**4. Concluding remarks**

protrusion occurs at the transition zone, which comprises the cells between the last proximal root hair cell in the radicle and the lower basal cells of the hypocotyl [69]. In *Arabidopsis* Col seeds that have been previously stratified, the radicle protrusion can initiate as early as 32 HAI. By this time, and immediately prior to the radicle emergence through the endosperm, the cells in the transition zone had incremented their size by 44% while the cells in the radicle 10% and in the hypocotyl 30%. By 40HAI, the radicle has already protruded and the elongated cells in the seedling have increased their size by 15% in the radicle, 52% in the hypocotyl, and 108% in the transition zone. Elongation is often accompanied by an increase in DNA content without

168 New Challenges in Seed Biology - Basic and Translational Research Driving Seed Technology

In the micropylar endosperm of tomato seeds, an important mobilization of protein bodies occurs, but it seems that there is no cell degradation as the radicle protrudes. Instead, a process similar to cell separation to allow radical protrusion was suggested [70]. A similar process was observed in celery seeds, where the radicle tip also seems to penetrate the micropylar endosperm by separating the endosperm cells, but, since the embryo needs to grow before germination is completed, cell degradation for storage mobilization occurs in the endosperm adjacent to the embryo [27]. The expression of *LeEXP8* and *LePG1* in the embryo elongation

Major reserve mobilization occurs once germination has concluded, and these reserves are utilized to feed the growing seedling rather than to fuel radicle protrusion. However, in cereal grains, the preparation for starch and oligosaccharide mobilization occurs within the first hours of germination [15]. In cereals, the endosperm is a nonliving storage tissue, and the endosperm cell walls protect its contents from enzymatic attack. Accordingly, the degrada‐ tion of cell walls is a limiting step in storage reserve mobilization that is induced by the GA

In most endospermic seeds this tissue is still living. Mannans in the endosperm cell walls of date palm (*Phoenix dactylifera*) and coffee are mobilized to support embryo development. It has been proposed that the mobilization of storage xyloglucans can be coupled to the growth rate of the seedling by transglycosylation. In legumes, endosperm galactomannans seems to function as reserves; they can constitute up to 30% of total seed dry weight. In fenugreek and *Schizolobium parahyba*, the cell walls of the endosperm are thickened with galactomannan and in some cases the cytoplasm is nonexistent. In *Tamarindus indica* and *Hymenaea courbaril*, reserve xyloglucans are stored between two primary walls and are degraded without hydrolyzing both walls [39]. During germination of celery seeds, the surrounding endosperm degrades leaving a small amount of un‐degraded polymers of the cell wall, except for the micropylar endosperm, in which only some protein bodies are mobilized and the rest of the cells persist until the radicle pushes through; once radicle protrusion has started, these micropylar cells

zone of tomato seeds has been reported at the onset of radicle protrusion [51, 68].

produced by the embryo (at the scutellum) and secreted to the aleurone layer [2].

*3.4.5. Cell wall participation in the mobilization of stored reserves*

subsequent mitosis (endoreduplication) [69].

are degraded [27].

In‐depth temporal screening of cell wall‐related transcripts and proteins has provided an important overview ofthe possible actors involved in the five stages during germination where wall modification is involved, as described above. In *rice*, a valuable integrative effort using ‐ omics approaches has been done to understand seed germination [23]. This analysis suggest‐ ed that the changes in transcript levels during early germination (3–12 HAI) drive the subsequent changes in the metabolome (12–24 HAI) of germinating seeds, supporting that most of the changes observed at the transcriptional level are related to the cellular processes involved in germination. Other authors have associated transcript abundance with specific seed compartments and some enzymatic activity assays, demonstrating the relevance of understanding tissue‐specific expression profiles [17, 38]. Much of the information available related to CWMPs still needs to be validated through enzymatic activity or in vivo interac‐ tion assays. Only about 121 (∼12%) of the total cell wall‐related genes are experimentally validated [48]. Also, many cell wall‐related proteins belong to families of unknown function. The ‐omics approach can be useful to propose hypothesis of wall‐modification complexes, whose activity could be regulated at several levels, and coordinated by unknown function proteins that could act as scaffolding proteins and direct this complex activity to specific polysaccharides. Cosgrove [44] proposed that CWMPs could be functionally classified into primary or secondary modifiers, but this idea has not being reflected in other studies. Following Cosgrove, the analysis of cell wall modification considering an alternating activi‐ ty of primary or secondary modifiers could facilitate the understanding of the dynamics of cell wall modification during seed germination. For instance, expansins could be primary modifiers as they affect cell wall loosening and extensibility, but they do notremove ortransfer polysaccharides into the wall during imbibition; other primary modifiers could be PMEs, as their activity precedes PGs and promotes cell expansion or cell separation, or the resulting exposed GAL residues can be cross‐linked with Ca2+ and promote wall stiffening. Secondary modifiers would include GHs and GTs that would act on exposed residues either promoting cell expansion, separation, or stiffening. In assays to study mucilage properties, sequential treatment with different hydrolases allows solubilization of other components, which are masked to the activity of other enzymes [55, 71]. The alternate perspective of primary and secondary modifiers could help in identifying potential interactions in silico and tested in vivo.

Spatial transcriptomic analyses that include the different seed compartments and the analy‐ sis of cell wall composition changes using specific antibodies for in situ localization of the different polysaccharide epitopes in seed tissues provide valuable information. Although *Arabidopsis* is the best‐known plant model, several authors demonstrate that comparative analysis allows higher resolution of tissue‐specific cell wall microdomains that are not achievable in *Arabidopsis*[8, 10, 60]. As an example, Lee et al. [59] describe the presence of LM13 epitopes in the inner and outer cell walls, but absent in the transverse cell walls of the endosperm in *Lepidium* seeds; in tobacco, which has a thicker endosperm than *Arabidopsis* or *Lepidium*, XGs were abundant in the embryo, and at the micropyle (rich in heteromannans), these polysaccharides were only present in the middle lamella and intercellular regions. Thus, the analysis of cell wall‐modification processes would benefit from the multispecies compar‐

ison of in situ localization of polysaccharide epitopes in seed tissues. The characterization of wall microdomains could be combined with the valuable information generated by ‐omics technologies, to propose new hypothesis of regulation and coordinated activity of CWMPs. Ultimately, the activity of these CWMPs must be confirmed by in situ localization, in vivo protein interactions, and enzymatic activity. By combining the resources available for model species with the selection of other plant systems with bigger‐easy‐to‐handle seeds, it could be possible to achieve a comprehensive view of seed‐compartment functions and regulation during germination. The endosperm role during germination is fundamental in endosper‐ mic seeds; however, in non‐endospermic seeds, this role must befall on either the embryo or the testa. Since the testa is a nonliving tissue, the radicle most certainly assumes part of this regulatory role, but a comparative analysis is needed to ascertain this supposition and to determine if some of the endosperm functions are developed by the testa while still in the maturation program. The occurrence of endospermic and non‐endospermic seeds within the same taxa is relatively common in legumes such as *soybean*, which could offer a model for analyzing transcriptomic differences within embryo compartments comparable to the differences described between the endosperm and the radicle.

ROS participation in germination is supported by several reports and transcriptomic profiles of germinating seeds [9, 14, 17]. However, the actual role of ROS and ROS‐related enzymes in promoting cell wall loosening needs to be further analyzed, since the physiological concen‐ trations of ROS during germination do not seem to be sufficient to induce wall extension, and attempts of increasing ROS concentration lead to wall breakage [5]. Müller et al. [72] de‐ scribe abnormal rupture of the micropylar endosperm of *Lepidium* seeds treated with H2O2, while the treatment with myrigalone A [73], which inhibits the hormone‐mediated accumu‐ lation of ROS during germination, also induces abnormal endosperm breakage. These observations further support the notion of ROS as a signaling agent that induces down‐ stream activation of CWMPs than inducing wall loosening on its own.


