transfusion takes place [1, 104, 116].
An immediate blood transfusion can be applied for the first time in an emergency situation with a very minor risk of serious transfusion reaction. Horses can develop alloantibodies within 1 week of transfusion. Thus blood typing and crossmatching are recommended before a second transfusion is given. A second blood transfusion may be given confidently without a blood crossmatch within 2-3 days of the first transfusion. Blood typing and alloantibody screening can be used for the transfusion needed patient to find the most suitable donor horse. Blood typing and antibody screening before initial transfusion are more important for horses. Because subsequent blood transfusions are anticipated and if sensitized to other blood group factors broodmares may produce foals with neonatal isoerythrolysis (NI). For detection of equine RBC antigens Ca and Aa, a rapid agglutination method has been developed. It can be more suitable for pretransfusion testing [99, 103, 104].
Blood is collected from the jugular vein of the donor horse. For this purpose two way used; direct needle cannulation or catheteri-zation. When a large volume of blood is required, a 10 or 12 gauge catheter is recommended. A 14 gauge catheter is also sufficient. Plastic bags and vacum-collection glass bottles in sizes ranging from 450 mL to 2 L are suitable for blood accumulation. Anticoagulation with 3.2% sodium citrate is enough when blood is received for immediate transfusion. In saline-adenine-glucose-mannitol solution red blood cell concentrates stored and they can be used for transfusion for up to 35 days after blood accumulation. Equine blood storage condition resemble to canine and human blood storage condition. According to both in vitro tests and human parameters after 35 days of storage equine erythrocytes remain appropriate for transfusion. Fresh frozen plasma is obtained by separation of erythrocytes and plasma. Both of them can be used alone. RBC survival evaluation sould be doen in vivo [104, 117].
To allow separation of red blood cells by gravity sedimentation the blood is stored in a refrigerator at 5 C for 48 hours in an upright position. Then the plasma is decanted into a sterile 3-L bag with sterile plastic connecting tubing using gravity. 3-L bags containes a constant weight of plasma (3.4 kg). The red cell fraction is thrown out. The plasma bags are sealed, labeled with the horse’s name and the date of decantation. They are stored at -20 C until needed for plasma transfusion [112, 118].
In acute blood loss cases, PCV is usually impractical for estimation of volume to be transfused because it does not exactly indicate blood loss. Instead of this the volume of blood needed are predicted by estimation of blood loss and evaluation of clinical parameters. Fluid shifts will replace much of the circulating volume so between 25% and 50% of the total blood lost should be replaced by transfusion. Pay attention sould be give to that up to 75% of RBCs lost into a body cavity like hemoperitoneum are within 24-72 hours autotransfused back into circulation. Thus in cases of intracavitary hemorrhage lower percentages of blood volume replacement can be needed. To remove small clots and fibrin blood and plasma products should be given with an in-line filter [104, 119].
Blood should be given at a rate of approximately 0.3mL/ kg over the first 10-20 minutes for monitoring the transfusion reactions. Heart rate, body temperature, and respiratory rate sould be monitored. Additionally horses have to be monitored for signs of muscle fasciculation, piloerection, and urticaria. Urticaria, hemolysis, pruritis, edema, tachycardia, tachypnea, pyrexia, colic, changes in mentation and acute anaphylactic reactions are adverse reactions indicated in horses taking blood transfusions. The rate of adverse reaction to WB transfusion has been reported as 16% which are mild urticarial reactions and worsening hemolysis. Also 1 of 44 horses (2%) exhibit a fatal anaphylactic reaction [103, 113].
Transfusion reactions may vary from mild urticarial reactions to anaphylaxis. They are divided into immunogenic and nonimmunogenic reactions. Immunogenic reactions include anaphylaxis, hemolysis, fever, hives, acute lung injury, posttransfusion purpura, immunosuppression, and neonatal isoerythrolysis. Nonimmunogenic reactions include circulatory overload, bacterial contamination, citrate toxicity, coagulopathy, hyperammonemia, and transmission of disease. In horses that have received fresh frozen plasma serum hepatitis has been observed [52, 93, 112, 120].
In a second plasma or blood transfusion there exists risk for severe adverse reactions in dogs. Also there is a risk of development of neonatal isoerythrolysis in gravid mares. The risk is much more in whole blood transfusions [26, 33, 112].
In horses suffered from normovolemic anemia polymerized ultrapurified bovine hemoglobin (PUBH) improves hemodynamics and oxygen transport parameters. During infusion to be informed about any adverse reactions patients should be monitored closely. Intense pruritus, tachycardia, and tachypnea can be resolved shortly after stopping the infusion [121].
Eleven blood groups have been classified in cattle. The greatest clinical relevance is in groups B and J. The B group is extremely complex, thus closely matched transfusions are very difficult. Newborn calves do not have the J antigen. During the first six months of life they generally acquire it. Cows can be sensitized to erythrocyte antigens by vaccinations of blood origin like some anaplasmosis and babesiosis vaccines. As a result of this neonatal isoerythrolysis in subsequent calves occur. [1].
Seven blood groups have been classified in sheep. The B group in these animals is resemble to the B group in cattle, and the R group is resemble to the J group in cattle. For example, antigens are soluble and soluble antigens passively absorbed to erythrocytes. In the goat, five blood groups are identified which resemble to those of sheep [1].
Blood group A–O expression is affected by 16 porcine blood groups and the S gene. Carbohydrate antigens like AO blood group antigens and minor histocompatibility antigens can be important targets for the immune response to transplanted organs or tissues. These antigens remain an unknown and untested variable in many transplant studies using pigs. Depending, on work performed in some Europian country pig blood groups developed and expanded largely. The source of blood typing reagents is especially from isoimmune sera. Most antibodies behave as agglutinins and a few as hemolysins. Internationally sixteen genetic systems are recognized [2, 122-124].
In two domestic South American camelids, Ilama and alpaca, our knowledge is little about group variation. Six blood groups factors were identified (e.g A, B, C, D, E and F). from iso- and heteroimmune sera constituted for these animals [2].
In ruminants and camelids indications for WB and plasma transfusion are similar to horses. Chronic anemia may be a more common problem in ruminants. Gastrointestinal parasites, particularly Haemonchus contains, and ectoparasites (e.g. Haematopinus spp. and Linognathus spp.) are causes of chronic blood loss anemia, and iron-deficiency anemia. These can affect neonatal calves [104, 121, 125].
