Simulated parameters
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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However, there are still many areas of controversy surrounding it. We hope this book can contribute to guide the advance of this ancient medical art. In the present work, the reader will find texts written by authors from different parts of the world. The chapters cover strategic areas to collaborate with the consolidation of the knowledge in acupuncture. The book doesn't intend to solve all the questions regarding this issue but the main objective is to share elements to make acupuncture more and better understood at health systems worldwide.",isbn:null,printIsbn:"978-953-307-410-8",pdfIsbn:"978-953-51-6516-3",doi:"10.5772/905",price:119,priceEur:129,priceUsd:155,slug:"acupuncture-concepts-and-physiology",numberOfPages:248,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"a1b327d1a93e8dfd07289ab0a701aa39",bookSignature:"Marcelo Saad",publishedDate:"October 10th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/386.jpg",numberOfDownloads:43869,numberOfWosCitations:8,numberOfCrossrefCitations:1,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:12,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:21,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 22nd 2010",dateEndSecondStepPublish:"December 20th 2010",dateEndThirdStepPublish:"April 26th 2011",dateEndFourthStepPublish:"May 26th 2011",dateEndFifthStepPublish:"July 25th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"51991",title:"Prof.",name:"Marcelo",middleName:null,surname:"Saad",slug:"marcelo-saad",fullName:"Marcelo Saad",profilePictureURL:"https://mts.intechopen.com/storage/users/51991/images/system/51991.png",biography:"Marcelo Saad, MD, PhD (Brazil), is a physician, board certified in acupuncture. 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The constant evolution of technologies for future wireless networks, along with the demand for new multimedia applications (voice, video,...) have led to the creation of new technologies for wireless communications. This is becoming one of the main challenges for this second decade of the third millennium, where new communications technologies must be sensitive to the need for bandwidth with high speed access, broadband in large coverage areas and the provision of services to an increasing number of users to ensure the next generation networks support for the content of new multimedia applications. Moreover, new technologies are an effective way of reducing physical barriers to the transmission of knowledge and transaction costs over fixed networks [1] [2]. Along with the creation of these wireless technologies, one of the current operating modes that is emerging is the mesh mode.
WMNs (Wireless Mesh Networks) are a special kind of MANET (Mobile Ad Hoc Network) and this research started out from the study and development of the MANETs. Compared with traditional networks, WMNs have many useful characteristics and peculiarities, such as dynamic self-organization, self-configuring, self-healing, high scalability and reliable services and are able to balance traffic and provide support to drop connections to fixed or mobile clients. In this way, it can prevent the decline of its services and avoid problems with flows where there is a need for bandwidth and high rates that are constantly required. This is achieved through a reconfiguration that always seeks the best alternative path to a better distribution of network traffic. Currently, many standard groups are improving the specifications of mesh networks from IEEE 802.11s to Wi-Fi (Wireless Fidelity), IEEE 802.15.4 to Bluetooth and IEEE 802.16j to WiMAX (Worldwide Interoperability for Microwave Access) to multi-hop relay that will be the subject under study in this chapter.
The mobile WiMAX (Figure 1) is a technology based on IEEE 802.16 standard [3] developed as a feasible and attractive solution to these problems. It provides access to wireless broadband, especially an enabling context-sensitive network for the FI (Future Internet) with new multimedia applications, connectivity services for handover scenarios, long distances reaching the last mile, mobility management and mechanisms that improve communications with support for bandwidth and throughput metrics. These influence the network QoS (Quality of Service) with a certain level of end-to-end quality for multimedia applications through the management of layer 2 (Link Layer / MAC) and layer 3 (Network Layer / IP) for the provision of better services that give support to multimedia applications such as video stream and VoIP (Voice over Internet Protocol) that require real-time data delivery [4] [5].
IEEE 802.16 / WiMAX network architecture
However, it is not clear enough how far the behavior of the WiMAX mesh network can support real-time services such as video streaming and VoIP, especially in mesh operation mode. Thus, this study provides an analysis of this question by analyzing network performance measurements through the properties of an IEEE 802.16 mesh network in several real-time applications. The chapter helps investigate the influence of routing protocols and the benefits of QoS to the network, as well as measurements for clients in a WiMAX wireless mesh environment, by showing their impact on flows and the final quality of multimedia applications.
QoS metrics, known as the rate of packet loss, delay and throughput, are generally used to measure the impact of multimedia streams on the level of quality of service, viewed from the perspective of the network, but do not reflect the user experience or the quality. As a result, these QoS parameters do not reflect subjective factors associated with human perception. In order to overcome the limitations of the existing schemes to guarantee QoS in networks with multimedia streaming that take account objective and subjective factors, the tests also address the impact of QoS and routing protocols on final quality through the QoE (Quality of Experience) concepts. This is carried out by addressing the user\'s perspective as the end-to-end quality of the video stream, by studying, evaluating and validating the results of QoS and QoE incidents on the routing metrics [6].
This chapter will provide an overview of the main challenges of the WiMAX mesh mode with a focus on routing protocols and the effect of quality of service mechanisms on scenarios with mobile clients. The chapter will describe the importance of mesh networks and how they can provide quality service and quality of experience for customers. It will also explain the impact of multimedia applications on this network and the importance of choosing the best route to ensure the network provides higher quality communications.
This section has provided a brief introduction to the main aims of this chapter. The second section will describe the mesh networks and explain their topology and operations. The third section will examine the QoS in WiMAX mesh networks. The fourth section will focus on routing protocols and draw attention to their main advantages and disadvantages. The fifth section will show the results of simulation tests obtained from analyzing the routing protocols with QoS and QoE. The sixth section explains the significance of the findings and conclusions, and this is followed by the seventh section with the main references.
Wireless mesh operation mode is one of the most effective network branches among the emerging technologies. This network can connect multiple wireless access points (known as nodes) and form a mesh network, which is a network of connections that provides broad coverage and enables multiple paths and routes of communication. It is able to balance the traffic load and provide support for fault tolerance, so that if a node goes down, the network can self-configure and self-heal to find alternative routes of access [7].
WMNs can be seen as one type of MANETs [8]. An ad-hoc network (possibly mobile) is a set of network devices that want to communicate, but have no fixed infrastructure available and no pre-determined pattern of available communication links. The individual nodes of the network are responsible for a dynamic discovery of the other nodes that can communicate directly with them, i.e. what are their neighbors (forming a multi-hop network). Ad-hoc networks are chosen so that they can be used in situations where the infrastructure is not available or unreliable, or even in emergency situations. A mesh network is composed of multiple nodes / routers, which starts to behave like a single large network, enabling the client to connect to any of them. In this way it is possible to transmit messages from one node to another in different ways. Mesh type networks have the advantage of being low cost, easy to deploy and reasonably fault tolerant.
In another analogy, a wireless mesh network can be regarded as a set of antennas, which are spaced a certain distance from each other so that each covers a portion or area of a goal or region. A first antenna covers an area, the second antenna covers a continuous area after the first and so on, as if it were a tissue cell, or a spider web that interconnects various points and wireless clients. What is inside these cells and covers the span of the antennas, can take advantage of the network services, provided that the client has a wireless card with the interface technology.
Mesh networks are networks with a dynamic topology that show a variable and constant change with growth or decline, and consist of nodes whose communication at the physical level occurs through variants of the IEEE 802.11 and IEEE 802.16 standard, and whose routing is dynamic. The image below (Figure 2) shows an example of a mesh network. In mesh networks, the access point / base stations area is usually fixed.
Mesh network
To achieve these goals, WiMAX networks can be structured into two operating modes: PMP (Point-to-Multipoint) and mesh networks, and the second is the focus of this chapter. Mesh mode is a type of operation that can interconnect multiple mobile clients together with many WiMAX base stations (nodes) and form a network of connections so as to provide a wide coverage area for mobile clients. All the clients can communicate with each other and there is no need for an intermediate node to act as the mediator of the network. In this mode, the IEEE 802.16 can provide broadband access with wireless support both single-hop and multi-hop settings [2].
The basic topology of an IEEE 802.16 mesh network consists of two participating entities, called Base Station (BS) and Subscriber Station (SS), displayed below (Figure 3). The BS is the central node, responsible for coordinating all the communication and providing connectivity to the client stations (fixed or mobile).
Basic topology of a WiMAX network network
Mesh networks reverse the idea of using a wired network to the backbone network and wireless access in the last mile. The backbone of a wireless mesh network comprises the router nodes that interconnect with the customers. As the nodes in the backbone network of this type have a fixed location and only the clients can be mobile, they may readily be fed, since they have no limiting power, and thus can rid themselves of many of the constraints of ad-hoc networks.
