**2. Structuralization of geological literature**

Text data are usually consisted of sentences written by authors with personal understandings and opinions. Compared to metadata, text data are characterized by ambiguity, polysemy, and irregular input in the natural language. It is difficult for computers to read and understand. It is necessary to segment a piece of text into semantic word sequences for further computer processing. English and other Latin languages have relatively simple morphology, especially inflectional morphology, and are segmented by spaces between words naturally. For those languages, it is often possible to ignore the word segmentation task entirely. In contrast, there is no space between words in a few other languages, such as Chinese. It is difficult for a computer to identify the boundary of a meaningful word or phrase in Chinese [33, 34]. The methods of Chinese word segmentation were classified into dictionary-based, statistically based, and hybrid approaches [33]. The statistically based methods include machine learning and deep learning methods, such as hidden Markov model (HMM), maximum entropy Markov model (MEMM), conditional random fields (CRF), and long short-term memory (LSTM).

From another perspective, the methods of word segmentation can be divided into generic and specific domain methods according the usage scenarios. In the generic domain, because of the shortcomings of word segmentation rules, some new words, especially the professional terms, will be regarded as out-of-vocabulary and cannot be identified correctly. Geology, as a knowledge-intensive discipline, has a systematic domain-specific terminology. Most of geologic terms are not familiar with the public. Geological literature including the geological terms has their unique characteristics. For instance, the geological literature is always organized according to some fixed format and contains lots of professional geologic terms that only people with a background knowledge can read and understand. The geological literature is dominated by descriptive sentences and has little ambiguity in information expression. In geological literature written in Chinese, it is also featured by mixed writing of Chinese and English terms as well as compound terms consisted of multiple geological terms [2, 7]. The text data in the natural language are sequence data; the word usage and combination are only influenced by the context. Based on the characteristics of text data, machine learning method (e.g., CRF) and deep learning method of neural network (e.g., neural network (CNN), LSTM) have been introduced to segment geological literature in Chinese in recent 2 years with successful results [7, 34–36].

#### **2.1 Conditional random fields**

For a random vector (e.g., in NLP), the joint probability is a high-dimensional distribution, which oversteps the processing power of an ordinary computer and is difficult to monitor during data processing. To reduce the data size, the highdimensional distribution is divided into a series of production of conditional

probability based on the independence hypothesis [37]. The probabilistic graphical model is a graph to describe independence relationship between multivariate in a high-dimensional probabilistic model, thus to reduce computer load. The probabilistic graphical model includes both directed and undirected models. The directed graphical model indicates there is a causation relationship between the variables, such as Bayesian networks. The variables in undirected graphical model have dependency with each other, such as Markov networks and CRF, which is different from the causation relationships.

dictionary matching and manual label methods on the basis of geological literature in CNKI, geology dictionary, TCCGMR (the terminologies and classification codes of geology and mineral resources), and a generic corpus of Peking University. Second, the segmentation rules were trained to build geological word segmentation model by the hybrid corpus, and then the trained model containing word segmentation rules was used to segment geological literature in Chinese. The workflow is

In that study, a geology dictionary of 11,000 geological terms, the TCCGMR of 80,000 geological terms, and the generic corpus of Peking University were used to build the hybrid corpus. By this way, geological knowledge was introduced into the corpus to train the rules of word segmentation of geological literature. It is the most notable feature compared with other Chinese word segmentation machine. The three parameters of precision, recall, and F-scores were used to evaluate the performance of CRF-based word segmentation in that work. The result was showed in **Figure 3**. The hybrid corpus combining a generic corpus and a geological corpus has a better performance than either the generic corpus or the geological corpus alone. The precision of the hybrid training reaches 94.14%, which is 7.84% and 0.52% higher than that of CRF-PKU and CRF-GEO, respectively. The recall of hybrid corpus reaches 91.40%, which is 9.30% and 0.41% higher than that of CRF-PKU and CRF-GEO, respectively. The F-score of the hybrid corpus reaches 92.75%, which is 8.60% and 0.46% higher than that of CRF-PKU and CRF-GEO, respectively.

Geological Corpus

90.99 91.40

Precision/% Recall/% F-scores/%

*Performances of the CRF model in different corpus. CRF-PKU, generic corpus of Peking University; CRF-GEO,*

*geological corpus; CRF-GEO + PKU, the hybrid corpus combining generic and geological corpora.*

CRF-PKU CRF-GEO CRF-GEO+PKU

82.10

Generic Corpus

Geological word segmentation model

Word segmentation result

Performance evaluation

92.29 92.75

84.15

Input

shown in **Figure 2**.

*Geology dictionary and* TCCGMR

CNKI Raw Corpus *Manual Label*

86.30

**Figure 2.**

100

90

80

70

60

**Figure 3.**

**125**

*Matching*

*Text Mining to Facilitate Domain Knowledge Discovery DOI: http://dx.doi.org/10.5772/intechopen.85362*

