*Edited by Gaffar Sarwar Zaman*

The book presents a qualitative and quantitative approach to understand, manage and enforce the integration of statistical concepts into quality control and quality assurance methods. Utilizing a sound theoretical and practical foundation and illustrating procedural techniques through scientific examples, this book bridges the gap between statistical quality control, quality assurance and quality management. Detailed procedures have been omitted because of the variety of equipment and commercial kits used in today's clinical laboratories. Instrument manuals and kit package inserts are the most reliable reference for detailed instructions on current analytical procedures.

Published in London, UK © 2018 IntechOpen © Antoine2K / iStock

Quality Control in Laboratory

Quality Control in Laboratory


**2. Exploring the systemic level**

The systemic and structural level is probably the most abstract and hard to describe when trying to analyse how religion interacts with society in a whole and with other social systems to achieve goals related to the sustainability ideal, but it becomes a good guide into such dynamics.

Taking the social systems theory of Niklas Luhmann as a general framework [3] and trying to update and adapt it to new circumstances, we can develop a model that could reveal several hidden dynamics when religion is thought as part of a system aimed at becoming sustainable.

Luhmann never ignored religion as a relevant social sub-system, and he devoted several essays along his fruitful career to dissect the functions that religion could perform in highly differentiated societies [4]. From this broad view, to speak about social systems and sustainability would be a redundancy: indeed, per definition, a social system is an entity that subsists despite the odds and manages to articulate a network of meaningful communications in contrast with its noisy environment. The general idea is that a social system is a living case of social survival, and societies exist since a very early stage of human evolution, but they have evolved too, following a path that moved from more hierarchical to more meshed, differentiated and specialized structures, well ingrained and inter-dependent. It is apparent that that evolution has been positive and helped to better adapt to changing circumstances: a progress arrow can be described. In the developed stage, several sub-systems arise to better address their functional issues, like in the case of economy, politics, science, and others. Religion experiences its own evolution, from providing the description and meaning of the entire system, to be a part dealing with a specific issue: managing the residual contingency, or those problems other systems could not manage or fix. In this schema, religion becomes the ultimate resource for unsolvable or pending issues beyond the reach of economic, political, or scientific intervention [5].

Luhmann's analysis moved later to still more abstract functions, like dealing with the paradoxes that unavoidably engenders a self-referential social system like ours. In principle, the sustainability of a society depends on some balance between the differentiation process, that leads to a high specialization of each sub-system, and the integration or coordination of such autonomous instances, with their own approach and communication codes. Furthermore, a society is sustainable when it is able to keep a balance with its own environment – not just natural – and its internal functions. Each sub-system needed to contribute in the right way, performing its own function. The schema reserves a place and a role to religion as an instance able to tackle the hardest or ultimate issues through a code that distinguishes between immanence and transcendence, and remits to a transcending dimension what cannot be adjusted in the immanent or immediate reality.

The emergency we try to address under the label of 'sustainability' forces us to observe our social systems from a different perspective, even if in continuity with what sociologists like Talcott Parsons and Niklas Luhmann could observe from their systemic gaze. The point is not so much how stable societies are at the present, and how they manage to keep so, but rather, how they can preserve in the future a condition that is now perceived as deeply endangered by several disrupting factors. Even if we managed to survive as integrated societies until now, despite many disasters, plagues, and wars, we can no longer take for granted such resilience, when the current conditions appear as more threatening and climate change is setting a concerning trend, together with other contingencies we do not manage to address.

The new condition puts religion under a rising tension, since the growing uncertainties, or non-manageable risks, increase the pressure on the function that the social systems theory assigned to religion. However, providing hope or even to establish a sharp distinction between this-worldly and other-worldly expectations – the first one doomed and the second one open to be revealed – does help only in a relative way, and clearly reduces the range of religious function when the entire system is in question.

Religions have always played a 'vicary role' in many societies, trying to address sectors that were incompletely covered by secular means. In that sense, the idea of 'residual management' acquires a new meaning. In fact, Christian churches have engaged many times in supplying material and human resources to cover gaps in the education, health and welfare systems in many societies. It has been a constant provider of such services during the 19th and 20th centuries in many Westers areas, a model later transferred to other societies in worse conditions. Even today, and as the American anthropologist Tanya Luhrmann stated [6], many Churches in America provide assistance to people suffering psychological illnesses and difficulties, and in that way, they help to remedy a shortage in services that cannot be covered by other administrative or social resources of the Welfare State. The point is that religions show a very adaptive flexibility that clearly contributes to fill gaps and to render the entire social system more resilient and sustainable.

The question is: To what extent do evolved religions exhibit this adaptive capacity, and will become useful to address the far greater concerns linked to sustainability? My proposal has been to expand the basic idea of Luhmann's social systems theory to assume that religion has been vicarious and has always addressed residual problems or has filled empty gaps in the social fabric no other systems were able to fill, or simply inadvertently opened. If this is the case, then, this other great emergency, threatening humanity's future, will mobilize all the available resources to cope with this risk, not just providing hope, but encouraging practices and pressing to reach more reassuring conditions.

Obviously, it is not easy to compare churches interventions aimed at alleviating educational needs, physical or mental health demands, and other social deficits, and to remedy a state of things that could bring to a general collapse. In the first case, concrete actions are expected in schools, hospitals, and charities; in the present emergency it is less about assisting people in need, and more about changing minds and hearts and to interact with other social systems exerting some pressure or lobbying for sustainable management. Several churches and religions have already adopted such a strategy, at least at the level of their declarations and intentions. It is less clear to what extent these religious bodies could effectively interact with more powerful systems, like economy and politics, to achieve these goals.

A main difficulty can be devised in this context: even if religion can play a vicarious role at filling gaps left in the social fabric, it becomes harder to contrast the own dynamics presiding contemporary standard economy, based on increasing production and conspicuous consumption. In this case, religion does not just provide some remedies, without interfering with other system's development, but might contrast and even disrupt those same systems which follow a divergent logic. From a systemic point of view, such interferences should lead to new configurations and adaptations in each system, but the problem could subsist and leave open wounds in the social body.

I have mentioned from the beginning that the interaction works as a two-way dynamic: it is not just about how religions contribute to ensure a sustainable future, but how the model of sustainability can influence and help these religious entities. The expectation is that such a model informs and inspires these institutions, and that

#### *Sustainability and Religion: Mutual Implications DOI: http://dx.doi.org/10.5772/intechopen.104497*

they assume sustainability as their main goal, for them and for all the society in which they are inserted. That means a special emphasis and a style more sensitive towards the common shared future and a revision of everything from this priority. Religious organizations confront similar – or even worse – scenarios as every other social body, and they need to focus more on something that was quite neglected until recently, since the stability of those organizations was presumed. This shared sense of risk clearly invites to redefine priorities and to adapt the organization to whatever can ensure a viable future.

The suggested perspective should assist in redefining the mission of religious organizations according to the current emergency. The central question is whether withdrawing religion and churches from the equation, things could stay the same or even improve to reach better levels in sustainability rankings. This would be the ultimate test when trying to ascertain the function that religion still plays in societies struggling with several threats and a highly uncertain future. The expectation is that the religious sub-system can assume a greater commitment in this field, and that doing so, it focuses all its resources to contribute to assuring life and stability for next generations, beyond providing transcending hope. The point is that such development could work to reintegrate religion and its function into this new context that demands to all social systems to engage – each one in its own way – in improving the present conditions. Not just religion, but every social sub-system needs to review and update its priorities, functions and performance according to that broad goal: ensuring a sustainable future. That goal will redescribe the social system with its many components or sub-systems, now more encompassed according to the current emergencies. Religion will probably not play a leading role in this new configuration as in earlier times, but it could nevertheless make a difference in dealing with the described challenges. Religion is called to contribute to render a society more sustainable than other society without.

Until now, religion has been described in highly general and abstract terms, avoiding the reference to specific religious traditions. Probably each religion offers its own style and rules to better address the described challenge, and surely some religions will be better endowed than others to assume that task and to tackle those issues. We need to move further to practice this analysis in a more accurate and nuanced way. Some steps have been already done, and several studies have been published focusing on particular religious traditions [7–9], but more needs to be done in order to better specify the connections between each religious expression and the described agenda, however this work transcends the limits of the present essay.


*Edited by Sujit K. Bhattacharya*

Acute diarrheal diseases (ADD) are important causes of morbidity and mortality worldwide. ADD includes, among others, cholera, shigellosis, and rotavirus diarrhea. These diseases are known to cause sporadic epidemics. Cholera and shigellosis are known to even cause pandemics. The treatments of these two diseases have been simplified with the advent of oral rehydration therapy and effective antibiotics. However, development of drug resistance poses tremendous therapeutic challenges. This book includes chapters on new methods of classification of dehydration and this information will provide a better classification method of dehydration. Epilepsy and its association with acute diarrhea is a new area for research. Prevention of acute diarrhea in school children and treatment with herbal medicines are important areas to pursue further. The readers will find herein new concepts in diarrheal disease research and management.

