**5. Communication schemes for MU-MIMO systems**

Communication schemes for MU-MIMO systems include both uplink MU-MIMO (UL-MU-MIMO) and downlink MU-MIMO (DL-MU-MIMO). Case of uplink communication, users transmit signals to the base station. However, in the case of downlink communication, base station transmits signals to users. A representation of these systems is depicted in Figure 6. We assume that the base station is equipped with *N* antennas. Case of DL-MU-MIMO, the base station attempts to transmit signals to *K* users *U*1,. . . ,*UK* which are respectively equipped with antennas of numbers *M*1,..., *MK*.

For notations, if antenna *k* acts like a receiving antenna, it is denoted by *Rxk*. Otherwise, it is denoted by *Txk*.

**Figure 6.** MU-MIMO communication models: UL-MU-MIMO and DL-MU-MIMO

#### **5.1. UL-MU-MIMO**

8 Recent Trends in Multiuser MIMO Communications

*TxN*

. . .

BS

**Figure 5.** MU-MIMO configurations

MU-MIMO provides better performances.

**Table 1.** Comparison between SU-MIMO and MU-MIMO systems

*Rx*1

*Tx*1

*BS*1

*BS*2

*TxN*

. . .

*Tx*1

*TxN*

*U*1

✿✸

✒

③✿

✸

. . . *Rx*1

*U*1

✿✸

✒

✣✻

③✿

✸

✒

*RxM*1

. . .

*Rx*1

*UK*

❘

✲

✯

✇ �⑦

�

✲

(b) MU-MIMO

*Rx*1

*RxM*1

. . .

*Rx*1

*UK*

❘

�✣✻ ✒

�

✲

✯

✇

⑦

✲

. . .

*RxMK*

. . .

*U*

✿✸

③✿

. . .

*Tx*1

*BS*1

❃

*BS*2

❘

**Feature MU-MIMO SU-MIMO**

CSI Perfect CSI is required No CSI

**5. Communication schemes for MU-MIMO systems**

Advantage Multiplexing gain No interference

*TxN*

. . .

*Tx*1

*TxN*

(c) MU-MIMO with cooperation

order to achieve high throughput and to improve the multiplexing gain [16]. Finally, the performances of MU-MIMO and SU-MIMO systems in terms of throughput depend on the SNR level. In fact, at low SNRs, SU-MIMO performs better. However, at high SNRs level,

Main aspect BS communicates with multiple users BS communicates with a single user Purpose MIMO capacity gain Data rate increasing for single user

Communication schemes for MU-MIMO systems include both uplink MU-MIMO (UL-MU-MIMO) and downlink MU-MIMO (DL-MU-MIMO). Case of uplink communication,

Throughput Higher throughput at high SNR Higher throughput at low SNR

. . .

*Tx*1 *RxM*

(a) SU-MIMO

between BSs Cooperation

> Let *Xk*(*Mk* × 1), the transmit signal vector of user *Uk*; *k* = 1, . . . , *K*. We assume that data streams associated to user *Uk*; *k* = 1, . . . , *K* are zero mean white random vectors where :

$$\mathbb{E}\{X\_k X\_k^\*\} = I\_{M\_k};\ k = 1, \dots, K \tag{7}$$

E denotes the expected value operator.

The complex channel matrix relating user *Uk*; *k* = 1, . . . , *K* to the base station, *Hk* is of dimension (*N* × *Mk*). In presence of additive noise signal *b*(*N* × 1), the received signal vector at the base station, *y*(*N* × 1) is expressed in the slow fading model by:

$$y = \sum\_{k=1}^{K} H\_k \cdot X\_k + b \tag{8}$$

The noise signal vector is a zero mean white Gaussian variable with variance *σ*2 *b* . The uplink scenario should satisfy two constraints:

• It should be as many receive antennas at the base station as the total number of users antennas.

• Each user should have as many transmit antennas as the number of data streams.

In Figure 7, the block diagram for the UL-MU-MIMO includes a joint linear precoder and decoder. Linear precoders associated to users *U*1,..., *UK* will be respectively denoted by

**Figure 7.** Block diagram for the UL-MU-MIMO with coding techniques: *N* antenna BS and *K* multiple antenna users

*F*1,..., *FK*. The received signal vector at the BS is then expressed as :

$$y = \sum\_{k=1}^{K} H\_k \cdot F\_k \cdot X\_k + b \tag{9}$$

10.5772/57133

13

http://dx.doi.org/10.5772/57133

*Bk*; *k* = 1, ..., *K* is an additive noise signal vector of size (*Mk* × 1). Equation (11) could be

The second term of the sum in equation (13) represents the interference signal coming from multiple users. Processing techniques such as beamforming should be introduced in the block diagram of the MU-MIMO system for mitigating the effect of users interference and

MU-MIMO technology finds its applications in many areas and is nowadays exploited in

**3GPP LTE:** 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is one of the next generation cellular networks which exploit the MU-MIMO technology. Thanks to this technology, available radio spectrum 3GPP LTE networks could achieve higher

**Release 8 of LTE:** The first release of LTE (Release 8) was commercially deployed in 2009. Release 8 has introduced SU-MIMO scheme in the communication system model. This release only uses one transmission mode (Transmission mode 5) which has been defined for MU-MIMO systems. Transmission mode 5 supports rank 1 transmission for two User Equipments (UEs). In order to achieve the performances of MU-MIMO systems, feedback parameters such as the channel Rank Indicator (RI) and the Channel Quality Indicator

**Release 9 of LTE:** The second release of LTE (Release 9) provides enhancements to Release 8. The LTE Release 9 supports transmission mode 8 and includes both SU-MIMO and

**LTE advanced:** Other progress in LTE MIMO systems have been obtained through LTE advanced. The performed mode is the transmission mode 9. This mode allows for a

**Multiple-cell networks:** MU-MIMO systems have received wide spread success in wireless networks. Examples of applications include the multiple-cell networks [21] with multiple access channels where possible coordination among base stations is established. Figure 8 shows a MU-MIMO coordinated network in a cellular network. Three classes of cells are

The coordination between cells is performed by the Central Station (CS). The aim of this coordination is to mitigate the effect of inter-cells interference. Coding techniques should

= *Hk* · *Xk* +

improving the performances of the communication system.

many evolving technologies wich are described in the following.

spectral efficiencies than existing 3G networks [18][19].

possible switch between SU-MIMO and MU-MIMO.

be employed in order to mitigate the effect of interfering cells.

presented. These cells are referred as :

(CQI)/Precoding Matrix Indicator (PMI) feedback [17] are required.

*K* ∑ *j*�=*k*

*Yk* = *Hk* · *x* + *Bk* (12)

(*Hk* · *Xj*) + *Bk* ; *k* = 1, . . . , *K* (13)

Multi User MIMO Communication: Basic Aspects, Benefits and Challenges

also written:

**6. Fields of application**

MU-MIMO schemes.

• Coordinated cells • Central cell • Interfering cells

An estimate of the transmitted signal vectors denoted by *Yk*; *k* = 1, . . . , *K* are obtained by using the linear decoders *G*1,..., *GK*. The decoding process is such that :

$$\mathbf{Y}\_k = \mathbf{G}\_k \cdot \mathbf{y}$$

#### **5.2. DL-MU-MIMO**

DL-MU-MIMO communication model assumes that *K* users are simultaneously receiving signals from the base station. The transmitted signal vector *x*(*N* × 1) is expressed as the sum of signals intended to users *U*1,..., *UK*:

$$\mathbf{x} = \sum\_{k=1}^{K} \mathbf{X}\_k \tag{10}$$

The channel matrix between user *Uk*; *k* = 1, . . . , *K* and the base station is denoted by *Hk*(*Mk* × *N*). At each user, received signal vector of dimension (*Mk* × 1); *k* = 1, . . . , *K* is given by:

$$Y\_k = H\_k \cdot \mathbf{x} + B\_k \qquad ; \quad k = 1, \ldots, K \tag{11}$$

*Bk*; *k* = 1, ..., *K* is an additive noise signal vector of size (*Mk* × 1). Equation (11) could be also written:

$$Y\_k = H\_k \cdot \mathfrak{x} + B\_k \tag{12}$$

$$\mathbf{x} = H\_k \cdot \mathbf{X}\_k + \sum\_{j \neq k}^{K} (H\_k \cdot \mathbf{X}\_j) + B\_k \qquad ; \quad k = 1, \dots, K \tag{13}$$

The second term of the sum in equation (13) represents the interference signal coming from multiple users. Processing techniques such as beamforming should be introduced in the block diagram of the MU-MIMO system for mitigating the effect of users interference and improving the performances of the communication system.


**Preface to "Thermal Performance of Membrane Distillation"**

Membrane distillation (MD) is a thermally driven membrane operation able to theoretically reject 100% of all non-volatiles contained in aqueous streams. It is based on the evaporation of the feed to be treated at the feed–membrane interface, the migration of the vapor/volatiles through the micropores, and the condensation and recovery of the permeated species at the distillate side. Membranes used are hydrophobic and microporous. The driving force of the process is the difference of partial pressure created across the membrane, and the temperature at the feed–membrane interface has been shown to have the greatest impact on the transmembrane flux. However, the temperature at the feed–membrane interface is usually lower than the feed bulk temperature because of temperature polarization phenomena, with a consequent decrease in the process efficiency. In addition, during MD, the feed is cooled inside the module, not only due to the evaporation but also due to the heat lost by conduction through the membrane matrix and the heat lost towards the environment. Therefore, the effective temperature for the evaporation is further reduced. This Special Issue focuses on the research efforts made to improve the thermal performance of MD, including the development of new module designs and heat recovery systems, the preparation of new types of membranes, the use of renewable energies and the integration with other membrane units. For instance, the analysis of heat and mass transport correlations, the development of membranes for localized heating, the design of new modules and condensation devices, the use of solar energy, the integration of different MD configurations and the thermal efficiency of MD in specific applications, are presented and discussed.

My most sincere thanks to all the Authors who contributed to the success of this Special Issue.

**Alessandra Criscuoli** *Editor*


A Perspective on the Recent Amelioration of Co3O4 and MnO2 Bifunctional Catalysts for Oxygen Electrode Reactions

*Selvaraj Venkateshwaran, Karuppiah Selvakumar, Velu Duraisamy and Sakkarapalayam Murugesan Senthil Kumar*


**4. Towards trustworthy conversational agents for children**

In previous sections, we have demonstrated the potential impact of CAs on children underscoring the need for developing trustworthy CAs that are suitable for them. Previous research on AI and children [28] has acknowledged the need to move from the identification of ethical guidelines to practical implementation. Building upon our experience in the study described in Section 3, in this section, we introduce an adaptation of ethical guidelines for CAs that consider children as potential users, along with their implementation in a practical system design.