*Workflow of CRF-based word segmentation for geological literature in Chinese.*

93.62 94.14

CRF model is a discriminative graph model, while HMM is a generative graph model. The role of CRF model is to create the discriminant boundaries similar to the support vector machine model, which has a wide usage in the fields of NLP and bioinformatics. Compared with HMM and the maximum entropy model (MEM), the CRF model improves the accuracy and addresses the drawback of label bias [38, 39]. Text data are unstructured sequence data. The structuralization of geological text is a process of word segmentation or named entity recognition (NER), which divides the geological text into a series of semantic words. For natural language, the text is only influenced by the context, which is consistent with the assumption condition of the CRF model. The assumption condition is that multivariable obeys the Markov property. In other word, the label of part of speech at *n* position in NLP only has relationship with the word or character at *n-1* position. From the point of view of the graph model, *Yv* is a subset of *V* nodes set in the graph *G* ¼ ð Þ *V; E* ; the following equation is established:

$$p(Y\_v|X, Y\_w, w \neq v) = p(Y\_v|X, Y\_w, w \sim v) \tag{1}$$

where **X** ¼ f g *X*1*;X*2*;* …*;Xn* is the words or characters of text data in NLP, *w* � *v* denotes neighbor nodes of node *v* in the graph, and *Y* is the label set of part-of-speech f g *B; E; M; S* . For NLP, the graphical structure is chain-structured (**Figure 1**) [14–16].

According the factorization of joint probability distribution of undirected graph, the CRF model can be written as

$$p(\mathbf{Y}|\mathbf{X}) = \frac{\mathbf{1}}{Z(\mathbf{X})} \prod\_{i} e^{\sum\_{k} i\_{k} f\_{k}(\mathbf{X}, Y\_{i-1}, Y\_{i}, i)} = \frac{\mathbf{1}}{Z(\mathbf{X})} e^{\sum\_{i} \sum\_{k} i\_{k} f\_{k}(\mathbf{X}, Y\_{i-1}, Y\_{i}, i)} \tag{2}$$

In which *i* is the node position, *k* denotes the sequence number of feature function, and *λk* is the weight parameter. In Eq. (2), the feature function can be expressed in Eq. (3), which contains information of transfer and status features.

$$f = \sum\_{i}^{T} \sum\_{k}^{M} \lambda\_k f\_k(\mathbf{X}, Y\_{i-1}, Y\_i, i) \tag{3}$$

In CRF-based word segmentation, Wang et al. [7] designed a two-step workflow to segment geological literature in Chinese. First, a hybrid corpus was created using

**Figure 1.** *A chain-structured CRF graph [7, 40].*

*Text Mining to Facilitate Domain Knowledge Discovery DOI: http://dx.doi.org/10.5772/intechopen.85362*

dictionary matching and manual label methods on the basis of geological literature in CNKI, geology dictionary, TCCGMR (the terminologies and classification codes of geology and mineral resources), and a generic corpus of Peking University. Second, the segmentation rules were trained to build geological word segmentation model by the hybrid corpus, and then the trained model containing word segmentation rules was used to segment geological literature in Chinese. The workflow is shown in **Figure 2**.

In that study, a geology dictionary of 11,000 geological terms, the TCCGMR of 80,000 geological terms, and the generic corpus of Peking University were used to build the hybrid corpus. By this way, geological knowledge was introduced into the corpus to train the rules of word segmentation of geological literature. It is the most notable feature compared with other Chinese word segmentation machine. The three parameters of precision, recall, and F-scores were used to evaluate the performance of CRF-based word segmentation in that work. The result was showed in **Figure 3**. The hybrid corpus combining a generic corpus and a geological corpus has a better performance than either the generic corpus or the geological corpus alone. The precision of the hybrid training reaches 94.14%, which is 7.84% and 0.52% higher than that of CRF-PKU and CRF-GEO, respectively. The recall of hybrid corpus reaches 91.40%, which is 9.30% and 0.41% higher than that of CRF-PKU and CRF-GEO, respectively. The F-score of the hybrid corpus reaches 92.75%, which is 8.60% and 0.46% higher than that of CRF-PKU and CRF-GEO, respectively.

**Figure 2.**

probability based on the independence hypothesis [37]. The probabilistic graphical model is a graph to describe independence relationship between multivariate in a high-dimensional probabilistic model, thus to reduce computer load. The probabilistic graphical model includes both directed and undirected models. The directed graphical model indicates there is a causation relationship between the variables, such as Bayesian networks. The variables in undirected graphical model have dependency with each other, such as Markov networks and CRF, which is different

CRF model is a discriminative graph model, while HMM is a generative graph model. The role of CRF model is to create the discriminant boundaries similar to the support vector machine model, which has a wide usage in the fields of NLP and bioinformatics. Compared with HMM and the maximum entropy model (MEM), the CRF model improves the accuracy and addresses the drawback of label bias [38, 39]. Text data are unstructured sequence data. The structuralization of geological text is a process of word segmentation or named entity recognition (NER), which divides the geological text into a series of semantic words. For natural language, the text is only influenced by the context, which is consistent with the assumption condition of the CRF model. The assumption condition is that multivariable obeys the Markov property. In other word, the label of part of speech at *n* position in NLP only has relationship with the word or character at *n-1* position. From the point of view of the graph model, *Yv* is a subset of *V* nodes set in the graph

where **X** ¼ f g *X*1*;X*2*;* …*;Xn* is the words or characters of text data in NLP, *w* � *v* denotes neighbor nodes of node *v* in the graph, and *Y* is the label set of part-of-speech f g *B; E; M; S* . For NLP, the graphical structure is chain-structured

According the factorization of joint probability distribution of undirected graph,

∑*kλk f k* **X***;Yi*�1*;Yi* ð Þ*;i* ¼ 1

In CRF-based word segmentation, Wang et al. [7] designed a two-step workflow to segment geological literature in Chinese. First, a hybrid corpus was created using

...

