\r\n\t
",isbn:"978-1-83968-571-2",printIsbn:"978-1-83968-570-5",pdfIsbn:"978-1-83968-599-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"dd81bc60e806fddc63d1ae22da1c779a",bookSignature:"Dr. Sebahattin Demirkan and Dr. Irem Demirkan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10818.jpg",keywords:"Decision Making, Blockchain, Accounting, Earnings Management, Strategic Alliances, Innovation, Performance, Corporate Governance, Accounting Quality, Digital Assets, Internationalization, MNCs",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 28th 2021",dateEndSecondStepPublish:"February 25th 2021",dateEndThirdStepPublish:"April 26th 2021",dateEndFourthStepPublish:"July 15th 2021",dateEndFifthStepPublish:"September 13th 2021",remainingDaysToSecondStep:"6 hours",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Academician in the area of accounting who believes in the impact of interdisciplinary research. Dr. Sebahattin Demirkan's research interests are in the areas of financial accounting, capital markets, auditing, corporate governance, strategic alliances, taxation, CSR, and data analytics.",coeditorOneBiosketch:"Researcher of strategic management, corporate entrepreneurship, and international business; specific interests include innovation, the ambidexterity framework, inter-organizational relationships, and networks. Experienced in teaching graduate and undergraduate courses in strategy, entrepreneurship, and international business and management areas.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"336397",title:"Dr.",name:"Sebahattin",middleName:null,surname:"Demirkan",slug:"sebahattin-demirkan",fullName:"Sebahattin Demirkan",profilePictureURL:"https://mts.intechopen.com/storage/users/336397/images/system/336397.jpg",biography:"Dr. Sebahattin Demirkan is a Professor of Accounting. He earned his Ph.D. in Accounting/Management Science at Jindal School of Management of the University of Texas at Dallas where he got his MS in Accounting, MSA Supply Chain, and MBA degrees. He got his BA in Economics and Management at the Faculty of Economics and Administrative Sciences at Bogazici University, Istanbul. He worked at Koc Holding, a private venture capital firm, and the University of California, Berkeley during and after his education at Bogazici University. His research interests are in the areas of financial accounting, capital markets, auditing, corporate governance, strategic alliances, taxation, CSR, and data analytics. Dr. Sebahattin Demirkan has published articles in Contemporary Accounting Research, JAPP, JAAF, TEM, Journal of Management, and other top academic journals. He teaches several different classes in both undergraduate and graduate levels in Accounting and Analytics programs. He is a treasurer and vice president of the TASSA, board member of the BURCIN and member of the American Accounting Association.",institutionString:"Manhattan College",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Manhattan College",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:{id:"342242",title:"Dr.",name:"Irem",middleName:null,surname:"Demirkan",slug:"irem-demirkan",fullName:"Irem Demirkan",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000033HrA8QAK/Profile_Picture_1606729803873",biography:"Dr. Irem Demirkan earned her Ph.D. in International Management Studies and M.S. in Administrative Studies at Jindal School of Management at the University of Texas at Dallas, USA. She got her BA in Economics at the Faculty of Economics and Administrative Sciences at Bogazici University, Istanbul, Turkey. She worked in the finance and textile industries before joining to academia. Dr. Demirkan has published research in the areas of strategic management and corporate entrepreneurship in journals such as the Journal of Management, Journal of Business Research, Management Science, European Journal of Innovation and Management, IEEE Transactions on Engineering Management, among others. Dr. Demirkan currently teaches strategic management, entrepreneurship, and international business at Loyola University Maryland in Baltimore, MD.",institutionString:"Loyola University Maryland",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Loyola University Maryland",institutionURL:null,country:{name:"United States of America"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"7",title:"Business, Management and Economics",slug:"business-management-and-economics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"59308",title:"Multiagent Intelligent System of Convergent Sensor Data Processing for the Smart&Safe Road",doi:"10.5772/intechopen.73610",slug:"multiagent-intelligent-system-of-convergent-sensor-data-processing-for-the-smart-safe-road",body:'\nSmart&Safe City means the development and implementation of projects such as Smart Manufacturing, Smart Houses, Smart Light, Smart Energy, Intelligent Transportation System, Smart Road, and so on [1, 2]. The goal of Smart Technology Development & Safe City is to ensure the comfort and safety of human life in the urban infrastructure and efficient production in the industrial sector. Smart&Safe City components are integrated into a multimodal smart environment [3]. It provides interaction of cyberphysical devices, cloud computing resources and mobile communication systems. Smart Environment helps the artificial intelligence system to solve problems of automatic control or to support decision-making based on big data monitoring about the surrounding reality. It is based on the Internet of Things network platform for the collection and processing of sensor data. The platform includes the following:
Intelligent sensors (sensors, measuring devices, photo and video fixation devices).
Telecommunication networks of broadband data transmission (fiber-optic and wireless) and mobile communication systems.
Satellite navigation systems.
The paradigm of an intelligent multimodal environment includes three basic concepts such as ubiquitous (pervasive) computing and networking [4]; intellectual assistance (ambient intelligence) [5] and smart environments [6].
\nThe creation of smart road environment (SRE) is an important direction in the Smart&Safe City concept [7]. Environment is needed for the interaction of satellite vehicle monitoring systems, intelligent transport systems (ITS) [8], unmanned vehicles, intelligent road infrastructure components and mobile communication users. SRE includes a built-in intelligent functionality in the vehicles, objects of road transport infrastructure and intelligent system for monitoring and traffic management. It is based on the methods of monitoring and managing traffic flows [9], provides information and safety to road users. Research in this area relates to the creation of traffic monitoring systems [10], for example, using radio tags [11] or embedded monitoring complexes [12]. Monitoring technology includes stream sensor data processing (photos, video streams, telemetry data, user information), data mining, machine learning, forecasting, multiagent processing [13] and the convergence of computing models (clouds, fog and mobile computing) [14]. The monitoring tasks are as follows: monitoring the condition of the pavement, meteorological monitoring, monitoring of traffic flows and monitoring violations of traffic rules.
\nModern road transport infrastructure consists of a system of satellite navigation, traffic signal control, regulation of cargo transportation, information boards, detection systems of car numbers, registration of traffic accidents and violations. The intellectualization of the road transport infrastructure is to develop intelligent systems for monitoring and surveillance, parking management system, decision-making systems for traffic flows regulation, intelligent transport systems, and so on. The purpose of the SRE elements is to influence the behavior of cars, drivers and pedestrians in terms of optimizing transport routes and passenger flows, reducing security risks by preventing emergency situations.