#### **4.1 Adaptation of ethical guidelines to CAs and children**

A recent study aimed to adapt ethical guidelines for AI systems to the specific case of CAs and children [13]. A team of four experts in computer science, AI ethics and

children's rights evaluated each item of ALTAI (introduced in Section 2.3) in terms of relevance and particular considerations for CAs and children. A Delphi method approach [58] was followed to perform a risk level analysis [59] as follows. The experts rated each ALTAI item based on the likelihood of application and impact on children and CAs. Later on, the individual ratings were analysed to identify critical points and disagreements. Finally, the identified disagreements were discussed and resolved at an expert meeting in order to reach a consensus.

The quantitative analysis consisted of a risk level assessment, using the formula *Risk = Likelihood Impact* to measure the level of risk of the different items and sections in ALTAI. A thematic analysis was also carried out on the annotated comments provided by the experts. The study's main findings are summarized below.

and self-regulation, transparency can be applied by using a language that is adapted to the user's age.

• **Transparency.** According to the experts, transparency is an important factor that can help address several critical considerations mentioned before. The CA trustworthiness could be improved by providing information about the system's nature, privacy and limitations in a language that is appropriate for the user's age.

The findings of this study are largely consistent with previous work on ethical guidelines for AI [28, 51, 52] but provide a more in-depth perspective of the specific issues related to CAs and children. The risk level assessment revealed that although all the identified points are critical, some present higher risks than others (**Table 3**). Particularly, the concerns of children were rated as having a higher impact, while the likelihood of issues was higher for CAs, resulting in a higher overall risk for CAs. Based on these findings, "Privacy and data governance" and "Human agency and oversight" were identified as the two critical requirements that should be prioritized when developing CAs for children.

For more details on the methodology and results of this study, please refer to ref. [13]. The critical points identified in this study, which have been taken into account in subsequent work [60], were applied throughout an agent's design process as illustrated in the subsequent section.

### **4.2 Proposal of a system**

In this section, we introduce a previous study [60] that applied the presented guidelines (Section 4.1) to the development of a conversational agent intended to create a list of games and toys according to user preferences.

The design of the interaction is limited to one user at a time, and the system can ask questions to provide the user with a list of interesting items to choose from. The system can also ask for data such as hobbies, idols and cost limits to fill the user's profile and determine the restrictions on the products that can be offered. The algorithm for the general CA is presented in Algorithm 1.

*a Risk = Likelihood Impact. b*

*Total Risk = Risk on Children Risk on CAs.*

*High risk values are marked with an\* (5-9 for Risks to Children and Risks to CAs, and 18-81 for the Total Risk).*

#### **Table 3.**

*Risk assessment results based on expert evaluations of the likelihood and impact of questions in conversational agents and children.*

The article proposed modifications consider children as possible users, including changes to the design, technology, interaction and post-interaction phases. These are summarized in **Table 4** and described in detail in the following paragraphs.

• Regarding the design phase, the "game recommender system" is redefined as a "games wish list" to improve children's interest. A list of relevant stakeholders is defined to gather ideas on how to adapt the system to a children-friendly version. Stakeholders are involved in defining age ranges to be considered, as well as, in time limits for interaction, identification of positive game properties and evaluation of the system.

**Table 4.**

*Recommendations to the design of a CA that generates a list of preferred toys/games for children.*

Overall, the proposed modifications aim to improve the system's inclusivity and reliability, promote critical thinking and decrease overtrust and data disclosure.

### **5. Conclusions and challenges for future research**

The field of conversational agents with children has made significant strides in recent years. Although extensive research has been conducted on the ethical development of artificial intelligence (AI), in general, there has been a growing emphasis on employing AI in interactions with children. Such work has emphasized the need for systems that enhance opportunities while mitigating risks.

However, the development and deployment of conversational agents for children come with specific challenges and ethical and social responsibility concerns. This chapter is dedicated to exploring the particular considerations conversational agents should have when interacting with children.

The chapter reviewed related research on conversational agents and children, identifying popular topics as well as opportunities and risks. Worldwide ethical guidelines on the development of trustworthy AI were presented as a framework for the ethical design and deployment of conversational agents with children. These guidelines emphasize the importance of the protection of user rights in the development and deployment of AI technologies. Additionally, a case study was presented that demonstrated the significant impact conversational agents can have on children's learning and social development.

An adaptation of previous AI ethical guidelines to the specific case of conversational agents and children was also presented, highlighting the importance of data protection and human agency. The application of ethical guidelines to the design of a conversational agent presented in this chapter served as an example of how these guidelines can be integrated into the development process of these systems.

It is important to note that the variety of conversational agent systems requires personalized study and application of these guidelines for each case. Furthermore, even state-of-the-art technology may not be able to address some proposed considerations, such as ASR modules that cannot understand little children's speech in less popular languages. Therefore, researchers in this area should continue to strive towards achieving new breakthroughs that enable the development of more ethically sound devices for the benefit of future generations.

In summary, the development and deployment of conversational agents with children require a careful balance between innovation and ethical responsibility. Ethical principles should guide research and development, and systems should be designed with the safety, privacy and well-being of children in mind. By doing so, conversational agents have the potential to be a powerful tool for enhancing children's learning and social development.

### **Acknowledgements**

This work was carried out with the support of the Joint Research Centre of the European Commission in the framework of the Collaborative Doctoral Partnership Agreement No.35500.

### **Conflict of interest**

The authors declare no conflict of interest.

*Towards Children-Centred Trustworthy Conversational Agents DOI: http://dx.doi.org/10.5772/intechopen.111484*


**7. References**

272 Molecular Dynamics – Theoretical Developments and Applications in Nanotechnology and Energy

transport rates, MD studies become one of the most beneficial methods in studying MOFs. As is evident from the volume of literature cited, this area is growing rapidly. The development of quantitative information about mixture diffusion in MOFs is just beginning (section 3.2) whereas a significant number of studies have already considered single component gas diffusion in MOFs (section 3.1). Detailed understanding of mixture diffusion in MOFs will be very beneficial for design of MOF membranes, adsorbents, catalysts and sensors. Current opportunities and challenges of using MD simulations in assessing

The most significant opportunity of employing MD simulations for obtaining gas diffusivity in MOFs lies in areas where experiments for transport property of interest (transport diffusivities, energy barrier to diffusion) are challenging, not in reiterating properties that have already been addressed experimentally. Measuring diffusivity at a wide range of loadings in the pores at extreme conditions such as infinite dilute loading and/or saturation loading is experimentally difficult. MD simulations can provide information about gas diffusion in MOFs' pores under these conditions. Getting diffusivity data as a function of gas loading is crucial to design membranes, adsorbents, catalysts from MOFs that will work

As addressed in Section 5, the development of quantitative information about mixture diffusion in MOFs is just beginning. Since performing mixture MD simulations for MOFs with large frameworks and for gas mixtures at high adsorbed loadings are computationally demanding, theoretical correlations that predict mixture diffusion based on single component diffusion data are very useful. Recent research search showed that these models yield accurate results for at least simple chemical mixtures in MOFs. Testing and validation of theoretical correlations for predicting gas diffusivity in various subclasses of MOFs will

A great advantage of using MD simulations is to test hypothetical MOF structures for particular applications if the metric describing the performance of a material for the application can be directly calculated. For example, Düren and coworkers used GCMC simulations to design materials with large adsorption capacities for CH4.(Düren et al., 2004) In a similar way, MD simulations can be used to design materials with slow diffusivities for CH4 and fast diffusivities for CO2 to identify materials that will be promising in kinetic

The development of accurate classical interatomic potentials for describing gas diffusion in MOFs remains challenging. From the modeling perspective, it is important to use experimental diffusion data from a broad range of conditions to parameterize interatomic potentials whenever this is practical. However, as discussed in Section 4, the number of experimental data on gas diffusion in MOFs is very limited. Furthermore, developing potentials specific to a MOF structure is not the solution since hundreds of different MOF structures are available. Therefore, efforts to test and improve the transferability of potentials among related families of MOFs will have a great value. One of the major

be useful to widely utilize these correlations for different structures.

transport rates of gases in MOFs will be addressed below.

under a wide range of operating conditions.

separation of CO2 from CO2/CH4 mixtures.

**6.2 Challenges**

**6.1 Opportunities**

Recent Advances in Molecular Dynamics

Hill,New York

110, 9565-9570.

16618-16625.

*Eng. Sci.*, 55, 2923-2930.

Wiley & Sons,New York

*Eng.*, 16, 71-197.

Simulations of Gas Diffusion in Metal Organic Frameworks 275

Eddaoudi, M., Kim, J., Rosi, N., Vodak, D., Wachter, J., O'Keeffe, M.&Yaghi, O. M. (2002).

El-Kaderi, H. M., Hunt, J. R., Mendoza-Cortés, J. L., A.P. Côté, Taylor, R. E.,

Feynman, R. P.&Hibbs, A. R. (1965). *Quantum Mechanics and Path Integrals*. McGraw-

Francl, M. M., Carey, C., Chirlian, L. E.&Gange, D. M. (1996). Charges Fit to Electrostatic

Frenkel, D.&Smit, B. (2002). *Understanding Molecular Simulation: From Algorithms to*

Frost, H., Düren, T.&Snurr, Q. (2006). Effects of Surface Area, Free Volume, and Heat of

Greathouse, J. A.&Allendorf, M. D. (2006). The Interaction of Water with MOF-5 Simulated

Greathouse, J. A.&Allendorf, M. D. (2008). Force Field Validation for Molecular Dynamics

Hayashi, H., Cote, A. P., Furukawa, H., O'Keeffe, M.&Yaghi, O. M. (2007). Zeolite A

Jhon, Y. H., Cho, M., Jeon, H. R., Park, I., Chang, R., Rowsell, J. L. C.&Kim, J. (2007).

Jobic, H., Kärger, J.&Bée, M. (1999). Simultaneous Measurement of Self- and Transport

Jobic, H.&Theodorou, D. N. (2007). Quasi-Elastic Neutron Scattering and Molecular

Jorgensen, W. L., Maxwell, D. S.&Tirado-Rives, J. (1996). Development and Testing of the

Kapteijn, F., Moulijn, J. A.&Krishna, R. (2000). The Generalized Maxwell-Stefan Model for

Kärger, J.&Ruthven, D. (1992). *Diffusion in Zeolites and Other Microporous Materials*. John

Keil, F. J., Krishna, R.&Coppens, M. O. (2000). Modeling of Diffusion in Zeolites. *Rev. Chem.*

by Molecular Dynamics. *J. Am. Chem. Soc.*, 128, 10678-10679.

Application in Methane Storage. *Science*, 295, 469-472.

Frameworks. *Science*, 316, 268-272.

Potentials? *J. Comput. Chem.* , 17, 367-383.

*Applications*. Academic Press,San Diego

Polymers. *J. Phys. Chem. C*, 112, 5795–5802.