The most effective way to discover the operation of the mesh network is the routing protocol, which scans the different possible routes / paths of data flow, on the basis of a pivot table where devices such as BS select the most efficient route to follow to reach a goal, while taking into account that the greater the speed, the packet loss, or the faster the access to the Internet (and others). This scan is carried out several times per second and is transparent to the user, even when it occurs at re-routing access gateways, which are the nodes that have direct access to the internet.
An important feature of mesh networks is the concept of roaming, also known as a transparent handoff mobility scheme offering fast handoff in wireless networks. This makes it feasible for users to become mobile clients who can move around between network nodes without losing the connection at the time of exchange. The practical consequence is that the system allows geographical mobility. The system will always know which jumps are required for the request of a customer at any point in the network so that it can reach the Internet in the most efficient manner possible.
The growing interest in multimedia applications in mesh networks is accompanied by challenges that make the provision of QoS and group communication (multicasting) a more complex task. This complexity is the result, among other factors such as high mobility of the stations, which implies that there is a need to manage their locations and the environment and cope with the limitations of the devices involved, such as transmission quality in a wireless environment, bandwidth scarcity, etc.
Mesh networks have good prospects of being the solution to a series of problems in the provision of access services, since they are flexible, dynamic and potentially low cost [9]. However, for this to become effective there is much that needs to be improved and developed.
Besides routing, the major problems in mesh networks are scalability and security. The first can be defined as the level of acceptable service packages in the presence of a large number of nodes in the network. An important factor is the potential reduction in performance when there are an increased number of nodes. Hence, any protocol layers involved should be scalable. The security schemes proposed for ad hoc networks can be adopted for mesh networks, although most of these solutions have not been studied in depth and there are still problems that prevent them from providing authentication and reliability to clients.
Today the provision of QoS to any network is mandatory. When the mesh networks follow these steps, with the growth of multimedia applications, the services often seek a guaranteed bandwidth and QoS requirements, as a result of the growth of multimedia applications [2] [10]. In addition, they know that choosing the best path routing is an important decision for the WMNs to enable them to provide a wide range of services to different client types, each with their own peculiar characteristics. Provisioning QoS in mesh networks is not devoted to a single task layer. It requires the joint effort of all the layers, and specific strategies for signaling quality of service using resource reservation and QoS for the data link layer.
Owing to this and a number of other problems, when compared with other wireless network models, the mesh networks pose a special challenge, because the wireless environment is shared by adjacent nodes and the topology may change dynamically in the same way as the mobility of the nodes and input / output in the same network. As a result, QoS has become a key area of research of comparable importance to algorithms.
WiMAX has been developed with QoS in mind. Five different service classes have been introduced for different applications and packets from different service classes and are being handled on the basis of their QoS constraints. However, this mechanism can only be used in the PMP (Point-to-Multipoint) mode. In the Mesh mode, QoS is maintained on a message-by-message basis.
In PMP mode, the WiMAX MAC layer uses a scheduling service to deliver and handle SDUs (Service Data Units) and MAC PDUs (Protocol Data Units) with different QoS requirements. A scheduling service uniquely determines the mechanism the network uses to allocate UL (UpLink) and DL (DownLink) transmission opportunities for the PDUs. WiMAX defines five scheduling services:
Unsolicited grant service (UGS): This is designed for the real-time constant bit rate (CBR) applications such as T1/E1 and VoIP. Unsolicited data grants are allocated to eliminate the overhead and latency of the request/grant process. During the connection establishment phase, maximum sustained traffic rate is declared and BS assigns fixed bandwidth grants in each frame accordingly.
Real-time polling service (rtPS): This is designed to support real-time services that generate variable-size data packets on a periodic basis, such as MPEG (Motion Pictures Experts Group) video. In this scheduling service, the BS provides unicast polling opportunities for the MS to request bandwidth. The unicast polling opportunities are frequent enough to ensure that latency requirements of real-time services are met.
Extended real-time polling service (ertPS): This scheduling service combines features from UGS and rtPS service classes. An initial ensured bandwidth allocation is carried out as in UGS and then this allocated bandwidth can be decreased or increased as in the case of rtPS.
Non-real-time polling service (nrtPS): This scheduling service is the most appropriate for the delay tolerant applications. As in rtPS, dedicated periodic slots are used for the bandwidth request opportunity, but with much longer periods. In nrtPS, it is allowable to have unicast polling opportunities, but the average duration between two such opportunities is in the order of a few seconds, which is large compared to rtPS. All the MSs belonging to the group can also request resources during the contention-based polling opportunity, which can often result in collisions and additional attempts.
Best effort (BE): This provides very little QoS support and is applicable only for services that do not have strict QoS requirements. It is for the traffic with no minimum level of service requirements. Like in nrtPS, contention slots are used for bandwidth request opportunities as long as there is space available [1] [2].
Classifiers are also present in the MAC layer of both the Base Station and Subscriber Station, whose goal is classify and map service flow into a particular connection for transmission between the MAC peers. The mapping process associates a data packet with a connection, which also creates a link with the service flow characteristics of this connection [11].
In this architecture there are schedulers in both the Base Station (BS) and Subscriber Station (SS), whose goal is to determine the burst profile and the transmission periods for each connection, while taking into account the QoS parameters associated with the service flow, the bandwidth requirements of the subscriber stations and the parameters for coding and modulation. Figure 4 illustrates the WiMAX QoS Architecture in PMP mode.
Architecture for IEEE 802.16 QoS
In a WiMAX mesh network, a “Mesh BS” (MBS – mesh base station) provides the external backhaul link. The backhaul links connect the WiMAX network to other communication networks. There may be multiple Mesh BSs in a network; other nodes are known as ‘‘Mesh SSs” (MSS – mesh subscriber stations). In point-to-multipoint mode, the SSs are under the direct control of the BS. In Mesh mode, the uplink and downlink is not clearly separated and SSs can communicate with each other without communicating with the BS.
In the mesh mode, bidirectional links can be established between any of the WiMAX nodes, and the information is transmitted on a hop-by-hop basis. The system access follows a frame-based approach where each channel is divided in time into a series of frames. The number of frames in a series is defined during process of creating the network..
A frame is divided into two subframes: a control subframe and data subframe (Figure 5). The control subframes are used for carrying the information necessary for access control systems, bandwidth allocations, connection establishment and connection maintenance. The data subframes are used for carrying the packets of upper layers. The control subframe is divided into a number of transmission opportunities. The data subframe is similarly divided into a number of minislots.
There are two types of control subframes depending on their function. The first type of control subframe is the scheduling subframe in which nodes transmit scheduling messages. The second is the network configuration subframe in which nodes broadcast network configuration packets containing topology information, network provisioning information, and network management messages.
Mesh frame structure
The IEEE 802.16 mesh standard uses a combination of a 16-bit mesh node identifier (node ID) and a 16-bit connection identifier (CID) to identify the source and destination of every transmission. The CID in mesh mode is a combination of an 8-bit link ID and an 8-bit QoS description for the connection. All the communications occur in the context of a link, which is established between two nodes. One link will be used for all the data transmissions between two nodes. QoS is provisioned over links on a message-by-message basis. No services or QoS parameter are associated with a link, but each unicast message has service parameters in the header. Figure 6 shows the Mesh connection identifier (CID) construction which contains these service parameter fields.
QoS bits in the mesh CID
The 8-bit QoS in the CID contains three definable fields: Reliability, Priority/Class, and Drop Precedence. Reliability refers to retransmit or not (0 indicates no retransmit while 1 indicates retransmit). Priority/Class refers to the priority of the packet. Drop Precedence refers to the probability of dropping the packet when congestion occurs [12] [13].
In the mesh mode, a special MAC is defined in the IEEE 802.16, which provides two different types of scheduling mechanisms – centralized and distributed scheduling.
Centralized Scheduling (Mesh CS): the Mesh-BS is responsible for supplying resources for each link in response to resource requests. Mesh centralized scheduling messages transmitted in a scheduled control subframe are used for this purpose.
In centralized scheduling, when a node has packets to send to either other MSS or the MBS, it sends a request packet in the control subframe, using the Mesh Centralized Scheduling Message (MSH-CSCH message) to the MBS. The node sends one bandwidth request for each link it has and all requests belonging to that node are sent in one MSH-CSCH message. After receiving requests from all the MSSs in the network, the MBS applies its traffic scheduler to these requests, including its own traffic requests.
Based on the scheduler used in the MBS, these requests are granted, either wholly or partially. Then the MBS broadcasts these grants in a MSH-CSCH message. A grant packet describes the data subframe usage of a frame. This data subframe description belongs to a frame after the frame from which the grant is sent. Each MSS forwards this grant message to its children. However, these requests and grants only include the amount of data that a node can transmit [14]. Figure 7 illustrates how it works in mesh mode.
