Agents parameters for the scenario.
\r\n\tNot all mixtures of particles and liquids can be considered slurries. A slurry has its character quite different from the carrying liquid (sometimes referred to as the vehicle). A Newtonian liquid has its shear stress directly proportional to its rate of deformation, but this is seldom the case for a slurry. In general, slurries are referred to as non-Newtonian liquids and ways of dealing with them are important threads in this text.
\r\n\r\n\tPipe blockages and pipe wear cause high costs to industry, in both maintenance and loss of production. This waste, and environmental damage which comes with it, can be shown to be reduced by careful application of slurry technology. This book will welcome recent research efforts to understand slurries related to the above-mentioned topics.
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Challenges and issues involved in the development of autonomous systems deployable in dynamic and open environments have led to fields as multiagent systems [1]. It is a discipline that forms a profound interdisciplinary study of fundamentals such as autonomy, agency, negotiation, communication, interaction, and cooperation. The major objective of this field is to develop autonomous systems capable of coexisting and cooperating with people and other systems in the real world. The principal motivation of this effort to develop autonomous systems is related to how people live in a digital and interconnected world, where new challenges and opportunities are arising (e.g., Internet of Things (IoT), smart cities, and big data [2]) as consequence of technology is strongly embedded in our daily life. Thus, we are near to see in our local environment, autonomous systems like smart environments (rural and urban scenarios), humanoid robots, unmanned vehicles (aerial and ground), among other autonomous systems capable of supporting people in their daily life. An important feature of these systems is the autonomy because they must be capable of embodying self‐governance and decision‐making. In this sense, to ensure that the autonomous systems are useful, they should be endowed with the ability to exhibit a smart negotiation to achieve its goals through the cooperation. It is supposed that these properties enable distributed systems to improve their performance.
Negotiation enables multiagent systems to achieve their goals. Although there are several research achievements that concern to strategies and protocols in the field of negotiation nowadays, its implementation in applications in real world scenarios is still far to reach. In a general sense, the multiagent system (MAS) is a paradigm in the computer sciences and related areas where a system of interest is conceived as a set of autonomous entities called agents, as well as its interaction mechanisms. The agent is an autonomous entity with the ability to “sense” the environment through a set of physical or logical sensors and to “interact” or “modify” such an environment by a set of physical or logical actuators, as well. A kind of “intelligence” or “inference” mechanism is also conferred to an agent. Thus, actions to the environment are based on the sensors and the inference machinery.
The MAS approach has proved to be a suitable solution for problems of distributed nature, where the information, the control, the processing, or all of them are not centralized but rather distributed. Thus, a set of problems has been well studied and useful solutions have been obtained. The interaction among agents is generally considered as message passing based on a well‐structured interaction protocols. The content of the message is “information” that may lay in a context called ontology. Figure 1 depicts a general layout of a MAS accordingly Foundation for Intelligent Physical Agents (FIPA) [3, 4].
General layout of a multiagent system.
The Foundation for Intelligent Physical Agents (FIPA) is an IEEE organization promoting the technology and standardization of multiagent systems. FIPA defines a set of specifications in the basic layer for the agent communication, management, and message transportation, as well as specification for the abstract architecture and applications layers. The interaction protocols, communicative acts, and the content of the messages interchanged between the agents are covered by the specifications defined by FIPA. For example, the auction and call for proposal mechanisms among a set of agents are defined as interaction protocols in the FIPA specifications [5].
The automotive industry is moving toward the automated mobility. To achieve the goal of making mobility safer and having an optimized system for moving people in the world, a visionary technology is needed. The approach followed in this chapter is based on MAS applied to the automotive scenarios [6]. The internet of things (IoT) is part of the design, as it is a trending technology for the connected cars and smart cities.
The applications of MAS to automotive applications, like traffic management and load balancing problem, include multiple possibilities, since the agents could represent different actors in the implementation of the solution. For example, in [6], the authors identify five types of agents: pedestrians, vehicles, traffic lights, streets, and parking lots. In this chapter, we consider the use of coordinators, route agents and traffic light cycles (phases), as an extension to the entities involved in the traffic manipulation.
The use of pedestrians as agents suffers from the problem to manage the communication with other agents. For example, other agents like vehicles and traffic lights can be incorporated with electric source and wireless link that helps power sensor systems or technology to help them accomplish that purpose of communication. Though, pedestrians normally do not have the facilities to perform those functions, however, the benefits to consider pedestrians as agents can be substantial due to obvious reasons. An approach to incorporate pedestrians into the system is to use a mobile device, such as the smart phones. By using these devices to identify pedestrians, its sensors may allow to monitor the position of the pedestrians, among other cases.
Other examples of vehicles as agents are reported in [7] and [8]. These works consider the communication between vehicles to coordinate the routes, every vehicle should take to reach its destiny. Within this approach, every vehicle has information that helps them to accomplish their goal, which deals with moving from point A to point B in the shortest time possible. The agents or cars can share or keep this information according to its heuristics which are the rules they use to make any decision that push them closer to complete their goal. Making local individual decisions based on information gathered by themselves or cooperating with other agents help they accomplish a global goal of coordination between vehicles in such a way that every agent can reach their destiny in less time than picking the common fast routes, and sometimes creating bottlenecks on those streets or avenues.
Another popular approach is to focus on traffic lights since they are typically the most common points where traffic loads are introduced into the system. There are several papers focusing on intersections like [9–12]. These works focus on coordination of phases between different intersections. The hypothesis is that creating local solutions in each intersection will produce a better performance overall in the system as a whole.
In [13] the authors propose using a set of Q‐learning iterations to approach the optimal solution of load balancing. They also mentioned several methods to control the traffic lights and intersections using different techniques of the artificial intelligence, such as fuzzy rules, predefined rule‐based systems, and centralized methods. An important feature of this approach is that when controlling traffic lights and intersections, the phases that control traffic in different roads are a key element for the success of the goal of the system. Indeed, the coordination between changes of lights and what streets have preference before others are crucial to get a good traffic flow in the right direction. This feature is considered in this chapter.