ISBN 978-1-78923-865-5

Perspective of Recent Advances in Acute Diarrhea

Published in London, UK © 2020 IntechOpen © Dr\_Microbe / iStock


**2. Materials and Methods**

### *2.1. Cell Lines and Culture Conditions*

Prostate cancer cell lines, DU145, PC3, and LNCaP were purchased from the American Type Culture Collection (ATCC). LNCaP95 was a generous gift from Dr. Jun O. Luo (Johns Hopkins University, Baltimore, MD, USA). Cells were authenticated by short tandem repeat analysis (Takara Bio Inc., Shiga, Japan) and then tested by DDC Medical (Thermo Fisher Scientific, Waltham, MA, USA) in April 2018, to ensure that the cells were mycoplasma-free. Cells were maintained as monolayer cultures at 37 ◦C and 5% CO2. The cell line DU145 was cultured in DMEM supplemented with 10% FBS, PC-3 in RPMI 1640 with 10% FBS, LNCaP in phenol red-free RPMI 1640 with 10% FBS, and LNCaP95 in phenol red-free RPMI 1640 with 10% charcoal stripped serum (CSS). A docetaxel-resistant cell line variant, LNCaP95-DR, was developed over a period of 6 months by exposure to gradually increased concentrations of docetaxel (Sigma-Aldrich, St. Louis, MO, USA). A time-matched parental cell line, LNCaP95-C, was developed in a medium containing vehicle (DMSO) at the corresponding concentration. Finally, LNCaP95-DR cells were maintained in medium containing 15 nM docetaxel.


**1. Introduction**

Additive manufacturing, more frequently known as 3D printing, is the fabrication method of objects using a print head, nozzle, or other printer technologies to deposit material to the print surface [1]. The 3D printing process has been successfully used in many disciplines, such as aerospace, automotive, biomedical, and food industries. It is claimed to allow quicker and cheaper production of an object, especially intricate and delicate objects with complex geometry. Unlike other manufacturing means, 3D printing has a much higher automation level that further contributes to labor and cost reduction, reducing production time [2–5].

The civil engineering field also has developed to a stage where the design of the structure has become much more complex, while load-bearing necessities have not been reduced. Furthermore, to meet the demand for residential buildings as well as infrastructure objects, there is a high need to build faster and reduce building costs. There are high hopes that 3D printing, due to success in other fields, will also bear fruit in civil engineering applications. It has been estimated that 3D printing will reduce construction waste by 30 to 60%, decrease production time by 50 to 70%, and drop labor costs by 50 to 80% [6]. To show these improvements, cementitious material compositions are specifically designed to flow evenly and have proper layer adhesion to one another. Furthermore, 3D-printed concrete sections are more prone to acid attacks as well as shrinkage [7]. The weakest part of the 3D-printed section is claimed to be the layer connecting zone. They also show anisotropic behavior and insufficient insulating properties that lead to possible heat loss [8,9].

As 3D printing of structures are more frequently used to develop a structure where layers are put on top of one another and subjected to direct compression, it was predicted that the structure would work similarly to masonry structures. However, as not all the structures are subjected to direct compression, there is a need to gain data and knowledge on how printed structures act under other stresses. For instance, retaining walls have load applied on the side of them. Therefore, tensile stresses significantly affect the structure's load-bearing capacity.

This article investigates the early-age creep and drying shrinkage properties of 3Dprinted cement composite that are loaded in the direction parallel to layers and cast cement composite and compares them to one another.


*Article* **Anti-Biofilm E**ff**ect of Selected Essential Oils and Main Components on Mono- and Polymicrobic Bacterial Cultures**

**Erika Beáta Kerekes 1,\*, Anita Vidács 2, Miklós Takó 1 , Tamás Petkovits 1, Csaba Vágvölgyi 1 , Györgyi Horváth 3, Viktória Lilla Balázs 3 and Judit Krisch 2**

Received: 26 July 2019; Accepted: 10 September 2019; Published: 12 September 2019

**Abstract:** Biofilms are surface-associated microbial communities resistant to sanitizers and antimicrobials. Various interactions that can contribute to increased resistance occur between the populations in biofilms. These relationships are the focus of a range of studies dealing with biofilm-associated infections and food spoilage. The present study investigated the effects of cinnamon (*Cinnamomum zeylanicum*), marjoram (*Origanum majorana*), and thyme (*Thymus vulgaris*) essential oils (EOs) and their main components, i.e., trans-cinnamaldehyde, terpinen-4-ol, and thymol, respectively, on single- and dual-species biofilms of *Escherichia coli*, *Listeria monocytogenes*, *Pseudomonas putida,* and *Staphylococcus aureus*. In dual-species biofilms, *L. monocytogenes* was paired with each of the other three bacteria. Minimum inhibitory concentration (MIC) values for the individual bacteria ranged between 0.25 and 20 mg/mL, and trans-cinnamaldehyde and cinnamon showed the highest growth inhibitory effect. Single-species biofilms of *L. monocytogenes*, *P. putida,* and *S. aureus* were inhibited by the tested EOs and their components at sub-lethal concentrations. Scanning electron microscopy images showed that the three-dimensional structure of mature biofilms embedded in the exopolysaccharide matrix disappeared or was limited to micro-colonies with a simplified structure. In most dual-species biofilms, to eliminate living cells from the matrix, concentrations exceeding the MIC determined for individual bacteria were required.

**Keywords:** antibacterial activity; biofilm; polymicrobial biofilm; essential oil; food spoilage


**Acknowledgements**

The authors gratefully acknowledge full support and cooperation of the Springer press, UK for extending permission in publishing the research paper of the journal Environmental Monitoring & Assessment, vol. 184(12), pp:7561-77, 2012.

This study was partially supported by the European Fund for Economic and Regional Development (FEDER) through the Program Operational Factors of Competitiveness (COM‐ PETE) and National Funds through the Portuguese Foundation for Science and Technology (PEST-C/MAR/UI 0284/2011, FCOMP 01 0124 FEDER 022689).


*2.1. Samples of CCM*

The sugar beet pulp was from Kanevsky sugar refinery (Krasnodar, Russia) and wheat straw from Khimavtomatica SPA (Moscow, Russia). Dried and steam explosion-pretreated bagasse was provided by DNL (Rotterdam, The Netherlands), Florida Crystals (West Palm Beach, FL, USA), and BC International. Sunflower peels were from East Siberian Biotechnology Center PLC (Tulun, Russia). Raw and pretreated soy husks and oat bran were provided by ADM (Decatur, IL, USA). Dried and steam explosion-pretreated corn stalks were obtained from NREL (Golden, CO, USA), Dyadic International (Jupiter, FL, USA), and ADM (Decatur, IL, USA). Corn bran was obtained from Gruma S.A.B. de S.V. (San Pedro, Mexico). Wheat bran and brewing waste were from Bigor LLC (Moscow, Russia). Wet and dried distillers grains were provided by ADM (Decatur, Illinois). Original and ball-milled wood samples (pine, aspen, larch, hevea) were from the East Siberian Biotechnology Center (Tulun, Russia). Samples of bleached and unbleached softwood and hardwood pulp were produced from spruce and birch-aspen mixtures, respectively, from Arkhangelsk Pulp and Paper Mill (Arkhangelsk, Russia).


**1. Introduction**

Hyperspectral imagery with hundreds of narrow spectral channels provides wealthy spectral information. With very high spectral resolution, hyperspectral data has been of great interest in many practical applications, such as in agriculture, environment, surveillance, medicine [1–4] etc. Hyperspectral classification is a key technique employed in aforementioned applications. A majority of classification methods have been promoted in the last several decades to distinguish physical objects and classify each pixel into a unique land-cover label, such as maximum likelihood [5], minimum distance [6], K-nearest neighbors [7,8], random forests [9], Bayesian models [10,11], neural networks, etc., and their improvements [12–15]. Among these supervised classifiers, one of the most important classifiers is kernel-based support vector machine (SVM), which can also be considered as a kind of neural network. It can achieve superior hyperspectral classification accuracy via building an optimal hyperplane to best separate training samples.