Overview of scheduling in the mesh mode
Distributed Scheduling (Mesh DS): The neighboring Mesh SS responds to a request with a corresponding grant for a link between two Mesh SSs. Mesh distributed scheduling messages are exchanged to perform this operation.
The scheduling policy for accessing data slots in coordinated distributed fashion, is not specified in the IEEE 802.16 standard. The standard only defines the Mesh Distributed Scheduling Message (MSH-DSCH message), and specifies the scheduling to avoid collisions between messages of different nodes. The MSH-DSCH message contains the scheduling information organized in Information Elements (IE): Request IE, Availability IE, Grant IE and Scheduling IE.
The scheduling procedure follows a three-way handshake to reserve the minislots. First, a node sends an MSH-DSCH message to one of its 1-hop neighbors, requesting a set of data slots. In the message, the node also includes the set of data slots that it has available for reservation. The 1-hop neighbor grants the request by replying with another MSH-DSCH message that specifies a set of data slots that confirms the availability of data slots at both nodes. Finally, the first node confirms the reservation of this set of data slots by repeating the grant in another MSH-DSCH message.
In contrast with point-to-multipoint WiMAX networks, the standard does not define scheduling services for Mesh WiMAX networks [13].
Currently, one of the main areas of mesh networks that is being studied, is the routing protocol used to find the best path to the base stations (or access points). This allows customers who use this type of technology to take advantage of their services in a more effective way and with efficient communication, as well as transferring their data stream through the wireless communication environment [15]. Routing is a service in which the router evaluates the possible paths to transmit packets to their destination, and determines the best route this packet should follow [16].
The concept of network performance optimization is carried out through the construction of the routing tree selection which is characterized by the topological properties that are independent when the network is being formed. The construction of the tree and arrangement of the nodes allows a distribution of the nodes that leads to a better chance of routing and optimization. The correlations between the topological parameters of the tree and the efficiency of the network must be estimated, and those that show the strongest correlations should allow the creation of the best trees and thus provide some routing and topology optimization [17].
Currently there are a number of routing protocols with several differences and similarities between them, that show the particular advantages and disadvantages when applied to mesh networks. Among these various routing protocols, there is no exists single protocol that can be claimed as the best. The reason for this is that they have several peculiarities and there not exists a protocol that is considered to be optimal for all scenarios. Each protocol has a unique characteristic, which makes it either suitable for a particular application.
By studying the scientific and academic papers in mesh networks, it is clear there has been a notable growth in the number of research studies in this area [18] [19]. There are currently several projects spread across the networks, some on a large scale. This is because of the benefits that can be derived from this mode operation, including the cost-effective deployment of broadband, and ease of access. Another potential element of fundamental importance is digital inclusion and the Future Internet which can provide services and comprehensive long-range topology wireless, suitable for specific topologies, with the implementation of QoS to meet the requirements of situations such as the next generation networks and the ever-increasing demand for multimedia applications and real-time.
As discussed earlier, mesh networks are a promising technology. However, to develop their full potential as a product, mesh networks require research in fields related to all the layers of the TCP / IP stack. Specifically in the routing area, there is a need for new protocols and critical metrics. However, the adoption of routing protocols of ad hoc networks in mesh networks, although possible, causes a number of problems and has drawbacks, such as the large number of control packets used for these protocols. The dynamics of an ad hoc network requires the constant assessment of the network topology, which is different from a mesh network with a static topology. Thus, a mesh routing protocol should be a more stable and less costly network.
However, before understanding routing protocol operations, it is necessary to understand the operation of routing algorithms that are of two kinds: non-adaptive algorithms (static) that calculate the route when the network is initialized and not based on a network topology and adaptive algorithms (dynamic) that take into account the topology and where to search for information.
Adaptive routing algorithms can in turn be classified in two ways:
Distance Vector (DV): Due to its applicability to packet routing on the Internet, this became known as Routing Information Protocol (RIP) or Distributed Bellman-Ford (DBF). This algorithm operates by enabling each router to maintain a table (i.e. a vector) which provides the smallest distance to each known destination and determines which line should be followed to get there. In a distance vector. routing is defined as a metric unit that will be the cost value of a path between nodes of a network. This metric unit could be the physical distance between nodes, the amount of hops (hops), the delay in transmission, the node congestion and other factors.
Link State (LS): This dynamic algorithm was devised with the purpose of solving the problem of distance vector routing, since it used the number of hops to the destination, although a packet could reach a destination by going a short way, ie with few hops. However, the link bandwidth could be small and the delay be greater. As a result,, the link state has arisen to find efficient routes, and is not concerned about the number of hops or the conditions under which the network is located.
Among the ad-hoc networks, there are three basic types of routing protocols: proactive, reactive and hybrid [20] [21]. The proactive type requires us to maintain the route network for all possible destinations when there is a need to send a data packet. In reactive protocols, the nodes discover the destinations on demand. The hybrid protocols are those where there is only one set of nodes that provides periodically updated information on possible destinations.
This protocol requires all the network nodes to maintain routes to all possible destinations so that, when the need arises to send a data packet, the route that must be taken is known immediately. These protocols operate through their routing tables by exchanging messages continuously. Examples of proactive protocols are: : OLSR (Optimized Link State Routing Protocol), DSDV (Destination-Sequenced Distance-Vector) and WRP (Wireless Routing Protocol), the first, the OLSR is the representative of the protocols used for the following tests of this chapter.
The OLSR is a routing protocol developed for MANETs, and is an optimized link state protocol. The OLSR reduces the control packet size and the number of these packets that are sent to the network. This reduction in the number of control packets is achieved through the use of Multipoint Relays (MPR), which characterizes the OLSR. MPR is a node chosen from among the neighbors to send control packets, and the choice is made by the neighbors when there are only a hop of the node [22].
In the reactive protocols, the nodes discover the on-demand destinations, i.e. they do not require a route to the destinations where they have to send data, and seek the efficient use of resources like energy and bandwidth. Examples of reactive protocols are : AODV (Ad-Hoc On-Demand Distance Vector), DSR (Dynamic Source Routing) and TORA (Temporally Ordered Routing Algorithm). An examination of he AODV protocol, which is the representative of the reactive protocols used for testing, follows in this chapter.
The AODV routing protocol is a reactive protocol, i.e. the route to a destination node is discovered only when it wants to send a packet (data) to this node., This protocol enables dynamic routing, where the route of the packet can be changed in accordance with the route that the data is following, if the route used is unavailable. This discovery quickly results in new destinations [20] [23].
The AODV protocol is a protocol based on the Destination-Sequenced DistanceVector (DSDV) [19], and is created primarily to eliminate errors in DSDV, on account of the constant changes of topology and the large number of control messages between the network components. During the route discovery, the AODV protocol uses a traditional routing table as a storage mechanism. This only stores one entry, i.e. it only stores the next hop to the destination, unlike the DSR that stores multiple routes to the same destination and also stores the entire route from the source to a destination. The AODV is designed to be used in ad-hoc networks which have provided small numbers of nodes (up to thousands). The main purpose of the protocol is to adapt quickly and dynamically to the changing conditions of the network links, and find routes which can allow it to provide a desirable QoS. In this way it, avoids wasting bandwidth, minimizing memory usage and processing the nodes that act as routers.
The hybrid is a protocol where a certain set of nodes, (only a limited number of nodes) periodically updates the information nodes / routes of possible destinations, and attempts to make a suitable use of the two previous approaches. Examples of hybrid protocols are : HWMP (Hybrid Wireless Mesh Protocol), ZRP (Zone Routing Protocol) and FSR (Fisheye State Routing); the HWMP protocol is the representative of the hybrid protocols used for the following tests of this chapter. HWMP is based on AODV [22] and also has an optional routing protocol, called RAOLSR (Radio Aware OLSR) based on OLSR [23] [24].
HWMP is a hybrid routing protocol. It has both re-active and proactive components. The creation of HWMP is an adaptation of AODV to radio-aware link metrics and MAC addresses. It is the basic, reactive component of HWMP. The on-demand path setup is achieved through the path discovery mechanism that is very similar to that of AODV. If a mesh point needs a path to the destination, it broadcasts a path request message (PREQ) into the mesh network. The hybrid routing protocols combine the best features.
QoS routing is an important parameter for the provision of guaranteed QoS in mesh networks. This issue has been exhaustively studied in wireless mesh networks. The aim of QoS routing for these networks is twofold: to find a best feasible path for each incoming connection in the presence of the underlying link interference and to optimize the usage of the network by balancing the load.
This chapter evaluates the routing problem in the IEEE 802.16 mesh networks. Unlike other routing strategies, this chapter is concerned with providing paths, mainly at certain QoS levels that guarantee traffic flows. The simulations will evaluate multimedia applications such as VOIP, video conference and other multimedia streams that have grown over the Internet, and verify the best qualifications between QoS and routing protocols by evaluating the major impacts on these two important factors in the WMNs.