\nThe main elements of the SRE are as follows:
Intelligent real-time monitoring system
Real-time traffic information system for alerting and warning road users
System of accounting and analysis of road users’ social reactions [15]
Interactive journey planner system
Intelligent traffic lights systems
Intelligent signaling system
Surveillance cameras (CCTV) and photoradar complexes
Satellite systems of transport monitoring
Parking and loading areas information systems
Sensor systems for the movement of unmanned vehicles
Intelligent vehicle transport systems
Electronic payment systems for road services
An important element of SRE is an intelligent monitoring system for decision-making on the management of the road infrastructure objects. The system works with a network of spatially distributed photoradar vehicle detectors for road accidents, video surveillance cameras, vehicle information and communication systems (VICS), built-in car navigation equipment and mobile communication equipment. It is designed for the collection and sensor data processing. The monitoring objectives are analysis, assessment and forecast of changes in traffic situations to control the behavior of vehicles and road users and to alert police, emergency services, ambulance, maintenance and other services.
\nMonitoring of objects and incidents in the road infrastructure is carried out on the basis of the collection and sensor data processing obtained from ground platforms, aerial and space surveillance facilities. The main ground platforms in SRE are CCTV cameras and photoradar vehicle detector complexes (Figure 1).
\nCCTV cameras and photoradar vehicle detector complexes.
Photoradar complexes allow in an automatic mode to fix incidents on objects of road transport infrastructure, to collect and accumulate sensor data [16]. A lot of complexes receive a huge amount of data, which cannot be processed by a person in real time. Complexes can recognize objects in photos and in a video stream, measure the speed of vehicles in the control zone, automatically capture and save photos of violators, recognize license plates, collect and transfer data to the data center. However, the complexes do not have the capabilities of intellectual analysis and forecasting in real-time mode.
\nThe data flow diagram of convergence model.
Creation of a heterogeneous transport environment is required for the interaction of the complexes and the transfer of sensor data to the data center. The trend in the field of telecommunications consists of replacing wired networks with wireless channels for monitoring distributed objects [17]. A wireless network is necessary for the interaction of mobile and fixed elements of the SRE. It includes a segment of the Internet of Things for the data exchange between complexes, intelligent transport systems, surveillance systems, a segment of the cellular network for data exchange between users and a segment of the satellite navigation system. The heterogeneous network is realized through technologies of wireless sensor networks (WSN) [18], cellular networks, Wi-Fi networks and satellite networks.
\nModern approaches to distributed computing and storage of sensor data are based on the concept of convergence [19]. Convergence is defined as the interlinking of computing and storage technologies such as media, content and communication networks. Convergence in relation to network technologies means the process of telecommunication technologies’ convergence with the appearance of similar characteristics in network equipment, communication channels, network standards and protocols and data transfer processes. For example, the technology integration of mobile and cloud computing is the result of the convergence [20]. Another example is the convergence of cloud and fog computing models in a wireless sensor network [21], which is proposed to create a computing platform for distributed sensor data processing in the SRE. The convergent model of cloud, fog and mobile computing (Figure 2) is designed for sensor data processing, obtained from spatially distributed photoradar complexes, a video surveillance camera, navigation equipment, intelligent transport systems and mobile equipment.
\nThe diagnostic data of the complex.
The convergence network platform may include some hardware and software levels that are as follows:
The sensor nodes are associated with industrial controllers and sensors, directly implementing fog computing.
Clusters network segments with coordinators, cellular modems, router, which collects and transfers sensor data into the data warehouse.
Cloud computing clusters.
Warehouse of sensor data and monitoring results.
The user mobile devices for the organization of access to computing and information resources.
The first level of the platform is a fog computing model. It provides the collection and sensor data processing on distributed nodes of the sensor network, in measuring devices and automation devices. Fog computing model is also the platform for data storage services on end-terminal devices and network services for data transmission. Computation is performed by terminal devices with limited computing and energy resources, including WSN nodes, controllers, industrial equipment, household appliances with microprocessors equipment and sensor network nodes. Modern WSN nodes have sufficient processing power to organize distributed computing [22]. The fog computing model is the basis of the Internet of Things [23]. Fog computing platform is necessary for realization of multiagent processing of sensor data and consolidated storage of calculated results on sensor network nodes [24, 25].
\nThe second level of the convergent platform is implemented on the basis of the cloud computing model. Cloud platforms are now used in almost all areas of activity [26]. It is used for the ubiquitous network for access to a common pool of configurable resources (software, server, information, platform, etc.) at any time. The user uses the technology of “thin” client as a means of access to applications and data. The infrastructure of the information system is located at the provider of cloud services. The information is stored in cloud storage on the servers of the network. It is temporarily cached by the analytical processing [27]. The trend is the creation of distributed storage for BigData processing [28].
\nThe third level of the convergent platform is related to the data processing on smart phones and tablets for presentation of monitoring results to users with the visualizing events and making decisions to reduce road incidents [29]. Mobile computing model is the platform for human-computer interaction. It involves mobile communication, mobile hardware and software. Communication issues include ad hoc networks and infrastructure networks as well as communication properties, protocols, data formats and mobile technologies.
\nMonitoring of road infrastructure includes procedures that are as follows:
Vehicle detection and identification in a controlled section of the road with the measurement of its speed
Photography and video fixation of traffic rules violations
Collect data on the traffic flow parameters in all monitored areas and transfer to the data processing center via communication channels
Vehicle detection on demand and tracking them with visualization of routes of their movements on a cartographic basis
Photographs and video materials processing about violations
Accumulation and statistical data processing on offenses for periods of time to identify and analyze the dependencies of changes in violations and road accidents from the influence of various factors (weather conditions, traffic volume, repair work, city events, time of day, seasonal factors, etc.)
Spatial analysis of offenses to identify critical areas and “bottlenecks” in the road transport infrastructure and their dependence on changes in traffic conditions with visualization on the map
Intellectual data mining and forecasting of road traffic situations for making decisions to improve traffic safety
Multiagent approach is advisable for the implementation of monitoring procedures. It involves the use of software agents for data collection, data mining and forecasting, as well as to alert road users about road traffic situation via mobile and navigation equipment [30]. The data collection and initial data processing are realized in the fog computing layer by means of agents loaded into sensor nodes. Sensor units are connected to the photoradar complexes. Agents interact with server components of the monitoring system.
\nThe hypervisor is used to manage agents. It is consolidated computing resources for distributed data processing. Software agents are operated on the sensor nodes. Agents respond to requests, decide on the selection of data processing functions, clone and migrate to other network nodes. A feature of the agents is the behavior realization. The behavior is determined by the mathematical function, which implements the steps of sensor data processing. Other options determine the agent behavior in case of certain kinds of situations. The model of brokers is offered to agent interaction with server applications at the data center. Broker is an agent that runs on routers and realizes the storage, data protection, transmission and warehouse loading functions.
\nThe multiagent system includes the following software agents:
Agent for the synthesis and control of the photoradar devices queue for inquiry.
Agent for creating threads for asynchronous device polling.
Agents of data polling from devices, separated by geographic zones and by types (devices Cordon-Temp, KrisP, Parkon, etc.). The data polling from device sensors is carried out by the agents from different zones using the SNMP protocol.
Agents that keep event logs directly on the complexes. Each complex maintains a local database, recording events in the log files. A lot of local databases represent a distributed hierarchical data warehouse. Agents keep a log file of vehicle passages, a log file of traffic violations, a log file of telemetry parameters for device diagnostics, and so on.