Other important example that implements multiple agents as a solution to automotive scenarios is [14]. In this work, the focus is to make buses arrive on time to their stops. The system uses four agents: the bus vehicle, the bus route, the intersections, and the stages. The bus vehicle drives through the route informing the route agent their times, the route agent checks the time between the buses in the same route and if the buses are late or early, it communicates with the main agent, the intersection. The intersections analyze what to do; if the bus is to early then the stages where the bus is not currently transiting have priority to be set in the traffic light. On the contrary, if the bus is too late, the stages where the bus is going should have more probability of appearing in the traffic light. One important aspect to notice is the priority, having a greater priority does not mean that automatically that stage will be next. It only gives to the agent more tools to coordinate with other stages to be the one at the top, which is a goal. The stages need to coordinate and from that process, the next stage in the traffic light is selected. The coordination is selected by multiple factors, the number of buses in the lane, the green time required by the stage, the velocity of the vehicles in the lane, etc.
Based on the specifications defined by FIPA, several implementations provide frameworks for the development of MAS. For example, the JAVA Agent Development (JADE) Framework is a platform for the development of agent‐based applications. JADE is fully compliant with the FIPA specifications and provides a basic class for agent instantiation, communication protocols, ontology implementation, and graphical management tools. Figure 2 provides a reference model for the management of agents within the platform [15].
Agent management reference model in JADE.
When working with automotive traffic, it is difficult to find a real environment for testing. For example, closing a group of intersections and sending vehicles in a predefine pattern, are desired features for the experimentation process in MAS applied to automotive scenarios. Fortunately, there are some computer traffic simulators that, with some sort of work, could be coupled to MAS development frameworks such as JADE, which is one of the target environments of this chapter.
To mention some simulators, consider for example VISSIM, Paramics, Aimsun, Dynameq, MITSIMLab, Simulation Urban Mobility (SUMO), DRACULA, DynaMIT, MEANET, and MATSim [14]. The simulators provide different characteristics that made them ideal for multiple scenarios. However, SUMO [18] seems to be used more often because it is microscopic, free, and easy to use. This chapter will focus on SUMO to simulate required traffic patterns and to interconnect these results with the JADE development framework in the section devoted to the negotiation and coordination applied to traffic load balancing with the use of intuitive ideas and common sense decisions [19].
SUMO stands for Simulation Urban Mobility, and is an open source project to create a portable traffic simulator. This simulator provides a lot of characteristics that made it ideal for the experiments of the last scenario considered in this chapter. First, the interfaces are visual and easy to use, the way to create routes and export them to be used is very much like a city simulation game. In the interface, multiple lanes can be created for a single street, intersections can be configured to set the phases of the traffic light, and the behaviors that vehicles can perform. SUMO provides an API to manipulate the simulation and obtain information about the same, making it ideal to work with other systems like the JADE framework, which was successfully used in the construction of Multiagent systems [20].
SUMO provides the user with tools to easily represent real streets and roads, then insert into the simulation elements like vehicles, which try to behave as their counter parts in the real world. In this way, the simulations are quicker and cheaper than the real‐time events and allow to test the same rules in different environments in a practical way.
Within the SUMO simulator, some designs have been taken to fit with the scenarios required in this chapter. Basically, there are two kinds of simulation processes, macroscopic and microscopic. The macroscopic simulation focuses on the system as a whole. It considers the state of the system at every moment, density, speed, and volume of vehicles. On the other hand, the microscopic simulation focuses on the actions of individual members of the systems. Thus, the approach followed in this chapter is the microscopic simulations, since the actions of the agents can be easily applied to members of the simulation, and within the approach proposed in this chapter, it corresponds to vehicle and infrastructure actions.
The most common simulation scenarios of interaction between agents considered in this chapter are the intersections. Among the two most common intersections where vehicles interact are the crossroad and the T, as depicted in Figure 3.
To the left, a crossroad and to the right, a T form intersection.
A simulation is composed by several elements but mainly defined by two principal configurations, the network configuration and the traffic demand configuration [11, 12]. This configuration is done through xml files. The network configuration contains multiple components starting with the nodes and edges. A node represents a joint point between edges, while edges represent the roads through which traffic will be circulating. A node is simply a representation of a point in the map that only requires three elements, an identifier and a pair (
This work presents application scenarios that take advantage of the MAS in the automotive field. In this work, the cars and infrastructure devices, like semaphores, are considered to be agents. The agents are communicating with each other by using a wireless network, through the usage of well‐structured ACL messages. The agents send messages to know the status of the system, and based on that information, they can make decisions on how to use the available resources, for example, the roads.
In the approach proposed in this chapter, the infrastructure devices have information about routes they are managing. When a vehicle agent requests information about a specific route, the infrastructure device informs the status of the variables of such a route. Once the vehicle has the information, it evaluates which route is the best based on its goals and in some cases, the individual agents consider information about the preferences of other agents to get a suitable global solution.
In this way, a cooperative, distributed multiagent system can be used to improve dynamic routing and traffic management. Distributed artificial intelligence techniques, those applicable to MAS, could be used to solve decision‐making problems to solve city mobility issues with the new technology cars.
The QoS approach considers a method to calculate the best route for a vehicle based on a set of requirements, of the drivers as well of the infrastructure. It was firstly proposed in the telephony and computer network industries to measure the requirements of different users. To quantify the service of the network, several aspects of the service are considered such as the bit rate, mean of errors in the transmissions, throughout, jitter, transmission delays, or availability, among others. In QoS, a weight is assigned to each of the goals of the user, depending on the importance assigned to each aspect of the service they require. Then, a negotiation process is executed between the clients and the service network.
In the context of the automotive field, such interaction helps to find a better route for an agent, or rather the driver it represents. On one hand, as far as information of different routes is shared, the traffic management system (the network) tries to maintain a balanced traffic accordingly to its own goals. On the other hand, the vehicle agents have their own priorities. For example, it could be possible that for a specific type of driver, the distance it will travel is quite important; while for the another one, the number of turns it will make on its travel is the key parameter. Consider, the case of a big cargo truck versus a utilitarian car, for example.
In this approach, the information about traffic is currently used to decide whether to use a certain route or not. However, infrastructure typically does not take part in a system to keep the traffic balanced. It is supposed that the infrastructure could play an important role in the load balancing strategy. In this approach, the infrastructure may consider information about building constructions in certain areas. Thus, an objective of the infrastructure could be to reduce the traffic flow in those areas.
The implementation described in this chapter explains how a distributed system changes the perspective of the traffic in a city, and how important is to see it as part of a smart infrastructure where all agents play an important role. The definition of the objectives of the drivers and the infrastructure play a key role in this approach.