Studies with camelids and bovines has showed that the neonatal intestine can only successfully absorb colostral immunoglobulins for 12–24 hours postpartum. Passive transfer (FPT) is failed in 19% to 24% of neonatal camelids. A common indication for plasma transfusion in neonatal calves and crias is failure of transfer of passive immunity. Hyperimmune serum products are existing for subcutaneous and intramuscular dosing in ruminants. These are products with antibodies against E. coli, Pasturella, Aercanobacter pyogenes, Salmonella typhimurium and Clostridium [104, 126-129].
An integral component of neonatal camelid care is IV plasma transfusion. It is used for the purpose of antibody supplementation and fluid resuscitation in critical illness. Neonates are immunocompetent at birth but due to initial postpartum absorption of colostrum for passive acquisition of immunoglobulins (especially IgG) they are severely hypogammaglobulinemic [130, 131].
In cattle, the first blood transfusion should usually be safe, regardless of the donor. J-negative donor is ideal. Because agglutination reactions do not develop, routine crossmatching is not useful in ruminants. First transfusions are usually safe to apply without a blood cross-match but crossmatching is recommended when more than 48-72 hours have passed away since the first blood transfusion. Blood donors should not have disease like bovine leukosis virus, anaplasmosis, and bovine viral diarrhea virus [104].
Total blood volume estimated in cattle is 80 mL/kg. From the donor animal up to 20-25% of total blood volume can be removed. Usually needle cannulation or jugular catheterization used in this situation. Blood can be collected into bottles or bags using citrate anticoagulant (e.g CPDA-1) in equine transfusions [104].
Blood samples can be taken from the jugular vein in sheep. A 500 ml transfer bag system including a needle can use for the storage. These bags include 70 ml of CPDA-1-stabiliser. Then the blood should be put into four 150 ml transfer bags. These bags can be stored on a horizontal shaker. It shows the best preservation of platelet function. Also it can be used for the storage experiment consecutively [132].
Platelet count and aggregability of CPDA-1-stabilised ovine blood is kept most covenient at room temperature. It provides adequate haemostatic function for ex vivo experiments for one working day. In ovine blood functional loss and high percentage of platelets within aggregates can be observed at refrigerator temperature. This should be considered in blood transfusion in sheep [132].
In order to monitor transfusion reactions blood should first be transported slowly. Ruminant blood type discordance result in primarily complement-mediated hemolysis. Volume overload should not be given. Also in neonates and small ruminants volume should carefully be given [104].
Intestinal absorption of antibodies declines sharply within the first 24 hours postpartum. For treatment of crias with failure of passive transfer (FPT) IV or intraperitoneal administration of 20–40 mL/kg of camelid plasma is recommended. In compromised neonates requiring fluid resuscitation IV administration of plasma is generally preferred. It is used for the correction of FPT and colloid support. In foals during extensive plasma volume expansion careful monitoring is needed to prevent cardiopulmonary complications. Following IV plasma administration the cardiovascular and pulmonary effects of plasma volume expansion have not been specifically worked out in camelids. But in several species (i.e sheep and cat) plasma volume overexpansion depending on excessive IV fluid administration has been associated with reduced lung function and pulmonary edema formation in clinical and experimental settings. In addition according to measures in presumed hypovolemic human patients administration of colloids can induce a greater reduction in lung function than crystalloids [130, 133-137].
Measurable plasma volume expansion and a concurrent reduction in pulmonary functional residual capacity (FRC) is caused by IV administration of 30 mL/kg camelid plasma to neonatal crias. In healthy neonatal crias administration of this quantity of plasma seems to be safe. But with underlying cardiopulmonary or systemic disease changes in lung volume associated with plasma administration could create risks for crias (131).
Adverse effects of transfusing blood stored for prolonged periods in lamps is encountered more often in patients with reduced vascular nitric oxide levels because of endothelial dysfunction. These patients can benefit from transfusion of fresh PRBC if available. Also inhaled nitric oxide supplementation can prevent pulmonary hypertension associated with transfusion of stored PRBC [138].
In previously untransfused pigs, hemolytic transfusion reactions do not appear to develop. But there have been two reports about adverse reactions in pigs undergoing liver transplants by the use of A–O incompatible transfusions. Pulmonary hypertension and decreased fibrinogen with an associated increase in fibrin degradation products occured in pigs that received A–O incompatible transfusions [139]. In a study, two pigs that administered A–O incompatible blood transfusions during liver transplants died because of disseminated intravascular coagulation (DIC), bleeding and progressive hypotension [140].
Vital part of veterinary emergency and critical care medicine is transfusion medicine. It is also therapy of some disease of patient. Blood and blood products can be obtained through the purchase of blood products or donors. Potentially fatal adverse transfusion reactions risk is higher in cats than in dogs. Also, adverse transfusion reactions are very important for large animals. By using known donors and screening assays that permit detection of incompatibility of blood typing or crossmatching, the risk can be decreased in both species.
Today, artificial intelligence (AI) has confidently entered our lives. The first mention of it belongs to the mid-50s of the last century. Under AI, we usually understand it as the branch of computer science devoted to develop data processing systems that perform functions normally associated with human intelligence, such as reasoning, learning, and self-improvement (ISO/IEC 2382-1:1993 Information technology–Vocabulary–Part 1). According to this, over the decades, AI has found its application in expert systems supporting decision-making, in heuristic classification, computer vision, pattern recognition, understanding natural language, etc. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. Here, under AI systems (AIS), we understand systems that include data processing systems that perform functions by AI, in particular by modeling and logic reasoning.
\nNote. System is a combination of interacting elements organized to achieve one or more stated purposes (according to ISO/IEC/IEEE 15288 “Systems and software engineering–System life cycle processes”).
\nIf the modern human brain already possesses skills of adaptation to conditions of various uncertainties in the world around, artificial intelligence systems require creation of effective methods for cognitive solving actual practical problems. “Cognitive solving” means relating to or involving the processes of thinking and reasoning (Cambridge English Dictionary). The applicable mathematical methods are focused mainly on conditions of actions in the logician “if …, that …” according to the gathered information, and on an estimation of traced situations by a man-operator. At increase and expansion of uncertainty conditions, quite often, there are failures and errors because of complexity. It means that search of new methods for advanced solving of AIS practical problems today is very important.
\nIn the present chapter, various AIS for supporting decision-making in intellectual manufacture and robotics systems are analyzed. According to robotics, it is supposed that AIS may be used for solving multiple aerial, land, underground, underwater, universal, and special problems of creation and operation. At the same time, we would like to emphasize that the main efforts of this chapter are not focused on illustrating the capabilities of AIS, but on demonstrating the applicability of author’s probabilistic models and methods to improve some of the existing capabilities of AIS.