*Y1 Y2 Y3 Yn-1 Yn*

*X*=*X ,...,X ,X 1 n-1 n*

In which *i* is the node position, *k* denotes the sequence number of feature function, and *λk* is the weight parameter. In Eq. (2), the feature function can be expressed in Eq. (3), which contains information of transfer and status features.

*p Y*ð *v*j*X; Yw; w* 6¼ *v*Þ ¼ *p Y*ð Þ *v*j*X; Yw; w* � *v* (1)

*Z*ð Þ **X** *e* ∑*i*

*λk f k* **X***; Yi*�1*; Yi* ð Þ *; i* (3)

∑*kλk f k* **X***;Yi*�1*;Yi* ð Þ*;i* (2)

from the causation relationships.

*Cyberspace*

(**Figure 1**) [14–16].

**Figure 1.**

**124**

*A chain-structured CRF graph [7, 40].*

the CRF model can be written as

*p*ð Þ¼ **Y**j**X** 1

*G* ¼ ð Þ *V; E* ; the following equation is established:

*Z*ð Þ **X**

Y *i e*

> *f* ¼ ∑ *T i* ∑ *M k*

*Workflow of CRF-based word segmentation for geological literature in Chinese.*

**Figure 3.**

*Performances of the CRF model in different corpus. CRF-PKU, generic corpus of Peking University; CRF-GEO, geological corpus; CRF-GEO + PKU, the hybrid corpus combining generic and geological corpora.*

#### **2.2 Long short-term memory**

The text data are consisted of a series of sequential words or characters, which can be regarded as a special data of time series and can be processed by the methods used in the time series analysis. Words or characters in text data are not completely independent but are connected to and influenced by the adjacent words or characters. In the model of neural network, it contains three basic compositions: input layer, hidden layer, and output layer. The layers of ordinary neural networks are linked with each other by weights. The nodes in a same layer are independent and have no link with each other. If the ordinary neural network methods are used to process text data, the semantic information of context will be missing. Recurrent neural network (RNN) has a short memory by nodes connecting in the hidden layer, which can receive information from self-cell and other cells. RNN has been used in the fields of NLP and automatic speech recognition [41, 42]. RNN model has the drawback of vanishing gradient problem, which means RNN model only obtains the information that is limited in the adjacent node position [43]. To address this challenge, the LSTM model designed input gate, output gate, and a forget gate to obtain information of far nodes and regulate the information flow between the cells [44] (**Figure 4**).

$$i\_t = \sigma(W\_i \cdot [h\_{t-1}, \chi\_t]) + b\_i \tag{4}$$

2.Words grouped: Each word is grouped based on frequency and a ranking

is extracted and joined together randomly.

*Text Mining to Facilitate Domain Knowledge Discovery DOI: http://dx.doi.org/10.5772/intechopen.85362*

3. Random extraction and combination: Each group of words in the previous step

4.Training: With the previous processing, sentences are formed via combination

5. Testing and output: The resulting segmentation is post-processed and output.

In this research work, the significant highlight is that the training corpus is

corresponding sequences of the training corpus. The training corpus did not have any manual label information. The precision, recall, and F-scores reach 86.1%, 87.1%, and 86.6%, respectively. Compared with Wang et al. [7], the performance of CRF-based method is better than the Bi-LSTM-based segmenter based on the performance reported in their papers. But the Bi-LSTM-based method has a strong ability of identifying new words. The rate of out-of-vocabulary word identification

The nodes of content word and their links are the carrier of literature information and knowledge. In a large open knowledge graph, the key information was stored in in a triple format. Moreover, the bigram is also widely used in the text information representation. Wang et al. [7] used the bigram graph to represent the

The visualization was built based on the "from," "to," and "weight" variables. The variables of "from" and "to" indicate the sequence of content words in the content word corpus. In the content-word pairs, the former content word is defined as "from" variable, and the latter is defined as "to" variable. Their weights were defined by the co-occurrence frequency of content-word pairs. The bigram graph

In geological exploration, the anomaly information of geology, geochemical exploration, geophysical exploration, and remote sensing is important clues for mineral prospecting [46]. To state different anomaly information, literatures of geological exploration will have significant features in the term of word frequency. **Figure 5** shows the main information hidden in a single literature of geophysical exploration. In this visualization, geological terms (e.g., *aeromagnetic*, *gravity*, *magnetic*) and geophysical data processing terms (e.g., *inversion*, *horizontal gradient*, *information*) are all linked to the term *anomaly*. The visualization represents the

Geology research not only reveals the earth evolution and promotes our understanding of the Earth but also has a close relationship with the human society. One of the important roles of applied geology is to discover mineral deposits and provide raw material for economic construction and development. In the long geological

random. The segmentation rule was learned from the words and their

**3. Text information visualization and knowledge discovery**

**3.1 Information visualization of a single geological literature**

was used to visualize the nodes of content words and their links.

**3.2 Geological text mining for discovering ore prospecting clues**

hidden key knowledge in the geological literature.

algorithm.

reached 71.1%.

**127**

single geological literature.

based on deep learning.

$$f\_t = \sigma(\mathcal{W}\_f \cdot [h\_{t-1}, \mathfrak{x}\_t]) + b\_f \tag{5}$$

$$c\_t = f\_{\;t}c\_{t-1} + i\_t \tanh(W\_c x\_t + W\_c h\_{t-1} + b\_c) \tag{6}$$

$$\text{tot} = \sigma(\mathcal{W}\_o \cdot [h\_{t-1}, \mathfrak{x}\_t]) + b\_o \tag{7}$$

$$h\_t = o\_t \, ^\*\mathbf{tanh}(c\_t) \tag{8}$$

in which *i*, *f*, *c*, and *o* denote input gate, forget gate, cell vector, and output gate, receptively. σ denotes the activation functions. *W* denotes weight matrices and bias vector parameters which need to be learned during the training.