The car agents must define in a quantitative way, the goals and preferences of the drivers they represent. Based on the received information, vehicle agents may calculate the utility as follows:
such that:
Where
The goals that a vehicle agent considers are based on the driver preferences. For example, but not limited, to the following goals:
Minimize Travel Time,
Minimize Travel Distance,
Minimize/Maximize Arterial Streets,
Minimize Number of turns,
Minimize/Maximize Roadway classification changes,
Thus, for example, a cargo truck may confer big weight to the number of turns in the selection of its best route, as follows:
In a similar way, the other types of cars can define the preferences of their drivers in the negotiation of the best route based on the QoS approach. For additional information about the goal‐based QoS.
Figure 4 provides a conceptual diagram of the agent interaction proposed in this chapter for the architecture implementing the QoS approach. In the figure, the car agents “request” information about the “status” of the infrastructure is done by asking to the proper agent. With the information of the nearby lanes, the car agents can decide which one provides the best solution for the goals of the driver they represent. The diagram is supported in the JADE framework [15].
Conceptual diagram of the agent\'s interaction.
In this approach, the implementation considers the following aspects:
The number of car agents in the MAS is arbitrary. That is, it could be from two agents, i.e., one car and one infrastructure or route, to an open number of cars and routes.
The agents of the system are implemented on an embedded board, e.g., the Intel Galileo Board Single hardware Figure 5, based in the JADE framework [15] where human decision of driving agents are tried to be programmed algorithmically [16, 17].
The architecture distinguishes two types of agents: unsteady (e.g., routes) and steady (e.g., cars).
The architecture considers a load balancing algorithm among the car agents and route agents based on QoS.
The architecture considers that the route agents shall send their parameters of interest to all the car agents that request them. The parameters of interest are automatically updated in every 1 min.
The car agents use the information provided by the route agents to calculate its best route.
The distributed load balancing algorithm considers the infrastructure requirements, for example, to keep some route under some peak value of traffic density.
Example of agents’ communication.
For illustrative purposes, Table 1 summarizes the parameters for the experiments in the QoS approach. There are four routes available, each one known by an infrastructure agent. There are two vehicles that would receive information from such routes. According to the MAS, they will be “born” with some attributes that will receive through the arguments, which are described in Table 1.
Arguments | |||||||
---|---|---|---|---|---|---|---|
Agent name | Travel time | Travel distance | Arterial streets | Turns | Roadway changes | Vehicle? | Route number |
Route_1 | 0 | 0 | 0 | 0 | 0 | False | 1 |
Route_2 | 0 | 0 | 0 | 0 | 0 | False | 2 |
Route_3 | 0 | 0 | 0 | 0 | 0 | False | 3 |
Route_4 | 0 | 0 | 0 | 0 | 0 | False | 4 |
Vehicle_1 | 55 | 10 | 5 | 5 | 25 | True | 0 |
Vehicle_2 | 5 | 5 | 80 | 5 | 5 | True | 0 |
Agents parameters for the scenario.
The first group of five arguments (columns) represents the weight (importance) that each agent gives to that goal. The first group of four agents (rows) represents routes. The routes have a zero value on those arguments because they do not have such goals. Rather, their job is to inform those conditions to the vehicle agents.
The sixth argument, i.e.
In the current implementation, the vehicle agents ask for the normalized values of each condition every 10 s. As soon as it receives the values of each route, it calculates the best route based on the weights (driver preferences) given when it was born. The vehicle has a list of routes, in case there is a new one, it will add such route to the array list
The calculations were made analytically to compare against the results computed by the car agents. Table 2 shows the weights that each agent assigns to each goal.
Priorities/goals | Vehicle_1 weights (%) | Vehicle_2 weights (%) |
---|---|---|
Travel time | 55 | 5 |
Travel distance | 10 | 5 |
Arterial streets | 5 | 80 |
Number of turns | 5 | 5 |
Roadway classification changes | 25 | 5 |
Vehicle weights.
Tables 3–6 show the selection of the results of the utility of the routes in the experiments. These results agree with the expected values accordingly with the weights of the agents.
Route_1 | Vehicle_1 utility | Vehicle_2 utility |
---|---|---|
0.11 | 0.0605 | 0.0055 |
0.52 | 0.052 | 0.026 |
0.69 | 0.0345 | 0.552 |
0.88 | 0.044 | 0.044 |
0.45 | 0.1125 | 0.0225 |
0.3035 | 0.65 |
Route_1 utilities.
Route_2 | Vehicle_1 utility | Vehicle_2 utility |
---|---|---|
0.88 | 0.484 | 0.044 |
0.12 | 0.012 | 0.006 |
0.12 | 0.006 | 0.096 |
0.73 | 0.0365 | 0.0365 |
0.99 | 0.2475 | 0.0495 |
0.786 | 0.232 |
Route_2 utilities.
Route_3 | Vehicle_1 utility | Vehicle_2 utility |
---|---|---|
0.23 | 0.1265 | 0.0115 |
0.22 | 0.022 | 0.011 |
0.88 | 0.044 | 0.704 |
0.43 | 0.0215 | 0.0215 |
0.25 | 0.0625 | 0.0125 |
0.2765 | 0.7605 |
Route_3 utilities.
Route_4 | Vehicle_1 utility | Vehicle_2 utility |
---|---|---|
0.44 | 0.242 | 0.022 |
0.43 | 0.043 | 0.0215 |
0.19 | 0.0095 | 0.152 |
0.25 | 0.0125 | 0.0125 |
0.81 | 0.2025 | 0.0405 |
0.5095 | 0.2485 |
Route_4 utilities.
The agents obtain the maximum of all the routes, the result will be the best route. In this case, Route_2 will be the best for Vehicle_1 with a total utility of 0.786 and Route_3 will be the best for Vehicle_2 with a total utility of 0.760. Figure 6 shows a screenshot of the GUI of the Sniffer agent capturing the ACL messages of the interaction between the cars and routes. The main container with the administrative tools of the JADE platform, including the sniffer agent, is running in a laptop. The agents are running on an Intel Galileo development board.
Lane and junctions.
The coordination of agents is a key element in the MAS field. This coordination can be accomplished by using multiple methods. For example, if the agents are competing to obtain a resource, an auction can be a good mechanism.
The approach considered in this section is like the one provided in Ref. [21]. However, instead of focusing only in the buses, we will focus on all the vehicles going through an intersection. The proposed design has three main agents: the lane agent, the junction agent, and the phase agent.