\nFor this goal, the problem of planning the possibilities of functions performance on the base of monitored information and the problem of robot route optimization under uncertainties limitations are chosen. The choice of these problems in AIS applications is caused on the one hand by increase of quantity and a variety of specific uncertainties conditions, and on the other hand by an urgency and width of areas for their practical use. However, some relevant problems (such as the problems of robotics orientation, localization and mapping, information gathering, the perception and analysis of commands, movement and tactile, realizations of manipulations, and also rational control) for which different probabilistic methods are also applicable have been left out of the scope of work.
\nFor cognitive solving and improvements by the use of probabilistic methods, the chosen problems are transformed more specifically to:
problem 1 of planning the possibilities of functions performance on the base of monitored information about events and conditions, and
problem 2 of robot route optimization under limitations on risk of “failure” in conditions of uncertainties.
The proposed methods for cognitive solving AIS problems are based on theoretical and practical author’s researches [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37] and need to be used either in combination or in addition to existing methods. There, where often it is required prognostic analysis or where the used approaches are not effective, the proposed methods can be used as rational basis or alternative.
\nThe proposed and referred author’s methods and models can be used in AIS life cycle to form system requirements, compare different processes, rationale technical decisions, and estimate reliability, quality, and risks. The decisions, scientifically proved by the offered models and software tools, can provide purposeful essential improvement of quality and mitigation of risks and decrease expenses for created and operating systems. The spectrum of the explored systems by these methods includes systems (not only AIS) operated by government agencies, manufacturing structures (including power generation, coal enterprises, oil and gas systems), food storage, space industry, emergency services, municipal economy, etc. [15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]. The supporting software tools are original Russian creations registered by Rospatent [38, 39, 40, 41, 42, 43, 44]. They have been presented at seminars, symposiums, conferences, ISO/IEC working groups, and other forums since 2000 in Russia, Australia, Canada, China, Finland, France, Germany, Italy, Kuwait, Luxembourg, Poland, Serbia, the USA, etc. The software tools were awarded by the Golden Medal of the International Innovation and Investment Salon and the International Exhibition “Intellectual Robots,” acknowledged on the World’s fair of information technologies CeBIT in Germany, noted by diplomas of the Hanover Industrial Exhibition and the Russian exhibitions of software.
\nNote. The proposed methods below do not replace existing methods for robots actions (for example, the methods of solving the systems of differential equations, the methods of refreshed linear and geometric algebra, geometry, Lie groups, linearization, solving Jacobians and Hessians, Kalman filters, Lyapunov analysis, the methods of biomechanics, graph theory, Laplas transforming for large-scale dynamic systems, etc.) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14].
\nThe structure of the chapter research is shown in Figure 1. It provides an explanation of the essence of cognitive solving of problems on the base of probabilistic modeling, selection of some author’s probabilistic models applicable for cognitive solving problems 1 and 2, the practical steps to solve these problems, and five practical examples demonstrating system planning the possibilities of functions performance by using robot-manipulators (in space), by AIS for a coal company and by AIS used for a security service of floating oil and gas platform, example of forming input for probabilistic modeling from monitored data and example of robot route optimization under limitations on risk of “failure” in conditions of uncertainties. Various areas of the examples’ applications have been chosen purposely to demonstrate universality and analytical usefulness of the proposed methods and models. Appendices includes the proof for the proposed model of a quite general technology of periodical diagnostics of system integrity and some short models results to estimate quality of used information.
\nThe structure of the research.
This section explains the definitions and interpretations which can help to understand the proposed models and results of modeling complex systems in different application areas.
\nAIS itself can be considered as an interested system (for example, dispatching intellectual center) or as a part of other, more comprehensive interested system (for example, functionally focused robots in safety systems). The current information is processed in real time for performing the set or expected functions of interested system. To meet system requirements, the solutions of considered problems 1 (of planning the possibilities of functions performance) and 2 (of robot route optimization) are initiated along with the solutions of other problems.
\nThe cognitive solving of problems include improvements, accumulation, analysis, and a use of appearing knowledge, see Figure 2. Possible uncertainties for the given period (from initial time point t1 to future moment tx) may be considered by using proposed probabilistic modeling, prediction, and optimization.
\nThe essence of cognitive of solving of problems.
The solutions for problems 1 and 2 are estimated by probability of “success” and/or “failure” (risk of “failure”) during given prognostic time period. Thus, prognostic period should be defined so to be in time to recover capabilities (which can be lost), or to carry out preventive action (with which the initiation of solving the problem is connected). Such behavior means operation in real time.
\nIn each real case of modeling the term “success” should be defined in terms of admissible condition of interested system to operate for the purpose. The term “failure” means “unsuccess.” Generally, a “success” of interested system operation during the given time period means an admissible degree of integrity. Accordingly, “failure” for interested system during given time period means inadmissible degree of integrity at least once within this period. System (or system element) integrity is defined as such system (system element) state when system (system element) purposes are achieved with the required quality and/or safety. The risk of “failure” is understood as a probabilistic measure of “failure” considering consequences (according to ISO Guide 73).
\nNote. For example, an interested system is a dangerous manufacturing object. The object structure includes an AIS, which monitors events and conditions in and/or around its manufacture. Equipment parameters (temperature, pressure, and so forth) which should be in norm limits are traced. The “failure” of interested system operation may mean an incident or accident on object.
\nGenerally, from the point of view of formalization for each estimated variants (for problem 1 or 2), the interested system is logically decomposed to compound subsystems; see Figures 3 and 4. Each subsystem is a set of components (elements and/or other subsystems): for problem 1, this set covers the components participating in functions performance; and for problem 2, the set covers compound parts of a possible route of the robot in space. Complete set of these components formally characterizes a variant of decomposed system for solving problem 1 or 2. The analysis and optimization are carried out on complete set of all compared possible variants.
\nVariant of system decomposition.
Variant of subsystem decomposition.
Interpretation of such decomposition is the following:
\nThe subsystem from serial connected elements provides functions performance with admissible level of integrity (quality and/or safety) at given time, if:
“AND” 1st component, …, “AND” last element provide admissible level of integrity (quality and/or safety) at given time (for problem 1);
“AND” 1st compound part of the route, …, “AND” last compound part of the route are overcame successfully by the robot at given time (for problem 2).
The subsystem from parallel connected elements provides functions performance with admissible level of integrity (quality and/or safety) at given time, if:
“OR” 1st component, …, “OR” last component in the subsystem provide admissible level of integrity (quality and/or safety) at given time (for problem 1);
“OR” 1st compound part of the route, …, “OR” last compound part of the route are overcame successfully by the robot at given time (for problem 2).