Recent Advances in Infrared Nonlinear Optical Crystal

*Senthil Kumar Chandran, Chinnakannu Elavarasi, Srinivasan Manikam and John James Gnanapragasam*

### **Abstract**

The search and growth of nonlinear optical (NLO) crystals in the infrared (IR) area are significant and of high importance in the fields of NLO, signal communication, solid-state chemistry, and laser frequency conversion. Infrared NLO crystals have a wide IR transparent range, high laser damage threshold (LDT) value, and large NLO coefficients. This chapter presents the recent advances in IR-NLO crystals and especially emphasizes their crystal growth method, crystal structures, band gap value, LDT, and NLO properties. Based on its structural variety, it is categorized into chalcogenides, chalcohalides, oxides, halides, and oxyhalides. This chapter describes several kinds of IR-NLO crystals and their structural, band gap value, thermal, optical, LDT, and NLO properties and also describes the significance of these crystals in laser frequency conversion, optical parameter oscillator, and other optical applications.

**Keywords:** crystal growth, laser-induced damage thresholds, infrared crystal, transmittance, optical parameter oscillator

#### **1. Introduction**

Nonlinear optical (NLO) crystals are important for frequency conversion and are widely used in different laser-oriented applications. High-efficiency NLO crystals need in high-efficiency laser methods, so it is essential to growing novel NLO crystals with good properties. In the past five decades, many valuable NLO crystals in the near-infrared (NIR), visible, and ultraviolet areas have been commercialized, such as LiNbO3, LiB3O5 (LBO), β-BaB2O4 (β-BBO), KTiOPO4 (KTP), and KH2PO4 (KDP). These crystals are commonly used in basic science and technology, such as laser generation, artificial nuclear fusion, and so on. However, due to the increasing practical or market necessities, only a few crystals can be successfully used in deep-UV(DUV) and mid/far-IR areas. Nevertheless, NLO crystals that can powerfully produce highpower mid-IR lasers in the spectral area of 2−25 μm are very rare. Up to now, several useful NLO crystals have been originated and used in DUV, UV-Vis, and near IR, but they cannot be implemented in mid-infrared spectra because they have two atmospheric transparent regions, 3–5 and 8–14 mm, owing to strong absorption [1–5].

Second-order nonlinear optical (NLO) crystals are significant for producing coherent energy in the IR region (3−20 μm). IR lasers have several vital applications in various devices, such as optical parametric oscillators (OPO), remote sensing, optical sensing, instrumental spectroscopy, industry, military, analytical devices optical imaging, laser guidance, telecommunication, medical diagnostics, and longdistance communications. Such instruments are used to identify various elements and precise vibrational spectra [6–9]. Even after several years of deep research, only three unresolved NLO crystals have been commercially accessible in the mid and far-IR areas, namely, AgGaSe2, AgGaS2, and ZnGeP2. However, some inadequacies still exist in these IR-NLO crystals, such as the inherent efficiency loss arising from dual photon absorption, low MIR cut-off edge, non-phase matchable behavior, and low laser damage threshold. Already commercial ones cannot meet the commercial conditions because of their inherent disadvantages. Hence, it is essential to find out new efficient MIR NLO crystals with more stable efficiency [3, 7, 8, 10].

The large size high-quality mid-IR-NLO crystals for laser device applications are grown by Bridgman-Stockbarger (BS) method and molecular beam epitaxy (MBE) growth method [5]. The IR-NLO crystal can be separated into five categories: chalcogenides, chalcohalides, oxides, oxyhalides, halides, and chalcohalides [4]. This chapter will emphasize second-order NLO inorganic crystals in the MIR region. We did not focus on the commercially accessible LiNbO3, LiB3O5 (LBO), β-BaB2O4 (β-BBO), KH2PO4, and KTiOPO4 NLO crystals, these crystals have absorption bands in this region. Instead, this chapter focus on the chalcogenides, chalcohalides, oxides, halides, and oxyhalides. These crystals are promising materials for MIR applications due to they have wide transmittance in the MIR region [11].


3.3.2. Substitution Segments of DP30-CSSLs

In total, we identified 80 BC4 F2 plants from 171 CSSLs and named as DP30-CSSLs. Then, 24 BC4 F2 plants were selected and 44 BC5 F2 plants were obtained after continuous backcrossing. Finally, 18 BC6 F2 were obtained with a relatively complex genetic background to continue backcrossing. The 132 CSSLs formed a DP30-CSSLs population, and the target replacement segments of these CSSLs accumulated a total length of 536.4 Mb, covering 91.42% of the DP30 genome. The CSSLs were arranged according to the position of their target substitution segments. The 99 substitution lines contained only one substitution segment from DP30. The total length of these CSSLs substitution segments was 359.66 Mb and the coverage rate was 61.36% (Figure 3).

**Figure 3.** Graphic genotypes of the 132 DP30-CSSLs developed in this study. Red bars indicate homozygous substituted segments derived from DP30. Blue bars indicate heterozygous substituted segments derived from DP30. Gray bars indicate the genetic background of the recipient parent 93-11, "Chr."—chromosome.

Chromosomes with more than 90% coverage of the DP30 genome by chromosome substitution segments include chromosomes 1, 2, 3, 5, 7, 11 and 12. Among them, chromosome 5 had the highest coverage rate (97%), while the lowest coverage rate belonged to chromosome 8 (Table 2).

The average length of DP30-CSSLs population substitution target segment was 4.06 Mb, which ranges from 0.5–22 Mb. The length of the substitution segment of 25 CSSLs was less than 2 Mb; The length range of substitution segments of 55 substitution lines was 2–4 Mb and 30 CSSLs were 4–6 Mb and the length of the substitution segment of 6 CSSLs was larger than 8 Mb.

**Table 2.** Substitution segment analysis in DP30-CSSL.

CSSLs No.—number of substitution lines on chromosomes; Chr.-Length (Mb)—chromosome length; Coverage length (Mb)—covering length of the substitution segment to the chromosome; Percentage (%)—coverage of the substitution segment.

### *3.4. Wild rice QTLs in the DP30-CSSLs*

We detected the genotypes of all CSSLs in DP30-CSSLs (Table S3). In total, 36 QTLs were identified in the CSSLs.

### 3.4.1. Tiller Angle (TA) and Heading Date (HD)

Four QTLs (*qTA1.1, qTA7.1, qTA9.1* and *qTA2.1*) related to TA were detected in the CSSLs. Seven CSSLs, (RZ18, RZ20, RZ38, RZ104, RZ129, RZ130 and RZ168) had di fferent TAs those of 93-11 (Figure S2A,B). *qTA1.1* was located in the overlapping segment of RZ18 and RZ20, which belong to C1-26–C1-27 in chromosome 1. *qTA7.1* was detected in an overlapping segment of RZ104 and RZ168 that located in C7-2 on chromosome 7, the *PROG1* was also in the same position of chromosome [17]. *qTA9.1* and *TAC1* were detected in overlapping segments of RZ129 and RZ130, which was in the C9-14–C9-15 region of chromosome 9 [20] (Table 3). The TAs of the RZ38 plants were between 0◦ to 10◦, while 93-11 plants had TAs of 30◦ to 15◦, in both seasons (Figure S1A,B). The QTL *qTA2.1* in the segment of RZ38 was located near the molecular marker C2-25 on chromosome 2 (Table 3; Figure S6).

Three CSSLs (RZ142, RZ8 and RZ13) showed significant di fferences in HD when compared with 93-11 (Figure S2C). These three lines had an overlapping segment near C11-4 on chromosome 11, so we identified a new QTL (*qHD11.1*) in this region that has negative additive e ffects on HD (Table 3; Figure S6).

**Table 3.** List of 36 QTLs identified from DP30-CSSLs.

**Table 3.** *Cont.*

List of wild rice QTLs identified in this study. Position—physical position of molecular markers on chromosomes (Mb); length—length of the overlapping part of the lines; a—additive effect; Gene— cloned genes in the overlapping segments. TA— tiller angle; HD—heading date; PH—plant height; NGPP—number of grains per panicle; GWT—1000 grain weight; GL—grain length; GW—grain width; GLWR—grain length to width ratio; AL—awn length; SH—grain shattering; and CT—cold tolerance.