The lane agent represents one of the lanes of an edge. More precisely, let say there is a street section that goes from junction A to B, and that street goes in both directions A to B and B to A. Then, the lane agent 1 will be the lane closer to the right in the section that goes from A to B, while the lane agent 2 is the second closest to the right. A similar approach is applied to the section that goes from B to A. The lane closest to the right will be lane agent 3 and finally, the second closest lane is the lane agent 4. This approach could be followed incrementally. That is, the street can have one or more lanes going in the same direction which means that a street can have multiple lane agents assigned to them, as previously described. Figure 7 provides an illustration of the junctions and lane agents.
Connections and phase representation.
The objective for the lane agent is to keep the lowest number of automobiles in the street at any time. To accomplish that aim, the lane has a priority that is related with the capacity of the street and how close it is to reach its limit. This limit is when the lane reaches the priority 1, which means the street is almost empty. On the other hand, when the lane is at the priority 5, it means the street is at full capacity.
To calculate the lane capacity, multiple parameter are in play, for example, the length of each vehicle (C), the space between each vehicle (S), the number of vehicles (N), the length of the lane (L), and the maximum number a priority can reach (M). The following equation captures these parameters:
This represents an intersection in a real scenario, which is a junction between two or more streets, which also may contains a traffic light in it. The objective of this agent is to manage the traffic light cycles, which for the systems are called the phases, in such a way that the streets can allow traffic to move through the intersection. This agent is responsible for keeping the phases in a stack to inform what the current stage is and rotating the phases according to that stack.
This agent represents a traffic light cycle. The objective for this agent is to negotiate with other phases to go up in the stack from the junction agent. The phase has a priority to know what kind of actions it needs to negotiate with other agents and tries to stay as much time as possible at the top of the stack. To accomplish that aim, every phase agent has several seconds that can be used to negotiate with other agents.
A phase contains two arrays of elements, one with the time of the cycle and the other with a string representing the behavior that vehicles can have during that phase. These elements are represented as follows:
[31, 6]
[“GGGgrrrrGGGgrrrr”, “yyygrrrryyygrrrr”]
The array of string represents the behavior of the traffic lights during the cycle. For example, starting from the first lane at the top left in Figure 6, the vehicles can turn right in first lane and go straight, in the second lane. The same vehicles can go straight and turn right with precaution (this represents the lowercase g in the above character string). All the red lines in Figure 7 represent connections that vehicles cannot use during this phase.
This process starts when the lane agent calculates its priority. This may happen every certain quantum of time depending on the configuration of the system. The lane agent calculates its priority by checking the lane capacity with the formula seen in section describing the lane agent. That calculation returns the priority level the lane should have and if it is different from the current priority, then it sends a message to the junction agent notifying the priority change. A diagram representing this interaction by means of ACL messages is depicted in Figure 8. The junction agent receives the message and notifies the affected phases to calculate its priority. The phase agent will use the largest priority of the lanes that require the use of such a phase.
Diagram of negotiation process.
This simple system of three agents allows us to experiment with different methods of coordination. The proposed implementation method is to create a trade system where one phase exchanges time for the possibility of the get up in the queue of priorities. Each phase calculates the priority as per the total number of vehicles each lane supports and the current number in the lane at a specific stage. With that information, the lane can setup a priority from one to five, where five means it is critical for that phase to be next one in the cycle.
One important aspect to consider is the fairness of the system. That is, some traffic light phases will have more seconds to negotiate than others. The rule of the tomb is a strategy for a system that can be beneficial for the phases with lower number of seconds and the phases with more seconds to spend. For this reason, instead of using the second as a raw currency, this work proposes to use the concept of a unit.
A unit may represent several seconds. However, the units may vary depending on the phase. That is, the unit will be the expected time of the phase divided by five. In this case, five is the number of columns we want our agents to work with. Thus, the expected value will be in any case that corresponds to the middle column. Accordingly, in the negotiations, any phase will have the unit value of two columns to the left to spent, and the unit value of the two columns to the right to gain. Table 7 shows the unit values of the phase agents that it uses in the offering stage of the negotiate process.
Time (s) | |||||
---|---|---|---|---|---|
Priority | −2 | −1 | 0 | +1 | +2 |
1 | 0 | 0 | 0 | 0 | 1 |
2 | 0 | 0 | 0 | 1 | 1 |
3 | 0 | 0 | 1 | 1 | 2 |
4 | 0 | 1 | 1 | 2 | 3 |
5 | 1 | 1 | 2 | 3 | 4 |
Offer table.
The offer table contains the priority number as row and the number of units to gain, or lose, as columns. If the phase is at a certain priority and at a certain column, then with a simple lookup process, it is possible to determine the value that one phase agent should offer to take in the queue of another phase.
The accept table works in a similar fashion as the offer table. However, in this case when the phase receives an offer for its position in the queue, then it should check the accept table to decide whether to accept or reject the offer. The minimum value that the phase agent should accept is at the column and row of this table. Notice that there are some infinite symbols in the entries of the table. It means that for those situations, it does not matter the number of units offered, the phase will reject any offer, since that phase agent is at a situation where it is required to get into the junction cycle as soon as possible. Table 8 shows the unit values of the phase agents that it uses in the accepting stage of the negotiate process.
Time (s) | |||||
---|---|---|---|---|---|
Priority | −2 | −1 | 0 | +1 | +2 |
1 | 2 | 1 | 1 | 1 | 8 |
2 | 3 | 2 | 1 | 1 | 8 |
3 | 4 | 3 | 2 | 1 | 8 |
4 | 4 | 3 | 2 | 8 | 8 |
5 | 8 | 8 | 8 | 8 | 8 |
Accept table.
For example, if the phase agent has 15 s of green time, then the unit value is 3 s (15 divided by 5).In this mechanism, the phase is not allowed to get lower than two units (6 s) and not bigger than two units (6 s again). In this case, the negotiation units are in Table 9. Thus, if the phase is in priority 5, for instance, and currently has 15 s, then it will offer two units to the phase at the top in the queue. If that phase does not accept the offer, then the negotiation ends. However, if the phase at the top of the queue accepts, then the offering phase will take the place of the accepting phase in the queue. Accordingly, the offering phase will lower two units of time, with 9 s of green light, but up in the queue. The phase that accepted the offer will increase its time by two units, i.e., with 21 s of green light, but lower in the queue.