Each component after system decomposition is presented as a “black box.” For each “black box,” various probabilistic models can be applied for calculations and for building required probabilistic distribution function (PDF) of time between the next deviations from an established norm. A norm is connected with definitions of “success” and “failure,” it may be connected with the precondition to “failure” (to prevent “failure”—see Example 2). Focus on processes’ description allows to use only time characteristics (mean time or frequency of events), the dimensionless or cost characteristics peculiar for various applications.
\nAppropriate calculated probabilities of “success” and/or “failure” (risk of “failure”) in comparisons to real events during the prediction periods represent the knowledge of admissibility borders for probabilities of “success” and acceptability borders for risks of “failure.” The process of cognitive solving of problems 1 and 2 means not only the formation and use of this knowledge for interested system, but also the estimated quality of monitored and used information (including definition of input for continuous modeling).
\nThe proposed probabilistic methods for cognitive solving of problems 1 and 2 are based on selected probabilistic models which are implemented effectively in wide application areas. The main principle at a selection of models consists that useful knowledge should be result of their application in conditions of various uncertainties. Knowledge is understood as the form of existence and ordering of results of cognitive activity of human. In the applications to solv
Selected models for every system element, presented as “black box,” allow to estimate probabilities of “success” and/or “failure” during given prognostic period. A probabilistic space (
Not considering uncertainty specificities, in general case, intellectual operation of AIS component aims to provide reliable and timely producing complete, valid and/or, if needed, confidential information; see Figure 5. The gathered information is used for its proper specificity. And, the proposed models [18, 19] allow to estimate the intellectual operation processes on a level of used information quality, which is important for every AIS (information may be used by technical devices, “smart” elements, robotics, users, etc.).
\nQuality of used information (abstraction).
The proposed analytical models (“The model of functions performance by a complex system in conditions of unreliability of its components,” “The models complex of calls processing for the different dispatcher technologies,” “The model of entering into system current data concerning new objects of application domain,” “The model of information gathering,” “The model of information analysis,” “The models complex of dangerous influences on a protected system,” and “The models complex of an authorized access to system resources”) allow to estimate the probability of “success” and risks to lose quality of intellectual operation during given prognostic period considering consequences; see Table 1. Required limits on probability measures are recommended as produced knowledge for the best AIS practice (estimated on dozens practical estimations for various application areas).
\nThreats to AIS operation quality | \nEvaluated measure (required limits as produced knowledge for the best practice) | \nModel tittle | \n
---|---|---|
Information is not produced as a result of system unreliability | \nProbability of providing reliable functions performance during given time (no less than 0.99). Mean time between failures. System availability (no less than 0.9995) | \nThe model of functions performance by a complex system in conditions of unreliability of its components | \n
Delayed information producing (i.e., not in real time) | \nProbability of well-timed processing during the required term (no less than 0.95). Mean response time. Relative portion of all well-timed processed calls. Relative portion of well-timed processed calls of those types for which the customer requirements are met (no less than 95%) | \nThe models complex of calls processing for the different dispatcher technologies | \n
Producing of incomplete information | \nProbability that system contains information about states of all real object and coincides (no less than 0.9) | \nThe model of entering into system current data concerning new objects of application domain | \n
Information validity deterioration caused by: \n
| \nProbability of information actuality on the moment of its use (no less than 0.9). Probability of errors absence after checking (no less than 0.97). Fraction of errors in information after checking. Probability of correct analysis results obtaining (no less than 0.95) | \nThe model of information gathering. The model of information analysis | \n
Violation of information confidentiality | \nProbability of system protection against unauthorized access during objective period (no less than 0.999) | \nThe models complex of an authorized access to system resources | \n
Violation of secure system operation including \n
| \nProbability of faultless (correct) operation during given time (no less than 0.95). Mean time between errors. Probability of system protection against unauthorized access (no less than 0.99) | \nThe models complex of dangerous influences on a protected system. The models complex of an authorized access to system resources | \n
The next probabilistic model is devoted to estimate a probability of “success” and risk of “failure” on high meta level. This is based on studying the general AIS technology of periodical diagnostics of system integrity. Some general technologies were researched for “The models complex of dangerous influences on a protected system,” see Table 1. Here, the general case for AIS is presented.
\nFor system element allowing prediction of risks to lose its integrity during given prognostic period, there is studied the next general AIS technology of providing system integrity.
\nTechnology is based on the periodical diagnostics of system integrity (without the continuous monitoring between diagnostics). Diagnostics are carried out to detect danger sources occurrence from threats into a system or consequences of negative influences (for example, these may be destabilizing factors on dangerous enterprise). The lost system integrity can be detected only as a result of diagnostics, after which system recovery is started. Dangerous influence on system is acted step-by step: at first, a danger source occurs into a system, and then after its activation may be a loss of integrity; see Figure 6. Occurrence time is a random value that can be distributed by PDF of time between neighboring occurrences of danger Ωoccur(t) =
Some random events for technology: left—correct operation to provide system integrity; right—a loss of integrity during prognostic period Tgiven.\n
It is supposed that used diagnostics tools allow to provide system integrity recovery after revealing danger sources occurrence or the consequences of influences. Thus, the probability (
There are possible the next variants:
variant 1—given prognostic period Tgiven is less than the established period between neighboring diagnostics (Tgiven < Tbetw. + Tdiag);
variant 2—given prognostic period Tgiven is more than or equal to the established period between neighboring diagnostics (Tgiven ≥ Tbetw. + Tdiag).
Here, Tbetw. is the time between the end of diagnostics and the beginning of the next diagnostics and Tdiag is the diagnostics time.
\nFor the given period Tgiven, the next statements are proposed for use, see in detail [18, 19, 35, 36, 37].
\nUnder the condition of independence of considered characteristics, the probability of providing system integrity (probability of “success”) is equal to
for variant 1
\n
for variant 2
where N = [Tgiven/(Тbetw. + Тdiag.)] is the integer part, Trmn = Tgiven − N(Tbetw + Tdiag);
\nmeasure (b)
\nThe probability of success within given prognostic period
The modification of this model allows to use different values of diagnostics and recovery time [35, 36, 37]; for formulas (1)–(3), recovery time is equal to diagnostics time.
\nAll these models, supported by various versions of software tools, registered by Rospatent, may be applied and improved for solving quality and safety problems, connected with intellectual system presented as “black box” [18, 19, 38, 39, 40, 41, 42, 43, 44].