In addition, sparse representation based on an over-complete signal dictionary has gained great attention in the literature. Sparse representation-based classification (SRC) [16–18] and collaborative representation classification (CRC) [19,20] are proposed from a different aspect: they do not adopt the traditional training–testing fashion. Such classification methods do not need any prior knowledge about probability density distribution of the data. To further enhance the performance of SRC and CRC, Du and Li [21] utilized a diagonal weight matrix to adaptively adjust the regularization parameter. To address the issues of Hughes phenomenon in hyperspectral classification, majority of feature extraction and selection algorithms are utilized to delete redundant features from the original data. To further improve performance of hyperspectral classification, multi-features are extracted and employed for classification. For instance, Kang et al. combined spectral and spatial features through a guided filter to process pixel-wise classification map in each class [22]. Several studies [23–25] focused on integrating spatial and spectral information in hyperspectral imagery. In addition, texture features are considered to assist hyperspectral classification [26], and modeling of hyperspectral image textures is significant for classification and material identification.

Recent research has highlighted deep learning with deep neural networks, which can learn high-level features hierarchically. They have demonstrated their potential in image classification, which also motivated successful applications of deep models on hyperspectral image classification. The classic deep learning method is convolutional neural networks (CNN), which plays a dominant role in visual-based issues. The local receptive fields of CNN can extract spatial-related features at high levels. Fukushima [27] introduced the motivations of CNNs. Ciresan and Lee et al. [28,29] depicted the invariants of CNNs. Chen et al. proposed 2-D CNN and 3-D CNN [30] to capture deep abstract and robust features, yielding superior hyperspectral classification performance. Although CNNs are typical supervised models, a massive training dataset is needed to trigger their powers. Unfortunately, a limited number of labeled samples are usually given in hyperspectral imagery. Deep belief networks (DBNs) [31] and stacked autoencoders (SAEs) [32] are also very promising deep learning methods for hyperspectral classification with limited training samples.

In this paper, we mainly investigate the DBN for its suitability and practicality to hyperspectral classification. A novel hyperspectral classification framework is proposed based on an optimum DBN. To acquire desirable performance, we also promote an advanced algorithm to enhance the texture features of hyperspectral imagery. The main contributions of this paper are summarized below.

The rest of the paper is organized into four sections. Section 2 is a brief description of related work. In Section 3, we detail our proposed DBN model. Datasets and parameters setting are demonstrated in Section 4. Experimental results and discussions are depicted in Section 5. Section 6 draws the conclusion of this paper.

#### **2. The Related Work**

A deep belief network (DBN) is a model that is first pre-trained in an unsupervised way, and then the available labeled training samples are used to fine-tune the pre-trained model through optimizing a cost function defined over the labels of training samples and their predictions.

The original DBN, published in Science [33], uses a generative model in the pre-training procedure, and uses back-propagation in the fine-tuning stage. This is very useful when the number of training samples is limited, such as in the case of hyperspectral remote sensing. DBN can be efficiently trained in an unsupervised, layer-by-layer manner where the layers are typically made of restricted Boltzmann machines (RBM). Thus, to explain the structure and theory of the DBN, we first describe its main component, the RBM.

#### *2.1. Restricted Boltzmann Machines (RBM)*

An RBM generally uses unsupervised learning, which can be interpreted as stochastic neural networks. It was originally developed to form a distributed representation. It is a two layer-wise network, which is composed of visible and hidden units. Learning RBM only allows the full connection between visible and hidden units, and does not allow connection between two visible units or connections between two hidden units. With the given visible units, hidden units can be obtained via mapping of visible units. The activations of each neuron in hidden layers are independent. Meanwhile, with the given hidden units, visible units have the same effects. A typical RBM structure is depicted in Figure 1.

**Figure 1.** Architecture of Restricted Boltzmann Machines.

The visible units can be represented as **h**, and the hidden units can be expressed as **v**. The RBM model is a kind of energy-based models in which the joint distribution of the layers can be expressed as Boltzmann distribution. Energy-based probabilistic models define a probability distribution through an energy function as:

$$p(\mathbf{v}, \mathbf{h} | \theta) = \frac{\exp\left(-E(\mathbf{v}, \mathbf{h} | \theta)\right)}{Z(\theta)},\tag{1}$$

where the normalization constant *Z*(*θ*) is called the partition function by analogy with physical systems:

$$Z(\theta) = \sum\_{\mathbf{v}} \sum\_{\mathbf{h}} E(\mathbf{v}, \mathbf{h}; \theta) \tag{2}$$

A joint configuration of the units has an energy given by:

$$\begin{array}{rcl} E(\mathbf{v}, \mathbf{h}; \theta) &=& -\sum\_{i=1}^{n} a\_i \mathbf{v}\_i - \sum\_{j=1}^{m} b\_j \mathbf{h}\_j - \sum\_{i=1}^{n} \sum\_{j=1}^{m} \mathbf{v}\_i \mathbf{w}\_{ij} \mathbf{h}\_j \\ &=& -\mathbf{a}^T \mathbf{v} - \mathbf{b}^T \mathbf{h} - \mathbf{v}^T \mathbf{w} \mathbf{h} \end{array} \tag{3}$$

where *θ* = {*ai*, *bj*, *wij*}; *wij* represents the weight connecting the visible unit *i* and the hidden unit *j*; *ai* and *bj* denote the bias terms of visible and hidden layers, respectively; *n* and *m* are the total visible and hidden unit numbers; and *vi* and *hj* represent the states of visible unit *i* and hidden unit *j*.

Due to the specific structure of RBMs, visible and hidden units are conditionally independent, as given by:

$$\begin{array}{l} P(v\_i = 1 | \mathbf{h}, \boldsymbol{\theta}) = \sigma(a\_i + \sum\_i w\_{ij} v\_i) \\ P(h\_{\boldsymbol{\cdot}} = 1 | \mathbf{v}, \boldsymbol{\theta}) = \sigma(b\_{\boldsymbol{\cdot}} + \sum\_{\boldsymbol{\cdot}} w\_{i\boldsymbol{\cdot}} h\_{\boldsymbol{\cdot}}) \end{array} \tag{4}$$

where *σ*(•) is the logistic function defined as

$$\sigma(\mathbf{x}) = \frac{1}{1 + \exp(-\mathbf{x})} \tag{5}$$

Overall, an RBM has five parameters: **h**, **v**, **w**, **a** and **b**, where **w**, **a** and **b** are achieved via learning, **v** is input, and **h** is output. **w**, **a** and **b** can be learned and updated via the contrastive divergence (CD) method as

$$w\_{ij} \gets w\_{ij} + \lambda \left( P(h\_j|v\_i)v\_i - P(h\_j^r|v\_i^r)v\_i^r \right) \tag{6}$$

$$a\_i \gets a\_i + \lambda (\upsilon\_i - \upsilon\_i^r) \tag{7}$$

$$b\_j \gets b\_j + \lambda (h\_j - h\_j^r) \tag{8}$$

where *λ* denotes the learning rate, *P*(*hr j* |*vr i*) represents the reconstructed probability distribution, and *vr i* and *hr j* are the reconstruction of visible and hidden unit, respectively. Once the states of hidden units are chosen, the visible units can be reconstructed via the hidden units sampled via Gibbs method. Then, the states of hidden units are updated through the visible units, so that the hidden units demonstrate the features of reconstruction. The distribution of visible units approximates the distribution of the real data. The learning ability of an RBM depends on whether the hidden units contain enough information of the input data.

#### *2.2. Deep Belief Learning*

The learning ability of a single hidden layer is limited. To capture the comprehensive information of data, the hidden units of the RBM can be feed as the input (visible units) of another RBM. This kind of layer-by-layer learning structure trained in a greedy manner forms so-called Deep Belief Networks. In this way, DBN can extract deep features of image data. The structure of three-layer DBN is depicted in Figure 2.

**Figure 2.** An illustration of three-layer DBN with logistic regression.

The process of training of DBN consists of two parts: pre-training and fine-tuning. The pre-training is an unsupervised training stage that initializes the model in such a way to enhance the efficiency of supervised training. The fine-tuning process can be realized as supervised training stage, which adjusts the classifier's prediction to match the ground truth of the data.

#### **3. The Proposed Framework**

To extract more powerful and invariant features, we propose a novel DBN hyperspectral classification algorithm based on TFE. DBN is composed of several layers of latent factors, which can be deemed as neurons of neural networks. However, the limited training samples in the real hyperspectral image classification task usually lead to many "dead" (never responding) or "potential over-tolerant" (always responding) latent factors (neurons) in the trained DBN. Our proposed framework mainly consists of three steps: band grouping and sample band selection, TFE, and DBN-based classification.

