**3.1 Component based system design**

12 Will-be-set-by-IN-TECH

In Section 2.7 we have tried to evaluate effect of using key and speech inputs especially

As we discussed in Section 2.5, vocal communication increases the transparency of the human-robot communication to the audience. We could see from the experiment results that the enjoyment and the fulfillment of the demonstration from audience perspective was not let

One of the unexpected effect of using speech is, because it is very intuitive, it decreases the burden for remembering the commands. Error rate of key based commands is unexpectedly

Despite supportive evidence for speech input, about half of the demonstrators answered that they prefer using key input. In the question sheet, subject can leave comments. Subjects who are supportive to the speech input commented that they actually enjoyed reacting to mistakes made by speech recognition. This comment can be explained that by increasing the transparency of communication channel between human-robot demonstrators, the subject can observe what is happening in the situation (human demonstrator said the right thing, robot mistakes) and feel the mistaken response of the robot funny. On the other hand, subjects who are supportive to the key input commented that they want to demonstrate the exercise in a

We are currently preparing to do an evaluation which is more focused on measuring these effects and searching for a way to realize an appropriate interface for both people who prefers

As we have looked and discussed in this section, humanoid robot has different character than the other artifacts. *It has human shape* which can attract human to join in the activity. This character also gives some effect to *gain expectation to use natural communication method (voice)* as the human do. *It has physical body and exist in same world* which can give effect also to the observers. These characters are especially useful for applications such as exercise

However, this character sometimes gives negative effect to the usefulness. Because the human gain too much expectation to the robot to use natural communication method, human tends to use colloquial expression towered the robot, which is difficult for the robot to understand. In Section 2.7, we have seen the command acceptance rate using voice recognition evaluated by elderly users. In this experiment the command acceptance rate is low, not because voice recognition rate is low, but mostly because conversation patterns programmed to the dialog manager was not enough to understand the all varieties of colloquial expressions given by the

Because the robot has physical body and exist in physical world, the voice recognition system

Although for ideal benefits of using humanoid robots, above practical problems are need to

We are not only developing the applications for humanoid robots, but also developing a support tools for assist development of the communication functions for humanoid robots.

of the robot have to work under noisy condition of the real environment.

be solved beforehand to enhance the usefulness of the robots.

From the next section, we will introduce our development tools.

high, despite it's preciseness. This may also support the use of speech input.

focused to human-robot collaborative demonstration setup.

down by the imprecision in speech recognition.

**2.10 Summary**

more precise manner.

enjoyment or precision.

**2.11 Left problems**

demonstration.

users.

At present, we are developing a set of software called Open Source Software Suite for Human Robot Interaction (OpenHRI). Using OpenHRI, we aim to solve the above problems and enable the development of communication functions for robots. For this purpose, we employ the following approach.

**Introduce a uniform component model:** We construct our set of software on RT-Middleware, an object management group (OMG)-compliant robot technology middleware specification (Ando et al, 2005). The RT-Middleware specification can be used to connect all the components without requiring implementation issues to be taken into account. Further, because it is a standard architecture for building robotic systems, individual components developed in different institutes can easily be connected.

**Provide the required functions in a reconfigurable manner:** We implement various functions from audio signal processing to dialog management in a uniform and reconfigurable manner. The developer can develop the entire system at a comparatively less development cost. In addition, the system can easily be adapted to different environments for realizing accurate recognition.

Figure 11 illustrates the overall architecture of the components provided in OpenHRI. The software covers all the functions for the development of the communication system and also incorporates an interface for establishing connections with other components that can provide multi-modal information.

The component architecture of our software is based on RT-Middleware. RT-Middleware is a middleware architecture for robotic applications that has been standardized by the OMG.

In RT-Middleware, each function of the robot is implemented as a "node." An application system can be developed by selecting the required components and connecting them to each other (the connections are called "links"). Figure 12 shows the "RT-SystemEditor" development tool to edit the links between the components.

In the specification of RT-Middleware, a "data port" is defined as a connection point of a link that realizes the transmission of a data stream. A "service port" is defined as an entry point of

**3.1.3 Voice recognition and synthesis components**

used to give instructions that carry the same meaning.

humans do not say "walk N steps" to a robot on wheels.

"juliustographviz" tool.

cause this diversity.

synthesized voice as output.

**3.2 Incremental script development**

above (described in Section 3.2.4).

assume the following form:

*s*0 is the initial state.

**3.2.1 Formalization of state-transition model**

the ability to output the recognized result as an extensible XML stream.

with this, it is necessary for them to understand a variety of commands.

can accumulate the number of commands that the system can dealt with.

The voice recognition component is based on Julius (Kawahara et al, 2000) in combination with English and Japanese acoustic models. Our component is designed to possess the following features: (a) the ability to read grammar format in W3C-SRGS XML form and (b)

Speech Communication with Humanoids: How People React and How We Can Build the System 179

Figure 13 shows an example of voice recognition grammar specified using W3C-SRGS format. Voice recognition grammar can be visualized by combination of "srgstojulius" and

The voice synthesis component is based on Festival for English and Open\_JTalk for Japanese. The component accepts plain text as input and provides a data stream in the form of a

Commands given by the human to the robot are diverse. The following are the factors that

**The nature of language:** Human language is ambiguous, and different expressions can be

**Tasks:** Robots working in a life environment have to accept a variety of tasks. In order to cope

**Ability of the robot itself:** The diversity is also caused by the ability of the robot itself. A command from a human becomes effective due to the functions of the robot. For example,

Incremental development of the state-transition model has previously been conducted using the "extension-by-connection" method (described in the next section). In this section, we propose an "extension-by-unification" method that can cope with the diversities mentioned

A state-transition model is a modeling method in which the input and output of the system

where *I* represents the input alphabet, *O* represents the output alphabet, *S* represents the internal states, *γ* represents the state transition function, *λ* represents the output function and

The state transition function *γ* is defined in association with the state to the input.

The output function *λ* is defined in association with the state to the input.

*A* :=< *I*, *S*,*O*, *γ*, *λ*,*s*0 > (1)

*γ* : *S* × *I* → *S* (2)

*λ* : *S* × *I* → *O* (3)

The language comprehension system of the robot must be able to deal these diversities. In the script-based development approach, diversity has been dealt with by stacking a newly developed script onto the existing scripts. By accumulating a number of scripts, the developer

Fig. 12. Screenshot of RT-SystemEditor.

each function call for a service function. "Configuration parameters" are defined to configure each component.

OpenRTM-aist is an implementation of the RT-Component specification that supports C++, JAVA, and Python languages. It runs on various platforms such as Windows, Linux, Mac OS, and FreeBSD.

#### **3.1.1 Audio input/output components**

Audio input components accept the audio information from the sound device as input, convert it to a OpenRTM-aist data stream, and pass on the output to other linked components. These components also accept audio data streams from other components as input and pass on the output to the sound device.

We use portaudio, a cross platform audio input/output library, to implement both the audio input and output components. The components support both Windows and Linux platforms from monoral input to multichannel inputs.

#### **3.1.2 Audio filter components**

Audio filter components contain input and output ports.

The "sample rate conversion component" converts the sample rate of the audio stream by using an up/down sampling algorithm. The "echo cancel component" has two input streams; it subtracts the input of stream 1 from that of stream 2 by finding a maximum correlation. The "emphasis component" applies a signal processing algorithm to enhance or de-enhance the magnitude of the specified frequency in the data stream.

#### **3.1.3 Voice recognition and synthesis components**

The voice recognition component is based on Julius (Kawahara et al, 2000) in combination with English and Japanese acoustic models. Our component is designed to possess the following features: (a) the ability to read grammar format in W3C-SRGS XML form and (b) the ability to output the recognized result as an extensible XML stream.

Figure 13 shows an example of voice recognition grammar specified using W3C-SRGS format. Voice recognition grammar can be visualized by combination of "srgstojulius" and "juliustographviz" tool.

The voice synthesis component is based on Festival for English and Open\_JTalk for Japanese. The component accepts plain text as input and provides a data stream in the form of a synthesized voice as output.

## **3.2 Incremental script development**

14 Will-be-set-by-IN-TECH

each function call for a service function. "Configuration parameters" are defined to configure

OpenRTM-aist is an implementation of the RT-Component specification that supports C++, JAVA, and Python languages. It runs on various platforms such as Windows, Linux, Mac OS,

Audio input components accept the audio information from the sound device as input, convert it to a OpenRTM-aist data stream, and pass on the output to other linked components. These components also accept audio data streams from other components as input and pass

We use portaudio, a cross platform audio input/output library, to implement both the audio input and output components. The components support both Windows and Linux platforms

The "sample rate conversion component" converts the sample rate of the audio stream by using an up/down sampling algorithm. The "echo cancel component" has two input streams; it subtracts the input of stream 1 from that of stream 2 by finding a maximum correlation. The "emphasis component" applies a signal processing algorithm to enhance or de-enhance the

Fig. 12. Screenshot of RT-SystemEditor.