Time (s) | |||||
---|---|---|---|---|---|
Priority | 9 s | 12 s | 15 s | 18 s | 21 s |
1 | 0 | 0 | 0 | 0 | 1 |
2 | 0 | 0 | 0 | 1 | 1 |
3 | 0 | 0 | 1 | 1 | 2 |
4 | 0 | 1 | 1 | 2 | 3 |
5 | 1 | 1 | 2 | 3 | 4 |
Offer table for green time of 15 s.
To test the negotiation strategy described in the previous subsection, the first step is to simulate the basic scenario when coordination may occur.
Figure 10 shows a four‐road intersection in the SUMO simulator, which is used to simulate the negotiation process. The implementation of the four roads needs four phases to be fully functional. To represent the states of the phases, four cardinal points, North, South, East, and West, are considered Figure 9.
Four roads intersection.
Simulation values.
In phase 1, the cars can move in both directions, North‐South and South‐North. In phase 2, the cars go from North‐East and South‐West. In phase 3, the cars are allowed to move from East‐West and West‐East. Finally, in phase 4, the vehicles can go from West‐North and East‐South. With these four phases, all vehicles can move from one direction to all other different locations they require to fully travel the intersection, even considering right turns allowed at any moment with precaution.
To implement the negotiation system, the SUMO simulator is interfaced with the JADE development framework. JADE requires a JVM to be executed. To execute the runtime environment, a simple command can be used to accomplish that goal:
java ‐cp <classpath> jade.Boot
where the classpath is the place where the jade.jar file lives in the system. However, it will create and empty the platform and the container, solely with the basic structure and no other than the default agents.
JADE provides a set of classes in the JAVA language that can be used to create the agents that implement the different pieces of the negotiation system previously described. The most important class for this purpose is the agent. The agents have a unique identifier, denoted as AID, which is used to uniquely determine a specific agent. The AID can be obtained using the method getAID. The identifiers in JADE are using a convention like an email address, i.e., <nickname>@<platform‐name>; however, it is only a name and should be considered like that.
All the agents in JADE should extend the agent class. This inherits a set of methods to work in JADE framework. The two methods that require more attention are the setup and takedown.
The setup method is the place where the initialization of the agent occurs. It is used instead of the constructor method of a JAVA class. The agent class provides this different method, because it is safer to use and it can warrant that the system is up and running at that moment. This is something that cannot be possible with the traditional constructor. In the setup method, the agent parameters can be read to populate attributes by using the getArguments method.
The takedown method is invoked after the agent is terminated and this can be done by using the doDelete method in any place of the agent. The purpose of this method is to clean up any necessary objects or operations.
The communication between agents is the core functionality that needs to be implemented in JADE. To accomplish this task, JADE provides a behavior class. An agent can have different behaviors and all of them should be included using the addBehavior method. The behaviors are the mechanisms to implement the actions and methods of the agent.
There are a complete set of behaviors in JADE for different objectives. One shot behaviors, cyclic behaviors, generic behaviors, wake behaviors, and ticker behaviors, to mention some. One shot behaviors are implemented using the OneShotBehaviour class, this is meant to be executed only one time and after that delete the behavior from the agent. The cyclic behaviors use CyclicBehaviour class and they return false in the done method all the time, so this behavior repeats and keeps executing. The generic behaviors correspond to the Behaviour class, this is a vanilla class that can be extended and used as the user requires especially with communicative acts that requires several messages between agents. The waker behaviors relate to the WakerBehaviour class and will be executed after a certain condition is reached, commonly a time set like an alarm. Finally, the ticker behaviors use TickerBehaviour class and are repeated every certain interval of time.
The communication between the JADE agents with the simulator SUMO is required. For example, the lane agent requires to know the number of vehicles in the simulator lane, and the junction agent needs to modify the traffic light in the simulation according to the queue. To establish a communication between the two frameworks, this chapter uses the traSMAPI middleware.
TraSMAPI is a project from the University of Porto, which is an API to communicate with microscopic traffic simulator (like SUMO). This allows to get information and manipulate the different elements of the simulation like vehicles, traffic light, etc. One of the most important aspects is that it is written in JAVA, the same language as JADE allowing to easily integrate the multiagent environment with the Simulation [12].
The way TraSMAPI manipulates the simulation is through an interface created in SUMO, which is called TraCI (Traffic Control Interface) [22]. The interface can be accessed by enabling a remote port in SUMO. By using the command line, this can be done by adding the parameter—remote‐port [portNumber] or in the sumocfg gile adding the traci_server section like this:
<traci_server>
<remote‐port value=”portNumber”/>
</traci_server>
With this, a series of bytes can be sent through that port to the SUMO simulation, the bytes correspond to the values of the instructions required to interact, first byte reserved for the command and the following, for parameters required to get data or modify any characteristics of the running simulation.
To test the implementation, two types of traffic light scenarios will be used. One with the traditional static, or fix, times for the lights in the semaphores, and one with dynamic phases negotiation that use agents.
The vehicles will be generated using a fix number per hour. Two combinations will be used to simulate more traffic flowing from north‐south lines. In north to south lines, the flow will be a complete load of vehicles and in east to west lines, 50% of the full load. The full load will have values of 500, 750, 1000, and 1250 vehicles per hour. The details of the intersection are depicted in Figure 11. For the traffic, light phases, we will be using four phases as described in Table 10.
Static versus dynamic results.
Phase | Direction | Green time (s) | Yellow time (s) |
---|---|---|---|
1 | NS‐SN | 15 | 4 |
2 | NE‐SW | 6 | 4 |
3 | WE‐EW | 15 | 4 |
4 | WN‐ES | 6 | 4 |
Configuration of the phase.
The results using static (s) traffic lights and using dynamic (d) traffic lights are shown in Figure 11.
The result shows a similar behavior in the static traffic light and the dynamic ones using the negotiation mechanism with agents. At 1250 veh/h, both systems have difficulties to manage the vehicles load. Further experimentation is encouraged with different phases and times in the traffic light. It is clear that other coordination tables may be constructed to improve the balancing of vehicles under different load conditions and street configurations.