#### *3.1. Band Grouping and Sample Band Selection*

Compared to multispectral imagery, hyperspectral imagery with hundreds of spectral bands has relatively narrow bandwidths. The correlation between spectral bands needs to be considered. In our framework, we calculated all the pair wise correlation coefficient of bands, and then utilized the correlations between adjacent bands. The spectral correlation coefficients in different datasets are depicted in Figure 3.

**Figure 3.** The maps of correlation coefficients of spectral bands in different datasets: (**a**) Indian Pines; (**b**) University of Pavia; and (**c**) Salinas.

We can obtain the correlation coefficient between adjacent bands as:

$$\rho\_{i,j} = \text{corr}(\mathbf{B}\_i, \mathbf{B}\_j) = \text{cov}(\mathbf{B}\_i, \mathbf{B}\_j) / \sqrt{\text{var}(\mathbf{B}\_i)\text{var}(\mathbf{B}\_j)} \tag{9}$$

where cov is covariance and var means variance. B*i* and B*j* represent the *i*-th and *j*-th band channels, respectively. *i* = 1, 2, ... , *L* − 1. Here, *L* denotes the number of bands of the hyperspectral dataset. Based on Equation (9), the correlation coefficients between adjacent bands in different datasets are calculated, as shown in Figure 4. We can see that the highest correlation coefficient in Indian Pines is 0.9997, and the lowest correlation coefficient is 0.0686. The spectral bands of university of Pavia have strong correlations overall, where the highest correlation coefficient is 0.9998, and the lowest correlation coefficient is 0.9294. The highest correlation coefficient in Salinas is 0.9999, and the lowest correlation coefficient is 0.5856.

**Figure 4.** The correlation coefficients of adjacent spectral bands in different datasets: (**a**) Indian Pines; (**b**) University of Pavia; and (**c**) Salinas.

Here, we design an algorithm for grouping bands rationally.

Firstly, calculate the average correlation coefficients of the adjacent bands, denoted as *C*, which is utilized as the threshold in the following steps. It can be calculated through:

$$\overline{C} = \frac{1}{L-1} \sum\_{i=1}^{L-1} \rho\_{i,j} \tag{10}$$

where *j* = *i* + 1. If the correlation coefficients of adjacent bands are greater than *C*, these two bands are considered to have strong correlation.

*Remote Sens.* **2018**, *10*, 396

Second, search local minimum values from the correlation coefficients between the adjacent bands, denoted as ρmin, where ρmin = {*ρi*,*j*|*ρi*,*j* ≤ *ρi*+1,*j*+1 || *ρi*,*j* ≤ *ρi*−1,*j*−1}. All the elements in ρmin are compared with *C*. If the inequality {*ρi*,*j* ∈ ρmin} < *C* is satisfied, it indicates that the correlation between the *i*-th band and the *j*-th band is lower than the average correlation value, and the correlation between these two bands is considered to be weak. Then, the corresponding index group {*i*, *j*} is recorded and added to the set ρ*Loc*.

Third, band grouping depends on the stored index pairs in ρ*Loc*. For instance, with regard to index pair {*i*, *j*}, the *i*-th band is set as the end band of the former band group and the *j*-th band is set as the first band of the next band group. Thus, based on the aforementioned rules, all the bands are divided into different band groups {**G**1, **G**2, ··· , **G***K*}.

After dividing all the bands of hyperspectral dataset into different band groups, a sample band with the strongest and clearest texture features is searched and selected from each group.

To extract texture features, the gray level co-occurrence matrix (GLCM) has been employed successfully. GLCM [34] is defined as a matrix of frequencies which can extract second order statistics from a hyperspectral image. The distribution in the matrix depends on the angular and distance relationship between pixels. After the GLCM is created, it can be used to compute various features. We choose the five most commonly used features in Table 1 to select a sample band from each band group. The texture feature score of each band can be calculated by Equation (11):

$$T = \sum\_{i=1}^{5} F\_i \tag{11}$$

**Table 1.** Feature calculated from the normalized co-occurrence matrix *P*(*i*, *j*).

The sample band in each band group can be selected through:

$$\mathbf{g}\_k = \underset{\mathbf{B}\_{\parallel k}}{\text{argmax}} \left\{ T\_{\mathbf{B}\_{\parallel k}} \Big| \mathbf{B}\_{\parallel^k} \in \mathbf{G}\_k \right\},\tag{12}$$

where **G***k* represents the *k*-th band group of the dataset, *l k* ∈ {1, 2, ··· , *Nk*}, *Nk* is the number of bands in the *k*-th band group, and B*lk* represent the *l k*-th band in the *k*-th band group. Finally, the sample band set are comprised of {**g**1, **g**2, ··· , **g***k*}.

#### *3.2. Texture Feature Enhancement*

As an effective edge-preserving filter, guided filter (GF) was proposed by He in 2012. It can enhance the detail of an image. Texture feature is a kind of important spatial characteristics and also has long history in image processing. In this paper, we utilize the GF in each band group to enhance the texture features of the image.

The general guided image filtering was designed for gray-scale images or color images. It is very easy to extend to multi-channel image. Firstly, the guidance image in our proposed framework is multi-channel image, denoted as **I***M*, which is comprised of the copies of the band with the strongest texture features in each band group. We assume *qM* is a linear transform of **I***M* in a window *ωk* centered at the pixel *k*, and the multi-channel guided filter model can be expressed as

$$q\_i^M = \left(\mathbf{a}\_k^M\right)^T \mathbf{I}\_i^M + b\_k^M{}^\prime \/ \forall i \in \omega\_k \tag{13}$$

where **I***Mi* is a *C* × 1 vector, and *C* is the channel number of the input image, **a***Mk* is a *C* × 1 coefficient vector, and *qMi*and *bMk*are scalars. The guided filter for multi-channel guidance image becomes

$$\begin{array}{ll} \mathbf{a}\_{k}^{M} = \left(\boldsymbol{\Sigma}\_{k} + \boldsymbol{\varepsilon}\mathbf{U}\right)^{-1} \left(\frac{1}{|\boldsymbol{\omega}|} \sum\_{i \in \omega\_{k}} \mathbf{I}\_{i}^{M} p\_{i}^{M} - \mu\_{k} \overline{p\_{k}^{M}}\right) \\ \boldsymbol{b}\_{k}^{M} = \overline{p\_{k}^{M}} - \left(\mathbf{a}\_{k}^{M}\right)^{T} \boldsymbol{\mu}\_{k} \\ \boldsymbol{q}\_{i}^{M} = \left(\overline{\mathbf{a}\_{i}^{M}}\right)^{T} \mathbf{I}\_{i}^{M} + \overline{b\_{i}^{M}} \end{array} \tag{14}$$

where ∑*k* is the *C* × *C* covariance matrix of **I***M* in *ωk*, **U** is an *C* × *C* identity matrix, *pM* denotes a filtering input image which is given beforehand according to the application, *μk* is the mean of **I***M* in *ωk*, *pMk* is the mean of *pM* in *ωk*, and |*ω*| represents the number of pixels in *ωk*.

Then, the extending guided image filtering for multi-channel images will be applied to each band group. For instance, each channel of the guidance image *IM* in Equation (14) for the *k*-th band group **G***k* is the copy of the sample band **g***k* selected previously.

After guided filtering for all groups is completed, the output bands are restored to a hyperspectral image cube according to the band number. Finally, the reconstructed image data with enhanced texture features are obtained through the aforementioned steps. Figure 5 demonstrates the procedure of band grouping and TFE. We can see that, after sample bands with strongest textures are obtained, the reconstructed image data with enhanced texture feature can be achieved through the GF process.

**Figure 5.** The procedure of band grouping and texture features enhancement.

#### *3.3. DBN Classification Model*

In this section, a DBN-based framework for hyperspectral classification with feature enhanced data is developed.

Spectral information is the most significant and direct feature, and can be directly utilized for classification. Architectures of existing methods, such as SVM and KNN, can extract spectral features but not deep enough. Therefore, only a deep architecture can make full use of the texture enhanced hyperspectral image characteristics. However, as the training samples are limited, the overfitting problem often occurs if the network is too deep, so we advocate a novel DBN framework, which has only two hidden layers (Figure 6).

**Figure 6.** Our proposed DBN network for classification.

The input data consist of training samples that are one-dimensional (1-D) vectors, and each pixel of a training sample is collected from the texture enhanced HSI data. For ease of description, the first hidden layer is denoted as **h**1 and the second **h**2. The first layer is learned for extracting features from the input data, and the learned features are preserved in **h**1. Then, to pursue refined and abstract features, using the features contained in **h**1 as the visible data of the second layer, **h**2 keeps the refined features. This procedure is generally called recursive greedy learning for pre-training a DBN.

In practice, learning each layer is often performed through the *n*-step CD, and the weights are updated using Equations (6)–(8).

