**Dmitry A. Ruban 1,2,3**

Received: 31 October 2019; Accepted: 4 December 2019; Published: 5 December 2019

**Abstract:** Modern geoscience research pays significant attention to Quaternary coastal boulder deposits, although the evidence from the earlier geologic periods can be of great importance. The undertaken compilation of the literature permits to indicate 21 articles devoted to such deposits of Neogene age. These are chiefly case studies. Such an insufficiency of investigations may be linked to poor preservation potential of coastal boulder deposits and methodological difficulties. Equal attention has been paid by geoscientists to Miocene and Pliocene deposits. Taking into account the much shorter duration of the Pliocene, an overemphasis of boulders of this age becomes evident. Hypothetically, this can be explained by more favorable conditions for boulder formation, including a larger number of hurricanes due to the Pliocene warming. Geographically, the studies of the Neogene coastal boulder deposits have been undertaken in different parts of the world, but generally in those locations where rocky shores occur nowadays. The relevance of these deposits to storms and tsunamis, rocky shores and deltas, gravity processes, and volcanism has been discussed; however, some other mechanisms of boulder production, transportation, and accumulation (e.g., linked to seismicity and weathering) have been missed.

**Keywords:** bibliography; large clasts; Miocene; Pliocene; rocky shore; storm; tsunami


**1. Introduction**

Hyperspectral (HS) image classification always suffers from varieties of difficulties, such as high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixels. It is well known that increasing data dimensionality and high redundancy between features might cause problems during data analysis, for example, in the context of supervised classification. A considerable amount of literature has been published with regard to overcoming these challenges, and performing hyperspectral image classification effectively [1]. Machine learning techniques such as artificial neural networks (ANNs) [2], support vector machine (SVM) [3], multinomial logistic regression [4], active learning, semi-supervised learning [5], and other methods like hyperspectral unmixing [6], object-oriented classification [7], and the multiple classifier system [8] have been popularly investigated recently as well.

Multiple classifier system (MCS), which is also sometimes named as classifier ensemble or ensemble learning (EL) in the machine learning field, is a popular strategy for improving the classification performance of hyperspectral images by combining the predictions of multiple classifiers, thereby reducing the dependence on the performance of a single classifier [8–11]. The concept of MCS, on the other hand, does not refer to a specific algorithm but to the idea of combining outputs

from more than one classifier to enhance classification accuracy [12]. These outputs may result from either the same classifier of different variants or different classifiers of the same/different training samples. Previous studies have demonstrated both theoretically and experimentally that one of the main reasons for the success of ensembles is the diversity among the individual learners (namely the base classifiers) [13], because combining similar classification results would not further improve the accuracy.

MCSs have been widely applied to HS remote sensing image classification. Two approaches for constructing classifier ensembles are perceived as "classic", bagging and boosting [14,15], and afterwards numerous algorithms were successively derived from them. Bagging creates many classifiers with each base learner trained by a new bootstrapped training data set [16]. Boosting processes the data with iterative retraining, and concentrates on the difficult samples, with the goal of correctly classifying these samples in the next iteration [17,18]. Ho [19] proposed random subspace ensembles, which used random subsets of features instead of the entire feature set for each individual classifier. The rationale of the random subspace is to break down a complex high dimensional problem into several lower dimensional problems, thereby alleviating the curse of dimensionality. By integrating bagging and random subspace approaches, Breiman [20] proposed the well-known random forest (RF) algorithm [21,22]. The characteristics of RF, including reasonable computational cost, inherent support of parallelism, highly accurate predictions, and ability to handle a very large number of input variables without overfitting, make it a popular and promising classification algorithm for remote sensing data [23–25]. Generally, decision tree (DT) is used as the base classifier in ensemble learning because of its high computation efficiency, easy implementation, and sensitivity to slight changes in data. Recently, some researchers incorporated several prevalent machine learning algorithms into ensemble learning. Gurram and Kwon [26] proposed a sparse kernel-based support vector machine (SVM) ensemble algorithm that yields better performance compared with the SVM trained by cross-validation. Samat et al. [27] proposed Bagging-based and Adaboost-based extreme learning machines to overcome the drawbacks of input parameter randomness of traditional extreme learning machines. For a more detailed description about EL, refer to [28,29].

In a paper by Rodriguez and Kuncheva [30], the authors proposed a new ensemble classifier called rotation forest (RoF). By applying feature extraction (i.e., principal component analysis, PCA) to the random feature subspace, RoF greatly promotes the diversity and accuracy of the classifiers. Thereafter, several improved algorithms were proposed based on the idea of RoF, for example, Anticipative Hybrid Extreme Rotation Forest [31], rotation random forest with kernel PCA (RoRF-KPCA) [32]. Chen et al. [33] proposed to combine rotation forest with multi-scale segmentation for hyperspectral data classification, which incorporated spatial information to generate the classification maps with homogeneous regions.

A massive number of research studies show that RoF surpasses conventional RF due to the high diversity in training sample and features. Nevertheless, it is well documented in the literatures that PCA is not particularly suitable for feature extraction (FE) in classification because it does not include discriminative information in calculating the optimal rotation of the axes [30,34,35]. Although the authors explain that PCA is also valuable as a diversifying heuristic, it is expected to achieve better classification results if we try to find good class discriminative directions. Therefore, in this paper, we present an improved ensemble learning method, which uses the semi-supervised feature extraction technique instead of PCA during the "rotation" process of classical RoF approach. The proposed algorithm, named semi-supervised rotation forest (SSRoF), applies the semi-supervised local discriminant analysis (SLDA) FE method, which was proposed in our previous work [36], to fully take advantage of both the class separability and local neighbor information, with the aim of finding better rotation directions. In addition, to further enhance the diversity of features, we propose to use a weighted form of SLDA, which can balance the values of labeled samples and unlabeled samples. The main contributions of this paper are as follows: (1) an exploration of the benefit of the unlabeled samples in conventional ensemble learning methods; (2) an adjustment of the previous

SLDA technique to a weighted generalized eigenvalue problem; (3) the construction of an ensemble of classifiers, in which the weights can be randomly selected, thereby reducing the human effort for determining the optimal parameters.

The remainder of this paper is organized as follows. Section 2 describes the study data sets, and elaborates the proposed semi-supervised rotation forest algorithm. For better understanding, the SLDA feature extraction method is also briefly introduced. Section 3 reports the experiments and results. Finally, the conclusions are drawn in Section 4.

#### **2. Materials and Methodology**

In this section, we first introduce the experimental data sets, then we elaborate the proposed ensemble learning algorithm.

#### *2.1. Study Data Sets*

The experimental data sets include four HS images acquired by different sensors and resolutions. Each HS image is attached with a co-registered ground truth image.

#### *2.2. Weighted Semi-Supervised Local Discriminant Analysis*

Semi-supervised local discriminant analysis is a semi-supervised feature extraction method that has been applied in hyperspectral image classification. It combines the supervised FE method-local Fisher discriminant analysis and unsupervised FE method-neighborhood preserving embedding, and thus attempts to discover the local discriminative information of the data while preserving the local neighbor information [36]. Compared with other typical semi-supervised FE methods, SLDA focuses more on the exploration of local information, and gives a more accurate description of the distribution of samples. For better illustration, we first briefly review the feature extraction methods.