This chapter proposed the application of the MAS technology and concepts to the solution of problems in the automotive field. The MAS has provided suitable solution to problems of distributed nature, such as those present in the automotive field. The vehicles (both, cargo and utilitarian), the infrastructure (lane, semaphores, etc.), and even the pedestrian are suitable to be modeled as agents. This simplifies the modeling and simulation, and thus the construction of solution to problems of smart traffic systems. The communication mechanisms of the MAS are well suited to implement with simplicity, complex interaction protocols for the car‐2‐X communication. In particular, this chapter proposed the application of two mechanisms of the MAS to the automotive field. One the one hand, it proposed the utilization of QoS mechanism to the coordination between the cars and the infrastructure. On the other hand, it proposed the utilization of an auction‐based mechanism for the negotiation between faces in lane intersections.
By using the set of tools and techniques described in this chapter, solutions to intelligent traffic systems may be approached from the MAS field. The experimentation with the traffic simulators and the framework for the agent implementation seem to be a new way to design solutions that may be quite complex to implement with other approaches.
Legumes are plants that belong to the family,
Pesticides are chemical substances that are manufactured for the control of pests in domestic, agricultural and industrial settings, among others [3]. They are classified as herbicides, fungicides, bactericides and insecticides mainly for agricultural applications [4]. Insecticides comprise carbamates, organochlorines, organophosphates, neonicotinoids, pyrethroids; herbicides include benzothiazolyl urea, carbamic and sulfanilic acids, isoxazolyl urea, phenylpyrazole and pyridinium; while fungicides contain carboxamides, carbamates, dithiocarbamates, etc. [5, 6].
Heavy metals (HMs) are a group of metals that possess high atomic weights and densities beyond 5 g/cm3 [7]. They are usually derived from agricultural, mining and industrial activities, as well as effluents [8]. It has been observed that HMs for instance, lead (Pb), cadmium (Cd), zinc (Zn), nickel (Ni) and copper (Cu), build up in the soil and in plant uptake structures and evoke injurious effects on the environment and the health status of human beings [7, 9, 10]. Besides, HMs have the propensity to be toxic when the populace are exposed to them or when they are consumed in proportions beyond the acceptable daily limits [11].
Different food stuffs such as legumes, cereals, fruits, vegetables, etc. have been contaminated with pesticides and HMs in various parts of the world. It is noteworthy that the common environmental pollutants, pesticides and HMs, increasingly cause numerous health hazards to the populace because of their permeation and upsurge in the food chain, as well as their persistence in the bionetwork [12].
In 2020, [13] identified the organophosphate pesticides (malathion, parathion, ethion and carbophenothion) in cowpea (an African legume) from Gwagwalada market in Abuja, Nigeria, through the use of Gas Chromatography–Mass Spectrometry. The levels of the pesticides found in the cowpea samples exceeded the maximum residue limits set by the European Union and the Agency for Toxic Substances and Disease Registry. Additionally, [14] confirmed the contamination of cowpea by residues of organochlorine (endosulfan and lindane), and organophosphorus (malathion) pesticides in high levels in Northern Cameroun.
Furthermore, heavy metals including nickel, cadmium, manganese and cobalt were detected in cowpea by [15]. In the research, the concentrations of nickel, cobalt and manganese discovered in the legumes were below the acceptable limits by FAO/WHO, while the cadmium concentration was beyond the FAO/WHO limit permitted. However, in a study conducted in Saudi Arabian markets, it was discovered that kidney beans and haricots contained high levels of Mn, while peas had elevated levels of zinc beyond the standard permissible levels [16]. Hence, regular monitoring of food stuffs for HM content was advocated.
It has been observed that fertilizers and pesticides are responsible for the increased level of soil pollution by non-essential micronutrients such as arsenic, cadmium, mercury, nickel and lead [17]. These non-essential micronutrients in the soil are conveyed through various plants as they accumulate in the edible portions [18]. Subsequently, the general population and animals may be exposed to the residues of these environmental contaminants (pesticides and HMs) when they consume plants that have been polluted with them.
The aim of this review chapter is to underscore the acute and chronic health risks that human beings may be exposed to during their life time as a result of the consumption of legumes contaminated with pesticides and HMs. In addition, the mechanisms through which pesticides and HMs engender different undesirable health outcomes in biological systems were highlighted.
It is envisaged that the information in this review chapter will stimulate and enhance concerted efforts towards the regular monitoring of legumes and other foodstuffs for pollutants in order to attain food safety.
When human beings consume pesticides in legumes and other food sources through the dietary route, the pesticides may cause acute and chronic health risks through diverse mechanisms. For instance, organophosphate pesticides (chlorpyrifos, diazinon, dichlorvos, fenitrothion, malathion, parathion, etc.) bring about deleterious health effects by inhibiting the function of acetylcholinesterase, reducing the secretion of insulin, and perturbation of the metabolism of nutrients in living systems [19, 20, 21].
Moreover, organophosphate pesticides stimulate the release of reactive oxygen species and this phenomenon might cause oxidative stress [22, 23]. Oxidative stress refers to a disparity between the levels of prooxidants and antioxidants thereby culminating in damage to vital molecules including DNA, RNA, lipids and proteins in biological systems [24]. Oxidative stress has been identified as an important mechanism through which different kinds of pesticides cause biological injuries to human beings and animals.
The organochlorine pesticides are chlorinated hydrocarbons that persist in the environment and they include methoxychlor, dieldrin, chlordane, mirex and lindane, among others [25]. They cause the stimulation of the nervous system through the perturbation of action potentials, thereby leading to paralysis and death [26]. Dietary exposure brings about the bioaccumulation of organochlorine pesticides in the body, and this may terminate in derangements in human health [27].
Carbamates such as methiocarb, carbaryl, aldicarb, propoxur and carbofuran reversibly inhibit acetylcholinesterase in mammals and humans [19]. This group of pesticides are capable of causing apoptosis and necrotic changes in the cells of the immune system [28]. Consequently, the immune system becomes compromised and affected individuals become susceptible to various diseases when they are exposed to these pollutants in food and other sources.
Pyrethroids (e.g. allethrin, permethrin, cypermethrin and deltamethrin) are neurotoxicants and they disrupt the muscular structures and modify voltage-dependent sodium channels in the body [29]. They exhibit low acute toxicity to mammals and avian species. Conversely, at low levels, pyrethroids elicit acute toxicity to diverse aquatic species and arthropods [30].
Acute health risks may become visible instantaneously or within 24 hours sequel to exposure to a pesticide [31]. Pesticides can have access to human bodies through the dermal [32], ocular [33], oral and respiratory [34] routes. According to [35], the exposure of the populace to pesticide residues through the oral route is about five orders of enormity compared to the other routes. It has been affirmed that pesticides can produce acute health disorders such as hypersensitivity, asthma and mortality in people [8, 36, 37, 38].