**3.1.1 Audio input/output components**

on the output to the sound device.

**3.1.2 Audio filter components**

from monoral input to multichannel inputs.

Audio filter components contain input and output ports.

magnitude of the specified frequency in the data stream.

each component.

and FreeBSD.

Commands given by the human to the robot are diverse. The following are the factors that cause this diversity.

**The nature of language:** Human language is ambiguous, and different expressions can be used to give instructions that carry the same meaning.

**Tasks:** Robots working in a life environment have to accept a variety of tasks. In order to cope with this, it is necessary for them to understand a variety of commands.

**Ability of the robot itself:** The diversity is also caused by the ability of the robot itself. A command from a human becomes effective due to the functions of the robot. For example, humans do not say "walk N steps" to a robot on wheels.

The language comprehension system of the robot must be able to deal these diversities.

In the script-based development approach, diversity has been dealt with by stacking a newly developed script onto the existing scripts. By accumulating a number of scripts, the developer can accumulate the number of commands that the system can dealt with.

Incremental development of the state-transition model has previously been conducted using the "extension-by-connection" method (described in the next section). In this section, we propose an "extension-by-unification" method that can cope with the diversities mentioned above (described in Section 3.2.4).

### **3.2.1 Formalization of state-transition model**

A state-transition model is a modeling method in which the input and output of the system assume the following form:

$$A :=  *\tag{1}*$$

where *I* represents the input alphabet, *O* represents the output alphabet, *S* represents the internal states, *γ* represents the state transition function, *λ* represents the output function and *s*0 is the initial state.

The state transition function *γ* is defined in association with the state to the input.

$$
\gamma: \mathbb{S} \times I \to \mathbb{S} \tag{2}
$$

The output function *λ* is defined in association with the state to the input.

$$
\lambda: \mathcal{S} \times I \to \mathcal{O} \tag{3}
$$

Fig. 14. Example of state-transition model.

to represent the current state.

between the states.

Even the input to the system is same, the output of the sytem may be different, because the

Speech Communication with Humanoids: How People React and How We Can Build the System 181

We have explained the stat-transiton model in an equation form, however, the state-transition model can be also presented in a 2-dimensional diagram called "state-transition diagram." In the diagram, each state is represented by a circle, and the transition between states is represented by arrows. In this chapter, we annotate the transition conditions and the associative actions by including text over each arrow. We use a black circle (called a "token")

For example, Figure 14 represents a conversation modeled by the state-transition model. The model presented in Figure 14, the initial state of the system is in "TV control" state. When the model gets the instruction "Turn on" as an input, it will output the command "turn-on-TV" and state transition "(a)" will occur. Then the token turns back to the same "TV control" state. When the model gets the instruction "Video" as an input, state transition "(b)" will occur and the token will move to "VTR control" state. This time when the instruction "Turn on" is given, state transition "(c)" occur and output the command "turn-on-video". In this way, we can model the context by defining an appropriate state and state transitions

*O* = ("turn-on-tv", "turn-on-video",

As we have seen here, the expression in equation form has an advantage in formalization, while the expression in diagram form has an advantage in quick understanding. In later

 *s*0 *s*0 *s*0 *s*1 *s*1 *s*1 *s*0 *s*1

 *o*0 *o*2 *none none o*1 *o*3 *none none*

*A* := < *I*, *S*,*O*, *γ*, *λ*,*s*0 > (6)

*I* = ("Turn on", "Turn off", "TV", "Video") (7)

*S* = ("tv-control", "vtr-control") (8)

"turn-off-tv", "turn-off-video") (9)

(10)

(11)

internal state *st* will be updated each time the system gets the input.

Above example is expressed as follows in the equation form:

*γ* =

*λ* =

```
<?xml version="1.0" encoding="UTF-8" ?>
<grammar xmlns="http://www.w3.org/2001/06/grammar"
        xml:lang="en"
        version="1.0" mode="voice" root="main">
 <rule id="main">
   <one-of>
     <item><ruleref uri="#greet" /></item>
     <item><ruleref uri="#command" /></item>
   </one-of>
 </rule>
 <rule id="greet">
   <one-of>
     <item>hello</item>
     <item>bye</item>
   </one-of>
 </rule>
 <rule id="command">
   <one-of>
     <item>pick</item>
     <item>give me</item>
   </one-of>
   <one-of>
     <item>apple</item>
     <item>cake</item>
     <item>remote</item>
   </one-of>
 </rule>
</grammar>
     S2 S1
                             hello
                              bye
                                       S3
                     pick
                      S4
            give
                                                apple
                                                cake
                                               remote
                              m e
```
Fig. 13. Example of the voice recognition grammar and its visualization. The grammar is in W3C-SRGS form. "one-of" indicates the grammar matches either one of the child items. "ruleref" indicates reference to "rule" identified by the "id".

When the system is in state *st* and get input alphabet *it*, state transition to *st*+1 will occour as follows:

$$s\_{t+1} = \gamma\_{s\_t, i\_t} \tag{4}$$

At the same time, we get output alphabet *ot* as follows:

$$
\rho\_{t+1} = \lambda\_{s\_t i\_t} \tag{5}
$$

Fig. 14. Example of state-transition model.

16 Will-be-set-by-IN-TECH

S2 S1

S3

apple

cake remote

*st*+1 = *γst*,*it* (4)

*ot*+1 = *λst*,*it* (5)

bye

m e

Fig. 13. Example of the voice recognition grammar and its visualization. The grammar is in W3C-SRGS form. "one-of" indicates the grammar matches either one of the child items.

When the system is in state *st* and get input alphabet *it*, state transition to *st*+1 will occour as

pick

S4

"ruleref" indicates reference to "rule" identified by the "id".

At the same time, we get output alphabet *ot* as follows:

hello

<?xml version="1.0" encoding="UTF-8" ?>

xml:lang="en"

<item>hello</item> <item>bye</item>

<item>pick</item> <item>give me</item>

<item>apple</item> <item>cake</item> <item>remote</item>

give

<rule id="main"> <one-of>

</one-of> </rule>

</one-of> </rule>

<one-of>

</one-of> <one-of>

</one-of> </rule> </grammar>

follows:

<rule id="greet"> <one-of>

<rule id="command">

<grammar xmlns="http://www.w3.org/2001/06/grammar"

<item><ruleref uri="#greet" /></item> <item><ruleref uri="#command" /></item>

version="1.0" mode="voice" root="main">

Even the input to the system is same, the output of the sytem may be different, because the internal state *st* will be updated each time the system gets the input.

We have explained the stat-transiton model in an equation form, however, the state-transition model can be also presented in a 2-dimensional diagram called "state-transition diagram." In the diagram, each state is represented by a circle, and the transition between states is represented by arrows. In this chapter, we annotate the transition conditions and the associative actions by including text over each arrow. We use a black circle (called a "token") to represent the current state.

For example, Figure 14 represents a conversation modeled by the state-transition model.

The model presented in Figure 14, the initial state of the system is in "TV control" state. When the model gets the instruction "Turn on" as an input, it will output the command "turn-on-TV" and state transition "(a)" will occur. Then the token turns back to the same "TV control" state. When the model gets the instruction "Video" as an input, state transition "(b)" will occur and the token will move to "VTR control" state. This time when the instruction "Turn on" is given, state transition "(c)" occur and output the command "turn-on-video". In this way, we can model the context by defining an appropriate state and state transitions between the states.

Above example is expressed as follows in the equation form:

$$A \coloneqq  \tag{6}$$

*I* = ("Turn on", "Turn off", "TV", "Video") (7)

$$S = \text{("tv-control", "vt-control")}\tag{8}$$

$$O = (\text{"turn-on-tv"}, \text{"turn-on-video"}, )$$

"turn-off-tv", "turn-off-video") (9)

$$\gamma = \begin{pmatrix} \text{s\_0 s\_0 s\_0 s\_1} \\ \text{s\_1 s\_1 s\_0 s\_1} \end{pmatrix} \tag{10}$$

$$
\lambda = \begin{pmatrix} o\_0 \ o\_2 \ nonne \ nonne \\ o\_1 \ o\_3 \ nonne \ nonne \end{pmatrix} \tag{11}
$$

As we have seen here, the expression in equation form has an advantage in formalization, while the expression in diagram form has an advantage in quick understanding. In later

*λ*�

*λ*� *s*�

Δ*γ* includes both old state *S* and new state Δ*S* in its definition.

**3.2.3 Problems with the extension-by-connection method**

First, the state *SAC* is easily separable from state *SA* and state *SC*:

*γAC sA t* ,*i* = *γA*

*γAC sAC t* ,*i* = *γC*

*γC* contains state *SA* in its definition.

diverted for the extension of robot B.