The number of hops is the most common criterion that is adopted by traditional routing protocols. However, it is clear that these protocols are inadequate for multimedia applications, such as VoIP and video conferencing, which require QoS guarantees. Routing protocols with QoS, not only need to find the route with the shortest path, but the best route that meets the requirements of end-to-end QoS, regardless of the number of hops or how the routing protocols need to find the best routes through multiple hops. It, is necessary and important that the new protocols and routing algorithms also take into account the parameters and other measurements such as power consumption, the closeness of the backbone network output and especially the quality over quantity link for users and the quality of wireless communications, while taking into account attenuation, signal quality and interference.
The Simulations experiments were carried out with the aid of Network Simulator version 2 [25] to show the performance of some routing protocols with QoS as network measure in WiMAX Mesh Network. For the WiMAX Mesh simulations it was used a module developed by the Network and Distributed System Laboratory [26] with extensions to use on PMP and mesh mode. The results compare four routing protocols: AODV, OLSR, HWMP Proactive and HWMP Reactive. Figure tal show the topology used for the tests, a random topology.
The simulation scenario chosen for the experiments were formed in a randomly generated with sixteen nodes, but that could easily represent a pre-existing base stations in a city, a rural area or a group of cities in proximity.The base stations act as routers through which network traffic will be routed through them choosing the best path according to its algorithms so that traffic is routed between source and destination.
The scenario (Figure 8) aims to test the choices of the best routes according to the algorithms / routing protocols and verify the flow and the delay due to these choices. The results are found in the simulations are evaluated along with the following analysis of these.
Simulated Topology
Faced with this scenario, routing protocols, based on their algorithms must choose the best route for that traffic out of the source node (node 2/BS 2) and reaches the destination node (node 16/BS16) and there is the question. What\'s the best route? The red route or blue route? Will would other routes? Perhaps green route. Certainly there are several routes and choosing each one behind certain characteristics and particular outcomes to the performance of this network and its communication. Simulated parameters presented below (Table 1).
Cover Area | \n\t\t\t1km | \n\t\t
Frequency | \n\t\t\t3,5GHz | \n\t\t
Standard | \n\t\t\tIEEE 802.16 (MESH) | \n\t\t
Modulation | \n\t\t\tOFDM | \n\t\t
Router WiMAX Mesh Number | \n\t\t\t16 | \n\t\t
Simulation Time | \n\t\t\t60s | \n\t\t
Traffic | \n\t\t\tVideo and CBR | \n\t\t
Simulated parameters
In the first situation, the simulations were conducted with CBR traffic (1 MB), the transmission consists hop-by-hop by four routing protocols: AODV, OLSR, HWMP Proactive and HWMP Reactive. By the analyze of the throughput, achieved better performance result by HWMP Reactive (Figure 9). This result is because of the protocol in this scenario constantly keep checking the best route and always find a solution when faced with a new, always managing to optimize the flow through the best link at any given time.
Comparison of CBR traffic throughput for the four routing protocols
The result of the hybrid routing protocol show the better results in comparison with other protocols presented here. In other protocols, it takes a long time to find a best route for data flow and sometimes, take congested routes, which reduces the throughput of the network.
In the second situation, the simulations were conducted with Video and CBR traffic (as background traffic). The transmission consists hop-by-hop by four routing protocol: AODV, HWMP Proactive, HWMP Reactive, OLSR. When we analyze the throughput, we observed a better performance by AODV. This case was carried out by using the Evalvid tool [27] that allows control of real video quality called “Grandma”. The video simulations parameters presented below (Table 2).
In this particular case the transmitted traffic behind will focus on some decrease in the quality of connections that take the main traffic to the destination and make the hybrid routing algorithms are flawed when compared to non-hybrid and in this case, can best AODV results in selecting the best route and consequently better results regarding the flow, providing a certain QoS to the end customer and the quality of multimedia applications used. The AODV establishes the route more faster than other protocols, for this reason it had better throughput and better video performance.
Resolution | \n\t\t\t352 x 288 | \n\t\t
Frame Rate | \n\t\t\t30 Frame/sec | \n\t\t
Color Scale | \n\t\t\tY, U, V | \n\t\t
Packet Length | \n\t\t\t1052 | \n\t\t
Packet Fragmentation | \n\t\t\t1024 | \n\t\t
Video simulation parameters
Traditionally, the performance of network archictetures have been evaluated through Quality of Service (QoS) metrics. QoS is defined as the ability of the network to provide a service at an assured service level. QoS is also a commonly used metric set (e.g., throughput, packet loss, delay, jitter, handoff dropping and blocking probability) to represent the capability of a network to provide guarantees to selected network traffic. QoS considers parameters of a network that can be easily measured, but do not tell how the service is perceived by users. To satisfy the user-centric approaches, QoE is used to quantify the perception of the user about the quality of a particular service or network. The QoEmetrics confirm the previous statement.
The PSNR (Peak Signal to Noise Ratio) [6] [28] is the most traditional QoE/video metric, which estimates the video quality in decibels, comparing the original video with the video received by the user considering the aspects of luminosity. Figure 10 shows the better video quality using the PSNR statistics (Table 3).
For each PSNR range values, there is a qualification for the received video by the user. The Table II shows the PSNR range quality:
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
> 37 | \n\tExcellent | \n
31 – 37 | \n\tGood | \n
25 – 31 | \n\tFair | \n
20 – 25 | \n\tPoor | \n
< 20 | \n\tBad | \n
PSNR range
PSNR
The Structural Similarity (SSIM) [6] [28] metric evaluates the received video by the user taking into account the characteristics of the HVS (Human Visual System). The SSIM examines the color, light and structure similarity. The SSIM value is expressed by a number between 0 and 1, where 0 means zero correlation with the original image and 1 means the exact same video. As can be noted (Figure 11) by analyzing the QoS metrics, AODV has the best closeness in quality compared to the original video and HWMP Proactive worse.
SSIM
The Video Quality Metrics (VQM) [6] [28] as MSU VQM metric also compares the original video with the video received by the user. They are considered the most complete metrics because compare the following aspects: noise, distortion and color. Again, AODV (Figure 12) has the best values because the smaller the value of this metric, better the video quality.
VQM
The evaluation of routing protocols are clear when we look at the frames in Figures. The frame number 100 was selected to compare the quality. As we can see, the AODV presents the best results, followed by hybrid reactive, OLSR and hybrid proactive protocols respectively (Figure 13).
Result of frame 100 for the four routing protocols
The Figure 14 shows the delay average over time for the four routing protocols, showing their results in this network metric. AODV and HWMP reactive had the lowest delay.
Delay average over time
If a routing protocol takes longer time to find the best route and took and thus decide to use your communication path, in normal situations, present a lower performance as measures of network for those with a behavior in choosing the fastest route.
This chapter showed an initial study on wireless mesh networks, pointing out the main goals of interest, challenges and issues encountered, highlighting the advantages and disadvantages of this mode of operation. During the work, focused on the IEEE 802.16 popularly known as WiMAX, a technology standardized by the WiMAX Forum as an alternative wireless communication with wide area coverage and bandwidth, providing high speed and mobility, important peculiarities in the context of next-generation networks in Future Internet.
The chapter provides a more detailed study of the WiMAX mesh mode, pointing to two very important points for this type of network the next generation of wireless communications: algorithms / routing protocols and QoS, mainly to meet the demands for new multimedia applications as VoIP, telemedicine, videoconferencing and other real-time applications that require a large bandwidth with constant to meet the constant flow needs, providing quality network metrics such as throughput and delay and qualitative results regarding the perception of the end user when evaluated on the QoE metrics, with valid results on human perception of quality in real end user.
The studies and validated through simulation showing what the main advantages of routing protocols when incidents of random scenario presented here, however, it is noteworthy that these results are specific to this scenario, not ensuring that the protocols achieve similar results in any type of scenario. It is important to mention that the protocols have different results as there is a variability of scenarios or data flow, increasing them or decreased them and so, some protocols may have better results in some scenarios and worse in others, there is certain variability.
Conclusively, routing protocols have advantages and disadvantages and present very particular results, and there is a protocol that presents the best results ever, nor how to choose the best route, nor as to the best results of QoS and QoE.
Simulation results shown that the AODV protocol provides the best results when analyzed on the scenario shown to video traffic, however, the hybrid routing protocol that operates in a reactive mode, gives good results and operates in hybrid form, could be better than AODV depending on some parameter variations. This makes us believe that the hybrid reactive would be a protocol that can be relatively good in all cases, although not an optimal model, can be efficiently and effectively providing a good alternative routes and relative quality to the end user about the prospect of QoS and QoE. The hybrid reactive and AODV protocol gave good results as the data flow rate and video quality, but could have different results in other settings and with other simulation parameters.