\nSummaries for the last model are as follows:
The input for modeling include: frequency of the occurrences of potential threats (or mean time between the moments of the occurrences of potential threats which equals to 1/frequency); mean activation time of threats; mean recovery time; time between the end of diagnostics and the beginning of the next diagnostics; diagnostics time; and given prognostic period.
The calculated results of modeling include: the probability of providing system integrity within given prognostic period (i.e., probability of “success”); and risk to lose integrity (i.e., probability of “failure”) as addition to 1 for probability of “success.”
If probability of providing system integrity within given prognostic period for all points Tgiven from 0 to ∞ are computed, it means a trajectory of the PDF depending on characteristics of threats, periodic diagnostics, and recovery. And, the building of PDF is the real base to predict probabilistic metrics for given time Tgiven. In analogy with reliability, it is important to know a mean time between neighboring losses of integrity (MTBLI) like mean time between failures in reliability (MTBF), but in application to concepts of quality, safety, etc.
\nFor complex systems with serial or parallel structure, new models with known PDF can be developed by the next method [17, 18, 19, 20, 21]. Let us consider the elementary structure from two independent parallel or serial elements (Figures 3 and 4). Let the PDF of time between losses of i-th element integrity be Вi(t), i.e., Вi(t) = Р(τi ≤ t), then:
time between losses of integrity for system combined from serial connected-independent elements is equal to minimum from two times τi: failure of first or second elements (it means the system goes into a state of lost integrity when either first, or second element integrity is lost). For this case, the PDF of time between losses of system integrity is defined by the expression
\n
2. time between losses of integrity for system combined from parallel connected independent elements (hot reservation) is equal to a maximum from two times τi: failure of first and second elements (it means the system goes into a state of lost integrity when both first and second elements have lost integrity). For this case, the PDF of time between losses of system integrity is defined by the expression
By applying recurrently expressions (4) and (5), it is possible to build PDF of time between losses of integrity for any complex system with parallel and/or serial structure.
\nAs summary, the calculated results of modeling are: PDF of time between losses of integrity for system and each compound subsystems and elements; mean time between losses of integrity for system and each compound subsystems and elements (MTBLI as analog of MTBF).
\nFor example, integrated complex system, combined from intellectual structures for modeling interested system including AIS (Figure 7), can be analyzed by formulas (1)–(5) and probabilistic models described above and allowing to form PDF by (4) and (5). The correct operation of this complex system during the given period means: during this period both first and second subsystems (left and right) should operate correctly according their destinations, i.e., integrity of complex system is provided if “AND” integrity of first system left “AND” integrity of second system right are provided.
\nAn integrated complex system of two serial subsystems (abstraction).
All these ideas of analytical modeling operation processes are supported by the software tools [18, 19, 21, 23, 38, 39, 40, 41, 42, 43, 44].
\nWhat about new knowledge by using the proposed methods and models for cognitive solving of problems 1 and 2 of the chapter? A use of these methods and models on different stages of AIS life cycle (concept, development, utilization, support stages) allows to produce cognitive answers for the following questions:
What about different risks to lose integrity in operation?
What about the justified norms for values of monitored parameters?
What requirements should be specified to MTBLI and to repair time for different possible scenarios of operation?
Which information operation processes should be duplicated and how?
What processing devices and technologies should be used to achieve the necessary level of system integrity (quality, safety, etc.)?
What is the system tolerance to data flows changing?
What data flows and functional tasks may be the main causes of “bottlenecks”?
What data gathering technologies and engineering solutions can guarantee the completeness and actuality of used information?
What information verification and validation control should be used?
What qualification requirements should be for the users of AIS (from the AIS effectiveness and efficiency points of view)?
How dangerous are scenarios of environment influences and what protective technologies will provide the required security?
How the use of integrity diagnostics and security monitoring will worsen time-probabilistic characteristics of system?
What protection system effectiveness should be to prevent an unauthorized access?
What are the information security risks? etc.\n
The rationale answers allow to improve and accumulate knowledge concerning AIS.
\nThe proposed methods and models provide the next approach for cognitive solving problems 1 and 2.
\nIt is supposed that the terms “success” and accordingly “failure” are defined in terms of admissible condition of interested system to operate for the purpose.
\nNote. For example, for each parameter of equipment, the ranges of possible values of conditions may be estimated as “Working range inside of norm” and “Out of working range, but inside of norm” (“success”) or “Abnormality” (“failure”), interpreted similarly light signals—“green,” “yellow,” and “red.” For this definition, a “failure” of equipment operation characterizes a threat to lose system norm integrity after danger influence (on the logic level this range “Abnormality” may be interpreted analytically as failure, fault, losses of quality, or safety etc.). But the definition may be another: for example, a “failure” may be defined as incident or accident. For this definition, short-time being in the range “Abnormality” is not “failure,” because the incident or accident may not happen.
\nThere are four steps proposed for cognitive solving of problem 1 of planning the possibilities of functions performance on the base of monitored information about events and conditions; see Figure 8.
\nSteps for cognitive solving of problem 1.
Knowledge base (K-base) is defined as a database that contains inference rules and information about human experience and expertise in a domain (ISO/IEC 2382-1:1993).
\nMaximum of gain as a result of the functions performance under the given conditions and limitations on the acceptable risk of failure and/or other limitations
Maximum probability of “success” or minimum risk of “failure” under limitations
Note. For example, there are proposed the next general formal statements of problems for system optimization:
on the stages of system concept, development, production, and support: system parameters, software, technical, and management measures (Q) are the most rationale for the given period if on them the minimum of expenses (Zdev.) for creation of system is reached
at limitations on probability of an admissible level of quality
on utilization stage: system parameters, software, technical, and management measures (Q) are the most rational for the given period of operation if on them the maximum of probability of correct system operation is reached
at limitations on probability of an admissible level of quality
For limitation on
These statements (6), (7) may be transformed into the problems of expenses or risk minimization in different limitations. There may be a combination of these formal statements in system’s life cycle.
\nNote. A solution that meets all conditions may not exist. In this case, there is no optimal variant of planning the possibilities of functions performance on the base of monitored information. Additional systems analysis, adjustment of the criteria, or limitations is required (see, for example, ISO/IEC/IEEE 15288).
\nFor a robot, the concept of “failure” under uncertainty is defined as the “unsuccess” to achieve the goal within a given time. It is assumed that there are several possible routes to achieve the goal, and uncertainties may include both the conditions for robot operation (including random events in orientation, localization, and mapping in cooperation with drone for gathering actual data). The minimum risk of failure under the existing conditions and limitations is used as a criterion of optimization.