**Figure 1.** Experimental hyperspectral and corresponding ground truth images.

Let *xi* ∈ R*d* be a *d*-dimensional sample vector, and *X* = {*xi*}*ni*=1 be the matrix of *n* samples. *Z* = *T*T **X**, (*Z* ∈ R*r*×*n*) is the low-dimensional representation of the sample matrix, where *T* ∈ R*d*×*r* is the transformation matrix, T denotes the transpose.

Many dimensionality reduction techniques developed so far involve an optimization problem of the following form [40]:

$$T = \operatorname\*{argmax}\_{T} \left[ \frac{\left| T^{\top} S^{b} T \right|}{\left| T^{\top} S^{w} T \right|} \right] \tag{1}$$

Generally speaking, *Sb* (and *Sw*) corresponds to the quantity that we want to increase (and decrease), for example, between-class scatter (and within-class scatter). Equation (1) is equal to the solution of the following generalized eigenvalue problem:

$$\mathbf{S}^{b}\boldsymbol{\varphi} = \lambda \mathbf{S}^{w}\boldsymbol{\varphi} \tag{2}$$

where {*ϕk*}*dk*=1 is the generalized eigenvectors associated with the generalized eigenvalues {*λk*}*dk*=1 ,(*λ*1 > *λ*2 > ... > *λd*). *T* = {*ϕk*}*rk*=1 is composed of the first *r* eigenvectors corresponding to the largest eigenvalues {*λk*}*rk*=1. Particularly, when *Sb* is the total scatter matrix of all samples, and *Sw* = *Id*×*d*, where *I* denotes the identity matrix. Equation (1) turns into the PCA method.

#### 2.2.1. Local Fisher Discriminant Analysis (LFDA)

Suppose *yi* = *c* , *c* ∈ {1, 2, . . . , *C*} is the associated class labels of the sample vector *xi*. *C* is the number of classes. *nc* is the number of samples in class *c*, then ∑*Cc*=1 *nc* = *n*. Let *Sb* and *Sw* be the local between-class and within-class scatter matrices, respectively, defined by [41],

$$\begin{aligned} \mathbf{S}^{b} &= \frac{1}{2} \sum\_{i=1}^{n} \sum\_{j=1}^{n} \mathbf{W}\_{i,j}^{b} \left( \mathbf{x}\_{i} - \mathbf{x}\_{j} \right) \left( \mathbf{x}\_{i} - \mathbf{x}\_{j} \right)^{\mathsf{T}} \\ \mathbf{S}^{w} &= \frac{1}{2} \sum\_{i=1}^{n} \sum\_{j=1}^{n} \mathbf{W}\_{i,j}^{w} \left( \mathbf{x}\_{i} - \mathbf{x}\_{j} \right) \left( \mathbf{x}\_{i} - \mathbf{x}\_{j} \right)^{\mathsf{T}} \end{aligned} \tag{3}$$

then Equation (2) turns into a local Fisher discriminant analysis problem, where *Wb* and *Ww* are *n* × *n* matrices,

$$\begin{aligned} \mathcal{W}\_{i,j}^{b} &= \begin{cases} A\_{i,j}(1/n - 1/n\_c), \text{ if } y\_i = y\_j = c \\ 1/n, \text{ if } y\_i \neq y\_j \\ A\_{i,j}/n\_c, \text{ if } y\_i = y\_j = c \\ 0, \text{ if } y\_i \neq y\_j \end{cases} \end{aligned} \tag{4}$$

*Ai*,*j* is the affinity value between *xi* and *xj*. *Ai*,*j* is large if the two samples are close, and vice versa. The definition of *Ai*,*j* can be found in [42]. Note that we do not weight the values for the sample pairs in different classes. If ∀ *i*, *j*, *Ai*,*j* = 1, then LFDA degenerates into the classical Fisher discriminant analysis (FDA or linear discriminant analysis, LDA) [43]. Thus, LFDA can be regarded as a localized variant of FDA, which overcomes the weakness of LDA against within-class multimodality or outliers.

#### 2.2.2. Neighborhood Preserving Embedding (NPE)

NPE is an unsupervised feature extraction method that seeks a projection that preserves neighboring data structure in the low-dimensional feature space [44]. It can characterize the local structural information of massive unlabeled samples. The first step of NPE is also to construct an adjacency graph, and then compute the weight matrix *Q* by solving the following objective function,

$$\begin{aligned} \min & \sum\_{i} \left\| x\_i - \sum\_{j} \mathbf{Q}\_{ij} x\_j \right\|^2 \\ & \text{s.t. } \sum\_{j} \mathbf{Q}\_{ij} = 1 \end{aligned} \tag{5}$$

In other words, for each sample, we use its K-nearest neighbors (KNN) to reconstruct it. Thus, the goal of NPE is to preserve this neighbor relationship in the projected low-dimensional space,

$$\begin{aligned} \min & \sum\_{i} \left\| z\_i - \sum\_{j} \mathbf{Q}\_{ij} z\_j \right\|^2 \\ & \text{s.t. } \sum\_{j} \mathbf{Q}\_{ij} = 1 \end{aligned} \tag{6}$$

where *zi* = *T*T *xi*. Then we have

$$\text{minimrace}\left[\mathbf{Z}(\mathbf{I}-\mathbf{Q})^{\top}(\mathbf{I}-\mathbf{Q})\mathbf{Z}^{\top}\right] \tag{7}$$

By imposing the following constraint,

$$\sum\_{i} z\_{i} z\_{i}^{\top} = \mathbf{I} \Longrightarrow \mathbf{Z} \mathbf{Z}^{\top} = \mathbf{I}. \tag{8}$$

the transformation matrix can be optimized by solving the following generalized eigenvalue problem,

$$\mathbf{X}\mathbf{X}^{\top}\boldsymbol{\varrho} = \lambda \mathbf{X}\mathbf{M}\mathbf{X}^{\top}\boldsymbol{\varrho} \tag{9}$$

where *ϕ* denotes generalized eigenvectors, and *M* = (*I* − *Q*) T (*I* − *Q*).

## 2.2.3. Weighted SLDA

It has been demonstrated that the performance of LFDA (and all other supervised dimensionality reduction methods) tends to degrade if only a small number of labeled samples are available [40], while PCA or NPE (and other unsupervised feature extraction (FE) methods) will generally lose the discriminative information of labeled information. Thus, combining supervised and unsupervised FE methods [45] is believed to compensate for each other's weaknesses. In this paper, we consider the combination of the aforementioned LFDA and NPE methods. As mentioned above, feature extraction techniques can be transformed into eigenvalue problems, thus, a possible way to combine LFDA and NPE is to merge the above generalized eigenvalue problems as follows [40],

$$\begin{aligned} \beta \mathbf{S}^b \boldsymbol{\varrho} &= \lambda \beta \mathbf{S}^w \boldsymbol{\varrho} \\ (1 - \beta) \mathbf{X} \mathbf{X}^T \boldsymbol{\varrho} &= \lambda (1 - \beta) \mathbf{X} \mathbf{M} \mathbf{X}^T \boldsymbol{\varrho} \\ &\Downarrow \\ \left[ \beta \mathbf{S}^b + (1 - \beta) \mathbf{X} \mathbf{X}^T \right] \boldsymbol{\varrho} \\ &= \lambda \left[ \beta \mathbf{S}^w + (1 - \beta) \mathbf{X} \mathbf{M} \mathbf{X}^T \right] \boldsymbol{\varrho} \end{aligned} \tag{10}$$

where *β* ∈ [0, 1] is a trade-off parameter. Calculating the *Sb* and *Sw* of LFDA is time-consuming; an efficient implementation can be used according to [41]. Let *Sm* denote the local mixture scatter matrix,

$$\mathbf{S}^{\rm{uv}} = \mathbf{S}^{b} + \mathbf{S}^{\rm{uv}} = \frac{1}{2} \sum\_{i=1}^{n} \sum\_{j=1}^{n} \mathbf{W}\_{i,j}^{\rm{m}} \left(\mathbf{x}\_{i} - \mathbf{x}\_{j}\right) \left(\mathbf{x}\_{i} - \mathbf{x}\_{j}\right)^{\rm{T}} \tag{11}$$

where

$$\mathcal{W}^{m} = \mathcal{W}^{b} + \mathcal{W}^{w} = \begin{cases} A\_{i,j}/n, \text{ if } y\_i = y\_j\\ 1/n, \text{ if } y\_i \neq y\_j \end{cases} \tag{12}$$