However, the definition of state-transition function *γAC* is as follows:

following scenario:

to *AAC*.

C to this robot.

realize the function.

*t*,*i* = Δ*λs*� *t*

Extension-by-connection is a useful method, but it has the following problems.

,*i* (*s*�

Speech Communication with Humanoids: How People React and How We Can Build the System 183

The transition function of the accumulated part Δ*γ* needs to be defined based on the transition from the existing state *S* . Therefore, Δ*γ* will be a matrix of *S*� × *I*. Note that the new state Δ*S* can be expressed only by the newly defined part, but the transition of the accumulated part

The state-transition model is easy to understand in drawing a state-transition diagram. Extension-by-connection can also be carried out very easily by editing this diagram. There are several GUI that can add state-transition rules through the operation of mouse clicks.

As we can see in Equation 14 and Equation 16, the definition of Δ*γ*� requires both *S* and Δ*S*. This causes problems in the function development of robots. For example, let us consider the

1. Robot "A" has function A, and we have already developed a state-transition model *AA*to

2. For the robot "A" to accumulate function C, we have extended the state-transition model

3. We have developed another robot, "B," which has function B. And we want to add function

Here, the state-transition model for function C is already developed for robot A. We want to reuse the model for robot B. Here, we discuss whether such a diversion would be possible.

*ij* (*sA*

*ij* (*sAC*

Because states *SA* and *SB* are defined for different types of robots, A and B are not equal. In addition, because the transition for the function C is defined dependently on state *SA*, we cannot replace variables like *SAC* = *SBC*, which means that we cannot use *γC* to extend the state-transition model *AB*. The state transition of function C developed for robot A cannot be

Ideally, once a feature is developed, it would be possible to share with other robots that need the same feature. In order to achieve this, we introduce the extension-by-unification method.

*t* ∈ *S*�

*st*,*i* = *λst*,*i* (*st* ∈ *S*, *i* ∈ *I*) (17)

*SC* = *SAC* − *SA* (19)

*t* ∈ *SA*, *i* ∈ *I*) (20)

*t* ∈ *SAC*, *i* ∈ *I*) (21)

, *i* ∈ *I*) (18)

Fig. 15. Extension of a state machine using the extension-by-connection model.

discussion, we will use both the equation and the diagram forms to explain the concept quickly and formally.

State-transition model is very simple but very powerful modeling method and has been applied to very wide applications. Because the structure of state-transition model is very simple, it is frequently misunderstood that the state-transition model can only model simple behavior. However, it can model diverse behavior by applying some extensions (e.g. Huang et al (2000), Denecke (2000)).

#### **3.2.2 Extension-by-connection method**

The simplest way to extend state-transition model is as follows.

This process is illustrated in Figure 15.

Here, we formulate the above process. Let the existing state-transition model be *A* and the accumulated state-transition model be *A*� .

As explained in Section 3.2.1, the existing state-transition model *A* can be represented by following form.

$$A :=  \tag{12}$$

Here, *S* is the set of state *s* ∈ *S*. The transition function *γ* can be defined in any form. In this chapter, we use the matrix of *S* × *I*, in which the transition from state *st* to state *st*+1 can occur if *γSt*,*i* = *st*+1.

Similarly, we define the accumulated state-transition model *A*� as follows:

$$A' :=  \tag{13}$$

Then, the new state Δ*S* can be calculated as follows:

$$S' = \mathcal{S} \cup \Delta \mathcal{S} \tag{14}$$

Here, *S*� ∩ Δ*S* = ∅.

The new state transition Δ*γ* can be calculated as follows:

$$
\gamma\_{s\_{t\prime}i}' = \gamma\_{s\_{t\prime}i} \qquad \quad (s\_t \in \mathcal{S}, i \in I) \tag{15}
$$

$$
\gamma\_{s\_{l\prime}'i}' = \Delta \gamma\_{s\_{l\prime}'i} \qquad \quad (s\_l' \in S', i \in I) \tag{16}
$$

$$
\lambda\_{s\_{l,l}}' = \lambda\_{s\_{l,l}} \qquad \quad (s\_l \in \mathcal{S}, i \in I) \tag{17}
$$

$$
\lambda\_{s'\_{l'}i}' = \Delta \lambda\_{s'\_{l'}i} \qquad (s'\_l \in S', i \in I) \tag{18}
$$

The transition function of the accumulated part Δ*γ* needs to be defined based on the transition from the existing state *S* . Therefore, Δ*γ* will be a matrix of *S*� × *I*. Note that the new state Δ*S* can be expressed only by the newly defined part, but the transition of the accumulated part Δ*γ* includes both old state *S* and new state Δ*S* in its definition.

The state-transition model is easy to understand in drawing a state-transition diagram. Extension-by-connection can also be carried out very easily by editing this diagram. There are several GUI that can add state-transition rules through the operation of mouse clicks.

#### **3.2.3 Problems with the extension-by-connection method**

18 Will-be-set-by-IN-TECH

discussion, we will use both the equation and the diagram forms to explain the concept

State-transition model is very simple but very powerful modeling method and has been applied to very wide applications. Because the structure of state-transition model is very simple, it is frequently misunderstood that the state-transition model can only model simple behavior. However, it can model diverse behavior by applying some extensions (e.g. Huang

Here, we formulate the above process. Let the existing state-transition model be *A* and the

As explained in Section 3.2.1, the existing state-transition model *A* can be represented by

Here, *S* is the set of state *s* ∈ *S*. The transition function *γ* can be defined in any form. In this chapter, we use the matrix of *S* × *I*, in which the transition from state *st* to state *st*+1 can occur

> ,*O*, *γ*� , *λ*� ,*s*�

,*i* (*s*�

*t* ∈ *S*�

*A* :=< *I*, *S*,*O*, *γ*, *λ*,*s*0 > (12)

*S*� = *S* ∪ Δ*S* (14)

*st*,*i* = *γst*,*i* (*st* ∈ *S*, *i* ∈ *I*) (15)

0 > (13)

, *i* ∈ *I*) (16)

.

Similarly, we define the accumulated state-transition model *A*� as follows:

*A*� :=< *I*, *S*�

Fig. 15. Extension of a state machine using the extension-by-connection model.

quickly and formally.

following form.

if *γSt*,*i* = *st*+1.

Here, *S*� ∩ Δ*S* = ∅.

et al (2000), Denecke (2000)).

**3.2.2 Extension-by-connection method**

This process is illustrated in Figure 15.

accumulated state-transition model be *A*�

Then, the new state Δ*S* can be calculated as follows:

The new state transition Δ*γ* can be calculated as follows:

*γ*�

*γ*� *s*�

*t*,*i* = Δ*γs*� *t*

The simplest way to extend state-transition model is as follows.

2. Add a new transition from the existing state to the new state.

1. Add a new state to the existing state-transition model.

Extension-by-connection is a useful method, but it has the following problems.

As we can see in Equation 14 and Equation 16, the definition of Δ*γ*� requires both *S* and Δ*S*. This causes problems in the function development of robots. For example, let us consider the following scenario:

Here, the state-transition model for function C is already developed for robot A. We want to reuse the model for robot B. Here, we discuss whether such a diversion would be possible. First, the state *SAC* is easily separable from state *SA* and state *SC*:

$$\mathbf{S}^{\mathbb{C}} = \mathbf{S}^{A\mathbb{C}} - \mathbf{S}^{A} \tag{19}$$

However, the definition of state-transition function *γAC* is as follows:

$$
\gamma\_{s\_l^A, i}^{A\mathbb{C}} = \gamma\_{i\bar{j}}^A \qquad (s\_l^A \in \mathbb{S}^A, i \in I) \tag{20}
$$

$$
\gamma\_{s\_l^{A\mathbb{C}},i}^{A\mathbb{C}} = \gamma\_{ij}^{\mathbb{C}} \qquad (s\_l^{A\mathbb{C}} \in \mathbb{S}^{A\mathbb{C}}, i \in I) \tag{21}
$$

*γC* contains state *SA* in its definition.

Because states *SA* and *SB* are defined for different types of robots, A and B are not equal. In addition, because the transition for the function C is defined dependently on state *SA*, we cannot replace variables like *SAC* = *SBC*, which means that we cannot use *γC* to extend the state-transition model *AB*. The state transition of function C developed for robot A cannot be diverted for the extension of robot B.

Ideally, once a feature is developed, it would be possible to share with other robots that need the same feature. In order to achieve this, we introduce the extension-by-unification method.