\nThe next four steps are proposed for cognitive solving of problem 2 of robot route optimization under limitations on risk of “failure” in conditions of uncertainties, see Figure 9.
\nSteps for cognitive solving of problem 2.
The index i of the first part of the selected route is set to the initial value i = 1.
\nIf the set of possible options is exhausted and the goal is not achieved, it is concluded that the goal is unattainable with the risk of “failure” less than the acceptable risk (i.e., it means an impossibility of solving problem 2 in the defined conditions).
\nThus, for optimizing robot route in space (i.e., for the “successful” solution of problem 2) in real time, information gathering, probabilistic predictions for possible route variants, their comparison, the choice of the best variant, the implementation of further actions, the improvement, accumulation, systematization, and use of knowledge are being, see Figure 9.
\nNote. The proposed methods of solving problems 1 and 2 are essentially identical approaches based on the use of the same probabilistic models (Section 3). The only difference is that for the system planning the possibilities of functions performance (problem 1), the concept of “success” is used; and for the robot route optimization under limitations on risk of “failure” (problem 2), the concept of “failure,” which is defined as the lack of “success,” is used.
\nHere, problem 1 (of planning the possibilities of functions performance) is solved by the proposed approach on the base of information gathered from different similar projects, accumulated and systematized in K-base including history. Applicability of the proposed probabilistic methods and models on development stage is demonstrated to improve some of the existing capabilities of robot-manipulator. It is required to predict the possible period of robot-manipulator use in space. When planning the possibilities of performing the functions of the cosmonaut-operator, two variants were compared: first variant–without a use of AIS; second–by using some AIS for supporting decision-making and monitoring the status of the operator’s console, power units, central controller, and control handle for manipulator means.
\nA robot-manipulator as a system is composed on subsystems: an operator’s console, a power unit, a central controller with a handle of control and manipulator means. There are supposed that a frequency of anomalies is in average 1 times a year, mean activation time from anomaly occurrence to failure is about 3 days. Time between the end of diagnostics and the beginning of the next diagnostics is about 2 months, and the recovery time is about 2 days.
\nSystem decomposition is presented on Figure 10. We do STEPS 1–4 (Figure 8) and use formulas (1)–(3) for solving the problem for complex structure composed by elementary variants decompositions presented on Figures 3 and 4. Here, probability of “success” (
Probability of reliable operation of robot-manipulator as a system
Probability of reliable operation of every subsystem
Results of probabilistic modeling robot-manipulator operation.
Risks of “failure” (R) means addition to 1 for probability of “success.”
\nResults of modeling the first variant of project have shown the following (Figure 10): for operator’s console (first subsystem), power unit (second subsystem) and central controller with a handle of control (third subsystem) MTBLI = 8766 h, for manipulator means (including a hinge of roving of key, a hinge of shoulder, a hinge of roving of elbow, a hinge of elbow, a hinge of roving of brush, a hinge of brushes, a hinge of brush rotation, a device for grasping, videocamera—united as subsystem 4, which can operate if one of these means is available) MTBLI = 31,293 h, for all complex 1,…,4 MTBLI = 2672 h; probability of reliable operation of complex 1,…,4 during 8 h is equal to 0.979; probability of reliable operation of complex 1,…,4 during 48 h is equal to 0.965.
\nThe maximum probability of “success” and minimum risk of “failure” under limitations on the successful functions performance are used as a criterion.
\nThe results of first variant are used for estimating input for the second variant of modeling: every subsystem for second variant (for subsystems equipped by AIS) is characterized by MTBLI = 31,293 h in analogy to the subsystem 4 of first variant. Owing to AIS, the frequency of anomalies is about 0.28 year−1 (it is equal to 1/MTBLI), but the conditions of anomalies activation time are more strong: the mean time is 30 min. The time between the end of diagnostics and the beginning of the next diagnostics is 1 month, and the recovery time is about 1 day.
\nWhat about the risks of “failure” during period from 0.05 to 2 years?
\nAnalysis of modeling results proves: risks are very high despite the use of AIS with the described characteristics, see Figure 11.
\nRisks of “failures” depending on the prognostic period of use (from 0.05 to 2 years).
For a robot-manipulator used in space, new knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterizes inadmissible conditions for functions performance by robot-manipulator.
The probability of “success” on level 0.98 or risk of “failure” on level 0.02 during six sessions of cosmonaut work is inadmissible for reliable robot-manipulator operation more than 1–2 weeks in space.
For a robot-manipulator used in space, the level 31,293 h of MTBLI is inadmissible level for every compound subsystem equipped by considered AIS.
Analyzed project of robot-manipulator operation effectiveness can be added to K-base history as precedent of “unsuccess.”
For analyzed project, new research for decreasing risks with the proof of its efficiency on the basis of modeling is strongly required after improving characteristics for every subsystem of robot-manipulator.
In practice, many devices proper to intelligent manufacturing are sources of data monitored. This example explains how monitored data can be tailored in AIS for probabilistic modeling to solve both problems 1 and 2.
\nThe approach to form specific input for modeling is demonstrated on example of mean time Toccur for PDF Ωoccur(t) and mean time Tactiv for PDF Ωactiv(t) from random values τoccurrence and τactivation (Figures 6 and 12).
\nThe universal elementary ranges for monitored parameters.
The elementary ranges for monitored parameters from quality or safety point of view should be set. For each parameter, the ranges of possible values of conditions are set: “Working range inside of norm,” “Out of working range, but inside of norm,” and “Abnormality,” The condition “Abnormality” characterizes a threat to lose system integrity after danger influence (on the logic level this range “Abnormality” may be interpreted analytically as failure, fault, losses of quality, or safety etc.). The construction on Figure 12 allows to extract data for probabilistic modeling: time between moments of the occurrences of dangers (potential threats), activation time of occurred dangers, and recovery time.
\nFor example, from Figure 12:
Mean time between moments of the occurrences of dangers (potential threats) Toccur = (τoccurrence 1 + τoccurrence 2)/2
Mean activation time Tactiv = (τactivation 1 + τactivation 2 + τactivation 3)/3
Mean recovery time for Trecovery = (τrecovery 1 + τrecovery 2)/2
This example is auxiliary to understand some sources of input for the proposed models (Sections 3–5) used for the next examples.