Since Equation (3) can be expressed as

$$\begin{aligned} \mathbf{S}^{\text{w}} &= \sum\_{i=1}^{n} \sum\_{j=1}^{n} \mathbf{W}\_{i,j}^{\text{w}} \mathbf{x}\_{i} \mathbf{x}\_{j}^{\top} - \sum\_{i=1}^{n} \sum\_{j=1}^{n} \mathbf{W}\_{i,j}^{\text{w}} \mathbf{x}\_{i} \mathbf{x}\_{j}^{\top} \\ &= \mathbf{X} (\mathbf{D}^{\text{w}} - \mathbf{W}^{\text{w}}) \mathbf{X}^{\top} \end{aligned} \tag{13}$$

where *Dw* is the *n*-dimensional diagonal matrix with *Dwi*,*i* = ∑*nj*=1 *Wwi*,*j*. Similarly, *Sm* can be expressed as

$$\mathbf{S}^{\rm un} = \mathbf{X}(\mathbf{D}^{\rm un} - \mathbf{W}^{\rm un})\mathbf{X}^{\rm \top} \tag{14}$$

where *Dm* is the *n*-dimensional diagonal matrix with *Dmi*,*i* = ∑*nj*=1 *Wmi*,*j*. Therefore, the generalized eigenvalue problem of LFDA, namely Equation (2), can be rewritten as

$$\lambda \mathbf{X} \mathbf{L}^b \mathbf{X}^\top \boldsymbol{\varphi} = \lambda \mathbf{X} \mathbf{L}^w \mathbf{X}^\top \boldsymbol{\varphi} \tag{15}$$

where *Lw* = *Dw* − *Ww*, *Lb* = (*Dm* − *Wm*) − ( *Dw* − *Ww*), from which we can see that the eigenvalue problem of LFDA has a similar form with NPE, i.e., Equation (9).

Suppose the training sample vectors are arranged by *X* = %*XL*, *XU*&, where *XL* = .*xLi* /*nl i*=1 denotes the labeled samples, and *XU* = .*xUi* /*nu i*=1 denotes the unlabeled samples, where *n* = *nl* + *nu* is the total number of available samples. We can define the following matrices

$$\begin{aligned} \mathbf{P\_1} &= \begin{bmatrix} L^b & \mathbf{0\_{n\_l \times n\_u}} \\ \mathbf{0\_{n\_u \times n\_l}} & \mathbf{0\_{n\_u \times n\_l}} \end{bmatrix}, \mathbf{P\_2} = \begin{bmatrix} L^w & \mathbf{0\_{n\_l \times n\_u}} \\ \mathbf{0\_{n\_u \times n\_l}} & \mathbf{0\_{n\_u \times n\_u}} \end{bmatrix} \\ \mathbf{P\_3} &= \begin{bmatrix} \mathbf{0\_{n\_l \times n\_l}} & \mathbf{0\_{n\_l \times n\_u}} \\ \mathbf{0\_{n\_u \times n\_l}} & I\_{n\_u \times n\_u} \end{bmatrix}, \mathbf{P\_4} = \begin{bmatrix} \mathbf{0\_{n\_l \times n\_l}} & \mathbf{0\_{n\_l \times n\_u}} \\ \mathbf{0\_{n\_u \times n\_l}} & \mathbf{M} \end{bmatrix} \end{aligned} \tag{16}$$

Therefore, the weighted SLDA is equal to the solution of the following generalized eigenvalue problem

$$\begin{array}{c} \beta \mathbf{X} \mathbf{P}\_{\mathbf{1}} \mathbf{X}^{\top} \boldsymbol{\varrho} = \lambda \,\beta \mathbf{X} \mathbf{P}\_{\mathbf{2}} \mathbf{X}^{\top} \boldsymbol{\varrho} \\ (1 - \beta) \mathbf{X} \mathbf{P}\_{\mathbf{3}} \mathbf{X}^{\top} \boldsymbol{\varrho} = \lambda \,(1 - \beta) \mathbf{X} \mathbf{P}\_{\mathbf{4}} \mathbf{X}^{\top} \boldsymbol{\varrho} \\ \Downarrow \\ \mathbf{S}^{rb} = \mathbf{X} [\beta \mathbf{P}\_{1} + (1 - \beta) \mathbf{P}\_{\mathbf{3}}] \mathbf{X}^{\top} \\ \mathbf{S}^{rw} = \mathbf{X} [\beta \mathbf{P}\_{2} + (1 - \beta) \mathbf{P}\_{\mathbf{4}}] \mathbf{X}^{\top} \\ \Downarrow \\ \mathbf{S}^{rb} \boldsymbol{\varrho} = \lambda \mathbf{S}^{rw} \boldsymbol{\varrho} \end{array} \tag{17}$$

and *β* is the trade-off parameter. In general, 0 < *β* < 1 inherits the characteristics of both LFDA and NPE, and thus makes full use of both the class discriminative and local neighbor spatial information. In practice, searching for the optimal *β* is time-consuming and sometimes impractical if there are insufficient labeled samples available for validation. Several research studies suggest that ensemble learning methods can be employed to avoid the huge effort of searching for the optimal parameters [46,47]. On the other hand, different parameters also lead to diversity among features or classifiers, which benefits the generalization performance of the ensembles. Hence, we present an EL method based on the idea of RoF and the weighted SLDA algorithm.

#### *2.3. Proposed Semi-Supervised Rotation Forest*

Rotation forest was developed from conventional random forest to building independent decision trees on different sets of features. It consists of splitting the feature set into several random disjoint subsets, running PCA separately on each subset, and reassembling the extracted features [30,48]. By applying different splits of the features, diverse classifiers are obtained. The main steps of RoF are briefly presented as follows:

By substituting SLDA for the PCA method, we propose the following SSRoF ensemble algorithm. Apart from the different FE methods between Algorithm 1 and RoF, we use the different weights (*β*) to balance the discriminative information and structure information, thereby enhancing the diversity of features. Although the computation of the eigenvector matrix is repeated ten times (corresponding to different *β*) for each feature subset, it can be noticed that since the within-class and between-class scatter matrices are invariant for different weights, the computation cost is greatly reduced. Of course, the discrete values of *β* can be set by different steps; we recommend the values above by considering both the diversity and computation time.