It has been reported that chronic detrimental health risks do not manifest in human beings even within a day subsequent to pesticide exposure [31]. Chronic health risks evoked by exposure to pesticides in food such as legumes include congenital disabilities and decreased birth weight [36, 37]. Additionally, many organophosphate insecticides bring about declines in sperm counts, viability, density and motility; inhibition of spermatogenesis, reductions in testes weights, and sperm DNA damage in males [38]. Other chronic adverse effects of pesticides entail hindering the activities of hormones, their time of release, or mimicking these hormones, thereby culminating in reduced fertility and deformities in the male and female reproductive tracts [39]. These harmful effects may result in declines in the population of human beings affected. Also, pesticides perturb the immune system function and elicit carcinogenicity [40, 41].
Some investigators have asserted that organochlorine pesticides and their active metabolites are linked to neurological aberrations, cancers, hypertension, cardiovascular and dermatological disorders in humans [42, 43, 44]. Besides, the exposure of the populace to organophosphate, organochlorine and carbamate pesticides may evoke chronic neurological disorders such as Alzheimer’s and Parkinson’s disease [45]. According to [46], pesticides affect neuronal function negatively by hyperphosphorylation and the disruption of microtubules thereby ultimately causing Alzheimer’s disease.
Heavy metals can accumulate in the bones or adipose tissues of human beings through dietary intake (for example, the consumption of legumes and other crops), thereby leading to the diminution of critical nutrients and undermined immune defenses [8].
Lead induces oxidative stress in tissues and it is plausible that it brings about deleterious effects in humans that consume legumes and other plants contaminated with it through this mechanism [47]. Also, lead, a ubiquitous lethal HM, may activate the development of tumors through the production of reactive oxygen species, as well as the promotion of damage to DNA and its repair mechanisms in living organisms [48].
Furthermore, it has been documented that arsenic sets off neoplasms through the alteration of the genome of human beings, perturbation of DNA and induction of oxidative injury [49, 50]. Another common HM, mercury, generates carcinogenesis through oxidative damage and interference with the structure, mending and preservation of DNA in living organisms [51].
These myriads of mechanisms may be responsible for the untoward acute and chronic health hazards reported in human beings who have consumed foodstuffs such as legumes, cereals, fruits, vegetables, etc. that have been tainted with HMs over a period of time.
Heavy metal contamination is a health menace to both adults and children [52]. When HMs build up in human tissues internally, for instance, when legumes polluted with HMs are eaten for some time, they may harm the central nervous system, and produce seizures, headache and coma [8].
There is scientific evidence that lead may engender neurotoxicity, nephrotoxicity and impaired haeme synthesis [53]. When children have contact with cadmium through a range of routes, for example the oral route (by feeding on legumes such as chickpeas, cowpea, etc.), they become exceedingly prone to lead intoxication and permanent neurological abnormalities may ensue [54]. Other acute impacts of lead exposure documented in children are inattention, hyperactivity, increased dullness, irritability, headache, convulsion, coma and death [55, 56].
Copper is important for the normal functioning of the brain, but it can be noxious if the cellular concentration surpasses the metabolic requirement [57]. Elevated levels of copper can cause dysfunctions in the working memory of individuals [58], while short-term exposure to copper through dietary and other routes have been linked with stomach pain, haematemesis, melena, jaundice, anorexia and vomiting [59]. Moreover, rhabdomyolysis, cardiac and renal failure, hepatic necrosis, haemolysis, methemoglobinemia, encephalopathy, as well as mortality can occur in severe copper toxicity [60]. These harmful acute impacts of high copper levels may be attributed to its induction of oxidative stress, DNA damage and lessening of cell proliferation [61].
Moreover, the distortion of the concentrations of high-density lipoproteins and impairment of the immune system have been ascribed to excessive concentrations of zinc in the bodies of human beings [62]. People are usually exposed to zinc through the ingestion of contaminated food, for instance, legumes, pulses, among others [63].
Chronic health effects like diabetes, neurodegenerative diseases, renal damage, bone disorders and tumors of the breast, prostate and lungs have been elicited by Cd in people [64, 65, 66].
There are existing reports that indicate that the long-term effects of lead exposure through food and other routes are manifested as anemia, abdominal colic, miscarriages, male infertility, birth defects, renal diseases and behavioral dysfunctions in children [67, 68]. It has also been observed that lead may elicit decreased circulating maternal thyroid hormone thereby influencing growth patterns adversely [67, 68].
It is known that tremendous amounts of arsenic in the soil, food crops (including legumes, cereals, vegetables, etc) and groundwater can stimulate cancer and dermal aberrations, as well as disorders in the heart, stomach, intestines, liver, kidneys and brain, among others [62, 69, 70, 71, 72, 73].
Furthermore, excessive copper consumption can activate hepatic damage and other gastric-related problems in people [62, 74, 75]. Wilson’s disease, a form of chronic Cu toxicity in human beings, is characterized by alterations in mental states, motor disorders, dysphagia, incoordination, haemolytic anemia, renal and hepatic dysfunctions [72]. It has been shown that human beings become susceptible to chronic copper poisoning when they ingest food items (for instance, legumes, pulses, cereals, etc.) contaminated with copper probably through polluted irrigation sources [76]. Additionally, persistent bronchitis, emphysema, pulmonary disorders and fibrosis were reported in people following long-term exposure to nickel through dietary sources [77].
This review chapter highlighted the mechanisms through which the widespread pollutants, pesticides and HMs, evoke acute and chronic health disorders in human beings. Pesticides are usually applied in agricultural settings for the control of vectors and the enhancement of crop yield, while most HMs are utilized for industrial purposes.
Human beings may be susceptible to various health risks engendered by pesticides and HMs through the consumption of food crops, for example, legumes, contaminated with them. This may eventually pose serious threats to the wellbeing and survival of the populace except if regular monitoring of food items for the residues of these ubiquitous contaminants are conducted globally by the appropriate agencies.
It is envisioned that the information contained in this review chapter will provide a springboard for scientists, researchers and agriculturists, among others, to create innovative techniques for the minimization of human exposures to the residues of pesticides and HMs in order to forestall their pernicious effects in the general population.