As visible in Equation 26, the transition function *γ*� is an *S*� × *S*� matrix that only includes state *S*� in its definition. The extension-by-unification method does not require the definition of the

Speech Communication with Humanoids: How People React and How We Can Build the System 185

As noted in Section 3.2.3, in the conventional extension-by-connection method, the definition of the accumulated part of the state-transition model depends on information on the existing state. It is limited in terms of reusing scripts for this reason. The proposed extension-by-unification method does not have this problem. Using this method, we can

By using the above algorithms, the possibility of unification between scripts can be identified as "Unifiable," "Unifiable (Occurrence of isolated state)," or "Conflict". Similarly, scripts can be classified as "Executable" or "Unexecutable." By comparing a script and an adaptor definition for the existing scripts, we can obtain a list of scripts annotated with 6 (3 × 2) classes. Our script management server displays the above list at the bottom of each wiki page. By displaying the list, the developer can easily find a script that can be included in his/her current

Figure 17 shows overview of the editing system and Figure 18 shows example of using the

Wiki server (Docu m ents + Scripts)

Auto m aton unifier

Scripts

Current state

ID of m atched phrase

Unified auto m aton

Phrase m atcher

Hash m e m ory

Auto m aton controller

State m e m ory

Browser

Browser

Developer

Developer

original state in the accumulated part of the state-transition model.

significantly increase the reusability of the state-transition model.

**3.2.5 Visualization of unifiable states**

Adaptor

Fig. 17. Overview of the editing system.

application.

web based interface.

Input

Output

Fig. 16. Extension of the state-transition model using the extension-by-unification method.

#### **3.2.4 Extension-by-unification method**

In the extension-by-unification method, we extend the state-transition model by the following procedure:

This process is illustrated in Figure 16.

Here, we formulate the above process.

The existing state-transition model *A* can be represented by state *S*, state transition *γ*, and initial state *s*0:

$$A :=  \tag{22}$$

Similarly, the new state-transition model *A*� is represented as follows:

$$A' :=  \tag{23}$$

We accumulate the state-transition model *A*�� by unifying *A* and *A*� . First, we calculate state as follows:

$$\mathcal{S}^{\prime\prime} = \mathcal{S} \cup \mathcal{S}^{\prime} \tag{24}$$

Here, *S* ∩ *S*� �= ∅.

Next, the transition between the state *S*�� is calculated as follows:

$$
\gamma\_{s\_{l\prime}i}^{\prime\prime} = \gamma\_{s\_{l\prime}i} \qquad \quad (s\_l \in \mathbb{S}, i \in I) \tag{25}
$$

$$
\gamma\_{s'\_{l'}i}^{\prime\prime} = \gamma\_{s'\_{l'}i}^{\prime} \qquad (s'\_t \in \mathcal{S}^{\prime}, i \in I) \tag{26}
$$

$$
\lambda\_{s\_l, i}^{\prime \prime} = \lambda\_{s\_l, i} \qquad (s\_l \in \mathcal{S}, i \in I) \tag{27}
$$

$$
\lambda\_{\mathbf{s}'\_{l},i}^{\prime\prime} = \lambda\_{\mathbf{s}'\_{l},i}^{\prime} \qquad (\mathbf{s}\_{l}^{\prime} \in \mathbb{S}^{\prime}, i \in I) \tag{28}
$$

By defining initial state *s*�� 0 to be *s*�� 0 = *s*0, the extended state-transition model *A*�� will be as follows:

$$A^{\prime\prime} =  \tag{29}$$

As visible in Equation 26, the transition function *γ*� is an *S*� × *S*� matrix that only includes state *S*� in its definition. The extension-by-unification method does not require the definition of the original state in the accumulated part of the state-transition model.

As noted in Section 3.2.3, in the conventional extension-by-connection method, the definition of the accumulated part of the state-transition model depends on information on the existing state. It is limited in terms of reusing scripts for this reason. The proposed extension-by-unification method does not have this problem. Using this method, we can significantly increase the reusability of the state-transition model.


*3.3. HPP and IP Reduce the Production of Pro-Inflammatory Cytokines in LPS-Stimulated Human DC*

We recently demonstrated that HPP and IP are capable of reducing the production of pro-inflammatory cytokines in murine glia and macrophages [5]. We next sought to determine if they have similar immune modulating activity in human DC. To test this, DC were treated with either HPP or IP (500–1000 μM) for 6 h prior to stimulation with LPS (100 ng/mL) for 24 h. Cytokine concentrations were measured in cell supernatants by ELISA. Both HPP and IP treatment dose-dependently reduced production of the proinflammatory cytokines TNF, IL-6, IL-12p70, and IL-23, and this effect was most potent at the 1000 μM concentration (Figure 3A–H). As well as driving the production of proinflammatory cytokines, LPS treatment over time is usually accompanied by production of the anti-inflammatory cytokine IL-10 as a means of regulating inflammatory responses. Interestingly, IP treatment resulted in a significant enhancement of IL-10, while HPP treatment showed a similar, albeit non-significant, trend (Figure 3I,J). These results suggest that both IP and HPP are capable of reducing the production of pro-inflammatory cytokines in LPS-stimulated DC, whilst also promoting a more anti-inflammatory phenotype.


**6.2. Nuclear magnetic resonance**

Nuclear magnetic resonance (NMR) is mostly used to study inclusion complexation in solution and has been very useful in understanding the bonding configuration of functionalities present. This is mostly because when a guest is hosted the interior hydrogens are shielded by the guest resulting in a shift on the NMR spectroscopy [110]. NMR can also be used to determine the atoms that stabilize host-guest complexes using 13C-NMR by monitoring the shifts of the carbon atoms involved [110, 115]. Furthermore, NMR can also provide information on the orientation of the guest within the host's cavity [115, 116].


Biostimulants as Plant Growth Stimulators in Modernized Agriculture and Environmental Sustainability

*Muhammad Amjad Bashir, Abdur Rehim, Qurat-Ul-Ain Raza, Hafiz Muhammad Ali Raza, Limei Zhai, Hongbin Liu and Hongyuan Wang*

## **Abstract**

Plant growth stimulators (growth regulators + biostimulants; PGS) are chemical substances (organic/inorganic), helpful in plant growth and development. These are not considered as the replacement of fertilizers but can help in improved crop and soil quality. Both compounds can amplify the root biomass, nutrients translocation, enzymatic activities, crop yield, physiology, and nutrient uptake. Biostimulants are rich in minerals, vitamins, plant hormones, oligosaccharides, and amino acids. These compounds have a serious role to improve soil health, fertility, sorption, and desorption of nutrients. Hence, have a vital character in nutrients cycling, abiotic stress control, heavy metals bioavailability, and greenhouse gaseous emission. This chapter focuses on the discussions about the influence of plant growth regulators and biostimulants in crop production, soil health, heavy metal cycling, greenhouse gases emission with environmental sustainability. Whereas, the impact of biostimulants on greenhouse gases is a research gap.

**Keywords:** PGR, Biostimulants, Sustainable agriculture, sustainability

## **1. Introduction**

Modernized agricultural practices are focusing on sustainable environmental systems. The major challenges for agriculture scientists and experts are to improve the crop quality and yield with minimum inputs focusing on environmental sustainability. To fulfill this aim, various breeding programs are introduced but it is a time-consuming and species-specific method. Apart from this, a less time-consuming and cheaper method identification is a vital need, as the use of an organic substance can stimulate healthy plant metabolism and improve their growth and development functions [1].

In addition to increasing population, food security, and environmental pressure, modern agriculture is also facing the challenges of soil degradation and reduction of arable lands. About 24 billion tons of fertile soil has been depleted worldwide due to inadequate agriculture practices and erosion. In addition, drought stress,

#### *Technology in Agriculture*

salinization of cultivated lands, and natural disasters have poorly impacted agriculture. Approximately, 60–70% yield gap is due to abiotic stresses specifically salinity, heat stress, drought, nutrient deficiency, and hypoxia [2].

Biostimulant is a material, which when applied in minute quantity promotes plant growth. Here, the word "minute" describes how the biostimulants are different from soil amendments and nutrients that perform a similar task but are applied in higher amounts [3]. Do not confuse biostimulants with fertilizers because they do not supply nutrients directly to the plants. But facilitate the plant and soil metabolic processes to improve nutrient availability [4]. According to the new regulation (EU; 2019/1009), biostimulant is a fertilizing product that improves the plant's nutritional processes independent of its own nutrient content. Moreover, its goal is to achieve one or more following [2] characteristics of the plant and/or the plant rhizosphere.

i.nutrient use efficiency

ii.tolerance resistance to (a) biotic stress

iii.quality characteristics

iv. availability of confined nutrients in the soil or rhizosphere

Initially, plant biostimulants were used for organic production but as its benefits explored, it is now being adopted in sustainable agricultural practices and integrated cropping systems [2]. Biostimulants are the extracts derived from organic raw substances containing bioactive compounds. The common components of biostimulants are humic substances, mineral elements, amino acids, chitin, chitosan, vitamins, poly-, and oligosaccharides [1]. Biostimulants vary in their formulation and ingredients, but the major classification is on the basis of source and content (includes: hormone-containing products, amino acid-containing products), and humic substances [3].