*Article* **Knowledge, Attitudes and Practices of Eye Health among Public Sector Eye Health Workers in South Africa**

**Zamadonda Xulu-Kasaba 1,\*, Khathutshelo Mashige 1 and Kovin Naidoo 1,2**

**Abstract:** In South Africa, primary eye care is largely challenged in its organisational structure, availability of human and other resources, and clinical competency. These do meet the standard required by the National Department of Health. This study seeks to assess the levels of knowledge, attitudes, and practices on eye health amongst Human Resources for eye health (HReH) and their managers, as no study has assessed this previously. A cross-sectional study was conducted in 11 districts of a South African province. A total of 101 participants completed self-administered, closeended, Likert-scaled questionnaires anonymously. Binary logistic regression analysis was conducted, and values of *p* < 0.05 were considered statistically significant. Most participants had adequate knowledge (81.6%), positive attitudes (69%), and satisfactory practices (73%) in eye health. HReH showed better knowledge than their managers (*p* < 0.01). Participants with a university degree, those aged 30–44 years, and those employed for <5 years showed a good attitude (*p* < 0.05) towards their work. Managers, who supervise and plan for eye health, were 99% less likely to practice adequately in eye health when compared with HReH (aOR = 0.012; *p* < 0.01). Practices in eye health were best amongst participants with an undergraduate degree, those aged 30–44 years (aOR = 2.603; *p* < 0.05), and participants with <5 years of employment (aOR = 26.600; *p* < 0.01). Knowledge, attitudes, and practices were found to be significantly moderately correlated with each other (*p* < 0.05). Eye health managers have poorer knowledge and practices of eye health than the HReH. A lack of direction is presented by the lack of adequately trained directorates for eye health. It is therefore recommended that policymakers review appointment requirements to ensure that adequately trained and qualified directorates be appointed to manage eye health in each district.

**Keywords:** visual impairment; human resources for eye health; avoidable blindness; eye health; public health; eye health directorate


**1. Introduction**

With the vigorous development of mineral pipeline transportation technology, highpressure diaphragm pump operation, maintenance, and fault monitoring have become a concern. The high-pressure diaphragm pump provides the power required in the process of mineral transportation [1], and most of its failures are caused by one of the core components of the pump check valve. The check valve is a directional element used to control the feeding and discharging, so that the conveying medium flows in one direction and cannot flow back. Generally, the average price of each diaphragm pump exceeds CNY20 million, and the daily delivery of pulp exceeds 30,000 tons. If the stroke coefficient of the high-pressure diaphragm pump is 50 R/min, the feed and discharge check valves in a normal operating day need to act in a reciprocating manner 72,000 times. Therefore, the check valve is the most frequent and fault-prone component in a high-pressure diaphragm pump. Local faults and unplanned shutdowns in its operation state easily cause equipment pressure and flow fluctuations, resulting in equipment vibration and damage to the mineral transmission pipeline. To sum up, monitoring the operation state of the check valve is very important for the safe, stable, and efficient operation of mineral pipeline transportation. By detecting the vibration signal of the check valve and extracting the characteristics that can characterize the operation state for diagnosis and identification, the engineers and technicians can learn the operation state of the equipment in time and significantly improve the ability of operation and maintenance management. However, in the actual detection process, due to the complex field background noise, there is often strong impulse interference and random noise, making it difficult to distinguish the useful signal and noise interference, affecting the effective extraction of fault features.

Traditional signal processing methods are mostly based on Fourier transform, such as short-time Fourier transform, Wigner–Ville, power spectrum analysis, etc. [2–4]. Although

**Citation:** Yang, J.; Zhou, C. A Fault Feature Extraction Method Based on LMD and Wavelet Packet Denoising. *Coatings* **2022**, *12*, 156. https:// doi.org/ 10.3390/coatings12020156

Academic Editors: Ke Feng, Jinde Zheng and Qing Ni

Received: 23 December 2021 Accepted: 24 January 2022 Published: 27 January 2022

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

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

the above methods have been popularized in many fields of noise reduction, there are still some defects: they cannot decompose the signal adaptively. The vibration signal of the check valve is a typical non-stationary and nonlinear signal. Before processing and analyzing it, it is necessary to obtain the local properties of the signal in the time domain. Through the ongoing efforts of scholars in related fields, some breakthroughs have been made. The empirical mode decomposition (EMD) proposed by Huang et al. [5] can adaptively decompose the signal into a series of intrinsic mode functions (IMF). However, this method has problems such as the endpoint effect, mode aliasing, etc. In recent years, the local mean decomposition (LMD) [6] proposed by Smith is a new time-frequency analysis method, which can adaptively decompose the signal to be processed into several product functions (PF) with high to low frequency and has been gradually applied. Compared with traditional methods, LMD has the advantages of better adaptability and time-frequency aggregation, solves the problems of over the envelope and under the envelope of EMD, has fewer iterations, and effectively improves the accuracy of signal decomposition. Dong Linlu et al. [7] combined the respective advantages of LMD and the singular-value decomposition method to denoise the noisy micro-vibration signal. The results show that the proposed method can better remove the high-frequency noise of the micro-vibration signal, which lays a foundation for further analysis of vibration signals. To solve the influence of complex noise and measuring point position in the monitoring process, Wang Haijun et al. [8] proposed a processing method combining a data fusion algorithm and LMD. The results show that the combined method is better than the single digital filtering method, and the vibration monitoring of a hydropower plant is realized better. Wang Zhijian et al. [9] introduced the mask signal method to process the component signals screened after LMD decomposition, which effectively suppressed the noise and weakened the mode aliasing phenomenon in the process of signal decomposition. Through the signal's LMD decomposition, the complete time-frequency distribution information can be obtained, and it is convenient to extract the signal characteristics. At the same time, the latest applications of the LMD method in other fields have been reported. Gupta P et al. [10] proposed the method of combining LMD and an artificial neural network to complete the tool chatter feature extraction in the turning process. From the experimental analysis, it can be seen that the obtained safe cutting zone is important and can limit chatter. Liao L et al. [11] completed the extraction of low-frequency noise under the background of aerodynamic noise by using the improved LMD method. Through the wind turbine experiment, the results show that the signal noise extraction effect is good, and the calculation efficiency of the algorithm is good. To solve the problematic identification of welding quality, Huang y et al. [12] proposed a classification algorithm combining LMD and the deep belief network (DBN). Experimental results show that this method has a higher recognition rate than traditional classification methods. Lee CY et al. [13] introduced LMD, wavelet packet decomposition (WPD), and other ways to solve the problems existing in the rotor fault diagnosis of an induction motor. The results show that the model can reduce the dimension of the original data and has high robustness. However, due to noise interference, the extracted PF component will be distorted to a certain extent, which affects the interpretation of the useful components of the signal. Using wavelet packet to process the fault signal can effectively overcome the problem of low-frequency resolution in the high-frequency band of the traditional wavelet transform [14] and accurately describe the signal information, which is conducive to the effective elimination of noise information.

However, due to the bad working environment and changeable working conditions of the high-pressure diaphragm pump, some noise will still remain in the signal component decomposed by the time-frequency analysis method. If fault feature extraction and analysis are carried out directly, it will have a certain impact on fault diagnosis results. Therefore, it is also necessary to reduce the noise of the signal. Wavelet packet transform is based on wavelet transform. Compared with wavelet transform, wavelet packet transform has higher-frequency resolution in high-frequency and low-frequency bands and has good adaptability. In recent years, wavelet transform has been widely used in the noise reduction

of non-stationary signals. Kuanfang he et al. [15] proposed a wavelet packet denoising method for welding acoustic emission signals. The experimental results show that the proposed method can effectively process the acoustic emission signals of welding cracks. Wang X et al. [16] proposed a method based on optimized variational mode decomposition (VMD) and wavelet packet threshold denoising and applied it to remove strong white noise signals. The results show that this method retains the practical components of the signal well. Sun W et al. [17] used a wavelet packet to remove the noise in the collected bearing signal and then combined it with the LMD method to extract the fault feature. The results show that this method can effectively extract fault features.

Based on the above analysis, a fault feature extraction method based on local mean decomposition (LMD) and wavelet packet denoising is proposed in this paper. Firstly, the vibration signal of the check valve was decomposed by LMD, and the K-L divergence of each AM-FM component was obtained. The practical component signal was selected for reconstruction through the comparative analysis with the set threshold. Then, wavelet packet transform was used to denoise the reconstructed signal to reduce the noise interference. Finally, Hilbert envelope spectrum analysis asper formed on the denoised signal to extract the fault signal features. The effectiveness of the proposed method was verified by analyzing the actual fault data of the check valve.