Agents for uploading data from device logs to central storage. Agents work through the web interface and generate a lot of files in XML format. One file contains the data of one violation and is associated with a digital signature file and violation pictures.
Agent for aggregating data about recorded violations for a period of time. This agent generates a comma separated values (CSV) file containing rows with parameters of all violations for a given period. It is an element of a distributed fog database. A lot of CSV files on different devices form a distributed hypertable of summary data on violations over a period of time.
Agent for aggregating the values of the complex parameters over a period of time. This agent creates a CSV file containing rows with the values of the complex parameters. It is also an element of a distributed database. A lot of CSV files with parameters of different devices form a hypertable for their diagnosis over a period of time.
Agent for parsing the files with violation parameters and parameters of the complexes for loading data into the central cloud storage.
Data mining agents for the analysis of data violations. This group of agents analyzes the time series of the uploaded violations data over a time period to identify the dependencies of growth or to reduce violations from various factors.
Data mining agents for the analysis of device parameter. This group performs analysis of time series of device parameters to detect parameter deviations from the required and reference values (benchmarking). The tools include data visualization agent, data aggregation agent, data selection agent, data mining agent and data analyze agent [31, 32].
Agents for forecasting violations of traffic rules and agents for forecasting failures and errors in the operation of complexes. Forecasting is performed using the technique of deep machine learning based on the synthesis of a fuzzy neural network, its training and forecasting changes in the operating parameters of the complex.
Agents for data visualization on computers in the data center and on mobile units. A variety of agents form a distributed content management system. Agents are downloadable php and js scripts. They allow in standard browsers to present historical, current and forecast data in the form of graphs, tables and dashboards. The data correspond to the polling time and geographical coordinates of the complex location.
The data aggregation agent is needed to support the technology of work with database in the aggregation mode for the selection and visualization of hypertable data mart. When the mode is setup, the user should define a set of object properties (columns of values) that will be shown in the hypertable. Available properties can be selected from the drop-down list.
\nThe visualization agent allows to see information in the hypertable data mart. The hypertable is a nonstandard user interface for data visualization. It combines the functionality of a classic table with a tree structure. Elements of the hypertable can be located on distributed sensory nodes. Elements allow to view the dynamic changes of the values changes in real time. The data are grouped according to the parameters and levels of aggregation. A distinctive feature of the hypertable is that the number of rows is not a static value, a row character and functionality are not equal and some of them are the aggregates. The aggregates are nodal and show summary information on the relevant columns of the lower-level aggregation rows. The actual number of the hypertable rows varies dynamically, depending on the grouping of rows. Another feature of the hypertable is the ability to view quickly and analyze changes. The user can view hypertable change of any selected index for the period, as well as the predicted values for the specified forecast horizon. An example of data visualization, photoradar complex parameters, is shown in Figure 3.
\nDefuzzificator transforms an indistinct set to a completely determined exact decision y, representing the predicted condition of the photoradar complex.
The data analyze agent allows choosing the data needed for the analysis of a concrete situation. The data marts selection criteria can be quite complex. For this purpose, the system uses multilevel queries and filters that limit the data choice. The agent allows the personnel easily create queries to choose the right information.
\nThe diagnostic system is necessary for remote maintenance of photoradar equipment. The system should monitor the complex parameters, transmit telemetric information, predict possible malfunctions and automatically report about failures. The complex has a set of parameters such as supply voltage, response time, housing temperature, ambient temperature, and so on. Since the complexes are distributed over a large area, a multiagent remote diagnostic system is being developed to monitor their operation. Key element of a diagnostic system is the mechanism of forecasting a change, depending on its current parameters, level of external indignations and the influences. The data mining tasks and failures’ forecasting tasks are solved using deep machine learning and fuzzy neural networks based on the analysis of time series of complex parameters. The monitoring task for the equipment is determined by the high requirements for the uninterrupted operation of devices. In the event of emergency situations, the minimum time is allocated to correct the malfunctions. For a short time, it is necessary to determine the order of repair work and the required amount of resources such as the working time of specialists, the need to operate machinery and the required spare modules. The evaluation of the reliability of the complex is based on the data analysis on the device state at times and data analysis on the violations in the complex operations. Data for assessing the reliability of the complex include the following:
The work time of complex for the reporting data
The uninterrupted work time of complex
The time of fixing traffic accidents
The number of fixed objects
The number of recorded traffic accidents
The complex downtime for failure
The failure frequency
The cost of repairs
The number of errors in fixing traffic accidents
The purpose of the analysis is to identify the deviations of the complex parameters and to detect errors and malfunctions. The deviations of the parameters are exceeding thresholds, deviation of values from normative and normalized previous data. The results of forecasting are used to plan an unscheduled repair work in order to prevent possible failures. The work schedule depends on the following parameters: location of the complex; density of traffic on the repair location; availability of spare parts; nature of the malfunction; types of repair work carried out earlier with the device; frequency of malfunctions and required resources to restore functionality.
\nWe consider the system of forecasting of a qualitative condition of the photoradar complex on the basis of indistinct implication [33]. In case of N variables, rules of a conclusion have generally the following appearance: if x1 is A1 and x2 is A2 …. and xN is AN, then, y is B, where A and B are the linguistic values identified in the indistinct way through the corresponding functions. The x1, x2,… xN variables form an N-dimensional entrance vector x, the making argument of a condition in which А1, А2,…, АN and B designate sizes of the corresponding function of accessories μА(xi) (i = 1…N) and μВ(y), the function of Gauss defined in this case:
\nwhere с, σ and b are the parameters of the function of Gauss defining its center, width and form, respectively.
\nIf to consider that is available M-rules (and M-functions of accessory), the matrix of values of functions of accessory of the N × M size is formed:
\nRule 1: If \n
Rule 2: If \n
⋯
\nRule M: If \n
We present further sequences of the functioning of the diagnostics system of photoradar complexes with a conclusion of Mamdani-Zade in the form of the following stages:
\nThe first stage is the aggregation of the reasons for failures in the systems: The arriving value of the function μА(x) are aggregated in the algebraic form:
\nThe second stage is the aggregation effects of disruption of complexes: Each implication of the unique value of function μА → В is attributed. This operation is also carried out with the use of operation of algebraic work:
The third stage is the aggregation of results: At this stage, the operator of the sum is applied to aggregation of results of implication of many rules.
\nIn final part of the conclusion of Mamdani-Zade, the procedure of a defuzzification allowing to receive accurate value of an output variable—the predicted condition of photoradar complex is carried out (Figure 4).
\nThe scheme of HINN training for forecasting the complex state.