Various substances such as enzymes, micronutrients, proteins, amino acids, phenols, humic and fulvic acid, salicylic acid, protein hydrolases, and other compounds are sources as biostimulants. Moreover, living organisms i.e., bacteria and fungi that can induce changes among the organism present in plant or soil system are included in the group of biostimulants. Biostimulants are applied via soil application in the forms of granules, powders, capsules, or solutions, or as foliar sprays. Moreover, it can be also applied via fertigation through irrigation systems and foliar application [4]. Different substances are included under the term PGS. **Table 1** summarizes the definition and examples of PGS discussed.

**Table 1.**

*Concept of different plant growth stimulators.*

*Biostimulants as Plant Growth Stimulators in Modernized Agriculture and Environmental… DOI: http://dx.doi.org/10.5772/intechopen.98295*

In this chapter, we have focused on the plant growth stimulators (PGS) which comprise both plant biostimulants and growth regulators. The chapter consists of 8 sections, which describes the introduction and significance of the plant growth stimulators (Section 1), the influence of PGS on crop productivity (Section 2), the role of PGS in abiotic stress conditions (Section 3), the efficacy of PGS in soil health (Section 4), the role of PGS in heavy metals cycling (Section 5), its impact on greenhouse gas emission (Section 6), the constraints, challenges and future aspects (Section 7), and the conclusion of the chapter (Section 8), respectively.

## **2. Influencing the crop productivity with PGS**

Plant-derived biostimulants have been reported as an innovative tool to cope with agriculture challenges and environmental sustainability. Moreover, it has been reported that plant biostimulants impact plant growth hormones that improve the plant metabolic activities and ultimately enhance crop productivity [5]. Moreover, it is reported that plant biostimulants improve chlorophyll synthesis, the mineral status, and also synthesis and accumulate antioxidant metabolites. These antioxidants reactivate photosynthetic activity and improve plant growth [2].

Biostimulants are also responsible to increase the leaf chlorophyll content. Its application to vegetables and floriculture crops has been reported to build tolerance against biotic and abiotic stresses by improving the internal and external quality. Moreover, it also reduces the fertilizer requirement thereby is recognized as a step towards environmental sustainability [1, 6]. In addition, biostimulants also enhance the thiamine levels of green beans in proportion to the thiamine content it contains [7]. Furthermore, biostimulants also influence the mechanical properties of fruits and vegetables such as firmness. It might be due to the stiffness of the cell wall that results in extensibility reduction. Biostimulants also significantly improve the cell wall flexibility that helps increase the shelf life of fruits and vegetables, thus facilitate transportation and storage processes [4].

Non-microbial and microbial plant biostimulants also have a positive impact on crop productivity. It increases plant growth and development, nutrient uptake, and translocation consequently increases the yield and biomass production in horticulture and agronomic crops. Moreover, it improves nutrient soil solubilization (both macro and micronutrients), the plant root system architecture, and enhances soil exploration. Thus, it has also been shown to influence nutrient use efficiency specifically nitrogen in plants [2]. Plant biostimulants are also responsible to improve nutrient assimilation by improving the gene expression of functioning in the plant metabolism or due to the improved nutrient uptake and transport [8].

Biostimulants based on chitosan positively impact strawberry pulp firmness and improves the shelf-life by increasing the concentration of the phenolic compound in plants [9]. Moreover, it has been demonstrated that the application of biostimulants in the absence of fertilizers improves the radish shoot and root biomass [6]. Whereas the foliar application of seaweed extract influenced the growth of soybean possibly due to the identified minerals and plant growth regulators present in the biostimulant [10]. Moreover, seaweed is also reported to improve plant water and nutrient use efficiency due to the phytohormones involved. Furthermore, the potential outcomes of seaweed to minimized abiotic stresses and nutrient deficiencies can be expected [11]. Biostimulants have a promising role in improving plant growth, metabolic activities, better stress resistance, and reduction in fertilizer use [12]. Furthermore, **Table 2** summarizes the role of PGS and plants responses respectively.

#### **Table 2.**

*Plant growth stimulators and plant responses in different crops.*

*Biostimulants as Plant Growth Stimulators in Modernized Agriculture and Environmental… DOI: http://dx.doi.org/10.5772/intechopen.98295*

## **3. Limiting the abiotic stress by PGS**

Plants face multiple stressful events throughout their lifecycle. Stresses are classified based on the nature of the trigger factor as biotic and abiotic stresses. Biotic stresses are caused by a living organism such as insects microorganisms, weeds, etc. that impact plant growth, and productivity. However, the latter one is general associated with different environmental components that negatively impact plant development and survival. Drought, non-optimal temperatures, low soil fertility, and salinity are the most common abiotic stresses that limit agriculture production globally. Whereas, in the developing countries where the life of the majority depends on the agriculture sector, drought and nutrient deficiencies major issues [20].

Besides this, the rapid increase in population leading to urbanization and increases soil erosion has poorly impacted the prime cropland. Therefore, it is the need for time to utilize the less productive soils and enhance the crop yield and productivity [16]. Biostimulants ensure a promising role to improve the productivity of vegetables and also develop tolerance against stresses. Moreover, it positively impacts the plant metabolic activities in optimal and sub-optimal environmental conditions. In addition, it is very crucial to identify the proper timing for the application of biostimulants. It depends on the critical stages of development and crop species. To avoid unexpected results, minimize production cost and wastage of products, it is good to determine the exact dose and application time of biostimulants [20].

Diverse changes in temperature and precipitation have been reported due to climate change, this has resulted in severe drought conditions [16]. Drought has adverse effects on the plant gas exchange and causes changes in transpiration and photosynthetic rates, which ultimately results in yield losses. Biostimulants can be used to overcome water stress as it is effective to improve water use efficiency in plants [20]. It is reported that the micro-algal-biostimulants minimized the drought damaging effects on tomatoes and improve plant growth. It might be due to the presence of plant growth hormones, like abscisic acid that regulates transpiration and reduce water losses, present in biostimulant [21]. Microalgae improve the total flavonoid and phenolic content in plants which also increase the enzymatic activities of antioxidants such as catalase, superoxide dismutase, ascorbate peroxidase that consequently mitigate drought-induced oxidative damage [22].

Furthermore, biostimulants are also useful against nutrient deficiency. The results have shown that the application of biostimulants cannot replace fertilizers but it can contribute to overcoming nutrient deficiency and imbalanced conditions. It is responsible for improving plant root morphology which ultimately improves nutrient uptake, translocation, and assimilation [23]. Cold stress or low temperatures adversely affect plant metabolism and delay physiological processes. It also damages the cell membrane by destabilizing phospholipid layers. Biostimulants also help to stimulate biosynthetic pathways that increase the osmotic molecule accumulation, membrane thermostability, and overcome chilling injury. Moreover, the seed priming with chitosan also improved germination and plant growth under temperature stress [24].

The most abundant environmental stress is a salinity that adversely impacts plant metabolism and growth. With the application of biostimulants to salt stress environmental conditions, the damage can be minimized as it induces the accumulation of osmolytes to increase the osmotic potential of plant cells and enhance the level of protective molecules against oxidative stress [25]. Moreover, it has been reported that the protein hydrolysates-based biostimulant significantly mitigates

single as well as multiple stresses (nutrient stress + hypoxia or nutrient stress + salinity) in maize under hydroponic conditions [2].

Plant growth regulators also play a constructive role to mitigate the abiotic stress damages and improve plant development. Such as, salicylic acid is effective against drought as it increases the restoration process in plants. Moreover, putrescine fights against oxidative, drought, and salinity stress probably due to its acid-neutralizing abilities. Thus, plant growth regulators, plant growth-promoting rhizobacteria, and biostimulants could play a significant performance against environmental stresses [16].

## **4. Efficacy of PGS in soil health**

Plant growth regulators also effectively influence the soil properties and ultimately give a huge benefit to agriculture management. As describes earlier, plant biostimulants improve nutrient availability, uptake, translocation, and assimilation, which is beneficial to organic farming. In addition, incredible results can be obtained with the integrated application of biostimulants with chemical fertilizers in soil [26]. It contributes to improving the cation exchange capacity of the soil and enhances the solubility of the nutrients in soil solution which subsequently increases the nutrient availability for plant uptake [8]. Whereas, biostimulants are not nutrients and has no direct impact on nutrients bioavailability but they have a potential to reduce the application of mineral nutrients [6].