To fine-tune the DBN and accomplish classification, a Softmax layer is added to the end of the network.

Now, let **X** = {**x**1, **x**2,..., **x***K*} be a set of training samples and **Y** = {*y*1, *y*2,..., *yK*} be the corresponding labels, where **x***k* = [*xk*1, *xk*2,..., *xkL*] *T* is the spectral signature of the *k*-th sample with *L* bands. Utilizing the maximum likelihood method, the objective function can be written as

$$\mathbb{C}(\boldsymbol{\Theta}) = -\sum\_{k=1}^{K} \log(P(\boldsymbol{y}\_k|\mathbf{x}\_k), \boldsymbol{\Theta}) = -\sum\_{k=1}^{K} \log(\mathcal{S}\_{\mathcal{Y}\_k}(\mathbf{x}\_k, \boldsymbol{\Theta})) \tag{15}$$

where *K* is the number of training samples, (*P*(*yk*|**x***k*), θ) means the distribution of *yk* when given **x***k* with the parameters θ of the Softmax layer, and *Syk* (**x***k*, θ) denotes the output of the Softmax layer of the *k*-th training sample, that is

$$S\_{y\_k}(\mathbf{x}\_k, \boldsymbol{\Theta}) = \frac{\exp\{-\sum\_{m=1}^{M} \delta(y\_k = m) \boldsymbol{\Theta}\_m^T \mathbf{h}^{H\_L}\}}{\sum\_{n=1}^{M} \exp\{-\boldsymbol{\Theta}\_n^T \mathbf{h}^{H\_L}\}},\tag{16}$$

where *HL* is the number of the hidden layers, which is set to 2 in our proposed framework, and *M* is the number of the classes. θ*m* and θ*n* are the parameter vectors for the *m*-th and *n*-th unit of the softmax layer, respectively. **h** *HL* is the output of the *HL*-th hidden layer, which is calculated via the input data, the weights and bias from the first layer to the *HL*-th hidden layer. To optimize the objective function, the stochastic gradient descent (SGD) algorithm is used. Finally, the label of each testing pixel is determined via the weights and biases from aforementioned steps.


**5. Conclusions**

In regard to early warning signs and prodromal symptoms of AECOPD patients, COPD patients recognize the warning signs and prodromal symptoms according to their individual experiences. If they able to early recognize the warning signs and prodromal symptoms, they will able to manage it rapidly by themselves or their care givers. As a result, COPD patients will not have the declining of PFT immediately, decreasing of mortality rate, and have a better quality of life. Consequently,

healthcare professionals should help and provide the recommendation about early warning signs and prodromal symptoms of AECOPD to COPD patients in order to make it through COPD illness in a period of time during with the patients can learn this knowledge on their own.


*Article* **Enhancing the Protective Performance of Surge Arresters against Indirect Lightning Strikes via an Inductor-Based Filter**

#### **Mahdi Pourakbari-Kasmaei \* and Matti Lehtonen**

Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>Tel.: +358-46-538-48-72

Received: 11 August 2020; Accepted: 8 September 2020; Published: 11 September 2020

**Abstract:** Preventing the medium voltage (MV) transformer fault by protecting transformers against indirect lightning strikes plays a crucial role in enhancing the continuous service to electricity consumers. Surge arresters, if selected properly, are e fficient devices in providing adequate protection for MV transformers against transient overvoltage impulses while preventing unwanted service interruptions. However, compared to other protective devices such as the spark gap, their prices are relatively high. The higher the surge arrester rating and energy absorption capacity are, the higher the prices go. This paper proposes an inductor-based filter to limit the energy pushed into the surge arrester, and consequently to prevent any unwanted failure. An energy-controlled switch is proposed to simulate the fault of the surge arrester. Surge arresters with di fferent ratings, e.g., 12 kV, 18 kV, 24 kV, 30 kV, 36 kV, and 42 kV with two di fferent classes of energy, namely, type a and type b, are tested under di fferent indirect lightning impulses such as 100 kV, 125 kV, 150 kV, 175 kV, 200 kV, 250 kV, 300 kV, and 500 kV. Furthermore, these surge arresters are equipped with di fferent filter sizes of 100 μH, 250 μH, 500 μH, and 1 mH. Results prove that equipping a surge arrester with a proper filter size enhances the performance of the surge arrester significantly such that a high rating and somewhat expensive surge arrester can be replaced by a low rating and cheap surge arrester while providing similar or even better protective performance for MV transformers. Therefore, such configurations not only enhance the protective capability of surge arrester, but also reduce the planning and operating costs of MV networks.

**Keywords:** double exponential function; indirect lightning; medium voltage transformer; spark gap; filtered surge arrester; energy-controlled switch


**Acknowledgements**

**Figure 1** is published with the kind permission of SAGE Publications. Although partially modified, **Figures 3**–**5** are published with the kind permission of the Swiss Forestry Journal.


*3.3.1 Assessment of associated risk factors*

Clinicians should always ensure that a thorough history is taken for all children who are consulted. This must include all social, medical and psychological factors of the attending child patient. Clinicians must always take cognizance of the following points in order to assess a child holistically as these dynamics noted below are often the key risk factors which predispose a child to potential abuse or neglect:

• Households with a history of domestic abuse – this will not always be selfreported, however there will be anecdotal evidence that could be used parallel to the new information coming to light.

*Signs of Child Abuse and Neglect: A Practical Guide for Dental Professionals DOI: http://dx.doi.org/10.5772/intechopen.101564*


**5. Risk factors for occurrence of the trigeminocardiac reflex**

to occur in up to 67% after ophthalmic surgery [24]. Because of publication bias, the real prevalence may be substantially smaller, even the peripheral TCR has generally a higher prevalence than the central TCR. According to the senior author Schaller's experience, the central TCR occurs in up to 10–18% of the patients [1, 25–27]. In a retrospective time-series review of 125 patients operated for tumours of the cerebello-pontine angle, Schaller et al. noticed the TCR occurrence in 11%. [4] Three of these patients in this series developed asystole which lasted from 30 to 70 seconds [1]. In contrast to earlier studies on OCR, Schaller – for the first time – took into consideration both the heart rate and blood pressure and defined TCR as

heart rate and mean arterial blood pressure (MABP) 20% lower than the baseline [1].

In another retrospective time-series study, Schaller also showed the TCR occurrence during microvascular decompression of the trigeminal nerve for trigeminal neuralgia [27]. In this review on 28 patients, the prevalence of TCR was up to 18% with the same definition used as in his prior above-mentioned study [27]. TCR was also reported during transsphenoidal surgery for pituitary adenoma [11, 26, 28]. Among 117 patients who underwent transsphe‐ noidal surgery for pituitary adenoma, 10% developed a TCR during the surgical procedure [26]. Peripheral stimulation of the nasopharynx may also lead to (peripheral) TCR [11].

In several other neurosurgical procedures, there exists only case reports or small case series,

The TCR is a clinical phenomenon that reflects interactions of many organs to the brain [4, 12, 14, 19, 29–31]. Among all the organs, the connection between heart and brain is unique and presents a wide array of clinical manifestations when the TCR gets incited [16]. These clinical signs include drop in heart rate and blood pressure, asytole, ventricular tachycardia/fibrilla‐ tion, ST-T wave changes and other forms of arrhythmias [4, 5, 12–16, 29, 31–65]. Stimulus especially in form of stretch has found to be the strongest inciting factor for the TCR [1]. However, mild stimulus may also results into TCR episodes [3, 18, 35]. Chowdhury et al. reported that mild stimulus in the form of skin closure could able to produce transient asystole and questioned about the severity of stimulus and TCR manifestation. In another report, Chowdhury et al. further suggested that various manifestations of TCR episodes (bradycardia, hypertension, asystole) as well as different subtypes of TCR (peripheral and central) could manifest in the same patient [4, 13]. Again, in this report, transient asystole manifested during the skin closure as well [4]. Author postulated that the different sympathetic outflow responses could be due to different depths of anaesthesia, which coupled with different forms of TCR stimulation probably contributed to different haemodynamic responses in the same patient and obscured the classical manifestation of TCR [3]. Strikingly, the cardiovascular changes of TCR phenomenon mainly reported due to acute stimulation (peri-operatively) of trigeminal nerve; however, Chowdhury and Schaller highlighted the first description of chronic form of

As highlighted in a review by Chowdhury et al., the other rare cardiac perturbation i.e. coronary spasm in neurosurgical patients may be the mere manifestation of TCR events [16].

so that a robust prevalence of TCR occurrence is not (yet) known.

**4. Heart and the trigeminocardiac reflex**

TCR [40].