Since \n
The main weak spot in an implication method with a conclusion of Mamdani-Zade is subjectivity of the creation of a grid of rules and functions of accessory. This defect method can be eliminated by creation of the hybrid computing mechanism where implication of Mamdani-Zade is mediated by work of the neural network (NN), with the training mechanism inherent in it. Forecasting is the process of making predictions of the future based on past and present data. Forecasting accuracy is constantly being improved with the continual introduction of machine learning techniques. Time series sensor data are any data set that collects telemetry information regularly over a period of time. The fundamental problem for machine learning and time series is the same: to predict new outcomes based on previously known results. Time series and machine learning can be combined together in order to give the benefits of each approach. Time series does a good job at decomposing data into trended and seasonal elements. This analysis can then be used as an input for an NN model, which can incorporate the trend and seasonal information into its algorithm. The NN represents the parallel computing system consisting of a large number of elementary units of information processing—the neurons, accumulating experimental knowledge and providing them for the subsequent processing. The term “training” is understood as ability of NN to receive reasonable results on the basis of the data, which were not found in the course of training. The sequence of training on the basis of procedure of the return distribution is presented in Figure 5.
\nThe general structure of NN-mediating work of indistinct implication on forecasting with the indication of neuron minimizers and neuron adders.
This property is used at realization of hybrid indistinct neural network (HINN). We consider the sequence of functioning of HINN (Figure 6).
\nRoad area with photo-video fixing complexes.
On the first layer, the fuzzification is carried out. The formula of a fuzzifikation looks as follows:
where k is the quantity of functions of accessory (k = 1…M); j is the quantity of variables (j = 1…N); \n
It is necessary to consider that generally, the number of functions of accessory does not coincide with the number of rules. Therefore, if each xi variable has m functions of accessory, the maximum quantity of rules, which can be created at their combination, will make M = mN.
\nIn the second layer, the aggregation of values of the xi variables is carried out:
\nThus, the calculated parameters wk (k = 1…M) at the same time move further in the third layer (for multiplication on weight) and in the fourth layer for calculation of their sum in f2 neuron.
\nThe third layer when using a conclusion of Mamdani-Zade calculates the centers for k-rules for a formula: \n
After that aggregation of a consequence with the use of operation of algebraic work is carried out: \n
The fourth layer is presented by two neurons f1 and f2, which are carrying out results:
\nThe fifth layer is presented by the unique neuron, which is carrying out a defuzzification:
The algorithm of HINN training can conditionally be shared into two stages. At the first stage, parameters of the center of output functions of accessory in the third layer are subject to training. For this purpose, parameters of scales for the fixing of parameters of functions of accessory on the first layer (center, width and form) were determined as: \n
It should be noted that output signals y HINN replace with reference signals d from p of the training selections (the training examples x(l), d(l)), where l = 1…p. Then: \n
Further, the decision of system of the equations is carried out on the basis of pseudo-inversion of matrixes: Ap = d from p = A+d, where A+ is the pseudo-return matrix A.
\nAt the second stage, after fixing of values of linear parameters \n
where n is the number of iteration and η is the training speed parameter.
\nThis method was also used to forecast traffic accidents, based on the processing of accident statistics on controlled road sections. A method for forecasting road accidents was implemented depending on three factors: the amount of traffic flow per unit of time, the number of road accidents and the temperature indicators in the control areas. Before we begin to analyze how to conduct traffic accident inference with location and time information, a proper data structure is needed. When analyzing such spatial and temporal data, the use of matrix is widely accepted as the first choice. For temporal dimension, in order to match the time interval of traffic accident data, we select 1 hour as the time interval and divide 1 day into 24 slices. For spatial dimension, we mesh location into Δd latitude and Δd longitude. To guarantee each region in an approximate 500 m × 500 m square, which is a proper area for traffic accident analysis, we select Δd latitude = 0.004 and Δd longitude = 0.005 on a Penza region map (Russia). Therefore, we have a time index t and region index r for each element in the matrix. In this way, we have obtained grid data, if traffic accident happened n times in region r at time t, we define the risk level. Seventeen areas for traffic accident analysis were chosen for the prediction with installed photoradar complexes (Figure 7).
\nAn example of a graphical representation of the average transport speed.
To accumulate the statistics, their spatial and intellectual analysis, synthesis of graphs and reports to support decision-making, the system employs a special agent for remote polling of photo-video fixing complexes and automatic unloading of data on driver offenses and road accidents. Statistical data are presented in the form of time series or function graphs (Figure 8) of incidents, changes in speed and density of the flow of vehicles in controlled areas, ambient temperatures and are input parameters for training the neural network.
\nGraphs of the road incidents dynamics at six complexes (22 March–22 April 2017).
In the process of analyzing time series with the moments of road incidents, time intervals were chosen in which the number of incidents deviated from the average indicators. As an example, we present graphs of statistics on incidents collected from six complexes during the month (Figure 9).
\nResults of forecasting the number of road accidents (bold line indicates fact, dashed line indicates forecast before training and fine line indicates forecast after training).
Analysis of the data presented in the graphs showed anomalies. It is seen that for a month on five complexes (KD0173, KD0174, KD0183, KD0122 and KD0180) that the number of road incidents is fixed, which on the average is about 60–70 units with the exception of the KD0201 complex. However, after April 17, there is a decrease in the number of road incidents simultaneously on all the complexes.
\nTo determine the causes of anomalies and the forecast of incidents, meteorological data (temperatures, atmospheric pressure and precipitation) were collected at anomalous areas at similar time intervals.
\nIndicators in the form of the number of incidents, temperature values and traffic density values have become input parameters for training the neural network. The number of neurons of the first layer of the network was set to 18 and the number of rules as 9. After training the network, a forecast was made for road accidents (Figure 10).
\nThe results of the network showed an acceptable error in the forecast of an average of 13%. The model made it possible to determine the dependence of the number of incidents on the changes in traffic and on the temperature regime in the controlled sections of the road. In particular, the prognostic model showed the dependence of the level of incidents recorded by the Kordon-Temp complexes on the M-5 (Ural, Russia) route from changes in temperature and precipitation. It can be concluded that the neural network and the prediction system provide sufficient accuracy for the prediction model.
\nThe results of monitoring and analysis of traffic accidents, fixed by an intelligent monitoring system with photoradar complexes, are considered. A multiagent approach was developed to address the tasks of collecting and processing sensor data. Functionality of agents and brokers is defined as a mathematical function that determines the action to sensor data processing and the selection of behaviors to respond to emerging events. The system functionality is implemented by several agents that perform data collecting, cleaning, clustering, comparing time series, retrieving data for visualization in the dynamic hypertable form, preparing charts and reports, performing spatial and intellectual analysis, generating push notifications to mobile client, and so on. To accumulate the statistics, their spatial and intellectual analysis, synthesis of graphs and reports to support decision-making, the system employs special agents for remote polling of photo-video fixing complexes and automatic upload of data. The agent collects and downloads multimedia data such as photos and frames from the video stream, as well as various sensor data on traffic parameters.