The rest of the paper is organized as follows: In Section 2, we introduce the basic principle of the LMD algorithm and K-L divergence and describe the process of PF component selection. In Sections 3 and 4, we introduce the basic principles of Hilbert envelope demodulation and wavelet packet denoising. In Section 5, we describe the modeling process of check valve fault feature extraction based on LMD and wavelet packet denoising. Section 6 is experimental analysis. The effectiveness of the method proposed in this paper was verified by the collected actual fault data of the check valve. Section 7 concludes the paper.

#### **2. LMD Algorithm Based on K-L Divergence**

## *2.1. LMD Algorithm*

LMD is a time-frequency analysis method proposed by Smith. Its principle is to decompose the signal into product function components of different frequencies and a margin, and each component is obtained by multiplying a pure FM signal and envelope signal. If the original signal is *x*(*t*), the decomposition step can be described in the following form.

(1) Calculate all local extreme points of the original signal *x*(*t*), and then calculate the average value *mi* of adjacent local extreme points *ni* and *ni+*1:

$$m\_i = \frac{n\_i + n\_{i+1}}{2} \tag{1}$$

The moving average method is used to smooth the line composed of *mi*, and the local mean function *m*11(*t*) can be obtained.

(2) The envelope estimation value *ai* is calculated according to the adjacent extreme points *ni* and *ni+*1:

$$n\_i = \frac{|n\_i - n\_{i-1}|}{2} \tag{2}$$

Similarly, the envelope estimation function *a*11(*t*) is obtained by the moving average method.

(3) The local mean function *m*11(*t*) is separated from the original signal *x*(*t*).

$$h\_{11}(t) = \mathbf{x}(t) - m\_{11}(t) \tag{3}$$

(4) Divide *h*11(*t*) by *a*11(*t*) to obtain FM signal *s*11(*t*)

$$s\_{11}(t) = h\_{11}(t) / a\_{11}(t) \tag{4}$$

When *a*12(*t*) = 1, *s*11(*t*) is a standard pure FM signal, and when *a*12(*t*) is not equal to 1, *s*11(*t*) is taken as the original data, and the above processes repeated. Until *a*1(*n*+1)(*t*) = 1 is met, the pure FM signal *s*1*n*(*t*) is calculated, as shown in the following formula:

$$\begin{cases} \begin{array}{l} h\_{11}(t) = \mathbf{x}(t) - m\_{11}(t) \\ h\_{12}(t) = \mathbf{x}(t) - m\_{12}(t) \\ \vdots \\ h\_{1n}(t) = \mathbf{x}(t) - m\_{1n}(t) \end{array} \\\\ \begin{cases} s\_{11}(t) = h\_{11}(t) / a\_{11}(t) \\ s\_{12}(t) = h\_{12}(t) / a\_{12}(t) \\ \vdots \\ s\_{1n}(t) = h\_{1n}(t) / a\_{1n}(t) \end{cases} \end{cases} \tag{6}$$

The conditions for iteration termination are as follows:

$$\lim\_{n \to \infty} a\_{1n}(t) = 1 \tag{7}$$

(5) Calculate the envelope signal:

$$a\_1(t) = a\_{11}(t)a\_{12}(t) \cdots a\_{1n}(t) = \prod\_{q=1}^{n} a\_{1q}(t) \tag{8}$$

(6) The product function can be obtained by multiplying the pure FM signal *s*1*n*(*t*) and the envelope signal *a*1(*t*)

$$PF\_1(t) = a\_1(t)s\_{1n}(t)\tag{9}$$

(7) Separate *PF*1(*t*) from *x*(*t*) and find the signal *u*1(*t*). Then, repeat the above steps *k* times. When *uk*(*t*) becomes a monotone function, the operation is terminated.

$$\begin{cases} u\_1(t) = x(t) - PF\_1(t) \\\\ u\_2(t) = u\_1(t) - PF\_2(t) \\ \vdots \\ u\_k(t) = u\_{k-1}(t) - PF\_k(t) \end{cases} \tag{10}$$

The following formula can be obtained by combining all PF components and recombining.

$$\mathbf{x}(t) = \sum\_{p=1}^{k} PF\_p(t) + u\_k(t) \tag{11}$$

From the above description, it can be seen that the LMD is an adaptive decomposition method based on the local extreme points of the signal itself. The decomposition process is a multi-cycle iterative process.

#### *2.2. PF Component Selection Based on K-L Divergence*

Several PF components obtained by LMD have a different correlation with the original signal. To effectively screen component signals, appropriate screening methods must be adopted. K-L divergence is called directional divergence, describing the difference between the two probability distributions [18]. Let the two probability distributions be *p*1(*x*) and *p*2(*x*), then the K-L distance is defined as follows:

$$\delta(p\_1, p\_2) = \int p\_1(\mathbf{x}) \log \frac{p\_1(\mathbf{x})}{p\_2(\mathbf{x})} d\mathbf{x} \tag{12}$$

The K-L divergence values of *p*1(*x*) and *p*2(*x*) are:

$$D(p\_1, p\_2) = \delta(p\_1, p\_2) + \delta(p\_2, p\_1) \tag{13}$$

The vibration signals collected in the equipment characterize the vibration of the equipment. Therefore, assuming that the probabilities between the two signals are *p*1(*x*) and *p*2(*x*), the closer the vibration between them, the closer *p*1(*x*) and *p*2(*x*) will be, and the smaller the K-L divergence value will be.

Since several PF components are obtained through LMD, the solution of K-L divergence for the original signal *X* = {*x*1, *x*2 ... *xn*} and component *Y* = {*y*1, *y*2 ... *yn*} decomposed by the LMD algorithm is as follows:

(1) Solve the probability distribution of the above two signals and let the function *p*1(*x*) be the kernel density estimation of signal *X*:

$$p\_1(\mathbf{x}) = \frac{1}{nh} k \sum\_{i=1}^{n} \left[ \frac{\mathbf{x}\_i - \mathbf{x}}{h} \right], \mathbf{x} \in \mathcal{R} \tag{14}$$

where *k*(·) is the kernel function, which usually uses the Gaussian kernel function, which is *k*(*u*) = √12*π e*−*u*2/2, and *h* is a given positive number. Similarly, *p*2(*x*) can be obtained in the above way.

(2) The K-L distances *X* = {*x*1, *x*2 ... *xn*} and *Y* = {*y*1, *y*2 ... *yn*} are obtained by substituting the probability distributions of signal *δ*(*p*1, *p*2) and signal *δ*(*p*2, *p*1) into Formula (12).

(3) By substituting the calculated *δ*(*p*1, *p*2) and *δ*(*p*2, *p*1) into Formula (13), the corresponding K-L divergence can be obtained.

(4) Normalize all calculated K-L divergence values.

(5) Filter according to the preset threshold. If the component is less than the threshold, it will be regarded as the component containing an obvious fault signal. According to the data obtained in this study, the set threshold is 0.03.

#### **3. Hilbert Envelope Demodulation**

The Hilbert envelope demodulation method mainly converts the actual signal into an analytical signal by Hilbert transform and then takes the modulus to obtain its envelope. Assuming that the signal *x*(*t*) has amplitude modulation envelope *A*(*t*) and phase modulation function *ϕ*(*t*), its expression is as follows:

$$x(t) = A(t)\cos(2\pi ft + q(t))\tag{15}$$

The Hilbert transform of signal *x*(*t*) can be an approximately 90◦ phase shift of *x*(*t*):

$$
\hat{x}(t) = A(t)\sin(2\pi ft + \varphi(t))\tag{16}
$$

Construct the analytical signal *Z*(*t*) so that:

$$Z(t) = \mathbf{x}(t) + j\mathbf{\hat{x}} = A(t)e^{j\varphi(t)}\tag{17}$$

By calculating the modulus of the above formula, the following formula can be obtained:

$$|Z(t)| = A(t) \tag{18}$$

In the above formula, |*Z*(*t*)| is the envelope of the signal.