Qiu et al. [36] proposed a geological literature segmenter based on the Bi-LSTM model. The segmenter was carried out by the following stages (more details can be seen in the reference article):

1. Corpus construction: The corpus from domain-generic and domain-specific texts is collected and constructed.

**Figure 4.** *The cell of LSTM [43, 45].*

**2.2 Long short-term memory**

*Cyberspace*

[44] (**Figure 4**).

seen in the reference article):

*ct-1*

*ht-1*

*xt*

**Figure 4.**

**126**

*The cell of LSTM [43, 45].*

texts is collected and constructed.

*ct* ¼ *ft*

*ht* ¼ *ot*

vector parameters which need to be learned during the training.

σ · tanh

in which *i*, *f*, *c*, and *o* denote input gate, forget gate, cell vector, and output gate, receptively. σ denotes the activation functions. *W* denotes weight matrices and bias

Qiu et al. [36] proposed a geological literature segmenter based on the Bi-LSTM model. The segmenter was carried out by the following stages (more details can be

1. Corpus construction: The corpus from domain-generic and domain-specific

Neural Network Layer Pointwise Operation

The text data are consisted of a series of sequential words or characters, which can be regarded as a special data of time series and can be processed by the methods used in the time series analysis. Words or characters in text data are not completely independent but are connected to and influenced by the adjacent words or characters. In the model of neural network, it contains three basic compositions: input layer, hidden layer, and output layer. The layers of ordinary neural networks are linked with each other by weights. The nodes in a same layer are independent and have no link with each other. If the ordinary neural network methods are used to process text data, the semantic information of context will be missing. Recurrent neural network (RNN) has a short memory by nodes connecting in the hidden layer, which can receive information from self-cell and other cells. RNN has been used in the fields of NLP and automatic speech recognition [41, 42]. RNN model has the drawback of vanishing gradient problem, which means RNN model only obtains the information that is limited in the adjacent node position [43]. To address this challenge, the LSTM model designed input gate, output gate, and a forget gate to obtain information of far nodes and regulate the information flow between the cells

> *it* ¼ *σ Wi* � *ht*�1*; xt* ð ½ �Þ þ *bi* (4) *ft* ¼ *σ Wf* � *ht*�1*; xt* ½ � þ *bf* (5)

*ct*�1 þ *it*tanhð Þ *Wcxt* þ *Wcht*�1 þ *bc* (6) *ot* ¼ *σ Wo* � *ht*�1*; xt* ð ½ �Þ þ *bo* (7)

tanh

·

∗ tanhð Þ *ct* (8)

*ht*

*ht*

*ct*

In this research work, the significant highlight is that the training corpus is random. The segmentation rule was learned from the words and their corresponding sequences of the training corpus. The training corpus did not have any manual label information. The precision, recall, and F-scores reach 86.1%, 87.1%, and 86.6%, respectively. Compared with Wang et al. [7], the performance of CRF-based method is better than the Bi-LSTM-based segmenter based on the performance reported in their papers. But the Bi-LSTM-based method has a strong ability of identifying new words. The rate of out-of-vocabulary word identification reached 71.1%.


Contents

#### **Preface XI**

Chapter 7 **Ensiling Alfalfa (Medicago sativa L.) and Orchard Grass (Dactylis glomerata L.) Forage Harvested at 08:00 or 14:00, without Wilting or 1 or 2 h Wilting and with or without Use of Bacterial Inoculant 107** Ricardo D. Améndola-Massiotti, Renato González-Ortiz, Luis A. Miranda-Romero, Juan A. Burgueño-Ferreira and Pedro Topete-Pelayo

Preface

tions for their time and knowledge.

The book **Forage Groups** gathers information about forage plants that are used in different regions around the world as animal food. This book contains data from studies with native and cultivated species and presents information about cultivation and use of these forage plants, in a clear and direct way. The target readers are researchers, students, and farmers who will be able to apply the present knowledge on forage cultivation, harvesting, and con‐ servation. Another interesting point of view is the use of legumes in animal feed, beyond bringing a crucial analysis about carbon dioxide – CO2 produced in the pastures. The au‐ thors are from many countries around the world and this allows an analysis and exchange of information. The editors would like to thank the researchers and their staff and institu‐

> **Dr. Ricardo Loiola Edvan** Animal Science Department Piaui Federal University

> **Dr. Edson Mauro Santos** Animal Science Department Paraiba Federal University

Teresina, Brazil

Teresina, Brazil


**4. Experimental Methodology**

In this section, we present a series of experiments in order to evaluate the performance of the proposed WvEnSL algorithm for X-ray classification against the most efficient ensemble self-labeled algorithms i.e., CST-Voting, DTCo and EnSL which utilize simple voting methodologies. The implementation code was written in JAVA, making use of the WEKA 3.9 Machine Learning Toolkit [27].

The performance of the classification algorithms is evaluated using the following performance metrics: *F*-measure (*F*1) and Accuracy (*Acc*). It is worth mentioning that *F*1 consists of a harmonic mean of precision and recall while Accuracy is the ratio of correct predictions of a classifier.