In some future work, the authors intend to make optimizations in routing protocols and mechanisms include your choice between a more complete analysis taking into account other important points beyond the amount of jumps as energy consumption and output communications to the Internet outside the backbone of the mesh network with algorithms that also take into consideration the proximity.
Personality disorders are conditions that can have a destructive impact on an individual’s quality of life and social interactions. Indeed, a person with a personality disorder will experience serious difficulties managing emotions, behaving according to culturally acceptable cognitions, and interacting normally in everyday life [1]. In the United States, 9–15% of people develop a personality disorder and in most cases, this disorder is accompanied by numerous comorbid conditions [2]. The situation is even more worrisome considering that personality disorders are commonly associated with aggression, violence, criminal behavior, and violent recidivism worldwide [3]. According to a systematic review, 65% of incarcerated men and 42% of incarcerated women have a personality disorder [4]. These epidemiological data underline the importance of better understanding and treating personality disorders, particularly those in cluster B. According to the DSM-5, cluster B personality disorders are characterized by relational disorders and impulsive, emotional, and/or unstable behavioral manifestations [1]. They include disorders such as antisocial personality disorder (ASPD) and borderline personality disorder (BPD) and tend to be strongly associated with a variety of maladaptive behaviors, including addictive, suicidal, or aggressive behavior.
BPD is characterized by pervasive instability of affects, self-representations, and interpersonal relationships [1]. It also includes the presence of impulsiveness, paranoia, feelings of emptiness, and/or suicidal gestures. BPD has a lifetime prevalence of approximately 6% among both sexes [5]. In a population of adolescents, BPD traits are associated with high levels of delinquency, antisocial behavior, and all forms of aggression (e.g., sexual harassment, overt aggression, and violence). As such, the diagnosis of borderline personality is, according to some authors, a good predictor of violence and aggression [6]. Moreover, in hospital settings, 65% of patients with BPD report having used physical, verbal, or relational gestures that were aggressive [7]. According to several authors, aggressive behaviors among borderline patients are guided by emotions [8]. In fact, BPD patients are prone to overreact, which leads to irritability, outbursts of anger, and subsequent physical aggression.
The DSM-5 describes ASPD as a pattern of violation of, and disregard for, the rights and interests of others [1]. It is expressed through a lack of social conformity, use of deception for personal gain, lack of remorse, and irresponsible, irritable, or impulsive behavior. In the United States, the prevalence of ASPD is 3.63% in the general population and the prison population, as high as 21–47% [4]. Moreover, in young adults, self-reports of two antisocial characteristics (i.e., sensation-seeking and egocentricity) have been associated with relational aggression [9]. More generally, ASPD diagnosed in clinical populations has been shown to be a strong predictor of violence and aggression [6]. Further, high levels of aggression have been associated with ASPD regardless of gender [3]. Authors suggest that violent behavior by antisocial patients can be explained as being part of an instrumental goal, such as for the purpose of obtaining gratification [8].
Conceptually, aggression refers to intentional and observable action directed toward someone with the goal of physically or mentally harming them [10]. Aggression is said to be reactive when it occurs under provocation, threat, or frustration. It is expressed through outbursts of uncontrolled anger and cognitive scripts involving distinct expectations and hostile perceptions. Aggressive behaviors have disastrous economic, legal, and social consequences [11]. Since the impacts are observable at the individual, family, community, and national levels, many programs have been developed to prevent and reduce aggression. One potential area of intervention could consist of decreasing aggressive cognitions that cause the individual to perceive the world as a dangerous environment and to reconsider the use of aggression when a conflict occurs.
Relatedly, a meta-analysis of studies conducted with people without BPD or ASPD showed a strong relationship between aggression and the hostile attribution bias (HAB) [12]. According to Crick & Dodge’s [13] theory of social information processing, HAB refers to a tendency to attribute hostile intentions to others despite the intention behind their behavior being ambiguous [14]. In more than 100 studies, the positive relationship between reactive aggression and HAB has been demonstrated in clinical and normal samples of individuals of different ages and ethnicities [14, 15, 16].
In an ambiguous and provocative situation, people with BPD tend to interpret events (such as abuse or rejection) as threatening [17]. This leads them to be overly sensitive to rejection, behave impulsively, and feel negative emotions. According to several authors, dysregulation of affect and behavior, which is characteristic of BPD, is associated with various cognitive biases, such as the HAB [18]. In fact, a study by Arntz et al. [19] found that people with BPD showed a readiness to perceive a person as negative, aggressive, malicious, abusive, and rejecting. Thus, it is quite possible that the HAB can explain why people with BPD act aggressively toward others. Smeijers et al. [20] have shown that patients with BPD often produce a lot of hostile interpretation biases.
With regard to ASPD, few studies have tested the HAB as an explanatory variable for reactive aggression [20, 21]. According to Lobbestael et al. [21], ASPD traits and HAB (measured using thumbnails and images) were good predictors of reactive aggression. Further Smeijers et al. [20] found that people with ASPD performed many HABs when looking at facial expressions.
The HAB can be measured using self-reports [22], written vignettes [21], video vignettes [23], and computer tasks [20]. For example, in the study by Lobbestael et al. [21], HAB was measured using eight images from the thematic apperception test and eight text vignettes describing ambiguous and provocative scenes from daily life. Participants were asked to describe the scenes and rate the hostile, positive, negative, and neutral character of each scene on a 4-point scale, ranging from most plausible to least plausible. While all of the previously mentioned HAB measures provide interesting results, they are not without flaws. Indeed, these methods do not allow for the measurement of spontaneous inferences and rapid intention attribution processes that are characteristic of the HAB. The latter occurs in the early stages of social information processing [13]. Before providing their responses, participants have time to consider other, more socially acceptable interpretations.
To capture the first cognitive processes of real-time intention attribution, Gagnon et al. [24] developed an innovative measurement method based on the recording of brain signals. The aim was to present different scenarios on a screen that, in written form, describe a character performing ambiguous behavior toward the reader in a context-specific manner (see Table 1). The context was either hostile or nonhostile and the reader was asked to read the scenarios while imagining the intention of the character. Subsequently, the character’s actual intention was revealed through a final target word and event-related potentials (ERPs) were recorded. The intention could be either hostile or nonhostile. In principle, when the hostile or nonhostile nature of the intention was at odds with the hostile or nonhostile nature of the context, expectations about the intention of the character being portrayed were violated. According to Gagnon et al. [24], the ERP component N400 was observable when hostile expectations were violated. In the literature, N400 is described as a negative deflection occurring around 200–500 ms poststimulus presentation [24, 25]. Its amplitude is maximal in the centro-parietal regions of the brain and is triggered when the word presented is unexpected or inconsistent with the context in the scenario [25]. In a study by Gagnon et al. [26], the N400 directly measured expectation violation, and its amplitude was stronger among aggressive individuals compared to nonaggressive individuals during the hostile expectations violation than during the nonhostile expectations violation.
List | First sentence—context | Second sentence—behavior | Third sentence—intention | Condition |
---|---|---|---|---|
1 | You’re playing soccer against a team that has an aggressive style | On a breakaway, the defender trips you up | The defender wants to | Hma |
2 | You have soccer practice with your team | Hmi | ||
1 | You’re having dinner with friends and Sylvie, who’s obnoxious | She does not mention that your shirt is stained | Sylvie does not want to | NHmi |
2 | You’re having dinner with friends and Sylvie who’s nice | NHma |
Examples of scenarios under the four conditions of the hostile expectancy violation paradigm.
NHma = nonhostile match; NHmi = nonhostile mismatch; Hma = hostile match; Hmi = hostile mismatch. Here, the target word is in bold. Translation in English of a “Le défenseur veut vous
The main goal of this study is to examine the mediating role of the HAB (measured by EEG and self-report) in the relationship between cluster B personality traits and reactive aggression. To achieve this, we present several objectives and hypotheses. (1) First, we want to replicate and validate the HAB measurement method developed by Gagnon et al. [24]. Our first hypothesis is that N400 will be more pronounced in the right posterior brain regions during the hostile expectations violation. (2) Secondly, we aim to evaluate the predictive role of ASPD traits, BPD traits, and HAB (as measured by EEG and self-report) on self-reported reactive aggressive behaviors. (a) We hypothesize that ASPD and BPD traits will positively and significantly predict self-reported reactive aggression. (b) We hypothesize that ASPD and BPD traits will significantly predict self-reported HAB and hostile expectations violation. (c) We expect a neurophysiological and self-reported measure of HAB to significantly predict reactive aggression. (d) Finally, we expect that self-reported and neurophysiological HAB will mediate the relationship between cluster B personality traits and reactive aggression.
