**Laser Ablation Applications in Ablation-Resistance Characterization of Materials Provisional chapterLaser Ablation Applications in Ablation-Resistance Characterization of Materials**

Yonggang Tong, Xiubing Liang, Shuxin Bai and Qing H. Qin Yonggang Tong, Xiubing Liang, Shuxin Bai and Qing H. Qin

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/65108

### **Abstract**

Owing to the rapid heating and large power intensity, the laser beams were successfully used to characterize the ablation-resistant performance of materials, which provided us more knowledge about the usability of materials in the ablation environment and developing protection against laser irradiation. In this chapter, we comparatively introduced some experimental methods for ablation-resistance characterization of materials. The fundamentals of laser-material interactions were discussed from the physical and chemical aspects to help understand the laser ablation mechanism. Finally, we presented some practical applications of laser ablation in ablation-resistance characterization of ultra-high-temperature ceramics (UHTCs) and ceramic matrix composites and discussed the laser ablation behavior and mechanism.

**Keywords:** Laser ablation, Ablation resistance, Laser-material interaction, Ceramic matrix composite, Ceramic coatings


**Author details**

Gabriel Arismendi-Morillo1 \*, Alan Castellano-Ramírez1 and Thomas N. Seyfried2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Biological Researches Institute, Faculty of Medicine, University of Zulia, Maracaibo, Venezuela

2 Department of Biology, Boston College, MA, USA


*4.3. Algorithm Analysis*

The results obtained through the utilization of RF, SVM, and CatBoost algorithms surpassed those obtained through the utilization of MLR in all scenarios. The greatest error measured with RMSE was observed when the model was trained with S1 data, as depicted in Figure 5. The reason for this is that the connection between backscatter and yield is not linear, and MLR is not able to handle non-linear relationships. Although the relationship between VIs and wheat yield is primarily linear, it possesses enough non-linearity for other algorithms to yield superior results [106]. The capacity to handle non-linear relationships is a key advantage of some algorithms (SVM, RF, CatBoost), as it enables the analysis of complex multivariate relationships between different types of data, which is not feasible with MLR. The results obtained through the utilization of RF and SVM are comparable, with those obtained using the SVM model being slightly superior, which is in contrast to those reported by other authors [35,107] in the field of wheat yield prediction. Although RF generally outperforms SVM, in some areas of PA such as disease detection, SVM has performed better than RF [108]. However, in this study, the best results were achieved using the CatBoost algorithm, which is a member of the boosting algorithm family. The algorithms belonging to this family have produced inconsistent outcomes within the domain of PA. For example, Bebie et al. [25] reported the worst results when the boosting regression (BR) algorithm was used, while Heremans et al. [108] obtained the best outputs with the same algorithms. CatBoost, like Xtreme Gradient Boosting (XGBoost), is a gradient boosting algorithm that belongs to the next generation of boosting algorithms, and XGBoost has been used successfully in PA to predict monthly NDVI evolution [109]. However, the use of this group of algorithms is not as prevalent in PA as RF or SVM. As an example, the Scopus database revealed a limited number of articles, only seven, that employ CatBoost within any field of PA. In contrast, it is widely utilized in other areas such as industries, finance, healthcare, and online advertising.

Although in this case it has not been used because all the variables are quantitative, one of the main advantages of CatBoost over other algorithms is its ability to handle categorical variables because it can automatically deal with them without any additional pre-processing, such as 'one hot encoding' reducing considerably matrix dimensions. Moreover, CatBoost is specifically engineered to handle large datasets, as it facilitates training on graphics processing units (GPUs), thereby significantly decreasing computation time. In terms of performance, CatBoost has been shown to have high performance and generalization ability, outperforming other algorithms such as RF and the generalized

regression neural network (GRNN) algorithm [110]. Additionally, CatBoost has a built-in mechanism for handling overfitting, which can be a problem with other algorithms like deep neural networks (DNNs) [111] and missing values. Finally, CatBoost also has a built-in feature importance mechanism that allows users to understand the importance of each feature in the dataset.

#### *4.4. CatBoost Algorithm as a Tool for Processing Heterogeneous Data in Precision Agriculture*

Use of the CatBoost algorithm in PA can provide significant advantages in terms of scaling up results. This algorithm is based on gradient boosting and is specifically designed to handle both numerical and categorical variables. This characteristic makes it suitable for PA, where a large amount of heterogeneous data are generated.

Compared to traditional machine learning algorithms such as RF, CatBoost has demonstrated improved performance in terms of accuracy and speed. The algorithm utilizes decision trees as weak learners and combines them in an iterative manner to make a strong prediction model. This results in a model that can generalize well to new data and is able to handle large amounts of data more efficiently than traditional algorithms.

In PA, the use of remote sensing data is increasingly common. This technology allows the acquisition of information on the physical, chemical, and biological characteristics of crops. Integration of the CatBoost algorithm with remote sensing data can provide valuable insights into crop growth. Another advantage of CatBoost is its ability to operate effectively even in the presence of missing records in a database. This is a common challenge faced when utilizing information from multiple sensors, as failures of individual sensors can occur at any point in time. The application of techniques to address such situations is not ideal, as it involves the addition of estimated information, which does not enhance the model. Furthermore, CatBoost data does not require scaling, leading to reduced time and effort in data preprocessing.