**Algorithm 1:** Procedures of SSRoF

Input: Training samples *XL* = .*xLi* /*nl i*=1, testing samples *XT* = .*xTi* /*nl i*=1, unlabeled samples *XU* = .*xUi* /*nu i*=1, ensemble classifiers *L*, number of feature subsets *K*, ensemble L = ∅ Output: Class labels of *XT* For *i* = 1 : *L* 1. Randomly split the features into *K* subsets; For *j* = 1 : *K* 2. Randomly select a subset of samples from *XL* and *XU*, respectively, (typically 75% of samples) using bootstrap approach; 3. Perform the weighted SLDA algorithm by the subset of *XL* and *XU* to obtain the pairs of between-class and within-class scatter matrices in Equation (17); For *β* = 0.1 : 0.1 : 1 4. Obtain the eigenvector matrix *Tj*,*β* by solving Equation (17); End for End for For *β* = 0.1 : 0.1 : 1 5. Construct the transformation matrix *Tβ* = \**T*1,*β*, *T*2,*β*,..., *TK*,*β*+ by merging the eigenvector matrices, and rearrange the columns of *Tβ* to match the order of original features; 6. Build DT sub-classifier using *T*T*β XL*; 7. Perform classification for *T*T*β XT* by using the sub-classifier; End for End for 8. Use a majority voting rule for the *L* × 10 sub-classifiers to compute the confidence of *XT* and assign a class label for each testing sample;

#### **3. Experimental Results and Discussion**

In this section, we report the experiments on the four groups of hyperspectral images. First, the presented method is compared with several other EL algorithms to show the advantages. Then, we also introduce the performance evaluation of our method under different parameters.


**Meet the editor**

Dr. Maria C. Hernandez-Soriano, PhD, received a BS degree in Chemistry from the University of Granada, Spain, in 2006, where she completed a MS in Agricultural Sciences in 2007. She carried out her thesis studies within an international agreement between the High Council for Scientific Research in Spain, and the Katholieke Universiteit Leuven in Belgium, under the

direction of Dr MD Mingorance, Dr A. Peña and Prof. E. Smolders, receiving a PhD degree in Bioscience Engineering in June 2009. Dr Hernandez-Soriano has been awarded with a significant number of fellowships, including a Fulbright Postdoctoral Fellowship to conduct research in North Carolina State University, under the supervision of Prof. D. Hesterberg. She has made a significant number of research contributions that were published in prestigious international peer-reviewed journals in the area of Soil Science, Environmental Sciences, and Chemistry. Her work has been presented internationally in more than 20 conferences. Currently, she is holding a Research Position at the Katholieke Universiteit Leuven, where she is interested in gaining insights into the composition and molecular characterization of organic matter.

Contents

**Preface IX**

Chapter 1 **Soil Fertility Status**

**Part 1 Soil Characteristics Control Biogeochemical Processes 1**

**and Its Determining Factors in Tanzania 3**

Shinya Funakawa, Hiroshi Yoshida, Tetsuhiro Watanabe, Soh Sugihara, Method Kilasara and Takashi Kosaki

Chapter 2 **The Role of Aluminum-Organo Complexes in Soil Organic Matter Dynamics 17**

Maria C. Hernández-Soriano

**Part 2 Land Use Impact on Soil Quality 33**

Chapter 3 **Quantifying Soil Moisture Distribution at a Watershed Scale 35**

Chapter 4 **Pesticide Contamination in Groundwater**

Chapter 5 **Fire Impact on Several Chemical and**

**Part 3 Soil Fertility and Irrigation 87**

Chapter 6 **Nutrient Mobility and Availability with**

**in the Ofinso District, Ghana 51**

Andrea Rubenacker, Paola Campitelli, Manuel Velasco and Silvia Ceppi

**Vegetable Crops on Sandy Soils 89**  Shinjiro Sato and Kelly T. Morgan

**and Streams Draining Vegetable Plantations**

Benjamin O. Botwe, William J. Ntow and Elvis Nyarko

**Physicochemical Parameters in a Forest Soil 67**

**Selected Irrigation and Drainage Systems for**

Manoj K. Jha

## Contents

#### **Preface XIII**

	- **Part 2 Land Use Impact on Soil Quality 33**
	- **Part 3 Soil Fertility and Irrigation 87**

Contents VII

Chapter 16 **Earthworm Biomarkers as Tools**

**for Soil Pollution Assessment 305**

Maria Giulia Lionetto, Antonio Calisi and Trifone Schettino

	- **Part 5 Soil Salinity 175**
	- **Part 6 Soil Pollution Management 199**
	- **Part 7 Soil Pollution Assessment 253**

#### Chapter 16 **Earthworm Biomarkers as Tools for Soil Pollution Assessment 305**  Maria Giulia Lionetto, Antonio Calisi and Trifone Schettino

VI Contents

Chapter 7 **Forest Preservation, Flooding and Soil Fertility: Evidence from Madagascar 111**  Bart Minten and Claude Randrianarisoa

Liu Zhaohui, Song Xiaozong, Jiang Lihua, Lin Haitao,

**in the Composting of Livestock Manure 155**

Xu Yu, Gao Xinhao, Zheng Fuli, Tan Deshui, Wang Mei, Shi Jing and Shen Yuwen

Chapter 9 **Nitratation Promotion Process for Reducing Nitrogen Losses by N2O/NO Emissions**

Chapter 10 **Effect of Salinity on Soil Microorganisms 177**  Celia Maria Maganhotto de Souza Silva

**After Remediation Activities 201**

Ana Romero and Carlos Dorronsoro

**Pollution Soils by Use of Weeds 217**

Francisco Martín, Mariano Simón, Elena Arco,

Timothy J. Gish, John H. Prueger, William P. Kustas, Jerry L. Hatfield, Lynn G. McKee and Andrew Russ

**Pollution with Heavy Metals of Small Water Reservoirs 255**

Chapter 14 **Qualitative and Quantitative Assessment of Sediments**

Chapter 15 **Molecular Analyses of Soil Fungal Community – Methods and Applications 279**

and Elisabeth Francisconi Fay

**Part 6 Soil Pollution Management 199**

Chapter 11 **Arsenic Behaviour in Polluted Soils**

Chapter 12 **Restoration of Cadmium (Cd)**

Masaru Ogasawara

Chapter 13 **Herbicide Off-Site Transport 229**

**Part 7 Soil Pollution Assessment 253**

Bogusław Michalec

Yuko Takada Hoshino

Yasuyuki Fukumoto

**Part 5 Soil Salinity 175**

**Part 4 Soil Nitrogen Management 131**

Chapter 8 **Strategies for Managing Soil Nitrogen to Prevent Nitrate-N Leaching in Intensive Agriculture System 133**

Preface

USDA Yearbook of Agriculture, 1957.

of economic and environmental sustainability.

and the general public.

"Be it deep or shallow, red or black, sand or clay, the soil is the link between the rock core of the earth and the living things on its surface. It is the foothold for the plants we grow. Therein lays the main reason for our interest in soils." --- Roy W. Simonson,

The British naturalist Charles Darwin (1809–1882) was probably the first scientist to examine a soil profile and suggest factors responsible for the structure of the various layers. Nowadays, Soil Science is the science dealing with soils as a natural resource on the surface of the Earth, including soil formation, classification, and mapping. In addition, understanding the physical, chemical, biological, and fertility properties of soils is essential, and how these properties relate to the use and management of the soils. Soils play multiple roles in the quality of life throughout the world, not only as the resource for food production, but also as the support for our structures, the environment, the medium for waste disposal, water, and the storage of nutrients.