\nApplicability of the proposed probabilistic methods and models is demonstrated to improve some of the existing capabilities of AIS for a coal company. This subsection contains an explanation how problem 1 (of planning the possibilities of functions performance) may be solved for intelligent manufacturing by the proposed approach on the base of data monitored. This demonstrates AIS possibilities for a coal company on its operation stage.
\nLet a coal company (as system) is decomposed on 9 subsystems for studying efficiency. Of course, every subsystem also may be considered as complex system, for example, see Figure 7. Components from 1 to 6 united by multifunctional safety system of the mine, component 7 is associated with the washing factory, component 8 is associated with transport, and component 9 with port, see Figure 13: 1—the control system of ventilation and local airing equipment; 2—the system of modular decontamination equipment and compressed air control; 3—the system of air and gas control; 4—the system of air dust content control; 5—the system of dynamic phenomena control and forecasting; 6—the system of fire-prevention protection; 7—the safety system of washing factory; 8—the safety system for transport; and 9—the safety system of port. Information is monitored from different sources, accumulated in a database of dispatcher intelligence center, processed, and systematized (including systematization described in Example 2 to get input for modeling).
\nAn example of a coal company with AISs that transformed all system components to the level which is proper to skilled workers.
For planning possibilities of functions performance by AIS in this example, the probabilistic modeling is being to answer the next two questions:
How every responsible worker can know a residual time before the next parameters abnormalities?
What risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to skilled workers (Optimistic view on dangerous coal intelligent manufacturing)?
To answer the first question, the ranges of possible values of conditions are established: “Working range inside of norm,” “Out of working range, but inside of norm,” and “Abnormality” for each separate critical parameter of equipment. It is interpreted similarly by light signals—“green,” “yellow,” and “red,” as it is reflected on Figure 12. Some examples of parameters may include compression, capacity, air temperature (out, in, at machinery room), voltage, etc. The information from Example 6.2 and additional time data of enterprise procedures are used by AIS as input for using formulas (1) and (3) and Steps 1–4 (from Figure 8) in real time of company operation activity. Here, risks to lose the system integrity during the given period Tgiven means risks to be at least once in state “Abnormality” within Tgiven. The functions of modeling is performed on special servers (centralized or mapped); see details in [27, 36]. If virtual risks are computed by formulas (1) and (3) for all points Tgiven from 0 to ∞, the calculated values form a trajectory of the PDF. The mathematical expectation of this PDF means the mean residual time to the next state “Abnormality.” It defines MTBLI from this PDF. This output of probabilistic modeling can be transmitted to interested workers. Requirements to AIS operation quality are: quality measures of used information by AIS should meet admissible level recommended in Table 1.
\nThus, the answer on the first question “How responsible worker can know a residual time before the next parameters abnormalities?” is: the calculated mean residual time to the next state “Abnormality” (MTBLI for “red” range on Figure 12) can be transmitted in real time to responsible worker immediately after parameter value cross the border from “Working range inside of norm,” “Out of working range, but inside of norm” (from “green” to “yellow” range on Figures 12 and 13). It is possible as a result of implementation of the proposed approach—see example of implementation in [27, 36].
\nTo answer the second question, let the next input be formed from data monitored.
\nLet for every system component, a frequency of occurrence of the latent or obvious threats is equal to once a month and the mean activation time of threats is about 1 day. The system diagnostics are used once for work shift 8 h, a mean duration of the system control is about 10 min, and the mean recovery time of the lost integrity of object equals to 1 day. The workers (they may be robotics, skilled mechanics, technologists, engineers, etc.) are supported by capabilities of an AIS and a remote monitoring systems allowing estimating in real time the mean residual time before the next parameters abnormalities considering the results of probabilistic modeling. Formally they operate as parallel elements with hot reservation (structure on Figure 4, right). Owing to AIS support workers are capable to revealing signs of a critical situation after their occurrence. Workers can commit errors on the average not more often once a year (it is proper to skilled workers).
\nTo answer the question we do Steps 1–4 (from Figure 8) and use formulas (1)–(3) for solving the problem for complex structure, see Figure 13. Here, risks to lose system integrity means risks of “failure” for every subsystem which can be detailed to the level of every separate critical parameter of equipment.
\nThe fragments of built PDF on Figure 13 show: risk of “failure” increases from 0.000003 for a year to 0.0004 for 10 years and to 0.0013 for 20 years. Thus, the mean time between neighboring losses of integrity (MTBLI) equals to 283 years.
\nThese are some estimations for example assumptions.
\nThus, the answer on second question “What risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to skilled workers?” is: risks to lose system integrity may be 0.000003 for a year, 0.0004 for 10 years and 0.0013 for 20 years, herewith (MTBLI) is equal to 283 years. These are the Optimistic estimations for dangerous coal intelligent manufacturing that make sense to take over a desired level of AIS operation effectiveness.
\nNew knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterizes admissible conditions for functions performance by AIS for a coal company.
The probability of “success” on levels 0.99997 for a year, 0.9996 for 10 years and 0.9987 for 20 years or risk of “failure” on levels 0.000003 for a year to 0.0004 for 10 years and 0.0013 for 20 years (with predicted risks levels for discovered “bottlenecks”) are admissible.
Expected term in average 283 years and more is admissible systemic aim for providing safe company operation.
Analyzed project of AISs operation effectiveness (that transform all system components to the level which is proper to skilled workers of coal company) can be added to K-base history as a precedent of “success.”
This subsection continues an explanation on how problem 1 (of planning the possibilities of functions performance) may be solved for intelligent manufacturing by the proposed approach on the base of data monitored. This demonstrates the capabilities of AIS used for a security service of floating oil and gas platform on its operation stage. The difference from previous example is in more degree of uncertainties (because of high complexity) that allows to transform all system components to the level which is proper to medium-level workers of floating oil and gas platform. The same approach, structure, and formulas for probabilistic modeling are used.
\nLet a floating oil and gas platform is also decomposed on nine subsystems. Every subsystem is enumerated on Figure 14, and operates as parallel elements with hot reservation.
\nAn example of a floating oil and gas platform with AISs that transform all system components to the level which is proper to medium-level workers.
Components are: 1—a construction of platform; 2—an AIS on platform for robotics monitoring and control; 3—an underwater communication modem; 4—a remote controlled unmanned underwater robotic vehicle; 5—a sonar beacon; 6—an autonomous unmanned underwater robotic vehicle; 7—non-boarding robotic boat, a spray of the sorbent; 8—non-boarding robotic boat, a pollution collector; and 9—an unmanned aerial vehicle.