Protein hydrolysates, an important plant biostimulant is prepared using protein sources by the process of partial hydrolysis. It comprises amino acids, mixtures of poly- peptides, and oligopeptides. When applied as foliar spray or in the soil/root system, protein hydrolysates improve the microbial biomass and activity, soil respiration by providing a rich source of C and N to microbes. Moreover, it forms complexes and chelates with soil micronutrients (i.e., Zn, Mn, Fe, and Cu), improves nutrient availability, and ultimately improves plant nutritional status [8].

Another well-known plant biostimulant is "seaweed extracts". Due to its complex biochemical composition (minerals, antioxidants, polysaccharides, hormones, vitamins, pigments, fats, oils, acids), it is highly difficult to understand its mechanism. Likewise, protein hydrolysates, seaweed extract can be applied to soil as well as plants (foliar spray). It is responsible for improving soil retention, soil microflora as well as soil remediation. Moreover, it can be a rich source of nutrients and probably have hormonal effects. Whereas, multidisciplinary approaches are required to understand the complex interaction between bioactive compounds present in the extract [11].

#### **5. Role of PGS in heavy metals cycling**

The increase in anthropogenic activities including industrial and mining activities, urbanization, use of chemicals in agriculture has potentially increased the concentration of toxic elements in soil throughout the globe [27]. Moreover, wastewater mishandling also contributes to heavy metal contamination and induces toxic effects on plant metabolic activities, soil environment, groundwater, and ultimately human health [28]. One of the most toxic trace elements is chromium (Cr). It is harmful to the soil microbes as it depresses their microbial and enzymatic activities, as well as humans if enters the food chain [29]. Therefore, it is important to minimize or treat the negative impacts of Cr. Plant growth stimulators can also play a significant role in this regard.

#### *Biostimulants as Plant Growth Stimulators in Modernized Agriculture and Environmental… DOI: http://dx.doi.org/10.5772/intechopen.98295*

The application of micronutrient-amino acid is responsible to reduce the Cr stress in plants. It is reported that iron-lysine (Felys) application significantly improved plant growth and biomass. Additionally, with the increased nutrient uptake, gaseous exchange parameters also increased whereas it contributes to form complexes that reduce oxidative stress in plants. Therefore, the Cr toxicity caused due to contaminated water can be treated using Felys. Whereas, the mechanism behind the Felys attributes needs to be explored [30].

The foliar application of Zinc-lysine (Znlys) also improved rice growth and contributed to stimulating the anti-oxidant defense system, decrease oxidative stress, increase Zinc (Zn) uptake, and decreases Cr concentration in plants. Whereas, further studies are required to explore the mechanism of Znlys in mitigating Cr level in plants [31]. Moreover, Znlys is also effective to reduce cadmium (Cd) concentration in wheat along with improving Zn contents and plant growth in Zn deficient and Cd contaminated soil. It is useful to protect people from Cd risk and Zn deficiency [32].

Apart from biostimulants, plant growth regulators also contribute to phytoremediation. Plant growth regulators can be defined as the substance that can impact plant growth and is capable of altering biological processes in plants. Such compounds when produced inside the plant are known as plant hormones but when used as synthetic compounds to play a similar role are termed as plant growth regulators [33]. Auxins perform various biochemical changes in plant cells and their membranes which reduces the toxicity of metal ions [34]. Different hypotheses report that auxins cause changes in the cell membrane and alter its properties against toxic elements [33]. It also improves the rate of transpiration and ultimately heavy metal adsorption.

The use of cytokinins on *Alyssum murale* also improves the plant aerial biomass and transpiration due to the adsorption of heavy metals in soil solution. Likewise, the application of cytokinin on *Helianthus annuus* is helpful in the adsorption of Zn and lead (Pb). Studies reported that the usage of salicylic acid helps reduce the negative impacts of copper (Cu) and Pb in plants. Thus the use of plant growth stimulators is significant in heavy metal recycling [33].

## **6. Greenhouse gases emission and PGS**

Greenhouse gases (GHGs) are defined as the gases that obtain heat energy from the sunlight and limit the backward movement of radiations from the earth's crust thus resulting in global warming. The agriculture sector contributes a lot in this aspect by the emission of methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2). One of the major sources of GHGs emissions is paddy systems [35]. Moreover, the long-term application of manures also increases GHGs emissions [36].

With the increase in population, the demand for food has also increased. To fulfill this challenge, the farmers tend to apply more fertilizer to attain more yield. However, field fertilization is supposed to be a major source of GHGs emissions. It is reported that agriculture produces about 90% N2O and 20% CO2 worldwide. Depending upon the global warming potential, carbon dioxide and nitrous oxide are two important GHGs. Moreover, after CH4, and CO2, N2O is an important GHG that contributes about 6–8% of global warming [36]. Therefore, it is the need of today to introduce such agriculture practices that ensure high yield with minimum negative impacts on global warming and climate change.

Microbes-biostimulants specifically N-fixing microorganisms are responsible to increase N2O emission. Whereas few pieces of research have been reported in this

regard. Further studies need to be conducted to explore the impacts of different types of biostimulants on agro-ecosystems [37]. Additionally, the foliar application of plant growth regulators (abscisic acid and kinetin) has been shown to mitigate the N2O emission and also manipulate plant growth and development processes in the wheat cropping system. It is possibly due to the manipulation of anatomical and physiological processes. Thus, the application of plant growth regulators might be an effective tool for modernized agriculture and environmental sustainability [38]. Apart from this, there is a huge gap to study PGS for GHG emission and reduced agro-environmental pollution, especially from non-point sources.

## **7. Constraints, challenges, and future aspects**

Agriculture production is facing multiple challenges including food security, climate change, soil restoration, and environmental sustainability. Plant growth stimulators have the potential to overcome the biotic and abiotic stresses, improve crop productivity, better soil nutrient cycling, mitigation of heavy metal uptake, and GHGs emission. Due to the complex composition of biostimulants, their potential role and mechanism are still not clear. Therefore, further studies about the possible use of biostimulants to minimize GHGs emission needs to be studied.

Biostimulants are also expected to increase NUE, therefore, careful monitoring, climatic aspects, and related modeling need to be studied. Limited literature related to the impacts of biostimulants under sub-optimal nitrogen regimens is available. Further studies can bring huge benefits to modern agriculture. Moreover, open field studies should also be carried out in the future, which then moves back to the lab for further elucidation. Few field-based research using biostimulants has been reported which is a major constraint to adopt its application in farming. Furthermore, the farming community should be introduced with the product along with the cost–benefit analysis, thus moving towards environment sustainability.

Currently, biostimulants are gaining popularity in the market. Variety of biostimulants with different active ingredients (humic substances, seaweed extracts, microbial amendments, and amino acids) are estimated to account for \$2.6 billion and will reach about \$5 billion by 2025 [2]. Therefore, it is time to introduce new and beneficial biostimulants that have practical application and yield benefits for sustainable agriculture. Integration of plant biostimulants and growth regulators with nutrients and waste organics for different regions can be introduced with also helps in minimizing pollution and waste recycling.

## **8. Conclusion**

A sustainable agriculture management system needs to be adopted for dealing the adverse climatic conditions. Plant growth stimulators have positively contributed towards modern agriculture and have the potential to improve crop yield, plant growth, and development, handle biotic and abiotic stresses, minimize heavy metal translocation, and contribute to mitigating GHGs emissions. Biostimulants are rich in multi-nutrients but cannot replace fertilizers. Although it has a potential to improve soil quality and plant productivity even under stress conditions. Whereas, few researches regarding GHGs emission using biostimulants has been reported. It is a huge research gap. Whereas, different mechanisms need to be studied further to develop a better understanding and introducing useful products.

*Biostimulants as Plant Growth Stimulators in Modernized Agriculture and Environmental… DOI: http://dx.doi.org/10.5772/intechopen.98295*

## **Acknowledgements**

This study was financially supported by the National Natural Science Foundation of China (31972519).

## **Author details**

Muhammad Amjad Bashir1,2, Abdur Rehim1 \*, Qurat-Ul-Ain Raza1 , Hafiz Muhammad Ali Raza1 , Limei Zhai2 , Hongbin Liu2 and Hongyuan Wang2 \*

1 Department of Soil Science, Bahauddin Zakariya University, Multan, Pakistan

2 Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, P.R. China

\*Address all correspondence to<EMAIL_ADDRESS>and<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[19] Mola, I. Di; Cozzolino, E.; Ottaiano, L.; Giordano, M.; Rouphael, Y.; Colla, G.; Mori, M. Effect of vegetal- And seaweed extract-based biostimulants on agronomical and leaf quality traits of plastic tunnel-grown baby lettuce under four regimes of nitrogen fertilization. Agronomy **2019**, *9*, doi:10.3390/ agronomy9100571.