**Symmetries in Quantum Mechanics and Statistical Physics**

Editor **Georg Junker**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editor* Georg Junker Institute for Theoretical Physics I University Erlangen-Nurnberg ¨ Erlangen Germany

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Symmetry* (ISSN 2073-8994) (available at: www.mdpi.com/journal/symmetry/special issues/symmetries quantum mechanics statistical physics).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-2479-5 (Hbk) ISBN 978-3-0365-2478-8 (PDF)**

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**THE CARDIOVASCULAR SYSTEM – PHYSIOLOGY, DIAGNOSTICS AND CLINICAL IMPLICATIONS**

Edited by **David C. Gaze**

http://dx.doi.org/10.5772/2266 Edited by David C. Gaze

#### **Contributors**

Michihiro Suwa, Laurence Valeyrie-Allanore, Amandine Servy, Meriem Jones, Ana Olga Mocumbi, Steve Ogbonnia, Reza Amani, Nasrin Sharifi, Sebastjan Filip, Ali Nasimi, Mikhail Rudenko, Vladimir Zernov, Olga Voronova, Grazyna Lutoslawska, Xavier Figueroa, Tadashi Yoshida, David Newman, Robin Callister, Francisco Palmero, Cristina Guerrero, Ali Jalali, Parham Ghorbanian, Chandrasekhar Nataraj, Angela Tesse, Raul Martins, Nadezda Bylova, Gregory Arutyunov, Carmen Diniz, Joana Beatriz Sousa, Ana Leitão-Rocha, Shoichi Ezaki, Masanori Tamura

#### **© The Editor(s) and the Author(s) 2012**

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First published in Croatia, 2012 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

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2. Performing the right surgery

As in every aspect of medicine, the key to success in hand surgery is to make an accurate diagnosis based on a complete history and physical examination of our patient. Once you have an exact diagnosis you will know exactly what to operate and most importantly when to perform that operation. We, as surgeons, have that rush to try to solve everything as soon as it is presented to us. We know we can solve things, but we also have to be very patient to know the perfect timing and when to do it in order for our surgeries to be successful and to achieve the best results possible.


Acknowledgements

The three authors of this chapter, MRP, JUV, and AGM, are grateful to the projects UBACyT<PHONE_NUMBER>0088BA; PDTS-CIN 0362. This work was supported by CONICET; University of Buenos Aires; and COLCIENCIAS.


**2. Deformation of the detachment layers**

There are two main detachment layers of shale in the covering layers in the study region, i.e., the Lower Cambrian Niutitang Formation and Lower Silurian Longmaxi Formation. These two detachment layers controlled the strong deformation of the study region during the late Mesozoic and showed different roles in different zones.

**Figure 3.** Shear folds formed by shear deformation in the lower Cambrian detachment layer. (a) A-type shear folding; (b) pencil structure indicates SE-NW shear (location: Labachong, Luoyixi Town, Yongshun County, Hunan); (c, d) B-type

A Discussion on the Detachment Structural Deformation and Its Influence on Pore Structure…

http://dx.doi.org/10.5772/intechopen.72245

51

**Figure 4.** Assemblage of thrust faults and shear folds in the lower Cambrian (location: Fumeng Village, Liangshuijin

Town, Yuanling County, Hunan).

shear folding (location: Mengfu Village, Liangshuijing Town, Yuanling County, Hunan).

#### **2.1. Deformation of the Cambrian system**

The detachment layer is the Niutitang Formation (Є1 n) in the Lower Cambrian, which is mainly composed of black carbonaceous shale and black shale and about 70–200 m thick. This layer crops out in the region between Shizhu and Anhua-Xupu Fault, but the strong shearing along the layer only occurred in the region between the Cili-Dayong Fault and the Anhua-Xupu Fault.

Different types of small shear folds and thrust faults character the deformation of the detachment layer, including angular folds, congruous inverted folds, sheath folds and recumbent folds. B-type and A-type folds can be found (**Figures 3** and **4**). The fold hinges strike mainly northeastsouthwest. However, the fold hinges of sheath folds trend northwest-southeast, which indicated the direction of shearing. The axial planes of these folds mainly dip to the southeast at the angle of 40–70°. Combined with sheath folds, top-to-the-northwest thrusting occurred along this detachment layer, which is similar to the deformation of the Xuefeng Mountain.

## **2.2. Deformation of the Silurian detachment layer**

The Silurian detachment layer is composed of the Longmaxi Formation (S1 l) of the Lower Silurian. It is the thickest detachment layer (more than 2000 m) in the study region, which is mainly composed of shale with some silty mudstone and siltstone. This layer mainly distributes in Shizhu and Baojing; it also crops out to the west of Anhua-Xupu Fault. Field observations showed that the deformation of this layer mainly occurred in the region between Shizhu and Baojing (the region where the so-called narrow syncline style folds developed).