#### **4. Principle of Wavelet Packet Denoising**

As one of the more detailed signal analysis methods, the advantage of wavelet packet transform is that it can adaptively select the corresponding frequency band and divide the frequency band at multiple levels [19,20]. For a one-dimensional signal containing noise, it can be described as the following formula:

$$S(i) = f(i) + \mathfrak{a}\beta(i) \text{ ( $i = 0, 1, 2, \dots, n - 1$ )}\tag{19}$$

where *f*(*i*) is the actual signal, *S*(*i*) is the signal containing noise, and *β*(*i*) is noise. The process of wavelet packet denoising is as follows:

(1) Determine the corresponding wavelet base and decomposition level, and then start wavelet packet decomposition;

(2) According to the established entropy standard, the optimal tree is obtained, and the optimal wavelet basis is determined;

(3) The appropriate threshold is selected, and the high-frequency coefficients with different decomposition scales are quantified at the same time;

(4) The signal reconstruction operation is carried out according to the decomposition coefficients and quantization coefficients of the N-th layer wavelet packet.

The decomposition structure of wavelet packet is shown in Figure 1 [21].

**Figure 1.** Decomposition structure of wavelet packet.

#### **5. Fault Feature Extraction Model of Check Valve Based on Local Mean Decomposition Wavelet Packet Denoising**

Based on the above theoretical analysis, a check valve fault feature extraction method based on local mean decomposition wavelet packet denoising is proposed in this paper. The research outline of the proposed method is shown in Figure 2, and the method implementation process is shown in Figure 3. The specific steps are as follows:

**Figure 2.** Research outline of the proposed method.

**Figure 3.** Fault feature extraction process of check valve based on LMD and wavelet packet denoising.

(1) The experimental failure data of wear breakdown of check valve are selected, and then the above samples are decomposed into several PF components by LMD decomposition.

(2) According to the calculation method of K-L divergence, the K-L divergence between the original signal and each PF component obtained by decomposition is calculated. Then, all K-L divergence values are normalized.

(3) According to the set threshold, the calculated K-L divergence value is compared with it, and then any PF component less than the threshold is filtered.

(4) The filtered PF components are reconstructed, and then wavelet packet denoising is carried out.

(5) Hilbert envelope demodulation is performed on the denoised signal.


**3.2. MQWs solar cells** *p-i-n* **performance**

doped InP substrate with doping concentration about 5×1018 cm−3, while the intrinsic region consist of 10 nm In0.91Ga0.09As0.2P0.8 barriers alternating with 10 nm In0.81Ga0.19As0.4P0.6 quantum wells for ten periods with an additional 50 nm or 25 nm In0.91Ga0.09As0.2P0.8 barrier on the top quantum wells layer. The *p*-type electrode of all *p*-*i*-*n* structures solar cells consist of a Zn doped 50 nm *p*-type InP layer or 25 nm *p*-type InP and 10 nm *p*-type In0.47Ga0.53As with doping concentration about 3×1018 cm−3. The *n*-type Ohmic contacts were fabricated by using Ti (40

formed by conventional photolithography, and *p*-type contacts were formed by using Ti (20 nm)/Pd (20 nm)/Au (200 nm) metal deposited by electron beam evaporation. [8]The top In0.47Ga0.53As contact layer was removed from the window region by a selective wet etch (H2SO4:H2O2:H2O, 1:10:220) for 15 s, and about 15 nm SiO2 surface passivation layer was sputter

To optimize collection efficiency of photogenerated carriers in the multiple quantum wells and to minimize the reduction of *V*oc, a sufficiently large electric field across the intrinsic region is definitely required, in which the electric field intensity is 30 kV/cm or so, [23] and the barriers must be thermally or optically excited usually at 200~450 meV or less. [24] The electric field condition requires that the intrinsic region in the *p*-*i*-*n* structure should be especially thin, where the intrinsic region is required by choosing appropriate materials for the multiple quantum wells and barrier. [8]For the solar cells device structure with an intrinsic layer thickness of 250 nm, shown in Figure 9, the quantum wells electric field intensity is about 48 kV/cm at equilibrium condition, and 32 kV/cm at a maximum power state when operating

Incorporation of the multiple quantum wells region in solar cells device, not only improves photon absorption efficiency at longer wavelengths, but also increases the refractive index in the intrinsic region relative to the surrounding electrode contact layers, as also shown in Figure 9, which produces a slabby waveguide structure. [25] Waveguide mode accompanied by light scattered from metallic nanoparticles has been demonstrated by metal nanoparticles on siliconon-insulator photodetectors. [26] The scattering effect is achieved by depositing metal

window regions were

nm)/Au (200 nm) metal deposited by electron beam evaporation. 2 mm2

**Figure 9.** InP-based multiple quantum well solar cells with nanoparticles on the surface

deposited over the window area of all devices.

344 Solar Cells - New Approaches and Reviews

voltage is about 0.4 V.

The photocurrent response is shown in Figure 11 for an InP homojunction control device, a *p*-InP/ *i*-In0.91Ga0.09As0.2P0.8 /*n*-InP barrier control device, and a *p*-*i*-*n* multiple quantum wells solar cells device. These epitaxial layer structures for the control device and barrier control device and multiple quantum wells device were grown on 225 nm thicknesses intrinsic layer under identical reactor conditions. The photocurrent response only extends to 950 nm of the InP absorption edge for the InP homojunction control device, which is determined by room temperature photoluminescence measurements, but the photocurrent responses can extend to 1050 nm of the In0.91Ga0.09As0.2P0.8 absorption edges and 1150 nm of In0.81Ga0.19As0.4P0.6 absorption edges for the barrier control device and multiple quantum wells solar cells device, respectively.

**Figure 11.** Photocurrent response spectra for InP homojunction device, barrier device and quantum well solar cell de‐ vice

The maximum power curves are shown in Figure 12 for InP homo-junction control device, *p*-InP/*i*-In0.91Ga0.09As0.2P0.8/*n*-InP barrier control device and *p*-*i*-*n* multiple quantum wells solar cells device which all grown on 250 nm intrinsic layer thicknesses. Despite *V*oc drops to 0.63V for the homo-junction control device and barrier control devices, and drops to 0.53 V for the quantum wells solar cells device, the maximum power output of the quantum wells solar cells device increases 7.4% and 4.6% relative to the homo-junction control device and barrier control device, respectively. [8]

**Figure 12.** Power output curves for InP control device, barrier control device and quantum wells solar cells device

**Figure 13.** Photocurrent response spectra of quantum well solar cells with Au nanoparticles and SiO2 nanoparticles

To illustrate the effect of nanoparticle scattering on the solar cells device in improved photo‐ current response and optoelectronic conversion efficiency, Figure 13 shows photocurrent response spectra for quantum wells solar cells with either 100 nm diameter Au or 150 nm diameter SiO2 nanoparticles deposited on these solar cells surface, which plotted by photo‐ current response ratios relative to the spectrum for the solar cells device without nanoparticles. Au and SiO2 nanoparticles densities were employed with about 2.7×109 cm−2 and 2.1×109 cm−2, respectively. The nanoparticles deposition proceeding and photocurrent measurement apparatus are described in reference [27]. The incident light scattered by Au nanoparticles leads to a reduction of photocurrent response at wavelengths about 560 nm, at the same time, a phase shift is accompanied in the scattered wavelength near the nanoparticle plasmon resonance, which results in partially destructive interference between the scattered waves and the transmitted waves. [28] The scattering of incident light by the nanoparticles arise a broad wavelength range increased from 560 nm to 900 nm. But no surface plasmon polarization resonance is present for the SiO2 nanoparticles, and the transmission and photocurrent response are increased over the range from 400 nm to 1200 nm wavelengths. [29]

The maximum power curves are shown in Figure 12 for InP homo-junction control device, *p*-InP/*i*-In0.91Ga0.09As0.2P0.8/*n*-InP barrier control device and *p*-*i*-*n* multiple quantum wells solar cells device which all grown on 250 nm intrinsic layer thicknesses. Despite *V*oc drops to 0.63V for the homo-junction control device and barrier control devices, and drops to 0.53 V for the quantum wells solar cells device, the maximum power output of the quantum wells solar cells device increases 7.4% and 4.6% relative to the homo-junction control device and barrier control

**Figure 12.** Power output curves for InP control device, barrier control device and quantum wells solar cells device

**Figure 13.** Photocurrent response spectra of quantum well solar cells with Au nanoparticles and SiO2 nanoparticles

Au and SiO2 nanoparticles densities were employed with about 2.7×109

To illustrate the effect of nanoparticle scattering on the solar cells device in improved photo‐ current response and optoelectronic conversion efficiency, Figure 13 shows photocurrent response spectra for quantum wells solar cells with either 100 nm diameter Au or 150 nm diameter SiO2 nanoparticles deposited on these solar cells surface, which plotted by photo‐ current response ratios relative to the spectrum for the solar cells device without nanoparticles.