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[32] Rizwan, M.; Ali, S.; Hussain, A.; Ali, Q.; Shakoor, M.B.; Zia-ur-Rehman, M.; Farid, M.; Asma, M. Effect of zinc-lysine on growth, yield and cadmium uptake in wheat (Triticum aestivum L.) and health risk assessment. Chemosphere **2017**, *187*, 35-42, doi:10.1016/j.chemosphere.2017.08.071.

[33] Rostami, S.; Azhdarpoor, A. The application of plant growth regulators to improve phytoremediation of contaminated soils: A review. Chemosphere **2019**, *220*, 818-827, doi:10.1016/j.chemosphere.2018.12.203.

[34] Hac-Wydro, K.; Sroka, A.; Jabłońska, K. The impact of auxins used in assisted phytoextraction of metals from the contaminated environment on the alterations caused by lead(II) ions in the organization of model lipid membranes. Colloids Surfaces B Biointerfaces **2016**, *143*, 124-130, doi:10.1016/j.colsurfb.2016.03.018.

[35] Bashir, M.A.; Liu, J.; Geng, Y.; Wang, H.; Pan, J.; Zhang, D.; Rehim, A.; Aon, M.; Liu, H. Co-culture of rice and aquatic animals: An integrated system to achieve production and environmental sustainability. J. Clean. Prod. 2020, *249*, 119310.

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[38] Bordoloi, N.; Baruah, K.K.; Thakur, A.J. Effectiveness of plant growth regulators on emission reduction of greenhouse gas (Nitrous oxide): An approach for cleaner environment. J. Clean. Prod. **2018**, *171*, 333-344, doi:10.1016/j.jclepro.2017.09.284.

## **Chapter 17**


*Article* **Multi-Organism Composites: Combined Growth Potential of Mycelium and Bacterial Cellulose**

**Aileen Hoenerloh \*, Dilan Ozkan and Jane Scott**

Hub for Biotechnology in the Built Environment, School of Architecture, Planning and Landscape, Newcastle University, Newcastle upon Tyne NE1 7RU, UK<EMAIL_ADDRESS>(D.O<EMAIL_ADDRESS>(J.S.) **\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The demand for sustainable materials derived from renewable resources has led to significant research exploring the performance and functionality of biomaterials such as mycelium and bacterial cellulose. Whilst the growing conditions and performance of individual biomaterials are understood, to achieve additional new and enhanced functionality, an understanding of how biomaterials can be used together as composites and hybrids is required. This paper investigates the compatibility of mycelium and bacterial cellulose as two biomaterials with different qualities for the development of a large-scale biohybrid structure, the *BioKnit* prototype. Their compatibility was tested through preliminary design experiments and a material tinkering approach. The findings demonstrate that under optimal conditions mycelium and bacterial cellulose can grow in each other's presence and create composites with an extensive array of functions. However, there is a need to develop further fabrication settings that help to maintain optimal growing conditions and nutrition levels, whilst eliminating problems such as contamination and competition during growth.

**Keywords:** mycelium; bacterial cellulose; biocompatibility; knitted fabric; material tinkering


*Article* **A Three-Dimensional Constrained Ordered Weighted Averaging Aggregation Problem with Lower Bounded Variables**

#### **Yi-Fang Chen and Hui-Chin Tang \***

Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>Tel.: +886-9-12768867

Received: 25 July 2018; Accepted: 9 August 2018; Published: 13 August 2018

**Abstract:** We consider the constrained ordered weighted averaging (OWA) aggregation problem with a single constraint and lower bounded variables. For the three-dimensional constrained OWA aggregation problem with lower bounded variables, we present four types of solution depending on the number of zero elements. According to the computerized experiment we perform, the lower bounds can affect the solution types, thereby affecting the optimal solution of the three-dimensional constrained OWA aggregation problem with lower bounded variables.

**Keywords:** ordered weighted averaging (OWA) operators; constrained OWA aggregation problem; lower bounded variables


**16. Land units**

136 Soil Erosion Studies

Figure 8 shows slope map of the area. Class slope of 10 to %20 is most widespread one in the studied area. In slopes higher than %30, agricultural lands are less present due to difficulty in access and agricultural activities. The weighted mean of the slope of the studied area is

Landuse of the Sarab Drainage Basin are: Cultivation, range, forest and residential areas (villages) (Figure 9). Forest makes a small part in southeastern part of the basin and consists of *Quercus*, *Ulmus* and *Acear* species. There is also hand-planted forest consisting of *Pinus* species. In semi-arid zones, there is no forest and dry-farming lands mainly consisting of wheat, barley, sunflower and watermelon are present which due to not performing fallow, lack of vegetation after harvesting, ploughing in the direction of slope and nature of loesses, they are highly erodible. Rangeland mainly consist of *Artimisia*+*Dactylis glomerata*, *Artimisia* +*Poa bulbusa*, *Carpinus*+*Artimisia* and *Poa bulbusa*+*Medicago sativa* which due to not paying attention to the season for exploitation, they are overgrazed by tribal and villagers livestock

**14. Slope**

%16.5 .

Fig. 8. Slope map of the studied area

**15. Landuse**

and are erodible.

Figure 10 shows the land units of the area which are 23 units and more than 300 polygons. Each land unit is assigned letters and numbers. The first letter of the name of land unit is land use (A for dry-farming cultivation and R for rangelands). The second letter is for erosional features (SE surface erosion, RE rill erosion and SRE for surface and rill erosion) and latest number is for slope class (1 for class 0-%10, 2 for 10-%20, 3 for 20-%30 and 4 for more than 30 percent). Among the polygons, the ones which are accessible, having the largest area and are good indicators of similar polygons, were chosen (Figure 11), Rainfall simulator analyses were performed on each one with three repetitions and the resulted runoff and sediment were collected and measured.


**Development of High Efficiency Two-Phase Thermosyphons for Heat Recovery**

Ignacio Carvajal-Mariscal, Florencio Sanchez-Silva and Georgiy Polupan *National Polytechnic Institute of Mexico Mexico*


**4. Analysis of the effects of the programme integrating courses**

The effects of programme integrating courses have been analysed in a sequence of publications, several of them only published in Swedish [22, 25, 27–30]. In this section, the results of these publications will be summarised.


**4. Discussion**

A variety of target detection techniques have been published during the last few decades [12–15,25,59], with several studies applying support vector machines (SVM) [60] or Fisher's linear discriminant analysis (FLDA) [60] to solve target detection problems as a binary classification problem [61–65]. These algorithms require a number of classes, and their class distribution model must be known in advance. In order to avoid any biased selection of training samples, the partition must be performed randomly. In other words, training samples must be randomly selected from a dataset to form a training sample set for cross validation. As a result, such a validation is not repeatable and cannot be re-produced. The results are inconsistent. To alleviate this dilemma, this paper proposes a novel Constrained Energy Minimization (CEM) based technique that takes advantage of spectral and spatial information and developed Optimal Signature Generation Process (OSGP) in terms of the iterative process point of view to solve the issues mentioned above. CEM only requires one desired target information for the specific target of interest, regardless of other background information, which is its major advantage. Theoretically, CEM subpixel detection is generally performed by two operations that involve background suppression and matched filter [16]. First, it performs background suppression via the inverse of **R** so as to enhance a detected target contrast against the background. Second, CEM operates a matched filter using **d** as the desired matched signature so as to increase intensity of the target of interest. Since only one target signature can be used as the **d** in Equation (5), selecting an appropriate **d** is a very crucial step for detection results. Although CEM has many applications [27,30,31], very few studies investigated the issues of selecting a desire target signature. Therefore, this paper developed the Optimal Signature Generation Process (OSGP) to resolve this issue.

When compared to the classification based approaches that require very precise prior knowledge to generate a set of training samples and features, applying OSGP on the proposed CEM based methods required only one target signature information and provided stable results even if the initial desire target information is bias or not reliable. In the iterative process of OSGP in Figure 24, the iteration results of different desired signatures **d** after different numbers of iterations give a stable AUC result, so that the originally worse desired target obtains a relatively better desired target. Figure 25 shows different **d'**s have different results in the same algorithm. However, the **d'** iterated by OSGP used in CEM, Subset CEM, SW CEM, and ASW CEM can enhance the original desired target to some extent. Moreover, the results are approximately identical, meaning OSGP can determine the appropriate desired target automatically when selecting inappropriate **d** as initial, and the result is still very stable.

**Figure 24.** Iterative process of OSGP and corresponding AUC detection result.

**Figure 25.** AUC detection results of various algorithms executing five different **d** and corresponding **d'** generated by OSGP (**a**) CEM; (**b**) Subset CEM; (**c**) SW CEM; and, (**d**) ASW CEM.