98 Abnormal Heart Rhythms

Because of the still fragmented knowledge about the TCR and the consecutive limited therapeutic options, the risk factors gain increased importance. However, it is best known that such risk factors are not easy to proof in retrospective studies as they are predominantly available in TCR research. The risk factors already known to increase the incidence of TCR include (i) hypercapnia, (ii) hypoxemia, (iii) light general anaesthesia, (iv) age (more pro‐ nounced in children), (v) the nature of the provoking stimulus (stimulus strength and duration) and (vi) drugs. Recent systematic reviews, however, have identified several potential cofounders for these mentioned risk factors. Drugs known to increase the TCR include (i) potent narcotic agents (sufentanil and alfentanil) [66, 67], (ii) beta-blockers and (iii) calcium channel blockers [68]. Narcotics may augment vagal tone through their inhibitory action on the sympathetic nervous system [1, 69–71]. Beta-blockers reduce the sympathetic response of the heart and, by so doing, augment the vagal cardiac response resulting in bradycardia. Calcium channel blockers result in peripheral arterial smooth muscle relaxation and vasodilatation causing reduction in blood pressure. In patients undergoing trigeminal manipulations, this worsens the vagal effect that occurs in some patients. In previous publications about the TCR, the study design (mostly case control) may have led to different bias regarding the risk factors [39], such as recall bias and non-response bias to only mention two examples. From this point of view, a randomized controlled trial or a systematic review are requested to show best a cause-and-effect relationship for the risk factors.

In the recent years, it has more and more raised the question if the main search on risk factors is not a too linear thinking to overcome the complexity of the TCR. However, we think that risk factors are still an important key to better understand the TCR; even these risk factors have led to the development of the thinking model of TCR rather than to direct treatment conse‐ quences.


*3.3. Formulation of Optimized FS-NTF and FS-NTF-Loaded Hyaluronic Acid Hydrogel*

Finally, the optimized nanotransfersome formulation was developed with the use of lecithin (level: −0.99 ≈ −1) 75 mg, fluconazole (level: 0.32 ≈ 0.3) 60 mg, and sesame oil (level: +0.99 ≈ 1) 75 mg. The predicted and observed values for the responses of the optimized formula are summarized in Table 7 as suggested by the software based on an analysis of the actual and predicted data. Values of the expected and adjusted R2 of the studied responses were in close agreement, proving the significance and predictive capacity of the design. Moreover, the experimental and predicted ratios with a percentage error of less than 10% and acceptable residuals were found between the real and predicted responses, showing a lack of curvature in the responses and the validity of the model (Table 5).

Hyaluronic acid, a natural biomolecule, has shown its antimicrobial effectiveness against bacteria and fungi in a dose-dependent manner [30]. Another contemporary study revealed a fungistatic role of hyaluronic acid through its inhibitory effect on the fungal lysozyme and peroxidase system [11]. Further, hydrogel containing hyaluronic acid has an effective and safe coating, facilitating the proliferation of basal keratinocytes and promoting reepithelialization. This role of hyaluronic acid controls the hydration of ulcer tissues and also the inflammatory process [31]. Therefore, the hyaluronic acid hydrogel was formulated in the present study of the formulated FS-NTF and was incorporated to facilitate the application of the developed formulation at the oral mucosa.

**Table 7.** Composition of the actual and predicted responses of the optimal FS-NTF formulation.


*2.1. Description of the Study Area*

The Awash River Basin is one of the 12 river basins of Ethiopia which is found between latitudes of 7◦53 N and 12◦ N and longitudes of 37◦57 E and 43◦25 E [29]. The basin constitutes the central and northern part of the Rift Valley and is bounded to the west, southeast, and south by the Blue Nile, the Rift Valley lakes, and the Wabeshebele basins, respectively [17]. It covers a total area of 110,000 km2, with a length of 1200 km [23]. The basin is a home of about 15 million inhabitants [17]. This basin has been the most highly utilized basin in Ethiopia since modern agriculture was introduced, as early as the 1950s [30]. The basin is divided into upper, middle, and lower valleys. The mean annual rainfall of the basin varies from 1600 mm northeast of Addis Ababa to 160 mm in the northern part of the basin (Figure 1). The distribution of rainfall is bimodal in the middle and lower parts of the basin and unimodal in the upper part [29]. The distribution of rainfall in the basin is influenced by the Inter Tropical Convergence Zone (ITCZ).

**Figure 1.** Location map of the Awash River Basin.

The mean surface water resource of the Awash River Basin is approximately 4.9 × 108 m3 [31]. Irrigation used 44% from the surface water resources. More than 70% of large-scale irrigated agriculture in Ethiopia is found in this basin. The irrigation potential of the basin is estimated to be 206,000 ha as reported in the ministry of water and energy office. The total mean annual evaporation is 1810 mm and 2348 mm in the upper and lower parts of the basin [31]. The estimated mean annual runoff within the basin is about 4.6 km3 [17]. Many rivers are functional only in rainy seasons (July to September) especially in lowland parts of the basin. However, the Mojo, Akaki, Kessem, Kebena, and Mile rivers are functional throughout the year. Since the population are highly dependent on rainfed agriculture, this has made the population and the economy vulnerable to impacts of climate change and droughts [32].


3.3.1. Rutting Resistance

The wheel track test is used to study the rutting resistance of 1.0% and 2.0% ER-100 reinforced LCA-AMs and the original asphalt mixture. The specimens were cured at 25 ◦C for 72 h. The results are shown in Figures 7 and 8, where it can be seen that the accumulated deformation (rutting depth) of the asphalt mixture increases gradually with the increase of the number of load cycles, and the rutting depth growth rate of ER-100-reinforced LCA-AM is less than that of the LCA-AM without ER. After the same number of load cycles, the rut depth of ER-reinforced LCA-AMd is much smaller than that of the LCA-AM without ER. At the loading times of 45 min and 60 min, the rut depth of the LCA-AM with 1.0% ER is 21.2% or 25.5% smaller than that of the original asphalt mixture; the rutting depth of 2.0% ER-reinforced LCA-AM is 57.3% (59.9% smaller than that of the LCA-AM without ER). Meanwhile, the dynamic stability of ER-reinforced LCA-AMs is much higher than that of the LCA-AM without ER. The results indicate that ER can significantly enhance the high-temperature stability of LCA-AMs. The reason is that, after the curing of the ER, part of the strength of the asphalt mixture is provided by the ER, and the ER can form a spatial skeleton structure in the asphalt mixtures, which have greater strength and stiffness. Therefore, it can greatly improve the rutting resistance of the mixtures.

**Figure 7.** Rutting depth increase curve of asphalt mixtures.

3.3.2. Low-Temperature Crack Resistance

LCAs can decrease the formation temperature of asphalt mixtures by decreasing the viscosity of asphalt binders. However, if the reduction in viscosity is too large, it results in a reduction in the mechanical properties of the asphalt mixture (even at low temperature), which can easily break down during low-temperature crack resistance tests. In order to improve the low-temperature crack resistance of LCA-AMs, −10 ◦C and 0 ◦C SCB tests were conducted to evaluate the low-temperature cracking resistance of LCA-AMs with different dosages of ER. The results are shown in Figure 9 and Table 11, and the comparison of fracture energy of LCAs-AM is shown in Figure 10. According to Figure 9 and Table 11, the fracture energy and fracture toughness of LCA-AMs increase first and then decrease with the increase of ER dosages, and the LCA-AM with 0.6% ER has the highest fracture energy and fracture toughness. After curing, the ER acts as part of the skeleton structure in LCA-AMs. Therefore, a low content of ER can enhance the significantly increase the low-temperature crack resistance of LCA-AMs.

However, with the gradual increase of ER dosages, the increasing effect of the ER on the stiffness (mechanical performance) is much more obvious, and it may be too big and have a negative effect on the low-temperature crack resistance of LCA-AMs. The ER content in the LCAs-AM does not need to be high. The results show that one can significantly increase the low-temperature crack resistance of LCA-AMs by controlling the ER content in LCA-AMs.

**Figure 9.** Displacement and load curve of SCB tests: (**a**) −10 ◦C; (**b**) 0 ◦C.

**Figure 10.** Low-temperature fracture energy with different ER contents.

#### 3.3.3. Water Sensitivity

Immersion Marshall Test

The residual Marshall stability (RMS) of LCA-AMs with different dosages of ER were studied using the immersing Marshall test. The results are shown in Figure 11. From Figure 11, the Marshall stability of LCA-AMs with ER is higher than 8 kN both before and after the immersion condition. The RMS of LCA-AMs increases with the increase of ER dosage, and all the RMS of LCA-AMs are more than 80%. The results even satisfy the requirements of the specification of HMA.