cm−2 and 2.1×109

cm−2,

device, respectively. [8]

346 Solar Cells - New Approaches and Reviews

The photocurrent response is increased at near 960 nm and cut off at about 1200 nm for the solar cells devices deposited by Au nanoparticles. This phenomenon is attributed to the scattering of incident light into optical propagation path modes, which associated with the slabby waveguide formed by the multiple quantum wells region and surrounding p-layers and n-layers. [8]A standard calculation shows that the slabby waveguide supports two confined modes at 960~1200 nm wavelengths range. [30]Furthermore, the optical waveguide structure mode becomes better confined with increasing wavelength because of the depend‐ ence of the wavelength and semiconductor refractive indices, resulting in the waveguide modes efficiency increased. [26]The photocurrent response is improved in these wavelength ranges because of incident light coming into the waveguide mode, and substrate radiation modes leads to photon propagation path lengths increased dramatically within the multiple quantum wells region that associated with lateral photon propagation path rather than vertical path. Consequently, the efficiency of photon absorption is improved greatly. [8]

**Figure 14.** *J*-*V* and *P-V* curves measured for quantum well solar cell with and without SiO2 nanoparticles

Previously, some groups have reported that the short circuit current density and optoelectronic conversion efficiency are increased due to optical scattering from metal nanoparticles depos‐ ited on Si solar cells [31] and *a*-Si solar cells [32]. The photocurrent response is enhanced on the surface deposited nanoparticle quantum wells solar cells, which leads to the short circuit current density and optoelectronic conversion efficiency improved greatly under normal illumination incidence provided by a solar simulator with a Xenon arc lamp. The short circuit current density and voltage and the power output and voltage characteristics are shown in Figure 14 for the multiple quantum wells solar cells deposited SiO2 nanoparticles on the surface before and after. For a SiO2 nanoparticle surface density about 2.1×109 cm−2, the short circuit current density increased 12.9% and maximum power conversion efficiency increased to 17.0%. For Au nanoparticle surface density about 2.7×109 cm−2, the short circuit current density increased 7.3% and maximum power conversion efficiency increased only 1%.

The conversion efficiency and photocurrent response are improved substantially for the solar cells device structures whose quantum wells region bound with a lower refractive index substrate. A model developed by Soller and Hall [33] shows that when a horizontal electric dipole is located on a silicon insulator substrate, an excess of 80% of the light emitted by the electric dipole is coupled into the waveguide modes of the high refractive index Si insulator layer. [34]The ratio of the power of the electric dipole into waveguide modes fully to the total power of the electric dipole for the solar cells device structure over 600~1200 nm wavelengths occurs with a maximum efficiency no more than 10% in the course of the emission into waveguide, and leaky modes is in the range of 85~90%. This low efficiency is due to the small refractive index contrast to the solar cells device structure and could be improved with greater refractive index. [8]


**Jos´e Carlos S´anchez-Garc´ıa**

Full Professor in Entrepreneurship

Jose Carlos S ´ anchez-Garc ´ ´ıa has been the Dean of the Faculty of Psychology at the University of Salamanca for eight years. Currently he is the Director of the Doctorate Program in Psychology at the University of Salamanca and he is the President of AFIDE (Association for Training, Research and Development of Entrepreneurship). He is the author of numerous articles published in international high-impact journals. He is also the author of books and book chapters of a national and international character.


**4. Discussion**

Positive pairwise correlations were present between all four measures of individual deer: tick prevalence, musculature, coat condition, and body condition. That is, deer that had one health issue were more likely to also have another. This could simply reflect that some measures may be related (e.g., coat condition and overall body

#### *Changes to Health Parameters of White-Tailed Deer during a Drought in the US Virgin Islands DOI: http://dx.doi.org/10.5772/intechopen.108270*

condition have some common sources of overlap). However, other measures (e.g., tick prevalence and musculature) do not naturally overlap. Therefore, our results suggest health factors are indeed associated perhaps because the underlying stress of deer represents a common etiology. The population of deer on St. John are largely diseasefree [22], and demonstrate relatively high allelic diversity for an isolated island population [21] which may act as a mitigating factor for stress. However, this study demonstrates a strong response during peak drought for all of the variables.

Our observations suggest that deer on St. John were highly nutritionally deficient, particularly within the drought peak in July 2015, when deer faced severely limited feeding within a denuded landscape. During the peak drought period, deer appeared to be eating atypical forage, often called famine foods, in the absence of typical and available forage. Famine foods are eaten by animals when all known food sources have been exhausted. They often contain low levels of protein, are low in calories provided [31], and can be very energetically expensive to metabolize. Observations during the drought peak included a fawn eating dead leaves and adult deer spending considerable time and energy digging up tubers and roots. Dependable sources of water on St. John were also severely reduced in both quantity and quality, and deer were often seen near anthropogenic water sources such as pipes, foot baths, and shower areas. Their body condition during the height of the drought indicated both severe water and nutritional stress [23]. Below we analyze multiple indicators of deer health to show individual responses to drought onset, peak, and recovery.

#### **4.1 Indicators of deer health**

### *4.1.1 Body condition and coat condition*

Results for body condition followed the High-High-Low pattern, demonstrating a higher prevalence of poor health during both the drought onset and peak. This indicates that the deer were in a state of stress at the drought's onset and were without the reserves needed from the lack of food and water availability during the drought peak. Body condition scoring represents an amalgam of multiple health parameters representing external markers of internal health. Many of the factors that were evaluated with body condition, including coat condition and musculature, are complementary data for assessing the health and nutritional state of an animal. These factors are influenced by both current and past food availability, and can therefore indicate health in both the present and recent past. Prolonged nutritional challenges such as drought can cause individuals to exhaust stored fat reserves which can result in a deterioration in body condition [6]. In contrast, coat condition demonstrated a Low-High-Low pattern during the drought. This suggests that coat condition is not as sensitive to the onset stage of the drought as body condition, and coat condition is able to return to original condition during drought onset within the drought recovery period. Coat condition represents a sub-category of the body composition assessment, and one that can further indicate nutritional health. Essential vitamins, as well as fats and oils in the diet, are needed to provide coat and hair luster [32, 33].

#### *4.1.2 Muscle and muscle atrophy*

Results for musculature followed the High-High-Low pattern, demonstrating a greater prevalence of poor health both during the drought onset and peak. The response of musculature is similar to body condition in that the pattern indicates that individual deer were stressed at drought onset, indicating a deficiency in both food availability

and in high quality foods that contain adequate protein reserves to support muscle development and growth. Muscle is an energetically expensive tissue to build and maintain [34]. Muscle presence and appearance can be an indicator of overall health and an excellent external indicator of both health and protein reserves. However, with chronic and insufficient protein consumption, muscle catabolism can occur, resulting in muscle atrophy [33]. In addition, because more than 80% of protein in the animal body is dedicated to maintaining proper functioning of the immune system, disease state can indirectly result in muscle loss and atrophy. As a result, there can be a strong correlation between muscle appearance and the disease state of a deer. If muscle atrophy is present, this indicates that the animal body is using muscle as an energy source in the absence of any remaining fat reserves. More energy is released when a unit of fat is metabolized as compared to a unit of protein. Therefore, when a deer has metabolized most of its fat and is using muscle for energy, it will lose weight very quickly as subcutaneous fat is already depleted [35] and will appear gaunt and emaciated in appearance as a result [32]. In contrast, excellent musculature indicates that the deer is eating sufficient protein to both maintain muscle and to support the high protein demands of the immune system [36]. These changes in subcutaneous fat can be evident via visual assessment [24]. The results of this study add to evidence that both musculature and fat reservoirs are sensitive to drought even in the early onset stages within island populations.