Imidazolate Frameworks. *Nat. Mater.*, 6, 501-506.

James, S. J. (2003). Metal Organic Frameworks. *Chem. Soc. Rev.*, 32, 276-288.

Diffusivities in Zeolites *Phys. Rev. Lett.*, 82, 4260-4263.

Zeolites. *Micropor. Mesopor. Mater.*, 102, 21-50.

Liquids. *J. Am. Chem. Soc.*, 118, 11225-11236.

Systematic Design of Pore Size and Functionality in Isoreticular MOFs and Their

M.O'Keeffe&Yaghi, O. M. (2007). Designed Synthesis of 3D Covalent Organic

Potentials. II. Can Atomic Charges Be Unambiguously Fit to Electrostatic

Adsorption on Hydrogen Uptake in Metal-Organic Frameworks. *J. Phys. Chem. B*,

Simulations of IRMOF-1 and Other Isoreticular Zinc Carboxylate Coordination

Simulation of Methane Adsorption and Diffusion within Alkoxy-Functionalized IRMOFs Exhibiting Severely Disordered Crystal Structures. *J. Phys. Chem. C*, 111,

Dynamics Simulation as Complementary Techniques for Studying Diffusion in

Opls All-Atom Force Field on Conformational Energetics and Properties of Organic

Diffusion in Zeolites: Sorbate Molecules with Different Saturation Loadings. *Chem.*

Babarao, R.&Jiang, J. (2008). Diffusion and Separation of CO2 and CH4 in Silicalite, C168

Banerjee, R., Phan, A., Wang, B., Knobler, C., Furukawa, H., O'Keeffe, M.&Yaghi, O. M.

Banerjee, R., Furukawa, H., Britt, D., Knobler, C., O'Keeffe, M.&Yaghi, O. M. (2009). Control

Bär, N.-K., Ernst, H., Jobic, H.&Kärger, J. (1999). Combined Quasi-Elastic Neutron Scattering

Battisti, A., Taioli, S.&Garberoglio, G. (2011). Zeolitic Imidazolate Frameworks for

Bordiga, S., Vitillo, J. G., Ricchiardi, G., Regli, L., Cocina, D., Zecchina, A., Bj.Arstad,

Buch, V. (1994). Path-Integral Simulations of Mixed Para-D-2 and Ortho-D-2 Clusters - the

Chmelik, C., Kärger, J., Wiebcke, M., Caro, J., van Baten, J. M.&Krishna, R. (2009).

Chui, S. S. Y., Lo, S. M. F., Charmant, J. P. H., Orpen, A. G.&Williams, I. D. (1999). A

Ciccotti, G., Ferrario, M.&Ryckaert, J.-P. (1982). Molecular-Dynamics of Rigid Systems in Cartesian Coordinates: A General Formulation. *Mol. Phys.*, 47, 1253-1264. Clark, L. A., Gupta, A.&Snurr, R. Q. (1998). Siting and segregation effects of simple molecules in zeolites MFI, MOR, and BOG. *J. Phys. Chem. B* 102, 6720-6731. Darkrim, F.&Levesque, D. (1998). Monte Carlo Simulations of Hydrogen Adsorption in

Dietzel, P. D. C., Morita, Y., Blom, R.&Fjellvag, H. (2005). An in Situ High-Temperature

Dietzel, P. D. C., Panella, B., Hirscher, M., Blom, R.&Fjellvag, H. (2006). Hydrogen

Cylindrical Cavities of the Desolvated Framework *Chem. Commun.*, 959-961. Düren, T., Sarkisov, L., Yaghi, O. M.&Snurr, R. Q. (2004). Design of New Materials for

Eddaoudi, M., Li, H.&Yaghi, O. M. (2000). Highly Porous and Stable Metal-Organic

Single-Crystal Investigation of a Dehydrated Melal-Organic Framework Compound and Field-Induced Magnetization of One-Dimensional Metaloxygen

Adsorption in a Nickel Based Coordination Polymer with Open Metal Sites in the

Frameworks: Structure Design and Sorption Properties. *J. Am. Chem. Soc.*, 122, 1391-

Single-Walled Carbon Nanotubes. *J. Chem. Phys.*, 109, 4981-4985.

Chains. *Angew. Chem. Int. Ed. Eng.*, 44, 6354-6358.

Methane Storage. *Langmuir*, 20, 2683-2689.

Simulation. *Langmuir*, 24, 5474-5484.

3877.

83.

22-32.

1397.

*Science*, 283, 1148-1150.

Application to CO2 Capture. *Science*, 319, 939-943.

Investigation. *Micropor. Mesopor. Mater.*, 143, 46-53.

Orientational Effects. *J. Chem. Phys.*, 100, 7610-7629.

MOF-5. *J. Phys. Chem. B*, 109, 18237-18242.

Schwarzite and IRMOF-1: A Comparative Study from Molecualr Dynamics

(2008). High-Throughput Synthesis of Zeolitic Imidazolate Frameworks and

of Pore Size and Functionality in Isoreticular Zeolitic Imidazolate Frameworks and Their Carbon Dioxide Selective Capture Properties. *J. Am. Chem. Soc.*, 131, 3875-

and NMR Study of Hydrogen Diffusion in Zeolites. *Magn. Reson. Chem.*, 37, 79-

Separation of Binary Mixtures of CO2, CH4, N2 and H2: A Computer Simulation

Bjørgen, M., Hafizovic, J.&Lillerud, K. P. (2005). Interaction of Hydrogen with

Adsorption and Diffusion of Alkanes in CuBTC Crystals Investigated Using Infra-Red Microscopy and Molecular Simulations. *Micropor. Mesopor. Mater.*, 117,

Chemically Functionalizable Nanoporous Material [Cu-3(Tma)(2)(H2O)(3)](N).

Recent Advances in Molecular Dynamics

276-279.

10, 3244-3249.

8921.

636.

127, 17998-17999.

Simulations of Gas Diffusion in Metal Organic Frameworks 277

Li, H., Eddaoudi, M., O'Keeffe, M.&Yaghi, O. M. (1999). Design and Synthesis of an

Lii, J.-H., Allinger, N. L.&Yuh, Y. H. (1989). Molecular Mechanics. The MM3 Force Field for

Liu, B., Yang, Q., Xue, C., Zhong, C.&Smit, B. (2008a). Molecular Simulation of Hydrogen

Liu, D., Zheng, C., Yang, Q.&Zhong, C. (2009). Understanding the Adsorption and Diffusion

Liu, J., Lee, J. Y., Pan, L., Obermyer, R. T., Simizu, S., Zande, B., Li, J., Sankar, S. G.&Johnson,

Loiseau, T., Serre, C., Huguenard, C., Fink, G., Taulelle, F., Henry, M., Bataille, T.&Ferey, G.

Martin, M. G.&Siepmann, J. I. (1997). Predicting Multicomponent Phase Equilibria and Free

Martyna, G. J., Tuckerman, M. E.&Klein, M. L. (1992). Nose-Hoover Chains: The Canonical

Martyna, G. J., Tuckerman, E., Tobias, D. J.&Klein, M. L. (1996). Explicit Reversible Integration Algorithms for Extended Systems. *Mol. Phys.*, 87, 1117-1157. Mayo, S. L., Olafson, B. D.&Goddard, W. A. (1990). Dreiding: A Generic Force Field for

Millward, A. R.&Yaghi, O. M. (2005). Metal-Organic Frameworks with Exceptionally High

Mueller, U., Schubert, M., Teich, F., Puetter, H., Schierle-Arndt, K.&Pastré, J. (2006). Metal

Narehood, D. G., Pearce, J. V., Eklund, P. C., Sokol, P. E., Lechner, R. E., Pieper, J., Copley, J.

Pan, L., Sander, M. B., Huang, X. Y., Li, J., Smith, M., Bittner, E., Bockrath, B.&Johnson, J. K.

Pantatosaki, E., Pazzona, E. G., Megariotis, G.&Papadopoulos, G. K. (2010). Atmic

within Zeolite Imidazolate Framework-8. *J. Phys. Chem. B*, 114, 2493-2503. Park, K. S., Ni, Z., Cote, A. P., Choi, J. Y., Huang, R. D., Uribe-Romo, F. J., Chae, H.

Capacity for Storage of Carbon Dioxide at Room Temperature. *J. Am. Chem. Soc.*,

Organic Frameworks-Prospective Industrial Applications. *J. Mater. Chem.*, 16, 626-

(2004). Microporous Metal Organic Materials: Promising Candidates as Sorbents

Simulation Studies on the Dynamics and Thermodynamics of Nonpolar Molecules

K., O'Keeffe, M.&Yaghi, O. M. (2006). Exceptional Chemical and Thermal Stability

Ensemble Via Continuous Dynamics. *J. Chem. Phys.*, 97, 2635-2643.

Framework Material:[Zn(Bdc)(Ted)0.5]. *J. Phys. Chem. C*, 112, 2911-2917. Liu, J., Keskin, S., Sholl, D.&Johnson, J. K. (2011). Molecular Simulations and Theoretical

Hydrocarbons. *J. Am. Chem. Soc.* 111, 8551-8566.

ZIF-68 and ZIF-70. *J. Phys. Chem. C*, 115, 12560-12566.

(Mil-53) Upon Hydration. *Chem.-Eur. J.*, 10, 1373-1382.

Molecular Simulations. *J. Phys. Chem. C*, 94, 8897-8909.

R. D.&Cook, J. C. (2003). *Phys. Rev. B*, 67,<PHONE_NUMBER>15.

for Hydrogen Storage *J. Am. Chem. Soc.*, 126, 1308-1309.

Study. *J. Phys. Chem. C*, 113, 5004-5009.