**Figure 11.** Immersion Marshall stability and RMS of LCA-AMs with ER.

Freeze-Thaw Splitting Test

The freeze-thaw splitting ratio (TSR) of LCA-AMs with the different dosages of ER were studied using the freeze-thaw splitting test. The results are shown in Figure 12. It can be seen from Figure 12 that the TSR of LCA-AM without ER is lower than 75%. However, all the TSRs of LCA-AMs with ER are higher than 75%, and meet the requirements of HMA. The TSR increases first and then decreases with the increase of ER dosage, and the TSR of LCA-AMs with 1% ER is the highest. Therefore, due to the enhancement effect of ER, LCA-AMs have a good anti-water damage performance.

**Figure 12.** Indirect tensile strength and TSR of LCA-AMd with ER.

#### 3.3.4. Fatigue Resistance

The repeated SCB test was conducted to investigate the fatigue resistance of LCA-AMs. The results are shown in Table 12 and Figure 13. The stress ratios of the fatigue test were 0.3, 0.4, 0.5, 0.6 and 0.7, respectively.

It can be seen from Table 12 that, at 25 ◦C, the peak load and fracture toughness of LCA-AM with 1.0% ER are the highest, while the LCA-AM with 0.6% ER has the maximum fracture work and fracture energy. Compared with the LCA-AM without ER, the fracture energy of LCA-AMs with 0.3%, 0.6% and 1.0% ER increase by 26.7%, 109.3% and 60.5%, respectively. It can be seen from Figure 13 that under the same loading frequency, the fatigue life decreases gradually with the increase of stress ratio. When the stress ratio increases from 0.3 to 0.7, the fatigue life of the LCA-AM without ER decreases by 83.7%, which shows that the fatigue resistance of the asphalt mixture has an obvious stress dependence. When the stress ratio is 0.3, the fatigue life of the LCA-AM increases gradually with the increase of ER content. Compared with the LCA-AM without ER, the fatigue performance of LCA-AM with 0.3% ER decreased by 12.5%, while that of LCA-AMs with 0.6% and 1.0% ER increase by 15.3% and 48.9%, respectively. When the stress ratio is 0.7, the fatigue resistance of 0.3%, 0.6% and 1.0% ER reinforced LCA-AMs increases by 22.6%, 11.8% and 6.0%, respectively. This shows that ER can significantly increase the fatigue resistance of LCA-AMs. The LCAs-AM with 0.6% and 1.0% ER are much better than the LCA-AM without ER.

**Table 12.** Results of repeated SCB tests of LCA-AMs.

**Figure 13.** Fatigue life of LCA-AMs with ER.

#### **4. Conclusions**

In this paper, an environmentally friendly LCA asphalt modifier additive was developed, which can used to decrease the construction temperature of asphalt mixtures. The design and preparation parameters of LCA-modified asphalt mixtures were determined. The volumetric properties, mechanical performance and mixture performance of LCA-AMs were studied as well. The main conclusions are as follows:

It is feasible to improve the mixture performance by the enhancement of low dosages of ER. LCAs can facilitate the low-temperature preparation of asphalt mixtures, with remarkable environmental benefits. LCAs should find wide application prospects in asphalt pavement engineering.

**Author Contributions:** Conceptualization, Y.L.; methodology, Y.L., J.F., A.C. and S.W.; validation, F.W.; data curation, Y.L., J.F., A.C., F.W. and Q.L.; writing—original draft preparation, J.F. and F.W.; writing—review and editing, Y.L., A.C., S.W., Q.L. and R.G.; visualization, R.G.; supervision, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors acknowledge the financial supported provided by the National Natural Science Foundation of China (No. 52108415), the National Key Research and Development Program of China (No. 2018YFB1600200), the Natural Science Foundation of China (No. 51778515), the Key Technical Innovation Projects of Hubei Province (No. 2019AEE023), the Plan of Outstanding Young and Middleaged Scientific and Technological Innovation Teams in Hubei Province Universities (No. T2020010), the Key R&D Program of Hubei Province (No. 2020BCB064), the Scientific Research Fund Project of Wuhan Institute of Technology (No. K2021032), the Science and Technology Project of the Department of Housing and Urban-Rural Development of Hubei Province, and the test help from Shiyanjia Lab (www.shiyanjia.com).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no competing financial interest.

#### **References**


**1. Introduction**

Honeybees play a crucial role for the well-being of the humanity: on the one hand, they have the amazing capacity to produce honey and other secondary products (pollen, propolis, royal jelly, venom and wax) and on the other hand, they provide pollination services for plants, bringing an important contribution to the agricultural activities and food production. The

and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. 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, © 2017 The Author(s). Licensee InTech. 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.

bees represent a bio barometer for the preservation of the environment. In addition, their economic, cultural and religious values are very important [1].

The European Union is one of the most important honey producers of the world. Nevertheless, according to some official bodies, production levels seem to be declining [2]. Such decrease is attributable to the diminution of honeybee colonies, which is mostly related to the colony collapse disorder, but also to imports. The number of beehives, the quantity and quality of honey obtained are the main aspects evaluated at European level, to measure the impact of supporting policies.

Beekeeping—as agricultural activity—could be considered as multifunctional, because it performs several functions that contribute to society's welfare. At macro-level, beekeeping may play an essential economic role, creating a competitive advantage through innovation and improving the balance trade. This may be particularly true in some countries, rather than in others; however, even in those areas where the contribution of honeybee products to value added and trade of food product is limited, they represent an interesting alternative or complemental entrepreneurial activity. At micro-level, in fact, beekeeping creates well-being for communities, providing health products for population and decent revenues for farmers [1]. It also supports the sustainability of rural livelihoods, as, given the relatively low investment requirements, it is more easily engaged, compared to other rural and agricultural activities.

The key functions of beekeeping are: food security, environmental function, economical function and socio-cultural function. Beekeeping contributes to ensuring food security when people have physical and economic access to honey and other honeybee products or to other typical dishes that use honey as ingredient. The availability of honeybee products depends on local/national production and the capacity of import. The access to honeybee products depends on purchasing power of consumers and proximity to the markets. The food security has two main components: the quantity and the quality.

The quality of honey is one of the main aspects related to food security function of beekeeping. Three main components are usually taken into account in the case of honey quality: the organoleptic properties, the physical and chemical properties and the hygienic aspects (the latter is usually defined "food safety").

The organoleptic properties—aspect, consistency, colour, taste and smell are evaluated using the sensory analysis. This scientific method is used to establish the botanical origin of honey and its authenticity, but also to classify and define product standards. In addition, the method helps to identify the consumer preferences for different types of honey [3]. In EU countries, the evaluation of physical and chemical parameters of honey is made according to international legislation (Codex Alimentarius Standard), to European Union Directives and to National Legislation [4]. These parameters characterise the naturalness, maturity and unaffectedness of honey [5].

Unfortunately, the quality control of honey on the international market is sometimes vulnerable. According to Strayer et al. [6], the adulteration of honey could be economically motivated and influenced by several factors such as: the decrease of domestic production, lack of identity standards, scarce of analytical methods and trade policies. In the context of global market, there is a stringent need for finding solutions to limit the repercussions of the unfair practices on producers and consumers. The identification and characterisation of different types of honey via quality schemes and logos represent a protective solution that creates new opportunities for producers and consumers.

bees represent a bio barometer for the preservation of the environment. In addition, their

The European Union is one of the most important honey producers of the world. Nevertheless, according to some official bodies, production levels seem to be declining [2]. Such decrease is attributable to the diminution of honeybee colonies, which is mostly related to the colony collapse disorder, but also to imports. The number of beehives, the quantity and quality of honey obtained are the main aspects evaluated at European level, to measure the impact of

Beekeeping—as agricultural activity—could be considered as multifunctional, because it performs several functions that contribute to society's welfare. At macro-level, beekeeping may play an essential economic role, creating a competitive advantage through innovation and improving the balance trade. This may be particularly true in some countries, rather than in others; however, even in those areas where the contribution of honeybee products to value added and trade of food product is limited, they represent an interesting alternative or complemental entrepreneurial activity. At micro-level, in fact, beekeeping creates well-being for communities, providing health products for population and decent revenues for farmers [1]. It also supports the sustainability of rural livelihoods, as, given the relatively low investment requirements, it is more easily engaged, compared to other rural and agricultural activities. The key functions of beekeeping are: food security, environmental function, economical function and socio-cultural function. Beekeeping contributes to ensuring food security when people have physical and economic access to honey and other honeybee products or to other typical dishes that use honey as ingredient. The availability of honeybee products depends on local/national production and the capacity of import. The access to honeybee products depends on purchasing power of consumers and proximity to the markets. The food security

The quality of honey is one of the main aspects related to food security function of beekeeping. Three main components are usually taken into account in the case of honey quality: the organoleptic properties, the physical and chemical properties and the hygienic aspects (the

The organoleptic properties—aspect, consistency, colour, taste and smell are evaluated using the sensory analysis. This scientific method is used to establish the botanical origin of honey and its authenticity, but also to classify and define product standards. In addition, the method helps to identify the consumer preferences for different types of honey [3]. In EU countries, the evaluation of physical and chemical parameters of honey is made according to international legislation (Codex Alimentarius Standard), to European Union Directives and to National Legislation [4]. These parameters characterise the naturalness, maturity and unaffectedness of honey [5]. Unfortunately, the quality control of honey on the international market is sometimes vulnerable. According to Strayer et al. [6], the adulteration of honey could be economically motivated and influenced by several factors such as: the decrease of domestic production, lack of identity standards, scarce of analytical methods and trade policies. In the context of global market, there is a stringent need for finding solutions to limit the repercussions of the unfair

economic, cultural and religious values are very important [1].

has two main components: the quantity and the quality.

latter is usually defined "food safety").

supporting policies.