#### *4.1.3 Ticks*

The dynamics of infection often depend on the host's vulnerability, as poor body condition is likely to predispose individuals to infectious and parasitic diseases [6, 7]. Tick prevalence in this study followed a High-High-Low pattern, indicating that ticks were already both prevalent during both the onset and peak of the drought. The additive effect of this continued high level of parasitism has negative consequences for individual deer health for blood loss and the potential for disease development. A common parasite found on the deer of St. John included ticks. The two tick species found on St. John include the southern cattle tick (*Rhinocephalus* (*Boophilus*) *microplus* (Canestrini) and the tropical horse tick, *Dermacentor* (*Anocenter*) *nitens* Neumann [23]. High tick densities could result in associated health problems, including pruritis, alopecia, anemia, and low weight gain. In addition, ticks can deplete the iron resources of the deer through each blood meal taken, which can result in the development of iron-deficiency anemia and deprive tissues of necessary oxygen [37, 38]. There can be a considerable energy requirement to replace daily blood loss which could result in further accelerated nutritional decline and weight loss. Additionally, the relative energetic cost associated with compensating for blood loss is higher for animals in poor condition who can experience more pronounced energy and protein deficits compared to healthier animals [37].

#### *4.1.4 Stress hormones*

Stress hormones help the body handle adverse conditions, and levels can vary by situation and species [39]. Cortisol is released by the body when there is either an acute or chronic stressor. Cortisol can suppress the conversion of T4 thyroid hormone into T3, and lower circulating T3 levels can be an indicator that cortisol levels are high. Together, both T3 and cortisol levels create a complementary data set that demonstrates stress levels in individual animals [28].

Cortisol was the only measure from this study to display a "Medium-High-Low" pattern. This measure had statistically discernible (significant) differences when comparing *Changes to Health Parameters of White-Tailed Deer during a Drought in the US Virgin Islands DOI: http://dx.doi.org/10.5772/intechopen.108270*

each of the three time points with each other. Our data for all three phases of the drought show that the deer were already stressed in July 2015 at the start of the drought, and to a degree that shows widespread stress across the population. This was further exacerbated at the height of the drought, in March 2016, when the deer were in acute stress. The stress hormone data ranged from 24.3 to 110.1 ng/g for T3 and 26.5 to 258.5 ng/g for cortisol.

The cortisol values for St. John deer at the onset and recovery of the drought were within published values for animals within ongoing stressful situations, but cortisol values for deer during the peak of the drought were more consistent with wildlife undergoing acute trauma. For example, values ranged from 23.9 to 114.9 ug/g for musk deer in crowded conditions [40], and deer undergoing high parasite burdens had cortisol values that reached 93–144 ng/g [39] and could result in diminished body condition for deer over time [25]. These published values for deer with ongoing stress are consistent with the values found in this study for St. John deer during drought onset and recovery. However, the highest values found in our study at the peak of the drought (258.5 ng/g) were instead consistent with values that more reflected acute and traumatic events for animals. For example, koalas who had been in vehicle collisions (202 ng/g), were burn victims (200 ng/g), or were in an area during land clearance (669 ng/g) showed cortisol values similar to the deer of St. John during the peak of the drought [41]. High fecal cortisol levels can often be used as a predictor of mortality. Ring-tailed lemurs that died following traumatic events showed average fecal values of 51.1 ng/g [42]. The deer of St. John both endured sustained cortisol levels above that value for the duration of the drought (2015–2016), and also at values five times that amount at peak drought. This highlights the noteworthy resiliency of this population to continue to live with this ongoing stressor present on island for over 2 years.

Stress hormones are also important regulators of energy balance. When food availability diminishes to the point of starvation, cortisol remains chronically elevated, body condition declines, and fat stores are used for energy [43, 44]. Prolonged elevation of glucocorticoids can result in the suppression of reproduction, growth, immune function, and responses to pathogens and parasites [45].

### **5. Conclusions**

This study was able to describe the resilience of an isolated population of whitetailed deer on St. John, US Virgin Islands during a severe drought in 2015–2016. Health observations collected through three successive field seasons representing drought onset, peak, and recovery of the drought enabled novel work documenting physiological changes of deer under the stress of climate change For all six health measures in this study (musculature, coat condition, body condition, the presence of ticks, cortisol, and T-3 levels), there is an association between deer health and time relative to drought (onset, peak, or recovery phase). All measures indicate a rapid return to health following the drought peak.

This study provides a baseline foundation for future research needed to inform the extent to which isolated populations of herbivores cope with increasing climate variability. Further work is needed to explore health differences between males and females during various stages of drought. Future studies would also benefit from using tagging methods for a study design that enables direct observations to individual deer and clarifies how the population maintains resilience in extreme weather. In addition, future work may include a closer analysis of stress hormone levels related to reproduction and survival of individuals. Periodic re-evaluation of the St. John

deer population and health will add additional data to population densities that can be supported by the island ecosystem. This work provides a critical baseline to document physiological changes to an isolated Caribbean deer population, and the results of this work can be extended regionally throughout the Caribbean and to analogous species.


**Abstract**

A good functioning vascular access (VA) is a prerequisite to obtain a successful dialysis treatment. This chapter reviews VA management in advanced chronic kidney disease (CKD) patients drawn from the experience of a large network dialysis care provider with the following sections: overview on VA management in advanced CKD that follows patient pathway and patient profile, current practice patterns in line with best clinical practices; VA creation addressing crucial themes: when and what type of VA to construct, how to assess patient pre-emptively, how to proceed for the construction and monitoring to prevent early failures and complications; VA management with particular focus on clinical monitoring, surveillance and interventional procedures required to preserve patency and functionality of VA; the often-forgotten patient perspective is VA usage. What information to share, how to proceed for preventing pain, and fears related with VA needling? What should patients know about their VA and how to manage in daily life? Competences, skills and responsibilities of nursing staff when using and managing VA; and future of VA in terms of innovative concept for creating and maintaining VA conduits in dialysis patients.

**Keywords:** haemodialysis, vascular access, vascular access centre, arteriovenous fistula, arteriovenous graft, central venous catheter, vascular access complications, best nursing practice, value-based haemodialysis


**Acknowledgements**

This work is supported by the National Natural Science Foundation of China (Grant No. 11074221) and the Natural Science Foundation of Zhejiang Province, China (Grant No. Y6100381).


*7.3.3. Phase 3: life cycle environmental impact assessment*

LCA software is used at this phase for assessing the environmental impact of the systems. In this case, SimaProTM PhD <IP_ADDRESS> is used for the case study. The assessment method considered in this analysis is the Intergovernmental Panel on Climate Change (IPCC) 2007 GWP 100 years, which only includes the impact category of global warming potential (GWP).

air-conditioning systems. The operation stage of the CAC system presents the highest CO2

**Construction Operation Construction**

During the construction stage, ACASS presents 14.05% higher CO2

 **ACASS**

27.57

option for mitigating the environmental impacts related to global warming.

analysis is carried out considering 25 years of air-conditioning.

5.37% of the emissions at this stage.

%

**Figure 9.** Percentage of CO2

technologies.

systems.

**8. Parameterized cost**

emissions due to consumption of fossil fuel energy, while the ACASS system emits only

**SYSTEM Stage**

eq emissions at each life cycle stage for both air-conditioning systems.

5.37 13.52

system. This is attributed mainly to the difference in infrastructure demand for the construction of each system, and ACASS demands 79.20% more than CAC. It is important to note that the

According to the results, it can be concluded that the ACASS system reduces greenhouse gas emissions due to lower consumption of fossil fuel energy. Therefore, for geographical regions where enough solar energy is available, the implementation of ACASS systems is a viable

The high initial investment of the ACASS system might represent a barrier for its market, but it is necessary to continue working on the improvement of this type of systems, leading toward a massive production and aiming to reduce costs. As well, the intervention of decision makers is relevant to facilitate and incentivize the development of this kind of clean

Energy devices are expensive caused for the bin number of small parts to become a single product. In the air-conditioning systems, the parts for a roof device are based on three sec-

Population in general has no money for investment for the energy consumption for the next 20 years to pay now. Then financial is the key for the installation for renewable energy

tions: solar concentrators, thermodynamic cycle part, and auxiliary system part.

eq

61

eq emissions than the CAC

**Operation**

100.00

http://dx.doi.org/10.5772/intechopen.72188

**CAC**

Design and Construction for Hydroxides Based Air Conditioning System with Solar Collectors…

#### *7.3.4. Phase 4: interpretation*

A summary of the results and the corresponding discussion are carried out, and the final conclusions and recommendations are produced according to the results. In **Figure 8**, the ACASS system presents a reduction in CO2 e emissions of 77.34% compared with the CAC system, during the analyzed life cycle stages. In addition, employing the ACASS system also contributes to mitigate the environmental impacts due to the use of renewable solar energy. **Figure 9** shows the percentage of CO2 eq emissions at each life cycle stage for both

**Figure 8.** Total percentage of CO2 e emissions for the ACASS system compared against the CAS system at the stages of construction and operation.