Seventy-two French-speaking adults were recruited from university classes in two metropolitan universities, a list of former patients who consulted in a personality disorders clinic, and the general population through posters and announcements on Facebook and Kijiji. Interested individuals were then contacted by email to receive information about the study and to make an appointment for a laboratory visit. All participants were between 18 and 65 years of age, had normal vision with or without correction and had no history of psychosis, neurological disorder, or severe brain damage. Seventeen of them had been taking a central nervous system medication (e.g., anxiolytic, stimulant, SSRI, SNRI, and antidepressant) for at least 2 weeks prior to the day of the experiment. Before the visit, participants were asked not to use other drugs or alcohol for at least 1 week and 24 h prior to the experiment, respectively. Failure to comply with any of these instructions resulted in the postponement of the appointment. All participants received financial compensation of $25 at the end of the appointment. Nine participants were excluded due to attrition, a significant amount of missing data, a mother tongue other than French, or excessive artifacts on the EEG signals caused by eye movements. The final sample consisted of 63 participants (46 females and 17 males) with an average age of 29 (SD = 1.44) and 15 years of education (SD = .40).
The last questionnaire administered assessed age, gender, mother tongue, and education status.
The stimuli constituted 320 scenarios depicting social interactions encountered in everyday life and was developed by Gagnon et al. [24] to test hostile and nonhostile expectancy violations. Each scenario consisted of three sentences (see Table 1). The first sentence described a typically hostile or nonhostile context. The second sentence depicted a character whose intention was ambiguous, thus committing potentially provocative behavior to the reader. The last sentence included a final target word that resolved the ambiguity by clarifying the intention behind the behavior. The scenarios were created under four conditions—hostile match (Hma), hostile mismatch (Hmi), nonhostile match (NHma), and nonhostile mismatch (NHmi). When the conditions were hostile, the target word indicated hostile intent on the part of the character’s behavior. Conversely, when the conditions were nonhostile, the intention was depicted as nonhostile. Conditions were said to be a match when the hostile or nonhostile nature of the intention was consistent with the hostile or nonhostile nature of the context. Similarly, conditions were said to be mismatched when the hostile or nonhostile nature of the intention differed from the hostile and nonhostile nature of the context. Two lists of 160 scenarios (i.e., 2 × 40 scenarios for each of the four conditions) were used to balance the match and the mismatch conditions with the hostile and the nonhostile conditions across participants. For a given scenario, the match and mismatch versions shared the same behaviors and intentions but differed in the hostile or nonhostile nature of the context. The first two sentences were composed of a maximum of 25 words and the last sentence a maximum of eight words. The third sentence was phrased negatively in almost 50% of the scenarios for each condition. The two lists were administered alternately and equally across participants. A list of 20 additional scenarios (i.e., 5 × 4 scenarios for each of the four conditions) was developed for the purpose of practice and comprehension trials.
After completing the online questionnaire and giving their written consent, participants were invited to the laboratory to perform the experimental task. While their brain activity was recorded, they were asked to read the daily life interaction scenarios and visualize them as though they were actually experiencing them. As they read the first two sentences, the reader had to imagine why the characters were behaving in such a way toward them (intention attribution process). Once ready, they could initiate the presentation of the third sentence. For each scenario, a trial consisted of presenting the first two sentences for at least 1500 ms. After pressing the space bar on the keyboard, a delay of 500 ms without stimuli was followed by a fixation cross appearing in the center of the screen for 1000 ms. A third sentence was then displayed, word by word, in the center of the screen and ended with the target word. Each word was presented for 300 ms, with a delay of 200 ms between words. Finally, a fixation cross was displayed in the center of the screen for 2000 ms. The participant had to keep his eyes focused on the center of the screen and refrain from blinking from the appearance of the first cross until the disappearance of the second cross. In total, there were four practice trials followed by 10 blocks of 17 trials (170 trials). Each block consisted of 16 experimental trials (four scenarios for each of the four conditions: Hma, Hmi, NHma, and NHmi) and one trial used as a comprehension test. The comprehension trial was followed by a true or false question. The purpose of this question was to ensure that the participant was reading and understanding the scenarios. The participant could answer by pressing the letter N (true) or M (false). A correct/incorrect answer was followed by feedback (green or red cross, respectively). For our sample, the average rate of correct answers was 91.1%, indicating a high rate of comprehension. The experimental trials were presented in random order and without repetition. The blocks were separated by a break, the duration of which was determined by the participant. The words and fixation crosses were written in white, Helvetica font, size 14, bold, on a 17-inch (43.18 cm) black screen. The distance between the screen and the participant’s eye was 70 cm. Three characters corresponded to approximately 1° of visual angle. The experimental task was created using E-Prime 2.0 software (
The electroencephalography took place in a Faraday cage and under medium brightness. The brain activity of the participants was captured using 64 Ag/AgCI electrodes in an elastic cap. The position of the electrodes was done according to the International 10–10 System [40]. The right and left mastoids were used as references. One electrode was placed below the left eye to capture blinking and vertical eye movements. Two other electrodes were placed at the outer canthi of the eyes to capture horizontal eye movements. The signals were processed and recorded via a Biosemi ActiveTwo amplifier system (Amsterdam, Netherlands) at a sampling frequency of 512 Hz. Online, a 0.16 Hz high-pass filter and a 100 Hz low-pass filter were applied to the EEG signals. On Matlab, a 0.1 Hz high-pass filter and a 30 Hz low-pass filter were applied during offline analyses. The resulting signals were segmented in trials according to a time window of from 200 ms before, to 800 ms after the target word onset. The baseline time window ranged from −200 ms to 0 ms. Trials containing too many artifacts (i.e., eye or muscle movements) were rejected using an independent component analysis [41]. Rejection thresholds were applied for blink (i.e., > 80 mV within a time window of 150 ms) and for eye movement (i.e., > 35 mV within a time window of 300 ms). Electrodes with a noisy EEG signal (i.e., exceeding +/− 100 mV voltage) were interpolated by spherical spline. When more than seven electrodes were noisy in a trial, the trial was rejected. When the number of rejected trials was greater than 20 per condition, the participant was excluded from the sample. In our final sample, the percentage of rejected trials was less than 17.5% in the four conditions (i.e., 0–17.5% for Hma, 0–15% for Hmi, 0–12.5% for NHma, and 0–12.5% for NHmi). The trials were then averaged by condition (Hma, Hmi, NHma, and NHmi) and for each participant. On average, there were 39 trials per condition. The ERP amplitudes captured by the electrodes were averaged over six lateral regions and three midline regions on the scalp. The lateral electrodes were separated as follows: anterior left (AF3, AF7, F1, F3, F5, F7, FT7, FC1, FC3, FC5), central left (TP7, T7, C1, C3, C5, CP1, CP3, CP5), posterior left (P1, P3, P5, P7, PO3, PO7, O1), anterior right (AF4, AF8, F2, F4, F6, F8, FT8, FC2, FC4, FC6), central right (TP8, T8, C2, C4, C6, CP2, CP4, CP6), and posterior right (P2, P4, P6, P8, PO4, PO8, O2). The midline electrodes were analyzed as follows—anterior median (AFZ, FZ, FCZ), central median (CZ, CPZ), and posterior median (PZ, POZ, OZ).
Statistical analyses were performed to evaluate the voltage of the ERP amplitudes (dependent variable) according to the conditions (Hma, Hmi, NHma, and NHmi) and location of sensors on the scalp. Each subject being its own control, two repeated measures ANOVAs with Huynh-Feldt corrections were performed. The first ANOVA was for the lateral electrodes. The independent variables were intention (hostile, nonhostile), Consistency (match, mismatch), Hemisphere (left, right), and Location (anterior, central, posterior). Mean ERP amplitudes observed at midline regions were analyzed in a second ANOVA. The independent variables were Intention, Consistency, and Location. Given that our first objective was to demonstrate the presence of an N400 during expectancy violations (mismatch-match conditions), interaction effects involving the Consistency factor were looked at in the ANOVAs. To assess the role of the N400 in our mediation models, we selected regions showing greater negative amplitude (as shown in [24, 25]). Pearson’s correlations were performed between all variables. Therefore, several multiple linear regressions were conducted to assess whether antisocial characteristics, borderline characteristics, and the HAB (as measured by self-report or EEG) predicted scores on reactive and proactive aggression. A product-of-coefficient test for mediation analyses was performed by using bootstrapping procedures, a nonparametric resampling technique to test for indirect effects [42]. This method has been recommended as bootstrapping was found not to inflate Type I and Type II error rates and to have higher power [43]. In addition, bootstrapping does not assume multivariate normality. The significance of the mediation effect is determined when the 95% bias-corrected confidence intervals (CIs) do not contain zero. In the current study, estimates are based on 5000 bias-corrected bootstrap samples. All analyses were two-tailed, with an α level set at .05.