The B-type folding characters the deformation of the Silurian detachment layer. The congruent inverted fold is the main type of the folds in this layer (**Figure 5**). Most fold hinges strike A Discussion on the Detachment Structural Deformation and Its Influence on Pore Structure… http://dx.doi.org/10.5772/intechopen.72245 51

for illite-rich shale [37]. Shale gas and coalbed methane (CBM) reservoirs are in formally grouped as unconventional because gas is trapped in part by sorption processes [38]. Several previous workers have focused on the pore characteristics and adsorption gas characteristics of tectonically deformed coals [39, 40], which provide references to the study on deformed shale. Although previous studies have provided useful insight to evolution of pore structure in shale during thermal maturity and in CBM related to deformation structures, the prediction of structural deformation change in pore structure of shale in geological condition still remains quite challenging. In the present study, pore structure feature investigations are performed using two sets of shale samples (deformed shale and undeformed shale) collected from the typical shale bed of the Longmaxi Formations (Lower Silurian) of Western of the Xuefeng Mountain, South China. The influence of structural deformation on pore structure

There are two main detachment layers of shale in the covering layers in the study region, i.e., the Lower Cambrian Niutitang Formation and Lower Silurian Longmaxi Formation. These two detachment layers controlled the strong deformation of the study region during the late

composed of black carbonaceous shale and black shale and about 70–200 m thick. This layer crops out in the region between Shizhu and Anhua-Xupu Fault, but the strong shearing along the layer only occurred in the region between the Cili-Dayong Fault and the Anhua-Xupu Fault. Different types of small shear folds and thrust faults character the deformation of the detachment layer, including angular folds, congruous inverted folds, sheath folds and recumbent folds. B-type and A-type folds can be found (**Figures 3** and **4**). The fold hinges strike mainly northeastsouthwest. However, the fold hinges of sheath folds trend northwest-southeast, which indicated the direction of shearing. The axial planes of these folds mainly dip to the southeast at the angle of 40–70°. Combined with sheath folds, top-to-the-northwest thrusting occurred along

this detachment layer, which is similar to the deformation of the Xuefeng Mountain.

and Baojing (the region where the so-called narrow syncline style folds developed).

Silurian. It is the thickest detachment layer (more than 2000 m) in the study region, which is mainly composed of shale with some silty mudstone and siltstone. This layer mainly distributes in Shizhu and Baojing; it also crops out to the west of Anhua-Xupu Fault. Field observations showed that the deformation of this layer mainly occurred in the region between Shizhu

The B-type folding characters the deformation of the Silurian detachment layer. The congruent inverted fold is the main type of the folds in this layer (**Figure 5**). Most fold hinges strike

The Silurian detachment layer is composed of the Longmaxi Formation (S1

n) in the Lower Cambrian, which is mainly

l) of the Lower

was discussed.

50 Tectonics - Problems of Regional Settings

**2. Deformation of the detachment layers**

Mesozoic and showed different roles in different zones.

The detachment layer is the Niutitang Formation (Є1

**2.2. Deformation of the Silurian detachment layer**

**2.1. Deformation of the Cambrian system**

**Figure 3.** Shear folds formed by shear deformation in the lower Cambrian detachment layer. (a) A-type shear folding; (b) pencil structure indicates SE-NW shear (location: Labachong, Luoyixi Town, Yongshun County, Hunan); (c, d) B-type shear folding (location: Mengfu Village, Liangshuijing Town, Yuanling County, Hunan).

**Figure 4.** Assemblage of thrust faults and shear folds in the lower Cambrian (location: Fumeng Village, Liangshuijin Town, Yuanling County, Hunan).

**Figure 5.** B-type shear folds with SE dipping axial planes in the Silurian detachment layer (location: (a, b) Shaoha Village, Shaoha Town, Yongshun county, Hunan; (c, d) Heixi Town, Pengshui county, Chongqing).

**Figure 6.** Widespread thrust folding in the eastern Sichuan Basin (location: Wanfo Village, Gaoleshan Town, Xianfeng

A Discussion on the Detachment Structural Deformation and Its Influence on Pore Structure…

http://dx.doi.org/10.5772/intechopen.72245

53

**Figure 7.** Multi-layer detaching-thrusting deformation section shown by the seismic reflection. Tt3

, upper Permian; Tp1

, middle Cambrian; TЄ1

, lower Permian; Ts2

, lower Cambrian.

j, lower Triassic Jialingjiang Fm; Tp2

m, lower Ordovician Meitan Fm; TЄ2

x, upper Triassic

, lower

, middle Silurian; Ts1

County, Hubei).

Xujiahe Fm; Tt1

Silurian; To1

northeast-southwest; some AB-type inverted folds whose hinges strike north-east are also found. The axial planes of these folds mainly dip to the southeast with dip angle ranging from 20 to 60°. And some inverted folds with axial plane dipping to the northwest are also developed. In addition, many minor thrust faults top to the northwest are also developed in the layer. These structures imply that the top-to-the-northwest thrusting parallel to the bedding is the main deformation mechanism, and the inverted folds with axial plane dipping to the northwest resulted from the back thrusting.


3.2.1 Slow dynamic effects

Slow dynamics is by far the most typical characteristic of the nonclassical NMEM. It refers to the logarithm dependence recovery of the elastic modulus to the original initial value (i.e., at rest) after being excited and therefore softened by a large amplitude strain. The log-time evolution of the elastic modulus resembles to a creeplike behavior observed in quasi-static experiments. However, it is important to note that slow dynamics is more likely a new creep behavior due to the fact that modulus is not following the symmetry of the strain. Furthermore, contrary to creep experiments, slow dynamics is a reversible and repeatable behavior, and observations on rocks and some damaged metals are performed at dynamic strain levels two or three orders of magnitude below those of a typical creep experiment. Slow dynamics includes two different scale mechanisms: conditioning and relaxation.