Design and Construction for Hydroxides Based Air Conditioning System with Solar Collectors… http://dx.doi.org/10.5772/intechopen.72188 61

**Figure 9.** Percentage of CO2 eq emissions at each life cycle stage for both air-conditioning systems.

air-conditioning systems. The operation stage of the CAC system presents the highest CO2 eq emissions due to consumption of fossil fuel energy, while the ACASS system emits only 5.37% of the emissions at this stage.

During the construction stage, ACASS presents 14.05% higher CO2 eq emissions than the CAC system. This is attributed mainly to the difference in infrastructure demand for the construction of each system, and ACASS demands 79.20% more than CAC. It is important to note that the analysis is carried out considering 25 years of air-conditioning.

According to the results, it can be concluded that the ACASS system reduces greenhouse gas emissions due to lower consumption of fossil fuel energy. Therefore, for geographical regions where enough solar energy is available, the implementation of ACASS systems is a viable option for mitigating the environmental impacts related to global warming.

The high initial investment of the ACASS system might represent a barrier for its market, but it is necessary to continue working on the improvement of this type of systems, leading toward a massive production and aiming to reduce costs. As well, the intervention of decision makers is relevant to facilitate and incentivize the development of this kind of clean technologies.


**4.17. GLUT13; HMIT** *(SLC2A13)*

and an irregular vessel wall shape of arteries with increased elastic fibers. Furthermore, the animals displayed endothelial hypertrophy and disarranged elastic fibers that resulted in disruption of internal elastic lamina in the aorta. Neither group analyzed whether the muta‐ tions caused any dysfunction or loss of the target protein, therefore the reported phenotype of

GLUT12 was identified by 5' and 3' RACE-PCR from the human breast cancer cell line MCF-7. Strong GLUT12 expression is found in ductal cell carcinoma *in situ* when compared to benign ducts of breast cancer tissues. GLUT12 is mainly expressed in skeletal muscle, heart, small intestine, adipose tissue, and prostate. GLUT12 shows glucose transport when functionally characterized in *Xenopus laevis* oocytes that can be competed with fructose, galactose, 2-DG, and cytochalasin B. The transport activity of GLUT12 has been demonstrated to be affected by pH and in other reports by sodium, indicating its activity might be directly or indirectly

As a Class III isoform GLUT12 also contains a dileucine motif – both at the N-and C-terminal ends of the protein. Furthermore, the transporter does not contain GLUT12 contains a hydrophobic residue in position 314 which has been implicated in case of the isoleucine for GLUT2, 5, and 7 with the capability of fructose transport. This structural feature goes along with the substrate specificity of GLUT1. Endogenous as well as overexpressed GLUT12 protein localizes to intracellular compartments as well as to the plasma membrane in various cell lines. The N-terminal dileucine signal of GLUT12 is similar to that of GLUT8 which is the [DE]XXXL[LI] consensus sequence that represents a late endosomal/lysosomal sorting signal. However, GLUT12 does not colocalize with GLUT8, but rather resides in the Golgi network and at the plasma membrane. Plasma membrane associated GLUT12 is not endocytosed, which

The phenotypic characterization of mice deficient in GLUT4 indicates the presence of a second transporter that facilitates insulin-stimulated glucose transport. Due to its tissue expression and its biological characteristics, GLUT12 was studied for its ability to respond to insulininduced plasma membrane translocation of the protein. Indeed, in human skeletal muscle insulin induces an increase in plasma membrane GLUT12 which is comparable to the insulinstimulated GLUT4 translocation. Although GLUT12 expression in skeletal muscle is unaltered under pathophysiological conditions such as of obesity and type 2 diabetes these data imply that an additional transporter is expressed in human muscle that is insulin-responsive in a PI3 kinase dependent manner. Mice with transgenic overexpression of GLUT12 display increased insulin sensitivity in insulin-sensitive tissues, while basal (non-stimulated) glucose uptake into adipose tissue and skeletal muscle was unaffected [36] indicating that GLUT12 might contrib‐ ute to insulin-stimulated glucose uptake in those tissues, an effect not observed in heart muscle. Besides its potential importance for substrate transport in insulin-sensitive tissues, GLUT12 also appears relevant in prostate and breast cancer. However, the physiological role and

those mice remains inconclusive with respect to GLUT10 function.

indicates the absence of a continuous cycling mechanism for GLUT12.

**4.16. GLUT12** *(SLC2A12)*

32 Glucose Homeostasis

coupled to an electrochemical gradient.

relevance for GLUT12 remains elusive [37].

Screening of public expressed sequence databases with the GLUT8 protein sequence identified a rat EST clone that allowed cloning of the rat and human HMIT (*SLC2A13*) cDNAs from spleen and frontal cortex cDNA libraries. Despite low level expression in adipose tissue and kidney, HMIT is predominantly expressed in the brain, with high expression found in hippocampus, hypothalamus, cerebellum and brainstem.

The HMIT amino acid sequence contains all motifs known to be important for glucose transport activity. As for other Class III GLUT family members, HMIT is restricted to an intracellular location. Functional characterization of the protein in *Xenopus laevis* oocytes and in mammalian cells has been possible through the introduction of various mutations that yielded significant plasma membrane expression. Surprisingly, no sugar transport activity has been found for HMIT. Instead, HMIT has been identified as a H+ -coupled myoinositol symporter with a *Km* of about 100 µM. More recently, HMIT has been shown to transport inositol-3-phosphate (IP3). HMIT is inhibited by the common GLUT inhibi‐ tors phloretin, phlorizin and cytochalasin B, although at high concentrations. Transloca‐ tion of HMIT to the plasma membrane has been demonstrated to occur in PC12 cells or primary neurons upon depolarization or protein kinase C (PKC) activation resulting in functional HMIT at the plasma membrane evidenced by increased myoinositol uptake in those cells. However, those initial findings were not reproduced by other groups leaving uncertainty about a stimulus that induces plasma membrane translocation of the transport‐ er. In the brain, myo-inositol serves as the precursor for phosphatidylinositol, a key regulator for various signaling pathways. Dysregulation of the phosphatidylinositol signaling has been implicated in psychiatric illness such as bipolar disorder. Standard therapies (lithium, valproic acid and carbamazepine) alter neuronal growth cone morphol‐ ogy, a phenotype that is reversed by extracellular myo-inositol. Because of its predomi‐ nant expression in the brain compared to two other myo-inositol transporters that are sodium-coupled (SMIT1 and SMIT2), interest has been raised as to whether HMIT might play a role in the regulation of myo-inositol/phosphatidylinositol physiology in neurons. Mice deficient for the transporter demonstrated that HMIT is not involved in the neuro‐ nal transport of inositol from the extracellular environment.


**2. Methods**

A literature review was conducted of papers published up to 2021, which were identified by the Scopus search engine. The search terms were: Risk assessment AND Water OR Waters OR Estuarine OR Surface OR River OR Ocean OR Marine OR Lake AND Microplastics OR Microplastic on the article title. A total of 14 articles matched this search strategy, from which four were excluded because even though they identified MPs in water bodies, they carried out the ERA only for other pollutants (e.g., heavy metals), performed MPs measurements in sediments that were excluded from this review or were Reviews of toxicological articles. The eight selected articles were analyzed and scored based on the criteria observed in **Table 1**.

**Table 1.**

*Criteria to score the environmental risk analysis for MPs selected.*

The ERA criteria observed on the first two columns of **Table 1** were based on the articles made by Refs. [3, 4, 7], and the quality assurance column was based on the article made by Koelmans et al. [6]. Both the ERA criteria and the quality controls carried out in the experimental part of the article are considered for this review, as quality is the basis of good data collection and reporting how the experiment is performed is key to reproducibility and future standardization of methods.

The scores given to each criterion are:

	- One point will be assigned to each specified detection limit (one for size limit, one for shape limit, and one for polymer type limit) and one for each section of the experiment (one for sampling limit, one for separation limit, and one for identification limit).
	- In addition to the criteria established by Koelmans et al. [6], the detection limits must be specified to be able to interpret the results more accurately.