\nAnd let input for modeling is the same as in Example 6.3. Only one difference is because of complexity characteristics are proper to medium-level workers of floating oil and gas platform. For this example, it means workers and AIS can commit errors more often in comparison with skilled workers, for one element it is equal to 1 time a month instead of once a year.
\nFor planning possibilities of functions performance by AIS in this example, the probabilistic modeling is being to answer the question:
\nWhat risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to medium-level workers (realistic view on dangerous oil and gas intelligent manufacturing)?
\nTo answer the question, we do Steps 1–4 (from Figure 8) and use formulas (1)–(3) for solving the problem for complex structure, see structure on Figure 13. Here, risks to lose the system integrity mean risks of “failure” for every subsystem. The fragments of built PDF on Figure 14 show: from 0.0009 for a year to 0.0844 for 10 years and 0.25 for 20 years. Thus, MTBLI equals to 24 years. It is 11.4 times less often against the results of Example 6.3.
\nThese are some estimations for example assumptions.
\nThus, the answer on question is: risks to lose system integrity may be 0.0009 for a year, 0.0844 for 10 years and 0.25 for 20 years; herewith, mean time between neighboring losses of integrity is equal to 24 years. These are the realistic estimations for dangerous oil and gas intelligent manufacturing.
\nNew knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterize possible complex conditions for functions performance by AIS used for a security service of floating oil and gas platform.
The probability of “success” on levels 0.9991 for a year, 0.9156 for 10 years and 0.75 for 20 years or risk of “failure” on levels 0.0009 for a year, 0.0844 for 10 years and 0.25 for 20 years (with possible consequences) and expected term in average 24 years as estimation of mean time between neighboring losses of integrity are realistic view on dangerous floating oil and gas platform intelligent manufacturing.
For analyzed project new research to improve characteristics for the security service of floating oil and gas platform for decreasing risks with the proof of its efficiency on the basis of modeling is required.
Analyzed project of AISs operation effectiveness (that transform all system components to the level which is proper to medium-level workers of floating oil and gas platform) can be added to K-base history as precedent.
Applicability of the proposed probabilistic methods and models is demonstrated to improve some of the existing capabilities of rescue robot for route optimization. This subsection contains an explanation on how problem 2 may be cognitively solved. Similar problems of specific robot route optimization from point A (Start) to point F (Finish) can arise on water, under water (Figure 15), in burning wood (Figure 16), in the conditions of a city or in mountains (Figure 17), and in other situations in conditions of uncertainties. Specific cases of uncertainties can be connected additionally with complex conditions of environment and necessity of robotics orientation, localization, and mapping that influences on input for the proposed probabilistic models.
\nA system view on situation for robot route from point A (Start) to point F (Finish) on water and under water.
A system view on situation for robot route from point A (Start) to point F (Finish) in burning wood.
A system view on situation for robot route from point A (Start) to point F (Finish) in mountains.
Here, we demonstrate the proposed approach by a simplified example of moving a special rescue robot from point A to the final point F of the route (from where the SOS signals from tourists are following). It is required to optimize the route of the robot in space under uncertainty of weather, complex snow conditions in mountains to achieve the goal in 2 h with an acceptable risk of failure less than 0.1 (i.e., a probability of success should be more than 0.9). Interaction with the drone-informant is supposed, see Figure 17.
\nThe applications to cognitive solving the problem of robot route optimization are demonstrated by the next steps.
\nFor each variant, a set of system compared by modeling is defined: there are ABCF, AGKF, AHLDEF, and possible combinations. Inputs characterizing every part of route for each of the variants are formed by K-base and gathered data from drone-informant:
Frequencies of the occurrences of potential threats are for route ABCF σ = 1 time at 10 h, AGKF σ = 1.5 times at 10 h, AHLDEF σ = 2 times at 10 h (since 8.00 a.m. to 8.00 p.m.)
Mean activation time of threats Tactiv = 30 min
Time between the end of diagnostics and the beginning of the next diagnostics of robot availability Tbetw. = 2 min
Diagnostics time of robot availability Tdiag = 30 s
Recovery time of robot availability = 10 min (for modified model [42, 43, 44])
Given prognostic period Tgiven = 2 h
i = 1.
\nThe risk of “failure” in dependence on prognostic period during the robot route from point A (Start) to point F (Finish).
i = i + 1 = 2.
\nThus, the way ABCBGHLDEF is the result of optimization. The robot purpose was achieved owing to preventive measures which were defined by using risk control on the way (with probability of “success” more than 0.9).
\nNew knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterizes possible complex conditions for rescue robot route optimization under limitations on risk of “failure” in conditions of uncertainties. In particular, the information updates every 2 min for robot route optimization under limitations on risk of “failure” less than 0.1 is admissible for considered situation.
The acceptable risk 0.1 is justified; the predicted risks for all variants of the routes did not exceed 0.1.
Analyzed project can be added to K-base history as precedent.
The proposed approach to build and implement the probabilistic methods and models is demonstrated by application to cognitive solving:
The problem of planning the possibilities of functions performance on the base of monitored information about events and conditions
The problem of robot route optimization under limitations on risk of “failure” in conditions of uncertainties
There is proposed to carry out probabilistic prediction of critical processes in time so that not only to act according to the prediction, but also to compare predictions against their coincidence to the subsequent realities.
\nThe described analytical solutions are demonstrated by practical examples such as:
System planning the possibilities of functions performance in space by using robot-manipulators, by AIS for a coal company and for a floating oil and gas platform
Forming input for probabilistic modeling from monitored data
Robot route optimization under limitations on risk of “failure” in conditions of uncertainties
A cognitive solving of the chosen problems consists in improvements, accumulation, analysis, and use of appearing knowledge.
\nProofs for formulas (1)–(3)\n
\nAccording to the proof of formula (1): because between diagnostics system is not protected from threats an influence (a loss of integrity) will take place only after danger occurrence and activation during given time before the next diagnostic (Figure 6). A risk to lose integrity (i.e., probability of “failure”) is equal to Ωpenetr*Ωactiv(Treq) because these PDF are independent. The found probability of providing system integrity (probability of “success”) is equal to addition to 1.
\nThe proof of formula (1) is complete.
\nFor the special case, if Ωoccur(t) = 1 − exp(σt), σ = 1/Toccur, Ωactiv(t) = 1 − exp(t/β), β = Tactiv\n
\nNote. This formula (1) is used also for the estimation of system operation without diagnostics. There is supposed that before the beginning of period
According to the proofs of formulas (2) and (3), we consider independence. Then formula (2) means measure
\nFormula (3) means measure
The proofs for formulas (1)–(3) are complete.
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