2.3.3. In Vivo Assessment Efficacy of Se-NPs on *Vicia faba*

The inoculum of the pathogenic fungus *R. solani* was prepared according to Büttner et al. [48] that comprises mixing contents of 5 pure *R. solani* culture Petri dishes with 1000 mL of distilled water using electrical blender for two minutes. This experiment was carried out in the garden of Plant and Microbiology department, Faculty of Science, Al-Azhar University, Cairo, Egypt. The source of faba bean cv. Giza 716 was obtained from the Legume Research Department, Field Crop Institute, Agricultural Research Center, Egypt. The sandy loam soil was autoclaved (1.5 atm, 121 ◦C for 30 min) and distributed equally in disinfected pottery pots (30 cm in diameter) with 12 sterilized seeds per pot. The *Vicia faba* seeds were washed with distilled water then sterilized using 2% sodium hypochlorite for 2 min before conducting the treatments shown in Table 1.

**Table 1.** Treatments used in this study.


**Author details**

Ren Deqing1,2,3\* and Zhu Yongtian2,3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Physics & Astronomy Department, California State University Northridge, USA

2 National Astronomical Observatories/Nanjing Institute of Astronomical Optics & Technol‐ ogy, Chinese Academy of Sciences, China

3 Key Laboratory of Astronomical Optics & Technology, National Astronomical Observato‐ ries, Chinese Academy of Sciences, China


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[139] Byrne PF, Bolaños J, Edmeades GO, Eaton DL. Gains from selection under drought versus multilocation testing in related tropical maize populations. Crop Science. 1995;**35**:63-69

[140] Tollenaar M, Wu J. Yield improvement in temperate maize is attributable to greater water stress tolerance. Crop Science. 1999;**39**:1597-1604

[141] Edmeades GO, Bolaños J, Hernandez M, Bello S. Causes for silk delay in a lowland tropical maize population. Crop Science. 1993;**33**:1029-1035

[142] Chapman SC, Edmeades GO. Selection improves drought tolerance in tropical maize populations. II. Direct and correlated responses among secondary traits. Crop Science. 1999;**39**:1315-1324

[143] Edmeades GO, Daynard TB. The relationship between final yield and photosynthesis at flowering in individual maize plants. Canadian Journal of Plant Science. 1979;**59**:585-601

[144] Westgate ME, Bassetti P. Heat and drought stress in corn: What really happens to the corn plant at pollination? In: Wilkinson D, editor. Proceedings of the 45th Annual Corn and Sorghum Research Conference; 5-6 December 1990; Chicago. Washington D.C.: ASTA; 1991. pp. 12-28

[145] Zinselmeier C, Jeong BR, Boyer JS. Starch and the control of kernel number in maize at low water potentials. Plant Physiology. 1999;**121**:25-36. DOI: 10.1104/pp.121.1.25

[146] Jacobs BC, Pearson CJ. Potential yield of maize determined by rates of growth and development of ears. Field Crops Research. 1991;**27**:281-298

[147] Reed AJ, Singletary GW, Schussler JR, Williamson DR, Christy AL. Shading effect on dry matter nitrogen partitioning, kernel number and yield of maize. Crop Science. 1988;**28**:819-825

[148] Schussler JR, Westgate ME. Assimilate flux determines kernel set at low water potential in maize. Crop Science. 1995;**35**:1074-1080

[149] Zinselmeier C, Lauer MJ, Westgate ME, Boyer JS. Reversing drought-induced losses in grain yield. Sucrose maintains embryo growth in maize. Crop Science. 1995a;**35**:1390-1400

[150] Zinselmeier C, Westgate ME, Schussler JR, Jones RJ. Low water potential disrupts carbohydrate

metabolism in maize. Plant Physiology. 1995b;**107**:385-391

[151] Zinselmeier C, Habben JE, Westgate ME, Boyer JS. Carbohydrate metabolism in setting and aborting maize ovaries. Formation in maize hybrids representing three decades of grain 1-13. In: Westgate M, Boote K, editors. Physiology and Modeling. Kernel Set in Maize. CSSA Special Publications 29. Madison, WI: CSSA; 2000

[152] Fischer KS, Palmer AFE. Tropical maize. In: Goldsworthy PR, Fisher NM, editors. The Physiology of Tropical Field Crops. Imprint: New York John Wiley & Sons Ltd., United states US Publication. 1984. pp. 213-248

[153] Tollenaar M, Dwyer LM, Stewart DW. Ear and kernel. formation in maize hybrids representing three decades of grain yield improvement in Ontario. Crop Science. 1992;**32**:432-438

[154] Singletary GW, Below FE. Growth and composition of maize kernels cultured in vitro with varying supplies of carbon and nitrogen. Plant Physiology. 1989;**89**:341-346

[155] Li Y, Wang M, Zhang F, Xu Y, Chen X, Qin X, et al. Effect of postsilking drought on nitrogen partitioning and gene expression patterns of glutamine synthetase and asparagine synthetase in two maize (*Zea mays* L.) varieties. Plant Physiology and Biochemistry. 2016;**102**:62-69. DOI: 10.1016/j.plaphy.2016.02.002

[156] Kamara AY, Kling JG, Menkir A, Ibikunle O. Agronomic performance of maize (*Zea mays* L.) breeding lines derived from a low nitrogen maize population. The Journal of Agricultural Science. 2003;**141**:221-230

**467**

**Chapter 22**

**Abstract**

fabrics.