Healthy soil, meaning the 'fitness' (or condition) of the soil in relation to its inherent (or potential) capability, is productive, sustainable, and profitable. If soil can sustain biological productivity, maintain environmental quality, and promote the health of plants and animals, it is considered healthy. Understanding the impact of land management practices on soil properties and processes can provide useful indicators

*Soil Health and Land Use Management* is a book designed by InTech - Open Access Publisher in collaboration with recognized authors from Europe, North and South America, China, and Japan, unifying biological, chemical, and agricultural approaches to research on soil health. This book is intended to provide a broad vision of the fundamental importance of soil health and the development of feasible management and remediation strategies to preserve and ameliorate the fitness of soils. Simultaneously, this book also provides a deep insight into a suite of specific problems related with land management and soil pollution, from the impact of agricultural practices, to the introduction of novel methodologies for soil pollution assessment. Therefore, this volume will be a valuable reading to a global audience of scientists, researchers, environmental educators, administrators, technicians, managers, students,

## Preface

"Be it deep or shallow, red or black, sand or clay, the soil is the link between the rock core of the earth and the living things on its surface. It is the foothold for the plants we grow. Therein lays the main reason for our interest in soils." --- Roy W. Simonson, USDA Yearbook of Agriculture, 1957.

The British naturalist Charles Darwin (1809–1882) was probably the first scientist to examine a soil profile and suggest factors responsible for the structure of the various layers. Nowadays, Soil Science is the science dealing with soils as a natural resource on the surface of the Earth, including soil formation, classification, and mapping. In addition, understanding the physical, chemical, biological, and fertility properties of soils is essential, and how these properties relate to the use and management of the soils. Soils play multiple roles in the quality of life throughout the world, not only as the resource for food production, but also as the support for our structures, the environment, the medium for waste disposal, water, and the storage of nutrients.

Healthy soil, meaning the 'fitness' (or condition) of the soil in relation to its inherent (or potential) capability, is productive, sustainable, and profitable. If soil can sustain biological productivity, maintain environmental quality, and promote the health of plants and animals, it is considered healthy. Understanding the impact of land management practices on soil properties and processes can provide useful indicators of economic and environmental sustainability.

*Soil Health and Land Use Management* is a book designed by InTech - Open Access Publisher in collaboration with recognized authors from Europe, North and South America, China, and Japan, unifying biological, chemical, and agricultural approaches to research on soil health. This book is intended to provide a broad vision of the fundamental importance of soil health and the development of feasible management and remediation strategies to preserve and ameliorate the fitness of soils. Simultaneously, this book also provides a deep insight into a suite of specific problems related with land management and soil pollution, from the impact of agricultural practices, to the introduction of novel methodologies for soil pollution assessment. Therefore, this volume will be a valuable reading to a global audience of scientists, researchers, environmental educators, administrators, technicians, managers, students, and the general public.

#### XIV Preface

The first two chapters discuss the necessity of a thorough characterization of soil properties in order to develop management and remediation strategies, with a particular focus in the mineralogical composition and the turnover of organic matter. The chapters in section two present relevant case studies about the impact of land use on soil health, while section three and four introduce several strategies to improve land management. The last three sections encompass chapters dedicated to environmental scenarios of high concern, as being salt-affected soils or degraded, due to heavy metals or contamination from pesticides. Remediation strategies and longterm effectiveness are presented in section six, while section seven launches novel methodologies for highly efficient evaluation of soil pollution.

The sixteen chapters of this book can be read independently, but like the different components of soil, they mutually benefit and enrich each other, orchestrating a valuable composition that depicts current trends in soil health from a multidisciplinary approach.

For their excellent work, special thanks to the technical editor and sincere appreciation to Ms Ivana Zec, the Publishing Process Manager who coordinated the publication process of this volume, and assisted me and the authors in completing our tasks smoothly and in a timely manner.

> **M.C. Hernández-Soriano** North Carolina State University USA


**2.2 How the e-Books were made**

Information was elicited from students, practice teachers, tutors and other social work educators as to what they defined as the most important topics in the area of fieldwork placement. This was achieved with discussion groups in class and outside meetings with agency social workers. The participants on the NUI Galway Postgraduate Diploma in Practice Teaching, Supervision and Management course were also vocal in what they saw as important in the fieldwork relationship and development of the student's professional identity. Pre 2016 across a period of 2 years (2013–2015) a number of excellent podcasts and short videos had already been made by MSW students, practice teachers and visiting experts and it was expected to use these as a starting resource and add to it with current short video and aural podcasts. Over a period of 2 years NUI Galway has benefited from the expertise of acclaimed in-house and visiting professionals. Professor Mark Doel as external examiner of the MSW program at NUI Galway (2012–2015) gave generously of his time in contributing to two of the eBooks (eBook 1 and eBook 6 [35, 55]). In particular, his short video on 'Why is Groupwork Important?' [55] allowed the viewer not only to visually recognize and connect with the author, but be impressed by the sincerity of his delivery and practical wisdom. Marian Bogo and Pamela Trevithick [56] are eminent authors in the fundamental principles of social work practice, thus giving depth and credence to their expressed opinions within the eBooks. Both came to Ireland in 2015 and spoke eloquently on the topics of feedback and current social work practice with advice for practice teachers. Australian academics Lynn McPherson [56] and published author Helen Cleak [57] engaged the listener in both the safety issues for placement and the universally important elements of how to 'finish well' the placement experience.

The experts continued with Professor Pat Dolan [35], a UNESCO Professor and Director of the Child and Family Research Centre at NUI Galway, sharing his considered opinion on the video podcast in eBook 1 [35] 'What is Professional Identity?. He suggests for the listener a global view and one which has multi setting and dimensional elements. In many ways *all* of the contributors to the eBooks were experts in the messages or opinions they imparted because it was their imparted wisdom, put into words, which they believed to be most relevant in relation to the various settings and topics. Other Irish Universities also contributed, University College Cork were represented by their BSW/MSW staff Mary Wilson and Deirdre Quirke [55]. In all, the contribution of NUI Galway Professor of Social Work Caroline McGregor [56] provided an expert overarching view of the foundational principles of social work, presented in an engaging, modern and technological way on the MSW program.

Ultimately, it was the author's 11 years of professional experience with the NUI Galway Masters in Social Work program and her 35 years experience within the social work profession that consolidated all the information and eventually made the judgment on content. Attention is drawn to **Table 1**.

**101**

Feedback [56]).