Figure 1 shows differences in mean ERP amplitudes between mismatch and match conditions for the nine scalp regions. Mean amplitude differences indicate a negative deflection (N400) at around 350–650 ms during hostile expectancy violations (NHmi-NHma conditions). Based on visual inspection, the greatest deflections were at midline and right sites in the central and posterior regions. When nonhostile expectancies were violated (Hmi-Hma conditions), mean perceived amplitudes neared zero in the central and posterior regions. Figure 2 shows the topography of mean amplitude differences observed on the scalp from 350 to 650 ms (post target onset) during hostile and nonhostile expectancy violations. During the hostile expectancy violations, the N400 seems to appear in the central and posterior regions of the right hemisphere and the midline sites.
Difference between the mismatch and match conditions of the grand ERP averages obtained after presentation of the hostile or nonhostile target word for 9 brain regions. LA = anterior left; LC = central left; LP = posterior left; MA = anterior median; MC = central median; MP = posterior median; RA = anterior right; RC = central right; RP = posterior right.
The topographic map of ERP mean differences between mismatch and match conditions from 350 to 650 ms after presentation of hostile or nonhostile target words. On the left, nonhostile expectancy violation (hostile mismatch-match). On the right, hostile expectancy violation (nonhostile mismatch-match).
For the lateral electrodes ANOVA, there was an interaction effect between Intention, Consistency, and Location (F (2.124) = 5.90; p = .01), and between Intention, Consistency, and Hemisphere (F (1,62) = 5.21; p = .03). For these interactions, effect sizes were moderate (partial R2 = .08; partial R2 = .09, respectively). Simple effects for these last two interactions were assessed for Consistency factor by paired comparisons with post hoc Bonferroni adjustment. The levels of the Consistency factor (match and mismatch) differed significantly for the nonhostile intention in the central and posterior regions, with an adjusted alpha of .004. There was no difference between hostile mismatch and hostile match at anterior, central, and posterior sites.
For the midline regions ANOVA, there was an interaction effect between factors of Intention and Consistency (F (1,62) = 16.16; p = .00), between factors of Consistency and Location (F (2,124) = 10.59; p = .00), and between factors of Intention, Consistency, and Location (F (2,124) = 5.60; p = .01). Effect sizes for these interactions were high to moderate (partial R2 = .21; partial R2 = .15; partial R2 = .08). Simple effects for the last interaction were evaluated for the Consistency factor by paired comparisons with post hoc Bonferroni adjustment. The level of Consistency factor differed significantly for nonhostile intention at central and posterior regions on the scalp with an adjusted alpha of .004. There was no difference between mismatch and match for hostile Intention in anterior, central, and posterior regions.
These results confirm the presence of the N400 in central and posterior regions in the nonhostile intention condition (i.e., when hostile expectations were violated). In the hostile Intention condition (i.e., during nonhostile expectancy violations), the N400 was not significantly visible. Since the ERP waveform differences and the topographic map indicated a stronger N400 effect in the central and posterior regions of the right and midline sites, we selected MC, RC, MP, and RP regions for further analysis.
The scores of all self-report scores were normally distributed, except for antisocial behavior that had positive skewed distributions as observed in the general population.
Correlation coefficients of the variables of interest are presented in Table 2. Reactive aggression was significantly correlated with age, depression, antisocial traits, borderline traits, and nonhostile expectancy violations (hostile Intention condition) in the MC (r = −.29, p £ .05 two-tailed), MP (r = −.25, p £ .05 two-tailed), RC (r = −.35, p £ .01 two-tailed), and RP (r = −.32, p £ .01 two-tailed) region. In addition, antisocial traits were significantly correlated with gender, paranoid ideation and borderline traits, and indirect hostile attribution bias (r = .30, p £ .05 two-tailed). Borderline traits were significantly correlated with paranoid ideation, depression, and self-reported HAB. Reactive aggression was not significantly correlated with hostile expectancy violations (nonhostile intention condition). Because the correlation between hostile or nonhostile expectancy violations and aggression scores was more strongly consistent in the RC region than in the MC, MP, and RP regions, regression analyses were performed in the RC region.
Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Age | — | |||||||||||
2. Gender | ||||||||||||
3. Education | −.11 | −.12 | — | |||||||||
4. Paranoid idea | .13 | .01 | .09 | |||||||||
5. Depression | .07 | .06 | .08 | — | ||||||||
6. ASPD features | .08 | .04 | .19 | — | ||||||||
7. BPD features | .05 | .03 | .21 | — | ||||||||
8. REAG | .23 | .06 | .24 | — | ||||||||
9. HAB | .13 | .11 | −.11 | .25 | .14 | .16 | — | |||||
10. HN400RC | −.01 | −.08 | .03 | .00 | .10 | −.10 | −.11 | .18 | — | |||
11. NHN400RC | .14 | −.09 | −.13 | −.01 | −.00 | −.21 | .19 | .01 | −.08 | −.03 | — |
Correlation matrix.
p ≤ .05.
p ≤ .01.
REAG: reactive aggression; HAB: hostile attribution bias – SIP-AEQ; HN400RC: N400 effect in RC region for nonhostile expectancy violations; NHN400RC: N400 effect in RC region for hostile expectancy violations.
A first regression was conducted with ASPD features as the independent variable (Figure 3). Hostile expectancy violation (nonhostile intention condition) and self-reported HAB were the mediator variables and age, sex, education, BPD features, paranoid ideation, and depression traits served as covariates. Results showed a nonsignificant indirect effect for the hostile expectancy violation (indirect = −.00,
Mediation of antisocial characteristics—reactive aggression relationship by the hostile attribution bias and the N400 in hostile and the nonhostile conditions.
A third regression was conducted with BPD features as the independent variable, hostile expectancy violation (nonhostile intention condition), and self-reported HAB as mediator variables and age, sex, education, ASPD features, paranoid ideation and depression traits served as covariates (Figure 4). Results showed a nonsignificant indirect effect for the hostile expectancy violation (indirect = −.02,
Mediation of borderline characteristics—reactive aggression relationship by the hostile attribution bias and the N400 in hostile and nonhostile condition.
The first objective of this study was to replicate the measurement method of Gagnon et al. [24] and to validate their results. The aim was to present scenarios describing social interactions while measuring brain activity. In each scenario, characters acted in a provocative and ambiguous manner in both hostile and nonhostile contexts. Participants were asked to read the scenarios on a screen and imagine the intentions behind the behaviors presented. Subsequently, the characters’ hostile or nonhostile intentions were revealed through a final target word. As in the study by Gagnon et al. [24], we were able to observe the N400 ERP component in a time window ranging from 350 to 650 ms post-stimulus onset. Moreover, the amplitude of this deflection was more pronounced during the hostile expectancy violation in the central and posterior cerebral regions at the medial and right electrodes. This implied that participants attributed hostile intent to the characters when the context was hostile. This result has been corroborated by several other studies reporting a maximum amplitude N400 in the centro-parietal regions when expectations are violated [24, 25].
During the nonhostile expectancy violation, ERP amplitudes neared zero at approximately 350–650 ms. Therefore, when a nonhostile context was followed by ambiguous and provocative behavior, participants did not attribute a nonhostile intent to the behavior. Although consistent with findings reported in Gagnon et al. [24], this result appears inconsistent with the established assumption that the N400 would reflect an expectancy violation. Gagnon et al. [24] suggest this phenomenon possibly reflects a cautious interpretation, based on perceived cues, on the part of nonaggressive students. It is indeed possible that, in our study, nonhostile contextual cues conflicted with the ambiguous and provocative nature of the behavior. Therefore, the type of intent attribution depended on the weight the participant gave nonhostile cues versus provocative cues. In the end, in scenarios designed to violate nonhostile expectations, the participant may have had mixed views and not been systematically surprised to see hostile intent appear after a nonhostile context.
The second objective of this study was to demonstrate the predictive role of ASPD traits, BPD traits on self-reported aggressive behaviors. As expected, ASPD traits positively predicted reactive aggression in both models, which is consistent with the scientific literature [9, 21]. BPD traits were highly correlated with reactive aggression. However, when controlling for age, gender, education, depression, paranoid ideation, and ASPD traits, they did significantly predict reactive aggression in one model only. This result was surprising given that several studies have shown BPD to be a good predictor of reactive aggression [6, 7, 9]. In a recent longitudinal study, however, Penson et al. [29] showed that BPD characteristics were not sufficient in significantly predicting aggressive behaviors and rather, that ASPD characteristics were better predictors. Thus, it is likely that, in our regressions, ASPD traits were more effective predictors of reactive aggression than BPD traits. In addition, BPD traits and ASPD traits shared a high percentage of common variance (r2 = .24), possibly explaining the nonsignificant coefficient for BPD traits in the regression.