Exceptionally Stable and Highly Porous Metal-Organic Framework. *Nature*, 402,

Diffusion in Interpenetrated Metal-Organic Frameworks. *Phys. Chem. Chem. Phys.*,

of Carbon Dioxide in Zeolitic Imidazolate Frameworks: A Molecular Simulation

J. K. (2008b). Adsorption and Diffusion of Hydrogen in a New Metal Organic

Predictions for Adsorption and Diffusion of CH4/H2 and CO2/CH4 Mixtures in

(2004). A Rationale for the Large Breathing of the Porous Aluminum Terephthalate

Energies of Transfer for Alkanes by Molecular Simulation. *J. Am. Chem. Soc.*, 119,

Keskin, S.&Sholl, D. S. (2007). Screening Metal-Organic Framework Materials for

Keskin, S., Liu, J., Johnson, J. K.&Sholl, D. S. (2008). Testing the Accuracy of Correlations for

Keskin, S.&Sholl, D. S. (2009b). Efficient Methods for Screening of Metal Organic Framework

Keskin, S., Liu, J., Johnson, J. K.&Sholl, D. S. (2009a). Atomically-Detailed Models of Gas

Keskin, S., Liu, J., Rankin, R. B., Johnson, J. K.&Sholl, D. S. (2009b). Progress, Opportunities,

Keskin, S. (2010a). Comparing Performance of CPO and IRMOF Membranes for Gas Separations Using Atomistic Models. *Ind. Eng. Chem. Res.*, 49, 11689-11696. Keskin, S. (2010b). Molecular Simulation Study of CH4/H2 Mixture Separations Using Metal

Keskin, S. (2011a). Atomistic Simulations for Adsorption, Diffusion, and Separation of Gas Mixtures in Zeolite Imidazolate Frameworks. *J. Phys. Chem. C*, 115, 800-807. Keskin, S. (2011b). High CO2 Selectivity of A Microporous Metal-Imidazolate Framework: A

Keskin, S.&Kizilel, S. (2011). Biomedical Applications of Metal Organic Frameworks. *Ind.*

Kitagawa, S., Kitaura, R.&Noro, S. (2004). Functional Porous Coordination Polymers. *Angew.*

Krishna, R.&van den Broeke, L. J. P. (1995). The Maxwell-Stefan Description of Mass

Krishna, R.&Paschek, D. (2002). Self-Diffusivities in Multicomponent Mixtures in Zeolites

Krishna, R.&van Baten, J. M. (2011). In Silico Screening of Metal-Organic Frameworks in

Lee, T. B., Jung, D. H., Kim, D., Kim, J., Choi, K.&Choi, S.-H. (2009). Molecular Dynamics

Simulation Study on the Hydrogen Adsorption and Diffusion in Non-Interpenetrating and Interpenetrating IRMOFs. *Catalysis Today*, 146, 216-222. Li, H., Eddaoudi, M., Groy, T. L.&Yaghi, O. M. (1998). Establishing Microporosity in Open

Metal-Organic Frameworks: Gas Sorption Isotherms for Zn(Bdc) (Bdc = 1,4-

Molecular Simulation Study. *Ind. Eng. Chem. Res.*, 50, 8230-8236.

Transport across Zeolite Membranes. *Chem. Eng. J.* , 57, 155-162.

Separation Applications. *Phys. Chem. Chem. Phys.*, 13, 10593-10616.

Benzenedicarboxylate). *J. Am. Chem. Soc.*, 120, 8571-8572.

Mixtures in MOF-5. *Ind. Eng. Chem. Res.*, 48, 914-922.

14055-14059.

11786-11795.

*Res.*, 48, 2355-2371.

*Eng. Chem. Res.*, 50, 1799-1812.

*Chem. Int. Ed.*, 43, 2334–2375.

*Phys. Chem. Chem. Phys.*, 4, 1891-1898.

106.

13054.

Membrane-Based Methane/Carbon Dioxide Separations. *J. Phys. Chem. C*, 111,

Multi-Component Mass Transport of Adsorbed Gases in Metal Organic Frameworks: Diffusion of H2/CH4 Mixtures in Cu-BTC. *Langmuir*, 24, 8254–8261. Keskin, S.&Sholl, D. S. (2009a). Assessment of a Metal-Organic Framework Membrane for

Gas Separations Using Atomically Detailed Calculations: CO2, CH4, N2, H2

Membranes for Gas Separations Using Atomically-Detailed Models. *Langmuir*, 25,

Mixture Diffusion through CuBTC Membranes. *Micropor. Mesopor. Mater.*, 125, 101-

and Challenges for Applying Atomically Detailed Modeling to Molecular Adsorption and Transport in Metal-Organic Framework Materials. *Ind. Eng. Chem.*

Organic Framework Membranes and Composites. *J. Phys. Chem. C*, 114, 13047-

of Zeolitic Imidazolate Frameworks. *Proc. Natl. Acad. Sci. U. S. A.*, 103, 10186- 10191.

Recent Advances in Molecular Dynamics

403-411.

10132-10141.

Formulation. *Langmuir*, 19, 7977-7988.

*Phys. Lett.*, 501, 455-460.

*J. Solid State Chem.*, 178, 2420-2429.

*Phys. Chem. Chem. Phys.*, 11, 11389-11394.

Press,New York

114, 10527-10534.

468.

University Press,Delft

Simulations. *J. Phys. Chem. B*, 109, 15760-15768.

Material MOF-5. *Angew. Chem. Int. Ed.*, 45, 2123-2126.

Simulations of Gas Diffusion in Metal Organic Frameworks 279

Sholl, D. S. (2006). Understanding Macroscopic Diffusion of Adsorbed Molecules in

Skoulidas, A. I., Ackerman, D. M., Johnson, J. K.&Sholl, D. S. (2002). Rapid Transport of

Skoulidas, A. I.&Sholl, D. S. (2003). Molecular Dynamics Simulations of Self-Diffusivities,

Skoulidas, A. I., Sholl, D. S.&Krishna, R. (2003). Correlation Effects in Diffusion of CH4/CF4

Skoulidas, A. I. (2004). Molecular Dynamics Simulations of Gas Diffusion in Metal-Organic

Skoulidas, A. I.&Sholl, D. S. (2005). Self-Diffusion and Transport Diffusion of Light Gases in

Stallmach, F., Groger, S., Kunzel, V., Kärger, J., Yaghi, O. M., Hesse, M.&Muller, U. (2006).

Sun, X., Wick, C. D., Thallapallya, P. K., McGraila, B. P.&Danga, L. X. (2011). Molecular

Tafipolsky, M., Amirjalayer, S.&Schmid, R. (2007). Ab Initio Parametrized MM3 Force Field for the Metal-Organic Framework MOF-5. *J. Comput. Chem.*, 28, 1169-1176. Theodorou, D. N., Snurr, R. Q.&Bell, A. T. (1996). *Molecular Dynamics and Diffusion in*

Uemura, K., Matsuda, R.&Kitagawa, S. (2005). Flexible Microporous Coordination Polymers

Watanabe, T., Keskin, S., Nair, S.&Sholl, D. S. (2009). Computational Identification of a

Wehring, M., Gascon, J., Dubbeldam, D., Kapteijn, F., Snurr, R. Q.&Stallmach, F. (2010). Self-

Wesselingh, J. A.&Krishna, R. (2000). *Mass Transfer in Multicomponent Mixtures*. Delft

Xiong, R., Odbadrakh, K., Michalkova, A., Luna, J. P., Petrova, T., Keffer, D. J., Nicholson, D.

Xu, Q.&Zhong, C. (2010). A General Approach for Estimating Framework Charges in Metal

Organic Frameworks. *J. Phys. Chem. C*, 114, 5035-5042.

Frameworks: Argon in CuBTC. *J. Am. Chem. Soc.*, 126, 1356-1357.

Gases in Carbon Nanotubes. *Phys. Rev. Lett.*, 89,<PHONE_NUMBER>04.

Crystalline Nanoporous Materials Via Atomistic Simulations. *Acc. Chem. Res.*, 39,

Corrected Diffusivities, and Transport Diffusivities of Light Gases in Four Silica Zeolites to Assess Influences of Pore Shape and Connectivity. *J. Phys. Chem. A*, 107,

Mixtures in MFI Zeolite. A Study Linking MD Simulations with the Maxwell-Stefan

Metal-Organic Framework Materials Assessed Using Molecular Dynamics

NMR Studies on the Diffusion of Hydrocarbons in the Metal-Organic Framework

Mechanism of Hydrocarbons Binding to the Metal–Organic Framework. *Chem.*

*Microporous Materials. In Comprehensive Supramolecular Chemistry*. Pergamon

Metal Organic Framework for High Selectivity Membrane-Based Gas Separations.

Diffusion Studies in CuBTC by PFG NMR and MD Simulations. *J. Phys. Chem. C*,

M., Fuentes-Cabrera, M. A., Lewis, J. P.&Leszczynski, J. (2010). Evaluation of Functionalized Isoreticular Metal Organic Frameworks (IRMOFs) as Smart Nanoporous Preconcentrators of RDX. *Sensors and Actuators B: Chemical*, 148, 459-

Potoff, J. J.&Siepmann, J. I. (2001). Vapor-Liquid Equilibria of Mixtures Contaning Alkanes,

Rankin, R. B., Liu, J., Kulkarni, A. D.&Johnson, J. K. (2009). Adsorption and Diffusion of

Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A.&Skiff, W. M. (1992). Application

Rowsell, J. L. C., Spencer, E. C., Eckert, J., Howard, J. A. K.&Yaghi, O. M. (2005). Gas

Sagara, T., Klassen, J.&Ganz, E. (2004). Computational Study of Hydrogen Binding by

Sagara, T., Ortony, J.&Ganz, E. (2005a). New Isoreticular Metal-Organic Framework

Sagara, T., Klassen, J., Ortony, J.&Ganz, E. (2005b). Binding Energies of Hydrogen Molecules

Salles, F., Jobic, H., Maurin, G., Koza, M. M., Llewellyn, P. L., Devic, T., Serre, C.&Ferey, G.

Salles, F., Jobic, H., Devic, T., Llewellyn, P., Serre, C., G.Ferey&Maurin, G. (2010). Self and

Salles, F., Bourrelly, S., Jobic, H., Devic, T., Guillerm, V., Llewellyn, P., Serre, C.,

Sanborn, M. J.&Snurr, R. Q. (2000). Diffusion of Binary Mixtures of CF4 and N-Alkanes in

Sarkisov, L., Düren, T.&Snurr, R. Q. (2004). Molecular Modeling of Adsorption in Novel

Seehamart, K., Chmelik, C., Krishna, R.&Fritzsche, S. (2011). Molecular Dynamics

Nanoporous Metal-Organic Materials. *Mol. Phys.*, 102, 211-221.

Ruthven, D. M. (1984). *Principles of Adsorption and Adsorption Processes.* Wiley,New York Ryan, P., Broadbelt, L. J.&Snurr, R. Q. (2008). Is Catenation Beneficial for Hydrogen Storage

in Metal-Organic Frameworks? *Chem. Commun.*, 4132-4134.

Metal-Organic Framework-5. *J. Chem. Phys.*, 121, 12543-12547.

Carbon Dioxide, and Nitrogen. *AIChE J.*, 47, 1676-1682.

10191.

16914.

10046.

1354.

214713.

014706.

245904.

Simulations. *ACS Nano*, 4, 143-152.

Faujasite. *Sep. Purif. Technol.*, 20, 1-13.

*Chem. C*, 115, 10764-10776.