*Irish Field Education/Social Work Placement: The Making of Multi-Touch eBooks...*

(The importance of social work theory was not discounted but due to the wealth of textbooks in this area, a decision was made not to include this topic in the current

Initially all eBooks 1–7 were written in iBook Author and configured for MAC or iPad with iBooks 2 or later and iOS 5 or later, an iPhone with iOS 8.4 or later, or a

Subsequently eBooks 2–7 can now also be downloaded as The PC/Android versions through Amazon Books and Kindle. These are common operating systems and not outside the general capability of downloadable eBooks. Copies of the eBooks 2–7 [55–60] were also uploaded onto the University's MSW PC computers and each person included in the making of the eBooks were presented with a personal USB containing all seven multi-touch eBooks. Work is continuing on the uploading of eBook 1 to PC/Android format. IBAN numbers were obtained through NUI Galway, and each University Library in the Republic of Ireland was offered the eBooks 2–7 as a free downloadable resource. Since 2017 The National University of Ireland, Galway Library's depository of academic research named ARAN/IRIS also welcomed the resource [61]. This made the eBooks technically free to both a National and International audience. (All free links to eBooks 2–7 appear in the References

All multi-media for the eBooks was produced over a period of 2 years. Recording the audio podcasts mainly with a hand-held digital recorder proved to be the most reliable and immediate way to capture thoughts and ideas. Students used these in class, where, for example, in a post placement class they would divide into small groups and each group would make a podcast on their experience of 'supervision on placement'. With regard to the professionals, a number of practice teachers and tutors who were somewhat 'shy' of the video process choose to take away a digital recorder, record their piece on, for example 'Supporting the student on Placement'

Short videos and audio pieces heightened interest amid written research sections of the eBooks. What was the best way to produce the short videos? Would it be filming on an iPad, phone or in the Studio? Interestingly the iPad proved a superb mobile and immediate resource for gathering short videos. Students too enjoyed the freedom of being able to work on their short videos outside of class time when they had access to the class iPad (see student video in Book 6: Imaginative Groupwork [55], p. 9). Video recording within the NUI Galway Film Studio made the process more formal. NUI Galway affords its staff and students access to a small recording studio where, with the help of a professional technician recordings can be made. This model of filming worked well for interviews with leading authors such as the renowned author Pamela Trevithick (See Page 16 eBook 2 Social Work Placement: New Approaches. New Thinking. Assessment, Creative Supervision,

The use of both the aural podcasts and short videos helped to engage all the stakeholders within the placement experience. It presented to the students an application of learned theories, skills and roles, encouraging reflection, co-working, appreciating relevant evaluation and restyling action which was peer and expert led. The fundamental principle was one of 'sharing'. The sharing of information and the sharing of wisdom for all of the stakeholders involved. Permission for sharing

*DOI: http://dx.doi.org/10.5772/intechopen.89391*

**2.3 Downloading requirements and distribution**

**2.4 The nuts and the bolts of making the multi-media sharing**

and return the digital recorder to the Practice Learning Coordinator.

selection but possibly in the future).

MAC with OS × 10.9 or later.

section of this Chapter).

#### **Table 1.**

*Titles and contents of each eBook.*

*Irish Field Education/Social Work Placement: The Making of Multi-Touch eBooks... DOI: http://dx.doi.org/10.5772/intechopen.89391*

(The importance of social work theory was not discounted but due to the wealth of textbooks in this area, a decision was made not to include this topic in the current selection but possibly in the future).

#### **2.3 Downloading requirements and distribution**

*Global Social Work - Cutting Edge Issues and Critical Reflections*

the judgment on content. Attention is drawn to **Table 1**.

Language Professional identity Expectations and beginnings

Assessment Creative supervision

Feedback

intelligence

Failing forward

Finishing well

Imaginative groupwork

Home visiting—Urban and rural

Reflective practice influenced by emotional

Cultural competence Virtual training

Book 1 Social work placement: New approaches.

Book 2 Social work placement: New approaches.

Book 3 Social work placement: New approaches.

Book 4 Social work placement: New approaches.

Book 5 Social work placement: New approaches.

Book 6 Social work placement: New approaches.

Book 7 Social work placement: New approaches.

New thinking

New thinking

New thinking

New thinking

New thinking

New thinking

New thinking

*Titles and contents of each eBook.*

(2013–2015) a number of excellent podcasts and short videos had already been made by MSW students, practice teachers and visiting experts and it was expected to use these as a starting resource and add to it with current short video and aural podcasts. Over a period of 2 years NUI Galway has benefited from the expertise of acclaimed in-house and visiting professionals. Professor Mark Doel as external examiner of the MSW program at NUI Galway (2012–2015) gave generously of his time in contributing to two of the eBooks (eBook 1 and eBook 6 [35, 55]). In particular, his short video on 'Why is Groupwork Important?' [55] allowed the viewer not only to visually recognize and connect with the author, but be impressed by the sincerity of his delivery and practical wisdom. Marian Bogo and Pamela Trevithick [56] are eminent authors in the fundamental principles of social work practice, thus giving depth and credence to their expressed opinions within the eBooks. Both came to Ireland in 2015 and spoke eloquently on the topics of feedback and current social work practice with advice for practice teachers. Australian academics Lynn McPherson [56] and published author Helen Cleak [57] engaged the listener in both the safety issues for placement and the universally important elements of how to 'finish well' the placement experience. The experts continued with Professor Pat Dolan [35], a UNESCO Professor and Director of the Child and Family Research Centre at NUI Galway, sharing his considered opinion on the video podcast in eBook 1 [35] 'What is Professional Identity?. He suggests for the listener a global view and one which has multi setting and dimensional elements. In many ways *all* of the contributors to the eBooks were experts in the messages or opinions they imparted because it was their imparted wisdom, put into words, which they believed to be most relevant in relation to the various settings and topics. Other Irish Universities also contributed, University College Cork were represented by their BSW/MSW staff Mary Wilson and Deirdre Quirke [55]. In all, the contribution of NUI Galway Professor of Social Work Caroline McGregor [56] provided an expert overarching view of the foundational principles of social work, presented in an engaging, modern and technological way on the MSW program. Ultimately, it was the author's 11 years of professional experience with the NUI Galway Masters in Social Work program and her 35 years experience within the social work profession that consolidated all the information and eventually made

**100**

**Table 1.**

Initially all eBooks 1–7 were written in iBook Author and configured for MAC or iPad with iBooks 2 or later and iOS 5 or later, an iPhone with iOS 8.4 or later, or a MAC with OS × 10.9 or later.

Subsequently eBooks 2–7 can now also be downloaded as The PC/Android versions through Amazon Books and Kindle. These are common operating systems and not outside the general capability of downloadable eBooks. Copies of the eBooks 2–7 [55–60] were also uploaded onto the University's MSW PC computers and each person included in the making of the eBooks were presented with a personal USB containing all seven multi-touch eBooks. Work is continuing on the uploading of eBook 1 to PC/Android format. IBAN numbers were obtained through NUI Galway, and each University Library in the Republic of Ireland was offered the eBooks 2–7 as a free downloadable resource. Since 2017 The National University of Ireland, Galway Library's depository of academic research named ARAN/IRIS also welcomed the resource [61]. This made the eBooks technically free to both a National and International audience. (All free links to eBooks 2–7 appear in the References section of this Chapter).

#### **2.4 The nuts and the bolts of making the multi-media sharing**

All multi-media for the eBooks was produced over a period of 2 years. Recording the audio podcasts mainly with a hand-held digital recorder proved to be the most reliable and immediate way to capture thoughts and ideas. Students used these in class, where, for example, in a post placement class they would divide into small groups and each group would make a podcast on their experience of 'supervision on placement'. With regard to the professionals, a number of practice teachers and tutors who were somewhat 'shy' of the video process choose to take away a digital recorder, record their piece on, for example 'Supporting the student on Placement' and return the digital recorder to the Practice Learning Coordinator.

Short videos and audio pieces heightened interest amid written research sections of the eBooks. What was the best way to produce the short videos? Would it be filming on an iPad, phone or in the Studio? Interestingly the iPad proved a superb mobile and immediate resource for gathering short videos. Students too enjoyed the freedom of being able to work on their short videos outside of class time when they had access to the class iPad (see student video in Book 6: Imaginative Groupwork [55], p. 9).