**1. Introduction**

Ecofriendly Marigold Dye as

Natural Colourant for Fabric

This chapter highlights on the applications of marigold plant extracts as an antibacterial and antimicrobial best dyer for textiles. *Tagetes erecta* usually known as Marigold is a vital wellspring of carotenoids and lutein, developed as a nursery plant. Marigold blossoms are yellow to orange red in colour. Now a days, lutein is transforming into an unquestionably common powerful fixing, used as a part of the medicines, food industry and textile coatings. This has increased more noticeable vitality of marigold and its exceptional concealing properties. Regardless of the way that marigold blooms; its extract has been used as a measure of veterinary supports. The examination was directed to contemplate the usage of a concentrate of marigold as a trademark shading, which is antibacterial and antimicrobial. The marigold extract ability was focused on colouring of the cotton fabrics. Investigations of the dye ability, wash fastness, light fastness, antibacterial tests and antimicrobial tests can be endeavoured. Studies have exhibited that surface concealing was not impacted by washing and drying in the shadow/sunlight. These surprises reveal that the concentrate of marigold extract can be used for cotton

**Keywords:** dyeing, marigold, colour fastness, fabric, antibacterial, antimicrobial

Marigold is a common name of Tagetes species, a genus of herbs and the member of the Asteraceae family. It is a native of Mexico and other warmer parts of America and naturalised elsewhere within tropics and sub tropics. In India it is cultivated as garden plant. The flowers which are yellow to orange in colour with corymbose clusters are much used for garlands and functions and also used to

These plants do not require any additional care. They grow in all conditions of the environment. Marigold plants bare sun, heat, drought and grow in any well-drained soil. Marigolds are easy to grow even from transplants also. Marigold requires a mild climate for smooth growth and flowering. Mild climate is required during the growing period is 14.5° - 28.6°C and it improves the production of flowers at higher temperature (26.2° - 36.4°C). Depending on the climatic conditions, the marigold grows three times in a year – rainy, winter, and summer seasons. The

decorate households during celebration of festivals.

different coloured flowers are shown in **Figure 1**.

*Sujata F. Harlapur, Suneeta Harlapur*

*and Shantabasavareddi F. Harlapur*

#### **Chapter 22**

*Abiotic Stress in Plants*

1999;**39**:1315-1324

1979;**59**:585-601

1991. pp. 12-28

10.1104/pp.121.1.25

in tropical maize populations. II. Direct and correlated responses among secondary traits. Crop Science. metabolism in maize. Plant Physiology.

[152] Fischer KS, Palmer AFE. Tropical maize. In: Goldsworthy PR, Fisher NM, editors. The Physiology of Tropical Field Crops. Imprint: New York John Wiley & Sons Ltd., United states US Publication.

Stewart DW. Ear and kernel. formation in maize hybrids representing three decades of grain yield improvement in Ontario. Crop Science. 1992;**32**:432-438

[151] Zinselmeier C, Habben JE, Westgate ME, Boyer JS. Carbohydrate metabolism in setting and aborting maize ovaries. Formation in maize hybrids representing three decades of grain 1-13. In: Westgate M, Boote K, editors. Physiology and Modeling. Kernel Set in Maize. CSSA Special Publications 29. Madison, WI: CSSA;

1995b;**107**:385-391

2000

1984. pp. 213-248

[153] Tollenaar M, Dwyer LM,

[154] Singletary GW, Below FE. Growth and composition of maize kernels cultured in vitro with varying supplies of carbon and nitrogen. Plant

Physiology. 1989;**89**:341-346

[155] Li Y, Wang M, Zhang F, Xu Y, Chen X, Qin X, et al. Effect of postsilking drought on nitrogen partitioning

and gene expression patterns of glutamine synthetase and asparagine synthetase in two maize (*Zea mays* L.) varieties. Plant Physiology and Biochemistry. 2016;**102**:62-69. DOI: 10.1016/j.plaphy.2016.02.002

[156] Kamara AY, Kling JG, Menkir A, Ibikunle O. Agronomic performance of maize (*Zea mays* L.) breeding lines derived from a low nitrogen maize population. The Journal of Agricultural

Science. 2003;**141**:221-230

[143] Edmeades GO, Daynard TB. The relationship between final yield and photosynthesis at flowering in individual maize plants.

Canadian Journal of Plant Science.

[144] Westgate ME, Bassetti P. Heat and drought stress in corn: What really happens to the corn plant at pollination? In: Wilkinson D, editor. Proceedings of the 45th Annual Corn and Sorghum Research Conference; 5-6 December 1990; Chicago. Washington D.C.: ASTA;

[145] Zinselmeier C, Jeong BR, Boyer JS. Starch and the control of kernel number in maize at low water potentials. Plant Physiology. 1999;**121**:25-36. DOI:

[146] Jacobs BC, Pearson CJ. Potential yield of maize determined by rates of growth and development of ears. Field Crops Research. 1991;**27**:281-298

Schussler JR, Williamson DR, Christy AL. Shading effect on dry matter nitrogen partitioning, kernel number and yield of maize. Crop Science. 1988;**28**:819-825

[147] Reed AJ, Singletary GW,

[148] Schussler JR, Westgate ME. Assimilate flux determines kernel set at low water potential in maize. Crop

Science. 1995;**35**:1074-1080

1995a;**35**:1390-1400

[149] Zinselmeier C, Lauer MJ, Westgate ME, Boyer JS. Reversing drought-induced losses in grain yield. Sucrose maintains embryo growth in maize. Crop Science.

[150] Zinselmeier C, Westgate ME, Schussler JR, Jones RJ. Low water potential disrupts carbohydrate

**466**