*Mater.*, 143, 125-131.

of Zeolitic Imidazolate Frameworks. *Proc. Natl. Acad. Sci. U. S. A.*, 103, 10186-

Light Gases in ZIF-68 and ZIF-70: A Simulation Study. *J. Phys. Chem. C*, 113, 16906-

of a Universal Force Field to Organic Molecules. *J. Am. Chem. Soc.*, 114, 10035-

Adsorption Sites in a Large-Pore Metal Organic Framework. *Science*, 309, 1350-

Materials for High Hydrogen Storage Capacity. *J. Chem. Phys.*, 123, 214707-

to Isoreticular Metal-Organic Framework Materials. *J. Chem. Phys.*, 123, 014701-

(2008). Experimental Evidence Supported by Simulations of a Very High H2 Diffusion in Metal Organic Framework Materials. *Phys. Rev. Lett.*, 100, 245901-

Transport Diffusivity of CO2 in the Metal−Organic Framework MIL-47(V) Explored by Quasi-Elastic Neutron Scattering Experiments and Molecular Dynamics

G.Ferey&Maurin, G. (2011). Molecular Insight into the Adsorption and Diffusion of Water in the Versatile Hydrophilic/Hydrophobic Flexible MIL-53(Cr) MOF. *J. Phys.*

Investigation of the Self Diffusion of Binary Mixture Diffusion in the Metal Organic Framework Zn/Tbip) Accounting for the Frameowrk Flexibility. *Micropor. Mesopor.*

Xue, C.&Zhong, C. (2009). Molecular Simulation Study of Hexane Diffusion in Dynamic Metal Organic Frameworks. *Chinese J. Chem.*, 27, 472-478.

**14**

**Molecular Dynamic Simulation of Short**

*Institute of Metallurgy, Russian Academy of Sciences, Ural division*

Main concepts of Hydrogen permeability (HP) mechanism for the pure crystal metals are already stated. There are well-founded theoretical models and numerous experimental researches. As far as disordered systems (in which Hydrogen solubility is much more, than in crystal samples) are concerned, such works appear to be comparatively recent and rare. Particularly, they are devoted to Hydrogen interaction of with amorphous structures. Deficiency of similar researches is caused by thermo-temporal instability of amorphous

Unlike crystal alloy, where interstice volumes are presented discretely only by tetrahedron and octahedron cavities, small and big interstice cavities distribution in an amorphous alloy is close to Gaussian function (Polukhin and Vatolin, 1985, Polukhin et.al, 1984, 1986). Thus Hydrogen energy distribution function form in the amorphous alloys cavities is close to the main RDF peak which is approximated by Gaussian function. Inter-cavities transitions are strongly correlated, and the stationary states contribution to the Hydrogen atoms motion is

Amorfizator-elements (Si, B, C, etc.) insertion into the amorphous metals reduces number of large cavities (octahedrons) providing most energetically favourable Hydrogen migration path. It reduces metal absorption ability, as well as hydrogen diffusion motion intensity,

Amorphous alloys absorption ability of hydrogen is defined by number and size of cavities for hydrogen insertion, as well as the hydride forming elements (Ti, Zr, Hf, etc.) content in an alloy. Hydrogen diffusion factor in the amorphous alloys depends on its concentration.

Crystal and amorphous Palladium alloys are widely used in membranes for high pure Hydrogen producing. Literary data analysis shows possibility of filtering alloys production for hydrogen based on less expensive metals: V, Nb, Zr, Ta, etc., characterized by high Hydrogen solubility, which defines alloy Hydrogen permeability by diffusion factors as well as Palladium. Negative effect of these elements hydrides formation should be inhibited

**1. Introduction**

negligible.

materials structure and properties.

reducing Hydrogen permeability.

**Order and Hydrogen Diffusion in**

**the Disordered Metal Systems**

Eduard Pastukhov, Nikolay Sidorov, Andrey Vostrjakov and Victor Chentsov

 *Russian Federation*


*Edited by Gyula Mózsik,*

*Omar M. E. Abdel- Salam and Koji Takeuchi*

The capsaicin, a component of paprika, has been used in the culinary practice of every day nutritional practice. This agent is known to cause a variety of actions in the body through activating capsaicin-sensitive afferent neurons. A recently launched book entitled, Capsaicin-Sensitive Neural Afferentation and the Gastrointestinal Tract: from Bench to Bedside, is attractive for several reasons. First, Prof. Mozsik, a chief editor of this book, is known internationally as an expert in capsaicin pharmacology. Since he has worked for many years as a head of internal medicine, taking care of patients with various GI diseases, he is able to make a correct interpretation of various findings obtained in basic researches to clinical events. Second, although there are many articles about capsaicin, they mostly deal with basic research and finding but do not include much about clinical finding. Third, this book encompassed review articles written by internationally accepted scientists leading the field of capsaicin research, who highlighted the current state of knowledge on pharmacology, physiology and clinical phathophysiology of capsaicin-sensitive afferent neurons, and discussed directions for future research. Overall, this book is for people who are interested in the capsaicin action in body.

ISBN 978-953-51-1631-8

Capsaicin - Sensitive Neural Afferentation and the Gastrointestinal Tract:

from Bench to Bedside

Photo by Bulgnn / iStock

## Capsaicin Sensitive Neural Afferentation and the Gastrointestinal Tract: from Bench to Bedside

*Edited by Gyula Mózsik, Omar M. E. Abdel- Salam and Koji Takeuchi*

**CAPSAICIN - SENSITIVE**

**GASTROINTESTINAL**

**AND THE**

**BEDSIDE**

**Salam and Koji Takeuchi**

**NEURAL AFFERENTATION**

**TRACT: FROM BENCH TO**

Edited by **Gyula Mózsik, Omar M. E. Abdel-**


**2. Traditional versus modern hypnosis**

There are various types of hypnosis; however, the two distinct forms of hypnotherapy taught are the traditional, often referred to as, script-based approach, and the modern Ericksonian approach. They are sometimes better known as the direct or authoritative hypnosis and indirect or permissive hypnosis, respectively. The modern approaches are considered to be the conventional ways of inducing or achieving hypnosis.

**25**

*Hypnosis and Hypnotherapy: The Role of Traditional Versus Alternative Approach*

eye or counting backward to induce a hypnotic state [24].

among partygoers and comedy club attendees [3].

In our society today, three main types of hypnosis are used to hypnotize another person or hypnotize one's self. These three types of hypnosis are traditional hypnosis, Ericksonian hypnosis, and self-hypnosis. Each type of hypnosis varies from one another in terms of practice and use. The three types of hypnosis have a common denominator; that is, they all begin with some form of hypnotic induction like fixed

Is the most widely used basic form of hypnosis due to the belief that anyone can do it with very little instruction and training. Traditional hypnosis is believed to be the easiest form of hypnosis, it relies on simple suggestions and commands. Traditional hypnosis uses direct commands and suggestions to influence a person's feelings, thoughts, behaviors, and actions once the subject is in a hypnotic state. Traditional hypnosis will only work on people who are easy to follow orders, the hypnotist will need to know whether the patient is critical or not before the hypnotic session begins [3]. Often, it is not considered entirely effective for people that have analytical and critical thought processes. Commands and suggestions are usually interfered by the conscious mind; the critique nature of the conscious mind does not allow messages to be fully absorbed by the subconscious [3]. Traditional hypnosis is also the basis for stage hypnotism, which is popular in today's culture

Despite being largely unchanged as a directive approach since the 1930s, traditional hypnosis is still the dominant approach taught in countries like Australia [3]. The hypnotist will ask you to close your eyes, relax and forget everything in your mind in a traditional hypnotic approach. Once you enter a trance mode, he will give you hypnotic suggestion. For instance, if the patient wants to stop smoking, in the therapy session the hypnotist might ask the subject to visualize the harms of smoking, how awful it smells and hurts the nose and eyes. The therapist will give the subject suggestions that when not smoking you will be alright. Any time the subject remembers to smoke, he will remember the awful smell from a cigarette. This will help the subject to stop smoking and this is how traditional hypnosis works [3]. According to some studies, the success rate of this form of hypnosis rarely exceeds 30%; although the client may feel better, there may be higher relapse rate and the client may not always do better the next time something

In a situation when the traditional method does not work for the subject, the hypnotist might need to use another method like conversational hypnosis, etc. [3]. Unlike the traditional approach, conversational hypnosis is a type of hypnosis done to a subject or patient with the use of words and persuasion. It can also be used

The modern approach to hypnosis was developed by a renowned American psychiatrist and psychologist named Dr. Milton Erikson, thus named Eriksonian hypnosis. This method is built around understanding the client's unique situation and needs. The modern hypnosis approach will assist clients to build better cognitive skills and change the way they perceive a situation in and around challenging circumstances. Interventions are tailored toward the individuals' need, and this approach achieves a much higher success rate. Modern hypnosis particularly is an excellent approach for those who are skeptical of hypnosis as it uses metaphors

to treat stress, addiction, and phobia like in traditional hypnosis [3].

*DOI: http://dx.doi.org/10.5772/intechopen.91619*

**2.1 Traditional hypnosis**

similar happens [3].

**2.2 Modern hypnosis**

In our society today, three main types of hypnosis are used to hypnotize another person or hypnotize one's self. These three types of hypnosis are traditional hypnosis, Ericksonian hypnosis, and self-hypnosis. Each type of hypnosis varies from one another in terms of practice and use. The three types of hypnosis have a common denominator; that is, they all begin with some form of hypnotic induction like fixed eye or counting backward to induce a hypnotic state [24].


2.2.4. Biomass

Biomass power plants in California use a combination of raw biomass residues (including forest and agricultural residues from trees, foliage, roots and chips from wood processing residues) and, secondarily, municipal solid waste residues (mostly cellulose) [42]. A total installed capacity of 1325 MW is reported [36], which includes biogas (*cf.* Section 2.2.5). The WECC heat and power co-generation model from the Ecoinvent database [38] was selected to represent biomass electricity generation, and all energy and environmental impacts of this multi-output process were allocated on an energy content basis. This model is limited to the use of woodchips as feedstock; however, given that

biomass-fired plants contribute just 2% to the total electricity generated in-state (see Section 2.3), such simplification was deemed acceptable.

#### 2.2.5. Biogas

Biogas is a mixture of gases produced from the anaerobic decomposition of municipal solid waste (kitchen waste, garden waste), livestock manure, food processing waste, agricultural wastes and industrial wastewater. The process occurs in a controlled environment such as in airtight containers, in floating covers on lagoons or directly in landfills [43].

The resulting biogas is mostly composed of methane (CH4) and carbon dioxide (CO2), but also contains other hydrocarbons and significant traces of ammonia (NH3) and hydrogen sulfide (H2S).

The Ecoinvent WECC biogas-fired heat and power co-generation model [38] was selected to assess biogas electricity generation, and all energy and environmental impacts of this multi-output process were allocated on an energy content basis.


**6. Identification based on physiological and biochemical properties and sequence analysis of the 16S rRNA gene**

Identification of the SRB by morphological, physiological, and biochemical characteristics can be conducted according to Bergey's Manual of Determinative Bacteriology (ninth edition, 1994), where SRB belong to the seventh group and are called "dissimilatory sulfate- or sulfur-reducing bacteria" [11]. This group is divided into four subgroups (**Figure 5**).