Video recording within the NUI Galway Film Studio made the process more formal. NUI Galway affords its staff and students access to a small recording studio where, with the help of a professional technician recordings can be made. This model of filming worked well for interviews with leading authors such as the renowned author Pamela Trevithick (See Page 16 eBook 2 Social Work Placement: New Approaches. New Thinking. Assessment, Creative Supervision, Feedback [56]).

The use of both the aural podcasts and short videos helped to engage all the stakeholders within the placement experience. It presented to the students an application of learned theories, skills and roles, encouraging reflection, co-working, appreciating relevant evaluation and restyling action which was peer and expert led. The fundamental principle was one of 'sharing'. The sharing of information and the sharing of wisdom for all of the stakeholders involved. Permission for sharing

all of the gathered material was collected and all participants in the eBook project appreciated that their podcasts and videos would be on general release once the eBooks were published.

#### **2.5 Commonalities of each eBook**

Throughout each of the e-Books there were a number of commonalities. (a) Each opened with an introduction video made by the author (approx. 3 min). (b) Throughout the eBooks opinion pieces on specific topics were presented as short videos (approx. 3 min) and/or aural podcasts (approx. 3 min) by students, practice teachers or tutors connected with the National University of Ireland, Galway MSW program. (c) In order to have a ready-made discussion tool each e-Book finished with sections on: 'The University's Responsibility', 'New Approaches. New Thinking', 'Questions' and a relevant seven slide 'Power point/Keynote presentation'.


*3.2. Hardness and Roughness Evolutions*

Figure 4 shows the evolution of the hardness and roughness before and after the different SMAT and SMAT + polishing treatments for the Ti-Nb wall. For the polished sample, the maximum hardness was reached for 25% of Nb (Figure 4a). According to the literature [42,43], this increase is due to the presence of a small fraction of ω-phase, especially when the composition is around 25% Nb. The amount of omega phase was increased by the successive remelting and tempering from the successive layer by layer deposition generated by the DED-CLAD process. This tempering promoted the formation of the ω-phase, which explains the increase in the microhardness, as was also observed for TiMo alloys [44].

**Figure 4.** Evolution of (**a**) the hardness (HV0.3) and (**b**) the roughness (Ra) as a function of the Nb alloying amount for the P, S and P + S conditions of the TiNb walls.

After the SMAT modifications, it was first interesting to notice that the hardness evolutions for the S and S + P samples are almost equal (Figure 4a). This meant that the polishing has allowed to remove a few microns to smooth the roughness (Figure 4b) while remaining in the hyperdeformed thickness. The range of hardness increase by shot peening depended slightly on the alloying amount. The maximum increase was obtained due to the appearance of the kink bands in the Ti25Nb, which made it possible to change hardness from 250 to 400 Hv (60% increase). Sadeghpour et al. [45] have recently shown that there is a relation between the presence of omega phase and the formation of kink bands due to the accumulation of simple dislocation and dislocation channels, which correlates with our study. The strong internal misorientation of the 50 to 100% Nb containing samples also increased the hardness but to a lesser extent (15 to 40% increase).

The evolutions of roughness presented in Figure 4b shows that the roughness after SMAT was inversely related to the hardness. When the material was hard, the roughness increase remained moderate. This is because—for a given impact energy—the balls created deeper craters when the material was softer (Figure 4a). Because of the polishing procedure, the P and S + P samples have the same low roughness, independently of the initial hardness. These modifications of hardness versus roughness made it possible to dissociate the effect of the roughness from the other effects (chemistry and SPD induced by SMAT). Indeed, several papers have suggested that the surface roughness plays a major role in cell biology: the higher the roughness, the higher the cell adhesion [46–48].


1. Introduction

Hydrophilic membranes based on hydrogels can be defined as a polymeric material, which is insoluble in water, can absorb it and retain a significant fraction in its structure [1]. The material that forms the membranes with these characteristics is composed of two water-insoluble crosslinked hydrophilic polymer systems, due to the existence of a three-dimensional network connecting their chains, and is perceived that water was retained in its structure [2–4].

gel process, it is possible to obtain nanoparticles of pseudoboehmite, having as precursors

Obtaining Hydrogels based on PVP/PVAL/Chitosan Containing Pseudoboehmite Nanoparticles for Application…

http://dx.doi.org/10.5772/intechopen.72007

143

The sol-gel process consists of synthesis of materials, caused by the transition of a sol system, dispersion of colloidal particles (dimension from 1 to 100 mm) in a fluid; in a gel system, rigid structure system of colloidal particles or chains polymerization by immobilizing the liquid

Pseudoboehmites obtained by the sol-gel process have been successfully used in drug delivery

The aim of this chapter is to obtain a polymer system based on PVP, PVAl and chitosan containing pseudoboehmite nanoparticles, through the action of ionizing radiation, although the excellent biomedical properties in clinical practice of PVP-based hydrogels are confirmed, the difficult handling of these materials has been observed on their poor mechanical properties. Therefore, it is important to study new systems that maintain the properties required for these materials and at the same time to implement the physical:chemical and mechanical properties

The following reagents were used: aluminum nitrate Al(NO3)3, supplied by Dinâmica LTDA; ammonium hydroxide (NH4OH), supplied by Audaz Reagente Tecnológico; polyvinyl alcohol, supplied by Bandeirante Química; poly(N-vinyl-2-pyrrolidone) (PVP), supplied by GAF Co.; chitosan, supplied by Polymar; poly(ethylene glycol), supplied by Oxiteno Brasil and agar sup-

The synthesis of nanoparticles of pseudoboehmite (PSB): The nanoparticles of the pseudoboehmite were obtained through the sol-gel process, according to Munhoz Jr. et al. [22], aluminum nitrate solution in H2O, solution of ammonium nitrate in H2O and solution of polyvinyl alcohol in H2O, which is used to increase the viscosity of the aluminum nitrate solution. The solution of aluminum nitrate and polyvinyl alcohol was mixed with the ammonium hydroxide solution. The obtained product was washed with distilled water and dried through air.

Obtaining chitosan solution: The chitosan was dissolved in acetic acid solution, 2 wt% in H2O, which was stirred for 48 h. After dissolution of chitosan, it was neutralized with 1 wt% sodium

Preparation of PVP/PVAl/PSB/chitosan membranes reinforced with PSB: Membranes with 3 wt%

The hydrogels were produced from a solution of PVP, PVAl, chitosan, agar and PEG in H2O, which was heated and subsequently PSB was added to the solution. The solution was poured

PSB were obtained. Table 1 shows the composition of the membranes obtained.

into polyethylene molds and after cooling, a physical gel was obtained.

aluminum nitrate and ammonium hydroxide [20].

phase [21].

systems [18].

2.1. Materials

plied by Oxide.

2.2. Methods

2. Experimental study

hydroxide solution in H2O until pH 7.

Hydrogels can be expanded by water and ion absorption, above the equilibrium state and retain their original shape and mechanical properties. In addition, they present permeability to biologically active substances with low molecular weight, being used in dressings directly on contact with the skin and can be used as dressing or bandage in burn wounds, vascular prostheses, artificial cartilage membranes for hemodialysis, among other applications [5, 6].