\nConvergent approach is the convergence of distributed data processing technologies (cloud, fog and mobile computing). The model is designed for the collection, processing and integration of sensor data obtained in the process of monitoring and control of spatially distributed objects and processes. Convergent model of distributed computing includes three levels of data processing. The first level is fog computing. Here, processing and aggregation of sensor data is realized by migrating software agents in heterogenic sensor networks. At the next level (cloud computing), sensor data and aggregates are implemented in the server cluster. The cluster includes the main server to control the hypervisor and network servers at local network. The third level is implemented on mobile systems, where agents are to retrieve and visualize the results of monitoring and intellectual analysis with geo-information technologies.
\nThe tasks of intellectual analysis and forecasting using methods of deep machine learning are solved. As a prognostic model, a hybrid fuzzy neural network was synthesized and its training was performed. The structure of the neural network is adapted to the problems of diagnosing and forecasting the operation of photoradar complexes, as well as for analysis and prediction of road accidents. As an example, consider the results of the intellectual analysis of unloading data collected from complexes in a month’s time in comparison with meteorological data in order to reveal the patterns of variation in the number and severity of road incidents. In the process of spatial analysis, similar sections of the road and transport infrastructure are identified by the number and type of traffic accidents. Clustering of such areas allows to define the most emergency areas. In the process of intellectual analysis of time series, time intervals are determined, in which an abnormal deviation of the incidents number from average indicators are occurred. A comparison of the time series of road accidents and time series of meteorological factors has shown that changes in the traffic situation in controlled areas are strongly dependent on weather conditions.
\nThe reported study was funded by Russian Foundation for Basic Research (RFBR) according to the research projects № 18-07-00975, 16-07-00031, 17-307-50010, 17-37-50033.
\nAortic stenosis can be defined as a narrowing of the left ventricular outflow tract (LVOT) and/or aorta at the level below the aortic valve, at the aortic valve, or above it. This narrowing produces a blood flow turbulence that is auscultated as a systolic murmur at the heart base, as well as increased blood flow velocity that can be detected and measured by Doppler echocardiography.
\nAortic stenosis is mainly considered to be a congenital defect found in many species including humans. In dogs, aortic stenosis has autosomal inheritance; however, the mode of inheritance seems to be more complex in monogenic traits.
\nVarious forms of aortic stenosis as well as its possible genetic background have been recorded in domestic animals since the late 1960s and 1970s [1]. In those times, the final diagnosis was mostly confirmed at necropsy. Currently, diagnosis is based on echocardiographic evaluation of the morphology of the left ventricular outflow tract and aorta and the velocity of blood measured by the continuous wave (CW) Doppler method after a murmur is detected. Prognosis depends on the severity of the stenosis being from no effect on life quality and expectancy in mild forms of the disease to decreased life quality and expectancy in moderate to severe forms due to possible complications. Those include syncopal episodes that can result in sudden death, tiredness on exertion, or in rare cases, congestive heart failure or infective endocarditis [2].
\nThe aim of this chapter is a review of the existing literature and our experience with clinical aspects of AS in dogs and cats. Genetic evidence for aortic stenosis has been shown in Golden Retriever, Newfoundland, and Dogue de Bordeaux; however, the genetic background of aortic stenosis at a molecular level remains unclear.
\nSubaortic stenosis (SAS) is common congenital cardiac defect in dogs [3, 4] and pigs [5]. In cats, SAS has not been so often described [1, 6, 7, 8].
\nSeveral classifications are used for aortic stenosis. According to anatomic location, aortic stenosis is classified into valvular (VAS), subvalvular (SAS), or supravalvular (SupAS) [9].
\nBased on functional characteristics of obstruction, subvalvular cases are further categorized as either fixed (static) or dynamic (labile) [2].
\nA dynamic form of subaortic stenosis can occur in the following instances: in a hypertrophied left ventricle (LVH) due to protrusion of the ventricular septum into the LVOT, systolic anterior movement of the anterior mitral valve leaflet (SAM) which occurs concurrently or in the absence of LVH, and in cases where aortoseptal angle is smaller than 180o [10].
\nThe subvalvular form—subaortic stenosis (SAS)—has been reported as the most frequently seen (in 95%) and can be presented as a complete or incomplete ring [1, 2, 11, 12, 13].
\nThe gross appearance of the lesions in SAS is variable [4, 14]. Current classification which is used by clinicians is based on anatomical and echocardiographic classification of SAS on the result of postmortem and angiographic studies of Pyle et al. [14, 15]. In a postmortem study performed on Newfoundland puppies, the gross lesions were classified according to severity with grades 1 through 3 [14]. Mild lesions (grade 1) are present as small (1–2 mm), raised white nodules on the endocardium of the ventricular septum below the aortic valve. In some dogs, the nodules are also found on the ventricular surfaces of the aortic valve cusps (Figure 1) [14]. Moderate lesions are present as a ridge of endocardial fibrous tissue that in most cases extends from the base of the anterior leaflet of mitral valve across the interventricular septum to beneath the aortic valve (Figures 2 and 3) [14]. In severe cases (grade 3), the fibrous band or ridge completely encircles the left ventricular outflow tract below the aortic valve and forms a concentrically narrowing tunnel (Figure 3). In most severe cases, anterior leaflet of the mitral valve and ventricular surfaces of the aortic valve are also thickened (Figure 3) [14].
\nGross pathologic specimen from a dog with severe subaortic stenosis. A subvalvular fibrous ring (lower arrow) below the aortic valve and a thickened valve above the fibrous ring of tissue can be seen. Ao—aorta, LV—left ventricle, LA—left atrium, and MV—mitral valve.
Gross pathologic specimen from a dog with severe subaortic stenosis. This is a close-up of a closed fibrous subaortic tissue that encircles the left ventricular outflow tract just below the entrance to the aorta.
Gross pathologic specimen from a dog with severe subaortic stenosis. A tunnel-like subaortic stenosis (upper 2 arrows) and a fibrous subaortic ring below the aortic valve is seen (lower arrow). Ao—aorta, LV—left ventricle, and LA—left atrium.
Microscopically, the zone of endocardial fibrous tissue below aortic valve contains proliferated mesenchymal cells, mucopolysaccharide ground substance, and foci of metaplastic cartilage [3, 4, 14].
\nOther cardiac lesions that develop as the consequences of the altered left ventricular outflow include compensatory left ventricular concentric hypertrophy [3] (Figure 3) and poststenotic dilatation of the aorta [4].
\nMicroscopic cardiac lesions also include foci of myocardial necrosis, fibrosis in the papillary muscles and subendocardium, thickening of the intramyocardial arteries [3], intimal proliferation of connective tissue, fibrous replacement of smooth muscle in the tunica media [16, 17], and luminal narrowing of intramural coronary arteries [18].
\nSeveral cardiac defects have been observed concomitantly with SAS in dogs. These defects include pulmonary artery stenosis (PS), patent ductus arteriosus, mitral valve dysplasia, ventricular septal defect, valvular aortic stenosis, aortic root hypoplasia, persistent left cranial vena cava, bicuspid aorta, quadricuspid aorta, tricuspid dysplasia, double chambered right ventricle, and supravalvular aortic stenosis [19, 20]. Coexistence of aortic stenosis and pulmonary artery stenosis is one of the most common complex cardiac malformations [13, 20].