However, more modern and complex classification of SRB is published in Bergey's Manual of Systematic Bacteriology (2005), where SRB are divided into different classes, for example, class IV, *Deltaproteobacteria*, including order II, *Desulfovibrionales*; family I, *Desulfovibrionaceae* (genus I. *Desulfovibrio*); or family II, *Desulfomicrobiaceae* (genus I. *Desulfomicrobium*) [10]. For details identification based on physiological and biochemical characteristics is necessary to use both Bergey's manuals.

As was mentioned above, the representatives of *Desulfovibrio* genus are often found in the animal and patients with IBD and healthy subjects, because it is necessary to pay attention to the more detailed steps for identification of the second subgroup where this genus belongs (see **Figure 4**).

#### **Figure 5.**

*The scheme of classification of dissimilatory sulfate- or sulfur-reducing bacteria group, according to Bergey's manual of determinative bacteriology (ninth edition, 1994) [11].*

#### **Table 1.**

*The differences between features of the second subgroup SRB genera according to Bergey's manual of determinative bacteriology [11].*

The second subgroup includes *Desulfovibrio* (Dvi), *Desulfomonas* (Dmo), *Desulfobulbus* (Dbu), *Desulfomicrobium* (Dmi), and *Thermodesulfobacterium* (Tdb) genera (**Table 1**).

Other SRB genera can be identified by Bergey's manuals [10, 11]. However, for complete identification based on morphological, physiological, and biochemical properties, the molecular methods, in particular the sequence analysis of 16S rRNA gene, are also necessary to be applied [50]. Except sequence analysis of 16S rRNA gene, it is important to confirm the SRB species by using primers of functional genes of dissimilatory sulfate-reduction, such as *DsrAB* and А*prBA* (**Table 2**).

Further on the example of one isolate of intestinal SRB, identification based on sequence analysis of 16S rRNA gene by using the universal primers will be described. The schema of this identification is presented in **Figure 6**.

**33**

**Table 3.**

PCR amplification.

[51] and Persing [52].

ized water, and 2.0 μl of DNA supernatant.

1492RPL 5'-GGTTACCTTGTTACGACTT-3' position 1510–1492

*Universal primers for amplification of 16S rRNA gene fragments.*

*Isolation and Purification of Sulfate-Reducing Bacteria DOI: http://dx.doi.org/10.5772/intechopen.86786*

DSR4R 5'-GTGTARCAGTTDCCRCA-3'

aprA-5-RV 5'-GCGCCAACYGGRCCRTA-3'

*The scheme of identification based on sequence analysis of 16S rRNA gene.*

**DsrAB gen**

А**prBA gen**

**Table 2.**

**Figure 6.**

*amplification.*

*DNA isolation*. Isolation and purification of DNA were carried out with a 72-h culture of SRB by using a "QIAmp DNA Mini Kit (QIAGEN), Cat. No 51304." One single SRB colony was taken from modified Postgate agar medium and suspended in 50 μl of deionized water in a screw cap micro-centrifuge tube. The samples were boiled at 98°C for 5 min prior to being centrifuged for 5 min/14,000 g to settle cell debris. In total 2 μl of supernatant, containing the genomic DNA, were used for

**Functional genes Primer sequence Amplicon length (pb)**

DSR1F 5'-ACSCAYTGGAARCACG-3' 1900

aprB-1-FW 5'-TGCGTGTAYATHTGYCC-3' 1200–1350

*Primers designed based on functional gens of dissimilatory sulfate reduction, which can be used for*

*Amplification of gene fragments*. Amplification of 16S rRNA gene fragments was carried out using the universal primers (**Table 3**) according to Weisburg et al.

*PCR procedure*. PCR was carried out on DNA isolated from SRB cells in a final volume of 20 μl consisting of 10.0 μl Taq PCR Master Mix Kit (Cat. No 201445), 0.1 μl of each primer, 0.1 μl uracil D-glycosylase (Cat. No. M0280 L), 7.7 μl deion-

The amplicons were amplified by a preliminary incubation at 94°C for 5 min (initial denaturation) and then 34 cycles of 94°C for 1 min (denaturation), 55°C for 1 min (annealing of primers), and 72°C for 2 min (polymerization), using a

**Primers Sequence Amplicon length (pb)** 8FPL 5'-AGTTTGATCCTGGCTCAG-3' position 8–27 Approximately 1500

806R 5'-GGACTACCAGGGTATCTAAT-3' position 806–787 Approximately 800

*Isolation and Purification of Sulfate-Reducing Bacteria DOI: http://dx.doi.org/10.5772/intechopen.86786*

**Table 2.**

*Microorganisms*

**32**

genera (**Table 1**).

*determinative bacteriology [11].*

**Table 1.**

**Figure 5.**

Optimal temperature range

Ability of bacteria to grow in the presence of sulfate

*manual of determinative bacteriology (ninth edition, 1994) [11].*

**Notes***: the feature is presence "+" or absence "–".*

and А*prBA* (**Table 2**).

The second subgroup includes *Desulfovibrio* (Dvi), *Desulfomonas* (Dmo), *Desulfobulbus* (Dbu), *Desulfomicrobium* (Dmi), and *Thermodesulfobacterium* (Tdb)

*The differences between features of the second subgroup SRB genera according to Bergey's manual of*

Other SRB genera can be identified by Bergey's manuals [10, 11]. However, for complete identification based on morphological, physiological, and biochemical properties, the molecular methods, in particular the sequence analysis of 16S rRNA gene, are also necessary to be applied [50]. Except sequence analysis of 16S rRNA gene, it is important to confirm the SRB species by using primers of functional genes of dissimilatory sulfate-reduction, such as *DsrAB*

**Features Dvi Dmo Dbu Dmi Tdb** Spiral or vibrio-shaped cells + — — — — Oval or rod-shaped cells — + + + + Movement with the polar flagella +/− +/− + +/−

*The scheme of classification of dissimilatory sulfate- or sulfur-reducing bacteria group, according to Bergey's*

+25…+40°C + + + + — +65…+70°C — — — — +

H2 + CO2 + acetate as a carbon source + + + + + Lactate + + + + + Propionate — — + — — Desulfoviridin + + — — —

Further on the example of one isolate of intestinal SRB, identification based on sequence analysis of 16S rRNA gene by using the universal primers will be

described. The schema of this identification is presented in **Figure 6**.

*Primers designed based on functional gens of dissimilatory sulfate reduction, which can be used for amplification.*

**Figure 6.**

*The scheme of identification based on sequence analysis of 16S rRNA gene.*

*DNA isolation*. Isolation and purification of DNA were carried out with a 72-h culture of SRB by using a "QIAmp DNA Mini Kit (QIAGEN), Cat. No 51304." One single SRB colony was taken from modified Postgate agar medium and suspended in 50 μl of deionized water in a screw cap micro-centrifuge tube. The samples were boiled at 98°C for 5 min prior to being centrifuged for 5 min/14,000 g to settle cell debris. In total 2 μl of supernatant, containing the genomic DNA, were used for PCR amplification.

*Amplification of gene fragments*. Amplification of 16S rRNA gene fragments was carried out using the universal primers (**Table 3**) according to Weisburg et al. [51] and Persing [52].

*PCR procedure*. PCR was carried out on DNA isolated from SRB cells in a final volume of 20 μl consisting of 10.0 μl Taq PCR Master Mix Kit (Cat. No 201445), 0.1 μl of each primer, 0.1 μl uracil D-glycosylase (Cat. No. M0280 L), 7.7 μl deionized water, and 2.0 μl of DNA supernatant.

The amplicons were amplified by a preliminary incubation at 94°C for 5 min (initial denaturation) and then 34 cycles of 94°C for 1 min (denaturation), 55°C for 1 min (annealing of primers), and 72°C for 2 min (polymerization), using a

**Table 3.**

*Universal primers for amplification of 16S rRNA gene fragments.*

thermocycler (model MJ Research PTC-200, USA). After the last amplification cycle, the samples were incubated further at 72°C for 2 min for complete elongation of the final PCR products and cooled at 10°C.

*Analysis of PCR products*. Analysis of PCR products was carried out by electrophoresis in 1.5% agarose gel, with field strengths of 5 V/cm. Electrophoresis time was 40 min. The 100 bp ladder (Malamité, Czech Republic) was used as a size standard and molecular weight markers. Isolation and purification of fragments from agarose were performed by centrifugation of gel strips containing DNA through aerosol filters. For purification of SRB amplicons, the commercial kit from QIAGEN "MinElute Gel Extraction Kit" was used. The sequence was carried out using a "genetic analyzer" and reagents "BigDye Terminator v3.1 Cycle Sequencing Kit." Search homologous deposited in the GenBank nucleotide sequence encoding the 16S rRNA gene, was performed using BLASTN and Blast2 programs.

The 16S rRNA gene amplicons which were used for sequence analysis were obtained by using the PCR method. The PCR products were separated by electrophoresis (**Figure 7**). Before sequence analysis the absorbance of amplicons (8FPL/806R, amplicon I about 800 bp; 8FPL/1492RPL, amplicon I about 1500 bp; 8FPL/806R, amplicon II about 800 bp; 8FPL/1492RPL, amplicon II about 1500 bp) was determined [50].

By comparison of individual sequencing data from the amplicons 1–5, the following gene for 16S rRNA sequence of the total length 1370 bp was completed:

**Black nucleotides** are a summary from 1, 2, 3, and 5: the best sequence data (totally four sequencing). **Green nucleotides** are a summary from 3 and 4: good sequence data (two sequencing). **Red nucleotides** are the rest of 4: the worse sequence data because they were received from one sequencing only, but its quality was excellent. The obtained sequence BLASTN was analyzed. The highest homology of SRB colony was identified with *Desulfovibrio piger* ATCC 29098 from GenBank.

The obtained sequence results of SRB isolated colony were also compared by BLASTN analysis with the nucleotide sequences of 16S rRNA gene of other strains (**Table 4**).

Thus, the nucleotide sequence of the 16S rRNA gene of SRB has the highest homology (99%) compared to deposited nucleotide sequence *D. piger* ATCC 29098 (AF192152) in the GenBank database.

**35**

**Table 4.**

**Figure 7.**

pletely to acetate [10, 11].