CEM technique only takes advantages of spectral information to detect target of interests. However, when spectral information is insufficient to distinguish between targets and some materials have similar spectral signature, this likely causes false alarms in the multispectral images. In this case, our proposed window-based techniques actually include spatial information into the CEM algorithms via fixed or adaptive windows to compensate for the insufficient spectral information. According to the experimental results and the resulting images in Figures 14–17, among our proposed local CEM algorithms, the Subset CEM, Sliding Window-based CEM (SW CEM) of the fixed window size, or Adaptive Window-based CEM (ASW CEM) enhances the contrast between the target and the background better than the general CEM. Because the autocorrelation matrix **R** of the CEM algorithm is different, CEM uses **R** of the full image, whereas our proposed local CEMs uses local autocorrelation matrix **S** in Equation (10) to suppress the background. According to effect of the background suppression [58], it is obvious that using local autocorrelation **S** is better than global

autocorrelation **R** in this study. Figures 26 and 27 show the RGB signatures corresponding to different objects in the study site. As seen, some RGB signatures of leaves and grass are very close to NGL. In the upper left of Region 1, as the grass is too similar to the sprout shown in Figure 26, the CEM detection is likely to give a false alarm. Because the **R** that is used by CEM is generated according to the pixel value of the full image, the difference between NGL and grass is not obvious in the full image. In the entire image, the house and soil are larger than the RGB difference between grass and the sprout, and so the grass is likely to be misrecognized as NGL. On the contrary, in our proposed CEM based algorithms using **S**, because **S** is generated by pixels around the current pixel value and the proportion of soil and house is not high in a small area, the difference in RGB values between grass and NGL is enlarged, and the grass is likely to be suppressed, thus reducing the false alarm rate. In the same way, the lower right of Region 2 also easily gives a false alarm. Because the pixel values of some leaves are very similar to NGL in the region shown in Figure 27, when **R** is used to suppress the background, it is likely to be influenced by pixel values with a larger difference, and this problem can be solved by using **S**.

**Figure 26.** RGB signatures corresponding to different objects in Region 1 [4].

**Figure 27.** RGB signatures with different objects in Region 2 [4].

ASW CEM can change the size of the sliding window, according to the ratio of NGL around the current pixel. When there are too many NGL in the window, the difference between them is likely to be enlarged, and NGL that is very different from the desired target will be suppressed, leading to detection omission. Therefore, the sliding window shall be enlarged to reduce the rate of NGL and enhance the difference from the background, thus increasing the detected value. On the contrary, if the rate of NGL is too low, then the difference between backgrounds increase, and the RGB values that are

similar to NGL is likely to be misrecognized. At this point, the sliding window is shrunk, the rate of NGL increases, the difference between NGL and background are more apparent, and the result value of the non-NGL is reduced for suppression. When NGL are enhanced and background is suppressed; their difference is enlarged, so as to highlight NGL.

Briefly, CEM technique was originally designed to catch (1) low probability of infrequent occurrence, (2) relatively small sample size, and (3) most importantly, the target pixel has spectrally distinct from its surrounding pixels [16]. Obviously, the NGL in RGB images shows the same features. This explains why the window-based CEM techniques can achieve satisfied results of NGL detection even only three spectral signatures is used.


**About the Editors**

#### **Catalin Constantinescu**

Dr. Catalin-Daniel Constantinescu is a senior scientific researcher affiliated with the CNRS (Centre National de la Recherche Scientifique) in Marseille, France. His research interests cover aspects related to laser and plasma processing of materials, particularly for developing thin films and nanostructures, with applications in microelectronics, photonics, and electrical engineering. Three main processing techniques are worth mentioning, including pulsed laser deposition (PLD), matrix-assisted pulsed laser evaporation (MAPLE), and laser-induced forward transfer (LIFT). However, several other physical/chemical deposition and processing methods, as well as lithographic methods, should be noted as well, such as colloidal lithography using an adapted Langmuir–Blodgett technique and laser dewetting.

With 77 published papers, 1,037 citations, and an h-index of 21 (Scopus, July 2023), the research of Dr. Catalin-Daniel Constantinescu covers various aspects of materials sciences, due to his background in chemistry, physics, and engineering. He has also co-organized several conferences and symposia, including the EMRS 2021 Spring Meeting (symposium H—Laser material processing: from fundamental interactions to innovative applications), has delivered over 100 lectures and speeches at national/international meetings in over 15 countries, and is a member and chairperson of different international conferences, a member of different expertise and jury committees, and an editor. He has coordinated several regional, national, and international research projects, including two European infrastructure project proposals, and has actively participated and/or organized several events to promote science to the public, including "Fete de la Science" and "La Nuit Europ ˆ eenne des ´ Chercheur·e·s".

#### **Lucian Petrescu**

Dr. Lucian-Gabriel Petrescu is a tenured Professor at the Politehnica University of Bucharest, Romania. With over two decades of teaching activities and research projects, his main research interests focus on magnetic materials' characteristics using vibrating-sample magnetometers (VSMs) and single sheet testers (SSTs) in order to connect magnetic properties with their engineering utility. Another important aspect of his research is related to the modeling of magnetic materials using different models for predetermining power losses at a high frequency.

Dr. Lucian-Gabriel Petrescu is an author of 79 research papers published in prestigious journals or presented at international conferences. His work has over 206 citations, with an h-index of 9 (Scopus, July 2023). He has led 4 research projects and been a part of 17 other national research projects and 1 international research project. He is the leader of the "Technical Magnetism Laboratory" at the Politehnica University of Bucharest.


**7.2 While interpreting OGTT result you will not get information concerning**

a. Patient preparation for and how the glucose was administrated.

b. The result shows assessment of glucose tolerance at the time of the test only and cannot provide any other information.

2.Usually no glucosuria during fasting but mainly post-prandial

*Oral Glucose Tolerance Test (OGTT): Undeniably the First Choice Investigation…*

diabetes

*DOI: http://dx.doi.org/10.5772/intechopen.96549*

intestine is efficient in glucose absorption.

a. Diagnosed diabetes mellitus b. Suspected Type 1 DM c. Unconscious patient

**Table 6.**

**129**

d. Patient who can not obey instructions e. Refusal to follow instructions

f. Not for diabetes follow-up except during treatment of secondary diabetes; eg acromegaly, glucagonoma, Cushing's syndrome, Phaeochromocytoma

g. Hospitalized, acutely ill or immobile patients

emptying.

3. It may be physiological, in pregnancy or in renal disease or early

e. A flat glucose tolerance curve can be a normal finding and is as a result of rapid metabolism and not of either deficient absorption or slow gastric

Under certain pathological conditions such as hyper- and hypothyroidism changes in the gastric emptying rate may significantly alter the shape of the glucose tolerance curves [79]. Rapid gastric emptying associated with duodenal ulcer and partial gastrectomy where plasma glucose rises rapidly within 30 minutes of glucose ingestion stimulating hyperinsulinaemia and resultant reactive hypoglycaemia though measurement of serum insulin levels does not reveal evidence of such direct relationship. In a healthy young adult with increase physiologic activities, there is associated rapid metabolism and when venous rather than capillary blood is analyzed, a flat curve can be a normal findings and not of either deficient absorption or slow gastric emptying. Hypoglycaemia in a fasting subject is normally prevented by hepatic gluconeogenesis. This stopped after glucose ingestion when blood glucose rises, and begun when plasma glucose is falling preventing fasting hypoglycaemia Reactive hypoglycaemia in either normal healthy young adult, patient with peptic ulcer or partial gastrectomy might, therefore, be due to the failure of the liver to resume glucose production sufficiently and rapidly. The normal exponential pattern of gastric emptying results in a very gradual decline of the rate at which glucose enters the intestine and this should provide ideal conditions for the liver gradually to resume glucose production. The absorption of glucose by the small intestine is highly efficient. After ingestion of a concentrated solution, a combination of slow gastric emptying, dilution within the duodenum, and active peristalsis ensure that within the jejunum the glucose solution no longer remains hypertonic. The small

Every dynamic test requiring appropriate patient preparation and procedure for the conduct of the test will not be without contraindication if result is to be reliable. Such contraindications for conduct of OGTT are shown in **Table 6**. The primary objective is to demonstrate presence of dysglycaemia in a condition that has long latent period, except when monitoring success of treatment in secondary causes of hyperglycaemia. Subject must be conscious and alert to obey order (in both preparation and conduct of the test), in a no stressful condition, physically or otherwise. Patient should be able to take the stated amount or an equivalent and under influence of no other condition except what is being investigated for.

solution

*Conditions under which OGTT should not be conducted or when procedure should be stopped.*

h. Vomiting during the procedure

i Patient who could not consumed the glucose

j. Patient who developed moderate to severe hypoglycaemia during the test k. Do not perform the test on patients with uncontrolled thyroid dysfunction, under physical stress, eg post surgery, trauma, infection or extreme psychological stress or in patient with hypokalaemic periodic paralysis

Therefore result will be better interpreted with the cognition of the above in mind.