\nSAS has been ranked the most common congenital heart disease (CHD) in dogs in most European studies accounting for 35% of all CHD. In the United States [12] and in a broad Italian study [20], SAS was on the second place (the most common being PS). However, these results must be taken carefully due to referral population included since a lot of cases were sent for ballooning. Of 4480 dogs included in this study, 976 dogs were diagnosed with congenital heart disease (CHD) of which 21.3% had subaortic stenosis (SAS), while valvular aortic stenosis (AS) was on the fifth place with 5.7% dogs diagnosed. The same study showed many multiple heart defects; the most frequent combination was SAS and PS (26.4%).
\nWe did a study on 9236 dogs, where cardiovascular disease was diagnosed in 6% of dogs, and from those, 12% represented congenital heart diseases of which 45% were aortic stenosis cases [21].
\nAccording to many epidemiological studies [20, 22, 23, 24, 25, 26, 27], affected breeds are: Boxers, German Shepherd, Newfoundland, Rottweiler, Golden Retriever, Pug, and Bouviers de Flandres. In the Italian study [20] and a Danish study [28], Dogue de Bordeaux was also shown to be significantly affected. German Boxers have proved to be the most sensitive breed in recent years [19, 21, 29, 30, 31]. Almost half of all the dogs in the Italian study diagnosed with SAS were Boxers. Boxers are also on top of the list of dogs with pulmonary artery stenosis (PS) and valvular aortic stenosis (AS). In Boxer breed, more male than female dogs are affected with SAS [20, 32]. Studies in cats did not show any breed predilection; aortic stenosis could be of all types described in dogs, with subvalvular stenosis being the most common [6, 7, 8, 33]. In our clinic, occasionally a cat with a fixed SAS is detected, usually due to an ausculted murmur. Dynamic left ventricular outflow tract stenosis is much more common in cats due to common occurrence of hypertrophic cardiomyopathy and systolic anterior motion of the mitral valve (personal unpublished data).
\nDogs with mild SAS live longer and mostly remain asymptomatic. Prognosis for the untreated condition in this group is good. Dogs with moderate and severe gradients have shorter life expectancy. They have increased risk of infective endocarditis. The majority of dogs with severe gradients (>80 mm Hg) die before 3 years of age. Median survival was 18.9 months [9, 26].
\nSubaortic stenosis can be a progressive disease that attains its maximal severity within the first 12–15 months [15]. In dogs that already have high aortic velocity, further progression is unlikely; however, dogs with mild stenosis might progress to a moderate stage [34]. Breeding studies also indicate that AS may not be present at birth but develops during the first 4–8 weeks of life, which suggests that AS is not a true congenital trait but develops postnatally [27].
\nThe etiology of SAS is probably multi-factorial [35]. In the literature, there are two hypotheses on how the fibrocartilaginous ring around the LVOT is formed. It could be derived from embryonal endocardial tissue that retains its proliferative capacity and has chondrogenic potential for some time after birth [14]. A more recent hypothesis suggests that certain anatomic characteristics of the LVOT, including an increased mitral-aortic separation, a decreased aortoseptal angle (AoSA), and a small aortic annulus may cause cellular proliferation in the LVOT because of shear stress caused by abnormal flow patterns [35, 36].
\nClinical signs such as weakness, syncope, and sudden death are more commonly seen in dogs with severe or moderate AS than in those with mild SAS [2, 9, 11]. Dogs with mild AS rarely show any signs at all [2, 37]. Careful physical examination reveals crescendo-decrescendo systolic murmur from grades 1 to 6. Final diagnosis has to be confirmed by two-dimensional and Doppler echocardiography, by which evaluation of morphologic characteristics, the type of stenosis, and the pressure gradient across the stenosis can be assessed [2, 11, 15].
\nCats are more often identified when clinical signs such as heart failure develop [38].
\nIn the early years of the 21th century, cardiac screening programs have been proposed due to high incidence of some congenital heart diseases. Aortic stenosis has been recognized as one of the most common heart defects according to high prevalence in breeds such as Newfoundland dogs, German Boxer, Golden Retrievers, and Rottweiler to name just the ones mostly affected. Therefore, screening programs were introduced to reduce the high prevalence among the breeding dogs. Some breeders became aware that these breeding programs could help to reduce the incidence of affected animals and to breed healthy puppies. In Italy, such a breeding program helped to reduce the high incidence of AS among boxers [32]. In the case of AS, screening involves careful auscultation to detect cardiac murmur, which is a hallmark of AS. In cases where murmurs are found, 2-D and Doppler echocardiography is carried out, where the morphology of the left ventricular outflow tract with the ascending aorta, specific lesions characteristic for AS/SAS, and increased velocity of the aortic flow can be identified [39].
\nFor a screening program to be effective, a good mutual relationship between the veterinarians involved in screening and pertinent kennel clubs need to be established. Kennel club committees responsible for breeding need to suggest to breeders to screen their sires and dams before breeding or define the screening as a condition for breeding into their rulebook.
\nStenosis across the left ventricular outflow tract into aorta produces a pressure gradient between the left ventricle and aorta, and the gradient is inversely proportional to the degree of the stenotic orifice. The resistance to flow through the stenosis produces a rise of pressure in the left ventricle through the systole; increased wall stress results in concentric hypertrophy of the ventricle. The flow through the narrow passage is like when we squeeze the hose with water – the velocity (v) of the flow will increase proportionally to the narrowing. The relationship between the pressure and the flow is described by a simplified Bernoulli equation:
\nPressure gradient (PG) = 4v2.
\nThe velocity of the flow or the pressure gradient is used to assess the severity of the stenosis; higher the velocity or pressure gradient, the more severe is the stenosis. However, interpretation of PG must be careful in sedated and excited animals, where there is a change in the resistance and flow [2].
\nAdditionally, the left ventricular wall diameter and cross-sectional area of the aortic orifice are both proportional to the stenosis and can be used to assess the severity [40]. In the hypertrophied ventricle, diastolic filling can be impaired which can cause mild left atrial enlargement.
\nTurbulent and high velocity flow through the aortic orifice can damage the cusps, and aortic insufficiency can occur consequently. Damaged cusps can predispose to infective endocarditis, as well.
\nAnimals with aortic stenosis can develop heart failure, although this scenario rarely occurs. Myocardial failure could be the one of the reasons for heart failure to develop; however, other complications such as aortic or mitral insufficiency can lead to this kind of progression.
\nDogs or cats with aortic stenosis can die suddenly or experience syncopal episodes. The cause might be the reflex peripheral vasodilation on exertion and bradycardia; on the other hand, sudden hypoxia due to exertion or subendocardial fibrosis can predispose to fatal arrhythmias that can also lead to fatal fibrillation [2].
\nArterial pulse in patients with aortic stenosis can be reduced in amplitude and can have a delayed systolic peak [2].