The author is grateful to the members of staff of the Faculty of Veterinary Medicine, University of Abuja, Federal Capital Territory, Nigeria, for their support.
The author declares that there is no conflict of interest.
IntechOpen - where academia and industry create content with global impact
",metaTitle:"Team",metaDescription:"Advancing discovery in Open Access for the scientists by the scientist",metaKeywords:null,canonicalURL:"page/team",contentRaw:'[{"type":"htmlEditorComponent","content":"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\\n\\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\\n\\nBut, one thing we have in common is -- we are all scientists at heart!
\\n\\nSara Uhac, COO
\\n\\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\\n\\nAdrian Assad De Marco
\\n\\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\\n\\nDr Alex Lazinica
\\n\\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
\\n"}]'},components:[{type:"htmlEditorComponent",content:"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\n\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\n\nBut, one thing we have in common is -- we are all scientists at heart!
\n\nSara Uhac, COO
\n\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\n\nAdrian Assad De Marco
\n\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\n\nDr Alex Lazinica
\n\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
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The forearc basin type and tectonic history are characteristic for each forearc territory, reflecting the differences in plate tectonic processes. Several major unconformity events seem to be synchronous for a forearc territory or whole forearc territories around Japan, suggesting that these events originated from more or less wider scale plate tectonic events. In the NE Japan forearc territory, the Oligocene unconformity can be the largest events, which transformed the forearc basin styles from the trench slope break-uplifted, fluvial system-dominated type to the tensional, deeper marine sloped type. In the SW Japan and Ryukyu forearc territories, the latest Oligocene to Middle Miocene gap was the transformation phase from the Palaeogene Shimanto-type forearc and accretionary complex, to the Neogene compressive, sloped to ridged forearc basins, developments of which have been interrupted by several unconformity events possibly related to changes in plate tectonic condition. 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Multidisciplinary Approach on Active Plate Margins"},signatures:"Tetsuya Sakai, Mototaka Saneyoshi, Yoshihiro Sawada, Masato\nNakatsukasa, Yutaka Kunimtatsu and Emma Mbua",authors:[{id:"167627",title:"Dr.",name:"Tetsuya",middleName:null,surname:"Sakai",slug:"tetsuya-sakai",fullName:"Tetsuya Sakai"},{id:"168875",title:"Prof.",name:"Mototaka",middleName:null,surname:"Saneyoshi",slug:"mototaka-saneyoshi",fullName:"Mototaka Saneyoshi"},{id:"168876",title:"Prof.",name:"Yoshihiro",middleName:null,surname:"Sawada",slug:"yoshihiro-sawada",fullName:"Yoshihiro Sawada"},{id:"168877",title:"Prof.",name:"Masato",middleName:null,surname:"Nakatsukasa",slug:"masato-nakatsukasa",fullName:"Masato Nakatsukasa"},{id:"168878",title:"Prof.",name:"Yutaka",middleName:null,surname:"Kunimatsu",slug:"yutaka-kunimatsu",fullName:"Yutaka Kunimatsu"},{id:"168879",title:"Dr.",name:"Emma",middleName:null,surname:"Mbua",slug:"emma-mbua",fullName:"Emma Mbua"}]},{id:"45402",title:"Foreland Basins at the Miocene Arc-Arc Junction, Central Hokkaido, Northern Japan",slug:"foreland-basins-at-the-miocene-arc-arc-junction-central-hokkaido-northern-japan",totalDownloads:2846,totalCrossrefCites:2,totalDimensionsCites:4,abstract:null,book:{id:"3500",slug:"mechanism-of-sedimentary-basin-formation-multidisciplinary-approach-on-active-plate-margins",title:"Mechanism of Sedimentary Basin Formation",fullTitle:"Mechanism of Sedimentary Basin Formation - 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From\r\n1964 to 1974, he worked as Assistant in Biochemistry at the School of MedicineUniversidad Nacional de La Plata, Argentina. From 1974 to 1976, he was a Fellowof the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor oBiochemistry at the Universidad Nacional de La Plata, Argentina. He is Member ofthe National Research Council (CONICET), Argentina, and Argentine Society foBiochemistry and Molecular Biology (SAIB). His laboratory has been interested for manyears in the lipid peroxidation of biological membranes from various tissues and different species. Professor Catalá has directed twelve doctoral theses, publishedover 100 papers in peer reviewed journals, several chapters in books andtwelve edited books. Angel Catalá received awards at the 40th InternationaConference Biochemistry of Lipids 1999: Dijon (France). 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Serves as a peer reviewer for biomedical journals. 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From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,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. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,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",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:null,institution:null},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"417317",title:"Mrs.",name:"Chiedza",middleName:null,surname:"Elvina Mashiri",slug:"chiedza-elvina-mashiri",fullName:"Chiedza Elvina Mashiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"352140",title:"Dr.",name:"Edina",middleName:null,surname:"Chandiwana",slug:"edina-chandiwana",fullName:"Edina Chandiwana",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"342259",title:"B.Sc.",name:"Leonard",middleName:null,surname:"Mushunje",slug:"leonard-mushunje",fullName:"Leonard Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"347042",title:"Mr.",name:"Maxwell",middleName:null,surname:"Mashasha",slug:"maxwell-mashasha",fullName:"Maxwell Mashasha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"2941",title:"Dr.",name:"Alberto J.",middleName:"Jorge",surname:"Rosales-Silva",slug:"alberto-j.-rosales-silva",fullName:"Alberto J. Rosales-Silva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"437913",title:"Dr.",name:"Guillermo",middleName:null,surname:"Urriolagoitia-Sosa",slug:"guillermo-urriolagoitia-sosa",fullName:"Guillermo Urriolagoitia-Sosa",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"435126",title:"Prof.",name:"Joaquim",middleName:null,surname:"José de Castro Ferreira",slug:"joaquim-jose-de-castro-ferreira",fullName:"Joaquim José de Castro Ferreira",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"437899",title:"MSc.",name:"Miguel Angel",middleName:null,surname:"Ángel Castillo-Martínez",slug:"miguel-angel-angel-castillo-martinez",fullName:"Miguel Angel Ángel Castillo-Martínez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"289955",title:"Dr.",name:"Raja",middleName:null,surname:"Kishor Duggirala",slug:"raja-kishor-duggirala",fullName:"Raja Kishor Duggirala",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jawaharlal Nehru Technological University, Hyderabad",country:{name:"India"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11423,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. 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