4 Honey Analysis

Even if compositional, sensory and safety characteristics of food are essential elements of food quality, they do not necessarily exhaust this feature, that goes beyond the observable characteristics of a product. In the last decades, the concept of quality in food (this applies also to honey) has enlarged from intrinsic attributes to extrinsic ones, focussing on some features of the production process such as its social and environmental impacts, animal welfare issues and the link of the food with a certain agricultural area. The latter aspect has received particular emphasis and attention in European countries. The quality recognition of honey (as for all food products) at European Union level can be achieved by obtaining two designations: Protected Designation of Origin and Protected Geographical Indication) [7]. According to Bertozzi [8] the use of geographical name for an agricultural product date from ancient times, "honey from Sicily" being a good example in this sense.

Protected Designation of Origin and Protected Geographical Indication tools, created within the Common Agricultural Policy, help beekeepers (and all farmers) to improve the marketing of honeybee products and to guarantee their authenticity and reputation. In the same time, they help consumer to have more trust they are buying high quality products that are connected to special places. Authenticity and traceability are the main aspects in the case of Protected Designation of Origin/Protected Geographical Indication recognition process [9]. The honey authenticity is linked to the specificity of the geographical area where it is produced: environmental factors (climate, soil and flora) and human factors (beekeeping knowledge and skills, traditional/innovative practices). There are several European countries where protected honeys with Protected Designation of Origin/Protected Geographical Indication status could be found: Greece, Spain, France, Italy, Luxembourg, Malta, Poland, Portugal and Ukraine.

Honey produced in Serbia, Romania and Italy reflects the rich diversity of melliferous plants but also the particular characteristics of regions. The quality recognition of honey is a volunteer system for beekeepers that allow valorising regional honey at European Union level. Obtaining Protected Designation of Origin and Protected Geographical Indication designation is made in compliance with European Union legislation.

In Italy, there are three types of honey with Protected Designation of Origin certification [10]: "Miele della Lunigiana", registered from 2004 [11], "Miele delle Dolomiti Bellunesi" registered from 2011 [12] and "Miele Varesino", registered, in 2014 [13]. "Miele della Lunigiana" belongs to Toscana Region (central Italy) and it is reserved for two types of honey: acacia honey and chestnut honey (one of the healthiest honeys due to its mineral content, antioxidant and antimicrobial properties). "Miele delle Dolomiti Bellunesi" is produced in the mountains of Belluno (in Veneto Region, North-East Italy) and could be found for various types: wildflower, acacia, lime, chestnut, rhododendron and dandelion. It is very appreciated not only for the floral variety, but also for other qualities such as purity, wholesomeness and lengthy shelf life. "Miele Varesino" is an acacia honey from the province of Varese (Lombardy Region, North-West Italy) with a high level of purity, due to the quality of acacia trees that grows widely in this region.

For the moment, in Serbia and Romania, there is no Protected Designation of Origin/ Protected Geographical Indication certified honey, but there is a huge potential for developing this protection in the future. In the case of Romania, the Ministry of Agriculture and Rural Development encourages the Protected Designation of Origin/Protected Geographical Indication honey certification and support this process by using the following arguments: the increase of value added of Romanian types of honey, the rise of consumer trust in the reliability of beekeepers who take care of the quality of honey, the creation of a balance between supply and demand by maintaining the quality, the facilitation of traceability and controls and the acceleration to attracting European Union funds [14]. Such strategies implemented by the Ministry of Agriculture and Rural Development could significantly contribute to the development of beekeeping sector.

In the case of Serbia, there is also a huge potential for Protected Designation of Origin/ Protected Geographical Indication certification of honey and other food products. An illustrative example is "Vlasina honey". A study about the attitudes of Vlasina honey producers towards geographical indications reveals that a small part of them know about this certification system, the results indicating the need for education and information in order to familiarise beekeepers with the procedure and the advantages of Protected Designation of Origin/ Protected Geographical Indication system [15]. "Vlasina honey" is on the list of local products supported by the European Union and the Government of Switzerland to receive technical assistance for the certification procedure. The specificity of "Vlasina honey" is given by the exceptional qualities of the region: the variety of medicinal plants and the clean environment. This type of honey is unique due to its flavour and therapeutic properties. The European recognition of "Vlasina honey" will increase the competitiveness of the beekeepers' association "Matica" and will open the opportunity to sell on international markets [16].

At present, a very small quantity of honey produced in Serbia, Romania and Italy is Protected Designation of Origin/Protected Geographical Indication protected. Hence, a question arises: what other tools could be developed to measure the quality of honey produced in these three countries? The price of different types of honey could be a real barometer for evaluating the quality? Or the high demand for export of local honey demonstrates its value?

Providing a good quantitative proxy able to describe honey quality is a hard task, as such concept and perception is heterogeneous across consumers. The widely used index to approach quality attributes of a food product is its unit value (price). It is worth remembering that price differences across products may be influenced (along with preferences for quality) by other factors, for instance, production costs and disposable income of consumers; nevertheless, price remains the most available datum that may be related to product quality, even if such correlation may be variable. In the case of honey, its quality is strictly related to product differentiation: the availability of different kind of honeys enlarges the choice set of consumers, increasing their satisfaction.

The above-mentioned considerations on honey would suggest analysing and comparing price trends for a set of different kind of honey, over time and across the three countries examined. Such a comparison would allow grasping some insights on the relative quality of each honey examined, assuming some price-quality relationship. Unfortunately, this strategy cannot be followed, mainly for a matter of data availability on comparability across countries and over time: price data on differentiated honey typology are rarely accessible and even when present, they are usually not gathered by official statistical bodies and are discontinuous over time. It is then clear that for any attempt to renders the concept of quality are necessary data continuous over time that are gathered and processed with homogeneous criteria over countries.

Such characteristics are fully satisfied by trade data, which are available at a high level of detail. Unfortunately, the maximum level of disaggregation for which data on traded products are released refers to "natural honey", without any further specification about the typology or characteristic of that food item. Even if the lack of information on product differentiation represents a limitation in examining quality differences among honey typologies in each country, using trade data has many advantages.

Such positive aspects are mainly due to both the opportunity to observe trade movements knowing both the value of honey traded (imported and exported) and its quantity. From this information we can derive the unit values (prices) of exchanged honey. Knowing the volume of trade, along with average import and export prices is highly valuable information as it allows analysing trade flows using a set of indexes. Such indexes, developed within the traditional trade theory of comparative advantage, tell us, among others, to what extent the honey sector in each country is competitive in its export performances, compared to the whole export of the same country. Also this trade index, along with export and import prices, may be an indirect measure of quality of honey exchanged by the selected countries.

It is quite intuitive that the ability of a product (honey) to be demanded beyond its domestic market, overcoming trade cost and cultural barriers may be seen as a combination of factors like its perceived quality that meets preferences of foreign consumers. For the same reason measures of competitiveness in trade are related on one hand to honey quality and on the other to the efficiency of beekeepers (and of their bees) to yield a product that satisfy consumers beyond the domestic market. For this reason the rest of the chapter is focussed on such topic, with the twofold objective to provide a description, even though indirect, of both the quality of the honey traded and the competitiveness of beekeepers and honey sector in the selected countries (Serbia, Romania and Italy).

The research subject is the analysis of production and trade of honey in three European Union countries: Romania, Italy and Serbia, with a goal to consider the dynamic of supply and trade of honey and point out the problem faced by producers. The goal of the study is to give a practical implication to the results obtained, by proposing concrete measures to improve the existing situation.