\nTo make a diagnosis of AS, a thorough auscultation of heart sounds and murmurs should be carried out. Auscultation is the basic diagnostic technique to uncover AS and every clinically important AS will produce an audible murmur. It needs to be performed carefully in a quiet environment with a dog standing still to be able to hear low intensity murmurs. Although the murmur grade is found to correlate with the severity of AS, it is important to detect also low-grade murmurs to identify dogs with heart defects [41]. Early diagnosis of murmurs due to congenital heart defects may enable early intervention, which may substantially affect long-term outcomes [42]. Many healthy boxers tend to have a soft systolic low-grade murmur; in a study of 201 healthy Boxers, the prevalence of 1–3 grade murmurs was 56%. Boxers with murmurs had higher ejection velocities than boxers without murmurs [43] and young boxers may more commonly have functional murmurs that can also cause mild increase in ejection velocity due to the physiologic changes. It has been hypothesized that young animals have a larger stroke volume compared to the size of the great vessels than do older animals. This can result in an increase in flow velocity producing turbulence, either in the aorta or in the pulmonary artery, and a resultant innocent heart murmur. The increase in the velocity and associated turbulence is usually mild, so the heart murmur is soft (i.e., grade 1–3/6). The innocent heart murmur generally disappears before 4 to 6 months of age, when the great vessels enlarge in diameter with growth. A notable exception is the Boxer breed, where a smaller left ventricular outflow tract is associated with systolic murmurs in otherwise normal adults [44].
\nAortic or subaortic stenosis produces a typical crescendo-decrescendo mid-systolic to holosystolic murmur heard best over the left heart base or also on the right side of the thorax. Loud murmurs tend to radiate peripherally, some can be heard over the carotid artery or over the head. Severe cases of AS have usually harsh, mixed-frequency murmurs of high grade on the scale from 1 to 6 [41]. Murmur intensity significantly correlates with aortic ejection velocity [13, 41, 45]. Identification of low-intensity murmurs correlates with the level of experience. A stress test increased murmur duration and aortic flow velocity [46]. Assessment of the duration of murmur frequency >200 Hz can be used to distinguish physiologic heart murmurs from murmurs caused by mild AS in Boxers and can be used as a complementary method [47].
\nDogs with mild-to-moderate AS usually produce a normal electrocardiogram on the standard ECG recordings, whereas cases with severe AS may show signs of LV hypertrophy in leads II, III, aVF, V2, and V4. Hypertrophied ventricle can be hypoxic; therefore, depression of the ST segment and T wave changes suggest myocardial ischemia or secondary repolarization changes. We may observe ventricular premature complexes in severe cases as well [45]. In cases where AS is combined with other defects, for example, pulmonic stenosis or tricuspid dysplasia, a right axis deviation might occur, depending on the severity of additional lesions. In our study, in boxers with AS/SAS, arrhythmias were observed in 21% of dogs, such as ventricular premature contractions, left bundle branch block and supraventricular tachycardia, atrial fibrillation, atrial premature contractions, sinus bradycardia, and ventricular preexcitation. Dogs with multiple arrhythmias have ussually also heart failure and/or have concurrent malformations [13]. Holter recordings are recommended in symptomatic dogs for detection of possible arrhythmias or S-T segment changes [2].
\nEchocardiography is the main noninvasive method for diagnosis of aortic stenosis. Two-dimensional mode is used to detect morphologic abnormalities associated with AS/SAS or supravalvular form. In severe cases, LV concentric hypertrophy, subendocardial hyperechogenicity, representing fibrosis (Figure 4), and a small subaortic cross-sectional area (Figure 5), is found with 2-D echocardiography. Left ventricular hypertrophy, demonstrated by M-mode, has a positive relationship with disease severity [40]. Subaortic fibrous hyperechogenic tissue protruding into the LVOT is seen in the right parasternal or left parasternal long-axis views (Figure 6 &
Two-dimensional echocardiographic image of a short axis of the left ventricle (LV), showing subendocardial fibrosis in the left ventricular free wall. MV—Mitral valve.
Two-dimensional echocardiographic image of a short axis at the base of the heart showing subvalvular (upper image) and valvular region (lower image) of the aorta (Ao). One can appreciate the small subvalvular circle compared to the bigger valvular circle. LA—Left atrium.
Subaortic fibrous hyperechogenic tissue protruding into the LVOT is seen in the right parasternal view in a young Newfoundland with severe subaortic stenosis. Ao—aorta, LV—left ventricle, and LA—left atrium.
A color-Doppler flow image of a Sphynx cat with fixed and dynamic subaortic stenosis and concentric hypertrophy of the left ventricle (LV) with concurrent mitral regurgitation (MR).
Continuous wave Doppler across the aortic orifice showing a high velocity jet (AS) of 4 m/s below the baseline, which gives a pressure gradient of 67 mmHg and an aortic insufficiency jet in diastole above the baseline(AI).
It is important to use low-frequency transducer for Doppler studies to ensure good penetration of tissues and adequate signal strength to obtain good flow recordings of maximal velocities. Diagnostic problem represents dogs with low intensity murmurs and subtle echocardiographic changes. No association was found between heart rate and aortic velocity [41].
\nAortic stenosis has been graded as “mild,” with pressure gradients (PG) either from 16 to 40 mmHg (corresponding to aortic velocities, (v), of 2.0–3.16 m/sec) or from 20 to 49 mmHg (corresponding to velocities of 2.25–3.5 m/sec, “moderate,” with PG either from 40 to 80 mmHg (v = 3.1.6–4.5 m/sec) or 50 to 80 mmHg (v = 3.5–4.5 m/sec), and “severe” with PG above 80 mmHg, corresponding to velocities over 4.5 m/sec [2, 15]. Pressure gradients derived by Doppler echocardiography showed good agreement with direct pressure measurements, especially for mean gradients [51].
\nThoracic radiographs may appear normal in dogs with AS/SAS; however, in severe cases, LV enlargement may be visible due to LVH and/or post-stenotic dilation of the aortic arch (Figures 9 and 10).
\nA dorsoventral thoracic radiograph of a 4-month-old Irish setter with severe aortic stenosis. A post-stenotic dilation of aortic arch is seen (arrow). Ao—aorta, RV—right ventricle, and LV—left ventricle.
A right lateral thoracic radiograph of the same dog as in Figure 9, showing a post-stenotic dilation of aortic arch (arrow). DV—right ventricle and LA—left atrium.
In cases where AS is combined with other defects, pertinent radiographic changes may be apparent. Congestive heart failure is rare in SAS, it might be observed in severe cases or with concurrent mitral regurgitation, aortic or mitral endocarditis [2].
\nAngiographic methods for further evaluation of aortic stenosis morphology are nowadays replaced with contrast computed tomography (CT) scans where needed in terms of interventional or surgical treatment plans. Cardiac CT angiography allows visualization of cardiac chambers and great vessels as well as coronary vessels through cardiac cycles retrospectively. Evaluation of the coronary arteries in the patient is commonly focused on determining if an aberrant vessel is present, which may relate to a pulmonic stenosis, which can be present concurrently with AS/SAS.