The *D. piger* belongs to sulfate-reducing bacteria which are usually considered

*The results of electrophoresis 16S rRNA PCR products (amplicons): 8FPL/1492RPL, 1500 bp (1); 8FPL/806R,*

**SRB strains Acc. No Identities Identity (%)** *Desulfovibrio piger* ATCC 29098 AF192152 1352/1368 99 *Desulfovibrio fairfieldensis* ATCC700045 U42221 1313/1374 96 *Desulfovibrio desulfuricans* Essex 6 AF192153 1299/1369 95 *Desulfovibrio intestinalis* DSM 11275 Y12254 1289/1373 94 *Desulfovibrio desulfuricans* MB AF192154 1293/1373 94 *Bilophila wadsworthia* ATCC49260 L35148 1242/1369 91 *Desulfovibrio vulgaris* subsp. Oxamicus DSM 1925 AJ295677 1089/1195 91 *Desulfovibrio longreachensis* ACM 3958 Z24450 1253/1374 91 *Desulfovibrio termitidis* DSM 5308 X87409 1237/1372 90 *Lawsonia intracellularis* NCTC 12656 U30147 1215/1374 88 *Desulfovibrio vulgaris* subsp. vulgaris DSM 644 M34399 726/834 87

*800 bp (2); 8FPL/1492RPL, 1500 bp (3); 8FPL/806R, 800 bp (4); M is marker (100 bp ladder).*

as a commensal bacterium in humans [5, 10, 50]. More recently, *D. piger* has attracted more interest as it was found to be the most prevalent species of SRB in feces of patients with inflammatory bowel disease [20, 21, 25, 31]. The obtained bacterial strains have such phenotypic features as the presence of desulfoviridin, cytochrome *c*3, and menaquinone MK-6. They oxidize organic compounds incom-

*Comparing the resulting sequence of 16S rRNA gene with other* Desulfovibrio *species.*

*Isolation and Purification of Sulfate-Reducing Bacteria DOI: http://dx.doi.org/10.5772/intechopen.86786*

#### **Figure 7.**

*Microorganisms*

was determined [50].

thermocycler (model MJ Research PTC-200, USA). After the last amplification cycle, the samples were incubated further at 72°C for 2 min for complete elongation

the 16S rRNA gene, was performed using BLASTN and Blast2 programs.

The 16S rRNA gene amplicons which were used for sequence analysis were obtained by using the PCR method. The PCR products were separated by electrophoresis (**Figure 7**). Before sequence analysis the absorbance of amplicons (8FPL/806R, amplicon I about 800 bp; 8FPL/1492RPL, amplicon I about 1500 bp; 8FPL/806R, amplicon II about 800 bp; 8FPL/1492RPL, amplicon II about 1500 bp)

By comparison of individual sequencing data from the amplicons 1–5, the following gene for 16S rRNA sequence of the total length 1370 bp was completed:

**Black nucleotides** are a summary from 1, 2, 3, and 5: the best sequence data (totally four sequencing). **Green nucleotides** are a summary from 3 and 4: good sequence data (two sequencing). **Red nucleotides** are the rest of 4: the worse sequence data because they were received from one sequencing only, but its quality was excellent. The obtained sequence BLASTN was analyzed. The highest homology of SRB colony was identified with *Desulfovibrio piger* ATCC 29098 from GenBank. The obtained sequence results of SRB isolated colony were also compared by BLASTN analysis with the nucleotide sequences of 16S rRNA gene of other

Thus, the nucleotide sequence of the 16S rRNA gene of SRB has the highest homology (99%) compared to deposited nucleotide sequence *D. piger* ATCC 29098

*Analysis of PCR products*. Analysis of PCR products was carried out by electrophoresis in 1.5% agarose gel, with field strengths of 5 V/cm. Electrophoresis time was 40 min. The 100 bp ladder (Malamité, Czech Republic) was used as a size standard and molecular weight markers. Isolation and purification of fragments from agarose were performed by centrifugation of gel strips containing DNA through aerosol filters. For purification of SRB amplicons, the commercial kit from QIAGEN "MinElute Gel Extraction Kit" was used. The sequence was carried out using a "genetic analyzer" and reagents "BigDye Terminator v3.1 Cycle Sequencing Kit." Search homologous deposited in the GenBank nucleotide sequence encoding

of the final PCR products and cooled at 10°C.

**34**

strains (**Table 4**).

(AF192152) in the GenBank database.

*The results of electrophoresis 16S rRNA PCR products (amplicons): 8FPL/1492RPL, 1500 bp (1); 8FPL/806R, 800 bp (2); 8FPL/1492RPL, 1500 bp (3); 8FPL/806R, 800 bp (4); M is marker (100 bp ladder).*

#### **Table 4.**

*Comparing the resulting sequence of 16S rRNA gene with other* Desulfovibrio *species.*

The *D. piger* belongs to sulfate-reducing bacteria which are usually considered as a commensal bacterium in humans [5, 10, 50]. More recently, *D. piger* has attracted more interest as it was found to be the most prevalent species of SRB in feces of patients with inflammatory bowel disease [20, 21, 25, 31]. The obtained bacterial strains have such phenotypic features as the presence of desulfoviridin, cytochrome *c*3, and menaquinone MK-6. They oxidize organic compounds incompletely to acetate [10, 11].

Moore W.E. found SRB for the first time in people's feces and identified it as *Desulfomonas pigra* [53], which subsequently is reclassified as *Desulfovibrio piger* [16]. The described bacterial strains are similar to that of Moore et al. [53] except for the G–C content of the DNA, which is 64 mol%. Obligate anaerobic, sulfate-reducing, non-saccharolytic, non-proteolytic, nonspore-forming, and Gram-negative bacteria that are straight and vibrio-like and have rounded ends (0.8–1.0 × 2.5–10.0 μm) [10]. These microorganisms use lactate, pyruvate, ethanol, and hydrogen as electron donors for sulfate reduction. They oxidize lactate and pyruvate incompletely to acetate. The optimum temperature for growth is +37°C. Growth is not affected by 20% bile. Colonies on anaerobic blood agar are translucent, 2 mm in diameter, circular, and non-hemolytic. Cells contain desulfoviridin and cytochrome *c*3. These bacteria isolated from human specimens (feces, peritoneal fluids, and intra-abdominal collections). The type strain, isolated from human feces, is ATCC 29098 [10].


*3.1. General Remarks*

Generally, survey-engineering uses a wide scope of measuring techniques; therefore, it is important to select the most suitable one for bridge deflection testing, especially since the structure span is 165 m. Therefore, the critical determinants, affecting the choice of measuring technique, are the bridge shape, its parameters, and possible access to the bridge (for mounting the deflection sensors). Deflections of this bridge were also measured by Rabiega et al. [28] using a laser rangefinder. The results obtained were unsatisfactory due to the belt conveyor vibration, affecting the receiver and transmitter.

The tested bridge was monitored using three independent measuring techniques: tachymetry, TLS, and photogrammetry. The measurement range of each applied method was the same. Thus, it can be assumed that those methods are mutually verified. It should be added that the measuring network with benchmarks was situated outside the testing bridge. In the analyzed case, the most important factors are the bridge length and the spatial nature of the bridge (the truss). The bridge length is important because the accuracy of determining the position of the measuring points falls with increasing sight length. It should be also added that before the main bridge testing, a preliminary examination of the selected methods was conducted to choose the most appropriate measuring shields, white spheres, and to determine the proper distances from instruments to the bridge. Additionally, various environmental conditions were also checked. The field testing was conducted after 40 years of bridge operation and a year after the bridge repair. Bridge deflections were recorded in three load scenarios:

Each load scenario (A, B, and C) lasted approximately 1 h, which gave 3 h for the full load cycle. In total, bridge testing took 12 h (four load cycles). The tachymetry method is a classical measurement method (one of the most accurate), and in this test, it was treated as the reference method. The bridge deflections were observed and compared to each other based on these techniques. To receive the reference stage for each applied method, the initial measurements were carried out under the static loading (without the limestone). All results obtained were referred to this stage.

Environmental conditions can have a significant impact on the accuracy of the laser scanning and photogrammetric measurements. Producers of measuring equipment (laser scanners) introduce limitations to the control systems of these devices to prevent measurements when external conditions exceed their values. The most commonly considered factors are vibrations, temperature, and atmospheric pressure. The instrument's internal software makes corrections automatically due to the refraction and curvature of the Earth. Some measuring instruments have the option of introducing additional factors, e.g., humidity. In summary, laser scanners have a zero and one environmental security system. This means that for the level of external factors set by the producer (or user), the instrument works correctly and after exceeding them, the instrument turns off. Factors affecting the measurement accuracy also include dust, fog, unfavorable lighting, and precipitation such as rain or snow. In addition, the impact of these factors may change over time. To check the influence of the selected environmental conditions, preliminary tests were conducted during the rain and high humidity (>80%). The obtained results were unsatisfactory due to the lack of repeatability of results (they deviated far above the standard error). Therefore, the authors tried to minimize the impact of external conditions on the accuracy of measurements by making them in the most favorable weather conditions.


**1. Introduction**

Crop supply is a global issue, particularly in the context of global climate change, rising population, and urbanization. With increasing food demand worldwide, agriculture production and food security should be guaranteed by ensuring biodiversity and limiting the environmental impacts [1]. This makes reliable information about crop spatial distribution and growing patterns crucial for studying regional agriculture production and supply, making political decisions, and facilitating crop management [2,3].

The classification of crop spatial distributions are valuable for agricultural monitoring and for the implementation and evaluation of crop management strategies [4,5]. Hence, crop type mapping is in high demand. Field research and remote sensing have always been the most important sources for obtaining agricultural information [6], and since the first launch of Earth observation satellites in 1972, continuous agriculture mapping and monitoring over large areas became possible with the Earth Observation (EO) data. Moreover, the new generation of EO data, nowadays, has increased the resolution of

**Citation:** Xie, G.; Niculescu, S. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France. *Remote Sens.* **2022**, *14*, 4437. https://doi.org/10.3390/rs14184437

Academic Editor: Jianxi Huang

Received: 1 August 2022 Accepted: 31 August 2022 Published: 6 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

sensors for agriculture uses, therefore since the last few decades, the science of agriculture mapping and monitoring has developed quickly, with diverse types of high spatial and temporal resolution EO data. For example, Sun et al. in 2019 [4] conducted a study of the crop types that were located at the lower reaches of the Yangzi River in China. They performed a classification of crop-type dynamics during the growing season by using three advanced machine learning algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF)) with a combination of three advanced sensors (Sentinel-1 backscatter, optical Sentinel-2, and Landsat-8). Arvor et al. in 2010 [7] provided a methodology for mapping the main crops and agricultural practices in the Mato Grosso state in Brazil; this study was performed by two successive, supervised classifications with the Enhanced Vegetation Index (EVI) time series from the MODIS sensor to create an agricultural mask and a crop classification of three main crops in the state. In another study by Forkuor et al. in 2014 [8], they found that an integration of multi-temporal optical RapidEye and dual-polarized Synthetic Aperture Radar (SAR) TerraSAR-X data can efficiently improve the classification accuracy of crops and crop group mapping in West Africa, in spite of excessive cloud cover, small sized fields, and a heterogeneous landscape. Furthermore, in the Finistère department, Xie and Niculescu 2021 [9] evaluated the multiannual change detections of different Land Cover Land Use (LCLU) regions, including agricultural land with accuracy indices between 70% and 90%, by using high-resolution satellite imagery (SPOT 5 and Sentinel-2) and three algorithms that were implemented: RF, SVM, and the Convolutional Neural Network (CNN).