ASPD traits failed to predict both HAB, as measured by self-report, and hostile or nonhostile expectancy violations. These findings are not consistent with the few studies evaluating HAB in ASPD [20, 21]. However, it is important to mention that the methodology used to measure the HAB could explain the conflicting data. We used the SIP-AEQ questionnaire to measure self-reported HAB and an electrophysiology method developed by Gagnon et al. [24] to measure hostile and nonhostile expectancies violations. As such, it is possible to expect different results across studies. Given that ASPD is characterized by a lack of conformity to societal norms [1], it is also possible that the individuals with ASPD in our study did not relate to the characters or that they experienced difficulty imagining the situations described in our task.
BPD traits also did not predict self-reported HAB and nonhostile expectancy violation. In contrast, they positively predicted the hostile expectancy violation (i.e., BPD traits negatively predicted N400). Thus, the higher the BPD traits, the stronger the hostile expectancy violation. In other words, when the context was hostile, people with high BPD traits made more hostile intent attributions than people with lower BPD traits. This result partially confirmed our expectations and was consistent with findings in Smeijers et al. [18]. In addition, several researchers have provided arguments regarding the meaning of such a prediction [17, 18, 19]. For example, Lobbestael and McNally [17] demonstrated that people with BPD were subject to interpretive biases related to rejection and anger. According to Baer et al. [18], people with BPD have negative beliefs about themselves and their environment. They also interpret and evaluate neutral and ambiguous stimuli negatively. Finally, according to Arntz et al. [19], people with BPD judge other people as negative, aggressive, and malicious.
Self-reported HAB did not predict reactive aggression in all models, which is in contrast with the numerous studies showing that self-reported HAB is positively related to reactive aggression [15, 16, 44]. It is possible that variability in HAB scores was too small in our study to observe correlations. Regarding the hostile and nonhostile expectancies violations as measured by the N400 effect, results showed an opposite relationship with reactive aggression than expected. First, given that HAB and reactive aggression are positively associated as reported in the literature [15, 16, 44], we assumed that N400 effect in the nonhostile intention condition (hostile expectancies violation) would negatively predict reactive aggression (more negative amplitude associated with higher aggression score). However, because of the chronic accessibility to hostile patterns, it is possible that an aggressive person would see aggression in all their social interactions [45]. Since mismatch and match conditions would have a similar effect in this case, their subtraction should have the effect of reducing the N400 (more positive amplitude going up) as aggressive traits increase. Second, we assumed that nonhostile expectancy violation would negatively predict reactive aggression as nonaggressive individuals would be more surprised to see a hostile intention appear after a hostile context. However, given that the N400 effect was nonsignificant when the intention words were hostile, it is difficult to infer the nature of the cognitive processes underlying this relationship. Also, it appears that two other studies have found a negative relationship between HAB and reactive aggression [46, 47] suggesting that the relationship between HAB and reactive aggression could be more complex than we may think and difficult to predict.
Finally, our final hypothesis that self-reported HAB, the hostile expectancy violation, and the nonhostile expectancy violation were mediators of the relationship between cluster B personality traits and reactive aggression, could not be confirmed. These findings were inconsistent with the few studies that have evaluated the mediating role of HAB in the relationship between these personality features and reactive aggression [20, 21]. When it comes to cluster B personality, it is possible that the N400 effect (expectancy violation) may be influenced by other mediators, like sensitivity to rejection, impulsivity, and dysfunctional beliefs [17, 18], which were not included in our study. Further studies are therefore needed to better understand the cognitive and affective processes underlying aggressive behavior in antisocial and borderline personality disorders.
This research project had several methodological limitations, such as sample size and heterogeneity. Future analyses using a larger sample would be warranted to better understand the nature of the observed relationships. In addition, our sample potentially over-represented students in the general population. Out of 63 participants, 49 were from an academic background. It would be interesting and beneficial to evaluate our measures on samples more representative of the clinical population.
In conclusion, our study replicated the measurement of expectation violations by electrophysiology and validated the presence of a strong negative deflection of ERP amplitudes at the time of hostile expectation violations, as demonstrated in the study by Gagnon et al. [24]. Additionally, our results show that antisocial traits and borderline traits were positively associated with self-reported reactive aggressive behaviors. Our mediation models involving intention attribution processes as mediators could not be confirmed and the unexpected results suggested that HAB and reactive aggression sustain a complex relationship. To better understand the meaning of the relationship between hostile and nonhostile expectancy violation and reactive aggression, more studies are in need to verify how N400 effect among aggressive and nonaggressive participants varies according to various parameters of the ERP task. Nonetheless, this study indicates that electrophysiological measurements can be more sensitive than self-report questionnaires when investigating the nature of cognitive processes associated with reactive aggression. Considering the contribution of socio-cognitive treatments that are offered to aggressive individuals (e.g., [48]), we believe that this study can help to open the way to other empirical studies using ERP tasks to understand the cognitions associated with reactive aggression among cluster B personality disorders.
This study was supervised by Dr. Jean Gagnon, PhD, from the Department of Psychology at the University of Montreal. The study was funded by a portion of the supervisor’s research funds. The project was supported by Dr. Monique Bessette, PhD, psychologist and director of the Victoria Institute for participant recruitment. The author would like to thank Dr. Pierre Jolicoeur, PhD, of the Department of Psychology for his support in the analysis of EEG signals. This study was supported by a research grant to JG and PJ from the Social Sciences and Humanities Research Council (SSHRC) (no 435-2018-0963).
There is no conflict of interest. This project was approved by the Research Ethics Board of Education and Psychology of the University of Montreal. All procedures were consistent with the Énoncé de politique des trois conseils (EPTC-2, 2018).
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. 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However, drug resistance owing to BCR-ABL mutations and overexpression is still an issue. Natural products are chemical compounds or substances produced by living organisms. They are becoming an important research area for cancer drug discovery due to their low toxicity and cost-effectiveness. Several lines of evidence show that many NPs such as alkaloids, flavonoids, terpenoids, polyketides, lignans and saponins inhibit CML cell proliferation and induce apoptosis. NPs not only differentiate CML cells into monocyte/erythroid lineage but also can reverse the multi-drug resistance (MDR) in CML cells. In this chapter, we review the anti-CML activity of various NPs.",book:{id:"5443",slug:"anti-cancer-drugs-nature-synthesis-and-cell",title:"Anti-cancer Drugs",fullTitle:"Anti-cancer Drugs - Nature, Synthesis and Cell"},signatures:"Kalubai Vari Khajapeer and Rajasekaran Baskaran",authors:[{id:"190496",title:"Dr.",name:"Baskaran",middleName:null,surname:"Rajasekaran",slug:"baskaran-rajasekaran",fullName:"Baskaran Rajasekaran"},{id:"190512",title:"Mr.",name:"Kalubai Vari",middleName:null,surname:"Khajapeer",slug:"kalubai-vari-khajapeer",fullName:"Kalubai Vari Khajapeer"}]}],onlineFirstChaptersFilter:{topicId:"1185",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:11,numberOfPublishedChapters:91,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:333,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:11,numberOfPublishedChapters:144,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:126,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:23,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:13,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"August 17th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:11,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:6,paginationItems:[{id:"82526",title:"Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge",doi:"10.5772/intechopen.105627",signatures:"Theocharis Kravaris and George A. 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Kleczyk, Karin Hayes and Rajesh Mehta",slug:"evaluating-similarities-and-differences-between-machine-learning-and-traditional-statistical-modelin",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Machine Learning and Data Mining - Annual Volume 2022",coverURL:"https://cdn.intechopen.com/books/images_new/11422.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:61,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}}]},overviewPagePublishedBooks:{paginationCount:11,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",institutionURL:null,country:{name:"Spain"}}}]},{type:"book",id:"7656",title:"Fuzzy Logic",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7656.jpg",slug:"fuzzy-logic",publishedDate:"February 5th 2020",editedByType:"Edited by",bookSignature:"Constantin Volosencu",hash:"54f092d4ffe0abf5e4172a80025019bc",volumeInSeries:3,fullTitle:"Fuzzy Logic",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"117248",title:"Dr.",name:"Andrew",middleName:null,surname:"Macnab",slug:"andrew-macnab",fullName:"Andrew Macnab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. Gonzalez-Sanchez",slug:"juan-a.-gonzalez-sanchez",fullName:"Juan A. Gonzalez-Sanchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico System",country:{name:"United States of America"}}},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}}]}},subseries:{item:{id:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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