Hydrogels obtained by ionizing radiation, having as precursors poly(n-vinyl-2-pyrrolidone) (PVP), agar and plasticizing agents such as polyethylene glycol that have been used in pharmaceuticals and have advantages over the previously obtained methods, they eliminate the sterilization phase, as this is still being obtained in the process of ionizing radiation [1].

Due to its properties, PVAl is one of the synthetic polymers that are used as a biomaterial, being of great importance in the industry, mainly in cosmetics, where it is used as an additive, providing texture to the products. Its main function is that of a plasticizer. Its use in the synthesis of hydrophilic membranes presents some restrictions when used alone, because the obtained membrane presents low elasticity and rigidity. To improve these properties, it is used in conjunction with other polymers [7, 8].

Chitosan is a natural polymer that can be obtained through the process of deacetylation of chitin, a polysaccharide of great abundance in nature, and present a similar chain to cellulose. Among its main characteristics, of great importance and industrial interest, it is atoxic and has an easy way to create gel [9].

Chitosan nanocomposites have been used in the cotton coating for application in dressings and bandage with the purpose of increasing the absorption of the exudate as well as improving the antibacterial activity [10].

Chitosan and PVAl are biocompatible polymers being in the composition of several hydrogels used today [11, 12]. Hydrophilic membranes based on PVAl and chitosan have been synthesized because the presence of chitosan improves the mechanical, hydrophilic and antibacterial properties of membranes obtained from PVAl [13, 14]. These membranes modified by the presence of nanoclays for use in dressings have been obtained with good mechanical and absorption properties [15, 16].

Hydrophilic membranes obtained from polyvinyl alcohol, poly(N-vinylpyrrolidone) and antibiotic containing chitosan have been synthesized by Yu et al. [17].

The pseudoboehmite obtained by the sol-gel process is a ceramic nanoparticle with high surface area, bioinert, which can be used in drug delivery systems [18]. Pseudoboehmite has the same structure as the boehmite (ɣ-ALOOH). It has an orthorhombic structure and presents two layers of octahedral oxygen partially filled with aluminum cations [19]. Through the sol: gel process, it is possible to obtain nanoparticles of pseudoboehmite, having as precursors aluminum nitrate and ammonium hydroxide [20].

The sol-gel process consists of synthesis of materials, caused by the transition of a sol system, dispersion of colloidal particles (dimension from 1 to 100 mm) in a fluid; in a gel system, rigid structure system of colloidal particles or chains polymerization by immobilizing the liquid phase [21].

Pseudoboehmites obtained by the sol-gel process have been successfully used in drug delivery systems [18].

The aim of this chapter is to obtain a polymer system based on PVP, PVAl and chitosan containing pseudoboehmite nanoparticles, through the action of ionizing radiation, although the excellent biomedical properties in clinical practice of PVP-based hydrogels are confirmed, the difficult handling of these materials has been observed on their poor mechanical properties. Therefore, it is important to study new systems that maintain the properties required for these materials and at the same time to implement the physical:chemical and mechanical properties


**4. Technological processes**

Most of the conventional methods for liposome production require an additional unit operation for size reduction and polydispersity, as they are top-down approaches. In this approach, liposomes are produced from the hydration of a thin film of lipids using Bangham's method (Bangham et al., 1965), multitubular system (Torre et al., 2007; Tournier et al., 1999), detergent depletion or emulsion methods, ether/ethanol injection, and reverse phase evaporation (Lasic, 1993; New, 1990). All of these processes are discontinuous and only the ether/ethanol injection and multitubular system are scalable. Shearing or impact strategies are

Technological Aspects of Scalable Processes for the

A B

acquisition (adapted from Zhang et al., 2008).

al., 2010).

Production of Functional Liposomes for Gene Therapy 277

to lipid/solvent flow) correspond to smaller liposome diameter (Janh et al., 2010). However, caution is required in this flow rate analysis as the final alcohol content in the liposome colloidal dispersion can change with FRR and as a consequence of particle size. Alcohol can be used to disrupt liposomes and, at low concentrations, liposome size can be increased due to alcohol incorporation into the bilayer. Precise analysis can be performed if the alcohol is removed after liposome processing. By decreasing the total volumetric flow rate, the residence time can be increased and a lower average vesicle diameter and narrower size distribution obtained. This behavior indicates that if increasing the total volumetric flow rate, the microchannel length must be longer to complete the alcohol diffusion, otherwise large particles will be obtained due to bulk mixing downstream of the channel (Janh et

Fig. 4. (A) Schematic representation of hydrodynamic focusing (HF) in a microfluidic device with four-channel intersection geometry. The organic solvent (miscible in water) containing the dispersed lipids is injected in the middle stream and hydrodynamically compressed by two aqueous (or buffered) streams. The flow is in the x direction and alcohol diffusion is in the y direction. (B) Schematic representation of the experimental apparatus for liposome production using HF: (1) microfluidic device; (2) water syringe pumps; (3) syringe pump for

The lipid concentration in alcohol is also another important parameter for controlling size. The development of vaccines for *in vivo* applications requires the highest drug-loading capacity. In terms of gene vaccines, the DNA-loading capacity correlates with the cationic lipid content, defined by the molar charge rate (R+/-) between the cationic charges (from the cationic lipid) and negative charges (from the DNA). The R+/- and total lipid content are project parameters for scaling up (or scaling out) processes. As an example, the R+/- of the tuberculosis gene vaccine is 10 and the total lipid concentration 64 mM (Rosada et al., 2008). These parameters require cationic liposome production in a high lipid concentration under unusual microfluidic conditions. Aiming to explore the effect of lipid concentration on microfluidic HF processes, we investigated the influence of high lipid content (100 mM) and lipid composition in ethanol (EPC or EPC/DOTAP/DOPE) as a function of average size. We understand that the studied molar concentration is greater than the lipid solubility in ethanol (approximately 4 mM for EPC) (New, 1990) and, in this case, the lipid dispersion offers an additional barrier to ethanol diffusion into water. Based on this assumption, we simulated the required microchannel length for complete ethanol diffusion from a central stream (after HF) as a function of the lipid concentration in the ethanol stream considering

lipids/ethanol stream; (4) collector flask; (5) stereo microscopy; (6) computer data

the general key for homogenation and reducing liposome size. Mechanical stirring, extrusion through orifices (French press), extrusion through membranes, high-pressure impactor, and microchannel microfluidizer are equipment used in high-energy processes.

Bottom-up processes are low energy processes inspired by biological systems and used for the development of functional nanomaterials, such as supramolecular structures, selfaggregated monolayers, Langmuir-Blodgett films, aggregated peptide nanotubes, and deposited polyelectrolites or proteins in multilayers (Mijatovic et al., 2005; Shimomura & Sawadaishi, 2001).

Microfluidic systems have been the main representatives of bottom-up processing for liposome production in continuous and scaled up processes. Different microfluidic systems can be applied to the production of liposomes and giant liposomes (Ota et al., 2009; Shum et al., 2008; Wagner et al., 2002). DNA complexation on the external surface of liposomes also constitute a promising technology for the production of gene vaccines.


**Author details**

Gabriel Adua Awejori and Mileva Radonjic\* Oklahoma State University (OSU), Stillwater, USA

\*Address all correspondence to<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.

*Review of Geochemical and Geo-Mechanical Impact of Clay-Fluid Interactions Relevant… DOI: http://dx.doi.org/10.5772/intechopen.98881*