\nPrognosis of animals with aortic stenosis depends on the severity of the disease. Mild stenosis usually does not affect longevity; however, the possibility of aortic endocarditis exists, and antibiotic prophylaxis is recommended for dogs and cats with aortic stenosis [52].
\nBalloon valvuloplasty, although with an average 50% reduction in PG after ballooning, has not proved to be a long-term solution, because in most dogs restenosis occured [53]; however, in some cases, it may reduce clinical signs [54].
\nNo clear benefit in survival times was seen for dogs that underwent balloon valvuloplasty versus dogs that were treated with atenolol [55].
\nA new technique with a high-pressure ballooning or a cutting balloon might represent an opportunity for better outcome for dogs with AS/SAS, but to date we have no long-term results [56]. Moreover, aortoseptal angle >160° was associated with better long-term outcomes of treated dogs with cutting and high-pressure balloon [57, 58]. Authors and also others recommend saving patients with moderate and severe AS/SAS against strenuous exercise. Administration of beta-blockers can decrease heart rate, prolong diastole and coronary filling, thereby reducing myocardial hypoxia and protect against arrhythmia. Dogs do clinically well on beta-blockers; however, a study proved no benefit in terms of survival versus untreated dogs with severe SAS [59]. There is no literature on evaluation of other medical treatment.
\nSurgical options such as closed transventricular valvotomy or open-heart surgery can present an option for dogs with symptomatic or severe AS/SAS; however, also these techniques did not provide long-term benefits or prevent sudden death. Additionally, they are not widely available, and they are risky and costly [60, 61, 62, 63]. Hopefully, this might change in the future with the development of minimally invasive techniques and their availability in veterinary medicine.
\nComparison of mixed and pure-breed dog populations showed a tendency toward higher incidence of AS in pure-breed dog populations [64]. Among pure-breed dogs, the incidence of AS is increased in herding, working, sporting, mastiff-like, and retriever breeds. The fact that the higher incidence of AS is associated with the increase of inbreeding coefficient in the population supports the suggestion that AS has a genetic component. Online Mendelian Inheritance in Animals (OMIA) database also reports AS in dog as heritable disorder with unclear mode of inheritance [65].
\nGenetic background of AS has been studied in several dog breeds with the aim to decipher its mode of inheritance and causal mutation for it. In the Dogue de Bordeaux, association of AS with several physiological parameters as left-basilar ejection murmur, increased aortic ejection velocity, smaller aortic annulus and decreased aortoseptal angle was discovered and genetic predisposition for AS in Dogue de Bordeaux has been proposed [28]. Familial nature of subvalvular aortic stenosis (SAS) was discovered in Golden retrievers [66] based on pedigree data, where SAS has been observed in several subsequent generations. Although a bit controversial, the most complete data about the genetic base of AS are available for Newfoundland dogs. In the study performed by Reist-Marti [67], an extensive pedigree data set comprising more than 230,000 Newfoundland dogs from European and North American population reaching back to the 19th century has been investigated. Similar to the situation in Golden retrievers, the autosomal inheritance was proposed. In addition, statistically significant association between the inbreeding level and incidence of SAS was also found. However, the most precise information about the putative molecular background of AS in Newfoundland dogs was discovered by Stern et al. [68]. The authors propose that a three-nucleotide insertion in the genomic region, coding for phosphatidylinositol-binding clathrin assembly protein (PICalM) is associated with the appearance of AS. The pedigree evaluation, similarly as in Newfoundland dogs, supported an autosomal dominant mode of inheritance. The authors demonstrated the presence of PICalM in the canine myocardium and in the area of the subvalvular ridge immunohistochemically, which is supporting the assumption that PICaIM has a role in development of AS.
\nIn Boxers, AS seems to have a genetic background too; however, the causal locus (loci) has not been identified yet. The higher risk for AS in Boxers might be associated with some breed-specific conformational traits, like small aortic annulus and steep aortoseptal angle [69]. The incomplete penetrance of modifier genes together with autosomal dominant mode of inheritance may be the expected genetic base for AS in Boxers [32].
\nDue to the rapid development of genome analysis in all species, several novel approaches are available also in dog genetics. From the genetic point of view, dog breeds represent a very special taxonomic group, characterized by extremely long regions of linkage disequilibrium (LD) compared to other species. This enables a very effective identification of causal genomic regions associated with monogenic genetic disorders using relatively small groups of animals in case versus control format of studies. The most frequently used strategy in this context is genome-wide association studies (GWAS), which can precisely map location of candidate genes in the genome. The candidate gene regions are then further screened for polymorphic sites using the targeted sequencing strategy in order to find causal mutation for genetic disorder (Figure 11). However, complex traits, where a larger number of loci are involved in phenotype shaping, represent a much more difficult task and normally require a larger number of individuals for genetic studies.
\nSummary of development and application of genetic markers for diagnosis of hereditary diseases.
The number of registered inherited disorders in dogs is permanently growing (over 400 disorders), and in many dog breeds, the point is reached where for the successful breeding against spreading genetic disorders within the breed requires new strategies in combination with currently available breeding schemes. The widespread use of a popular sire caused the overrepresentation of genomes of a low number of sires in many breeds. As a consequence, the effective population size reduced drastically and the risk for rapid dissemination of monogenic disorders within the population increased significantly. The accessibility of reliable genetic tests for detection of carriers of recessive disease-associated alleles represents an important tool for reduction or even elimination of genetic disorders from purebreed populations. Increasing the number of breeding animals (especially males), controlled introgression of genetic material into closed pure-breed populations, and application of advanced breeding strategies are measures, which will help the breeders to keep genetic pools of different dog breeds healthy.
\nAortic/subaortic stenosis has a guarded prognosis if moderate to severe; however, efforts have been made in several aspects to fight the disease. First, screening programs have lowered the incidence of the disease (Bussadori 2006, personal unpublished data), and secondly, interventional methods have advanced and might give better prognosis for severely affected dogs; on the other hand, there is still room for surgical methods to take place in veterinary medicine and be more readily available. The genetic background for aortic stenosis is not completely known; however, several mutations, associated with the disease in different breeds, allow development of strategies for genetic screening which would reduce the risk for the disease in pure-breed dogs.
\nThe authors acknowledge the financial support of the Slovenian Research Agency (research programs P4-0053 and P4-0092).
\nThe authors have no conflicts of interest to disclose.
"I work with IntechOpen for a number of reasons: their professionalism, their mission in support of Open Access publishing, and the quality of their peer-reviewed publications, but also because they believe in equality. Throughout the world, we are seeing progress in attracting, retaining, and promoting women in STEMM. IntechOpen are certainly supporting this work globally by empowering all scientists and ensuring that women are encouraged and enabled to publish and take leading roles within the scientific community." Dr. Catrin Rutland, University of Nottingham, UK
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