More importantly, many studies of crop mapping focuses on winter crop mapping. Dong et al. in 2020 [10] proposed a method called phenology-time-weighted dynamic time warping (PT-DTW) for mapping winter wheat using Sentinel-2 time series data, and this new method may exploit phenological features in two periods, with a NDPI (Normalized Difference Phenology Index) providing more robust vegetation information and reducing the adverse impacts of soil and snow cover during the overwintering period. Zhou et al. in 2017 [11] studied the feasibility of winter wheat mapping in an urban agricultural region with a complex planting structure using three machine learning classification methods (SVM, RF, and neural network (NN)), and the possibility of improving classification accuracy by combining SAR and optical data.

Besides the contributions of the new generation of EO data, the diversity of the classification approaches and methods have provided more resources for agriculture mapping and monitoring. The classical, direct extraction approach is the traditional and most used classification approach that is used to extract single or multiple crop types directly from satellite images [12–14]. Moreover, we also propose the hierarchical classification approach for crops mapping in this study. Hierarchical classification is well known for its capacity to solve a complex classification problem by separating the problem into a set of smaller progressive classifications; it produces a series of thematic maps to progressively classify the image into detailed classes. Wardlow and Egbert [12] investigated the applicability of time-series Moderate Resolution Imaging Spectro-radiometer (MODIS) 250 m normalized difference vegetation index (NDVI) data for large-scale crop mapping in the Central Great Plains of the U.S. The hierarchical classification scheme was applied in this study with high classification accuracy, and instead of directly solving a complex irrigated crop mapping problem, a four-level hierarchical classification framework was implemented to produce a series of crop-related thematic maps that progressively classified cropland areas into detailed classes. Ibrahim et al. in 2021 [15] have also employed the hierarchical classification scheme to map crop types and cropping systems in Nigeria, using the RF classifier and Sentinel-2 imagery. Firstly, they produced a land cover map with five classes in order to eliminate other land cover types, then the next classification was performed only on cropland, where the specific crop types and cropping systems were mapped. The results indicated that the crop types were well classified with high accuracy, despite the study area being heterogeneous and smallholder-dominated.

In recent years, most studies in the agricultural field have explored the performance of different classification algorithms. Random Forest (RF) is one of the most well-known and widely used algorithms in the field for its optimal classification accuracy, effectiveness on large data bases, and its capability of estimating the importance of the variables in the classification [8,16–19]. The RF classification algorithm is traditionally run as a Pixel-Based Classification, which has proven efficient and accurate in agriculture fields by many studies [16,20–22]. On the other hand, the advantage of Object-Based Classification (OBC) is well documented and many recent studies have the conclusion that OBC usually outperforms PBC for its higher classification accuracy, better potential for extracting land cover information in a heterogeneous area with small size field, and the capacity to produce a more homogenous class [23,24]. However, even though Object-Based Classification is better developed and considered as more accurate than PBC, both classification methods are able to achieve a great degree of accuracy.

Aside from mapping and analyzing the crop spatial distribution, understanding agricultural growing patterns is also a key element for crop management. Crop phenology monitoring and the identification of the main phenological stages are highly necessary for agricultural production predicting, efficient interventions of farmers and decisionmakers during the phenological phases such as fertilization, pesticide application, and irrigation [25]. In particular, germination is the most critical phase to be understood, and it is the starting point of the growing season. Based on the germination information, the farmer and decision-makers are able to make a future projection of the season, estimate the whole seasonal phenology for crop growth, and predict its production [25]. Furthermore, phenology is highly related to the seasonal dynamics of a growth environment, therefore, in the context of global warming, the phenology of many plants, especially crops, may have changed [6].

Crop phenology is usually monitored with optical satellite images using vegetation indices. For example, Pan et al. in 2015 [26] analyzed the phenology of winter wheat and summer corn in the Guanzhong Plain in the Shanxi Province, China by using Normalized Difference Vegetation index (NDVI) time series data and extracted seasonality information from the NDVI time series for measuring phenology parameters. The potential of another less-known index, the Normalized Difference Phenology Index (NDPI), is exploited by Gan et al. in 2020 [27] in order to detect winter wheat green-up dates. During the evaluation with three other indices (NDVI, EVI, and EVI2), the results indicate that NDPI outperforms the other indices with the highest consistency with the ground truth.

Compared to the optical data, SAR data is less used in agricultural areas. Nevertheless, lately, with the emergence of a new generation of high-resolution SAR data, in particular since the Copernicus program Sentinel-1 C-band high spatial–temporal resolution images became available, SAR data has begun to draw interest, especially for its advantage of having its own source of energy, making it nearly independent of weather conditions [8]. Thus, SAR backscattering coefficient time series data is now more frequently used for crop phenology monitoring. While optical data strongly depends on the chlorophyll content in the plants, SAR data can reveal the main changes in the canopy structure, identify significant phenological stages, and determine the main growing period with the signal that is received after interacting with the canopy of the plants. Therefore, studies of crop phenology monitoring using SAR data have increased considerably in recent years. Meroni et al. in 2020 [28] conducted a study of retrieving the crop-specific land surface phenology (LSP) of eight major European crops from Sentinel-1 SAR and Sentinel-2 optical data, where crop phenology was detected on the temporal profiles of the ratio of the backscattering coefficient VH/VV from Sentinel-1 and NDVI from Sentinel-2. They revealed that the crop phenology that was detected by Sentinel-1 and 2 can be complementary. Wali et al. in 2020 [29] introduced rice phenology monitoring in the Miyazaki prefecture of Japan by using Sentinel-1 dual polarization (VV and VH) time series data, and attempted to clarify the relationship between rice growth parameters and the backscattering coefficient using the combination of two linear-regression lines. Canisius et al. in 2018 [30] exploited SAR

polarimetric parameters that were derived from fully polarimetric RADARSAT-2 SAR time series data to predict the growth pattern and phenological stages of canola and spring wheat in the Nipissing agricultural district of Northern Ontario, Canada. Mandal et al. in 2020 [31] proposed a dual-pol radar vegetation index (DpRVI) from Sentinel-1 difference data (VV-VH) to characterize the vegetation growth of three crop types (canola, soybean, and wheat) from sowing to full canopy development, with the accumulation of the Plant Area Index (PAI) and biomass.

The feasibility and effectiveness of winter crop type mapping and phenology monitoring with optical or SAR satellite data has been proven by many studies in agricultural field, however, some limitations remain. For example, the potential of a vegetation index other than NDVI and EVI has rarely been explored, and the studies have never been performed in a coastal area with fragmented and small-scale fields. More importantly, almost all the research perform and evaluate a single classification approach or method, instead of comparing different approaches and methods for crop type mapping.

In this study, we introduce a methodology to map two winter crop types (winter wheat and winter barley) with Sentinel-2 optical data that was acquired during the growing season of the winter crops. Two different classification approaches (hierarchical classification and classical direct extraction) were performed using RF-supervised classification algorithms, and two classification methods (PBC and OBC) were operated and evaluated within the hierarchical classification framework. With the classification results of the winter crops, we are able to monitor their phenology with Sentinel-1 C-band SAR backscatter time series and precipitation data in order to understand their temporal behavior from sowing to harvesting, identify the three main phenological stages (germination, heading, and ripening, including harvesting), and study how crop phenology responds to weather conditions.

The main objectives of this study are listed as follows:

#### **2. Study Area**

The study area is located on the west coast of France in the north of the Finistère department and the region of Brittany (Figure 1).

The study area covers a land surface of 1034.41 km2, and extends between the latitudes of 48◦19 39N and 48◦40 41N, and the longitudes of 4◦12 50W and 4◦47 13W. According to French National Institute of Geographic and Forest Information (IGN), the northern part of Finistère is mostly dominated by plains, and the elevation of the area ranges between 0 m and 100 m [32]. The study area is mostly occupied by cropland, temporal or permanent grasslands, small area of forests and shrubs, urban agglomeration in the south, and a wetland area in the north [33]. Climatically, the north of Finistère is classified as type Cfb (temperate oceanic climate), according to the Köppen climate classification, and it is characterized by warm winters and cool summers. On average, the northern region of Finistère receives 941 mm of total precipitation per year, with the annual average temperature being 12.1 ◦C (7.7 ◦C and 16.8 ◦C are the monthly average temperatures for the coldest and warmest months, respectively), and therefore the warm temperate climate with frequent rainfall provides very favorable conditions for agriculture activities.

**Figure 1.** Location of study area, the north of the Finistère department, Brittany, France, as per the RGB band combination of a Sentinel-2 satellite image on 20 April 2019 and the distribution of agricultural land in 2019.

With such climate and topography conditions, agriculture is an important economic sector in the study area, and a considerable number of locals work in an agricultural or related sector in the department. There are 384,408 hectares of useful agricultural area in the department, so 57% of the department's surface is devoted to agricultural use [34]. One of main agricultural productions are crops, including corn, winter wheat, and winter barley, and vegetables [35].

Hence, it is important to develop a methodology to map one or several specific crop types and monitor their growth stages by using free access, high quality satellite images for crop production management. The north of the Finistère department was chosen as our first study area because of its favorable natural conditions, highly active agricultural activities, and its proximity, which facilitate the field research and interaction with farmers.

### **3. Data**

The study was executed in the Finistère department in France during 2019, using openaccess high-quality satellite data from the Sentinel platform. It is worth noting that the latest version of the graphic parcel register was published in 2019 by the French National Institute of Geographic and Forest Information, and this information was relevant to our study.

Due to the annual high-intensity precipitation there is frequent heavy cloud cover in the region, therefore, operable optical satellite images are very rare in the study area. Nevertheless, a Sentinel-2 optical satellite image was acquired in the spring, which is the growing season of the winter crops. In order to create a cloud-free time series of the study, the phenological phrases of winter crops from the SAR data were applied to the phenology monitoring process.

#### *3.1. Sentinel-2 Optical Data*

Sentinel-2 is an satellite imaging mission that is implemented by the European Commission (EC) and the European Space Agency (ESA) as a part of the Copernicus program [36]. The two identical satellites (Sentinel-2A and Sentinel-2B) provide continually

open-access, multispectral, wide swath (290 km), high spatial resolution (four bands at 10 m, six bands at 20 m, and three bands at 60 m), and high revisit frequency (five days with combined satellites) image data [36]. Due to frequent heavy cloud cover in the area, only one cloud-free level 2A atmospheric effect-corrected Sentinel-2 image from 20 April 2019 was acquired from the Theia platform (catalog.theia-land.fr) [37] (Table 1). Ten spectral bands (Table 2) were extracted for further processing and analysis.

**Table 1.** Sentinel-2 image used in the study.

**Table 2.** Sentinel-2 spectral bands used in the study.