Preface

This Edited Volume is a collection of reviewed and relevant research chapters, concerning the developments within the Quantum Electronics. The book includes scholarly contributions by various authors and edited by a group of experts in the field. Each contribution comes as a separate chapter complete in itself but directly related to the book's topics and objectives. The book includes chapters dealing with the topics: Photonic Quasicrystals for Filtering Application, Synthesis of Curved Surface Plasmon Fields through Thin Metal Films in a Tandem Array, Localized Excitation of Single Atom to a Rydberg State with Structured Laser Beam for Quantum Information and Single-Atom Field-Effect Transistor. The target audience comprises scholars and specialists in the field.

IntechOpen

Chapter 1

Abstract

teristics of PBG.

1. Introduction

sequences.

1

Youssef Trabelsi

Photonic Quasicrystals for

In this chapter, we study the properties of specific one dimensional photonic quasicrystal (PQCs), in order to design an output multichannel filter. We calculate the transmittance spectrum which exhibits a photonic band gap (PBG), based on the Transfer Matrix Method (TMM) and the two-fluid model. We show that the generalized Thue-Morse (GTM) and generalized Fibonacci GF(m, n) distributions provide a stacking of similar output multichannel with zero transmission when the input was a sharp resonance of peaks at given n ¼ 2pm where p, is a positive integer. Also, we consider GTM configuration and we apply a deformation y ¼ xhþ1 along the PQC filter, which enhanced the band width of each channel with respect to the number of peaks inside the main transmittance. Here, the coefficient h represents the deformation degree, x and y are thicknesses of the layers before and after the deformation, respectively. This improves the charac-

Keywords: hybrid quasiperiodic PC, superconducting materials, GTM sequence, GF sequence, multichannel optical filters, deformed 1D photonic quasicrystals

Photonic quasicrystals (PQCs) which are made of alternating dielectric and superconductor layers intervene in numerous researches due to their interesting optical properties [1–5]. This type of crystal is an artificial super lattice which is built according to quasiperiodic sequences. It is considerably different than photonic crystals (PCs) since it is a non-periodic structure with perfect long-range order and lack translational and it can be considered as an intermediate class between the random and periodic media. Our considered PQC consists of a stack of two different layers A and B which represent building blocks having a selfsimilarity distribution and long range order with no translational symmetry.

We mention that there are numerous examples of aperiodic chains constructed by a substitution rule. These chains allow forming many deterministic PQCs structures such as: Fibonacci, Thue-Morse, Rudin-Shapiro, Cantor, and Doubly periodic

Based on PQC heterostructure, many studies have been performed to carry out new optical devices. In this direction, the introduction of superconducting materials into the regular PQC photonic structure has been investigated in [5–7] in order to improve the characteristics of photonic band gap structures (PBGs) by changing the

operating temperature of superconducting layers.

Filtering Application


**4. Development of autoantibodies against self-antigens and the promotion of autoimmunity**

The autoimmune nature of OLP is evidenced by the chronic and protracted course of the disease, its later age of onset, its higher prevalence in women, its association with other autoimmune diseases, proven demonstration of the T-cell auto-reactivity and increased auto-cytoxicity against basal keratinocytes, its clinical, histological and immunological similarity to graft-versus host disease (GVHD) and the effectiveness of immunosuppres‐ sive therapies. Two theories have been advanced to explain the autoimmune nature of OLP, specifically the loss of self-tolerance of the basal keratinocytes: (1) impaired immunesuppression in OLP due to lack of Transforming Growth Factor-β1 (TGF-β1); and (2) loss of "immune-privilege" in OLP. [14, 20]

**Impaired Immune-Suppression in OLP (deficient TGF-β1):**TGF-β is believed to be important regulator of the immune system by inducing and increasing the differentiation of both Foxp3+ regulatory T cells (Tregs) (and Th17 cells). FOXP3 (forkhead box P3) also known as scurfin, is a protein involved in immune system responses. A member of the FOX protein family, FOXP3 appears to function as a master regulator (transcription factor) in the development and function of regulatory T cells. [21, 22]

TGF-β also appears to block the activation of lymphocytes and monocyte derived phagocytes. TGF-β1 levels and/or activity may be deficient for some of the following reasons: insufficient numbers of TGF-β1-secreting Th3 regulatory T cells; blockage of TGF-β1secretion; secretion of defective, non-functional TGF-β1; defective or inadequate TGF-β1receptor expression; or defective intracellular downstream signalling from the TGF-β1 receptors. Local overproduc‐ tion of IFN-γ by Th1 CD4+ T cells in OLP lesions would down-regulate the immunosuppressive effect of TGF-β1and so up-regulate keratinocyte MHC class II expression and CD8+ cytotoxic T cell activity explaining the immune responses to antigens including self antigens and the chronicity of OLP.

**Loss of "Immune-Privilege" in OLP:** The normal oral epithelium, similar to the eye, testes and placenta, may represent an immune-privileged site, by being able to induce apoptosis of infiltrating T-lymphocytes. In these sites, the stromal cells possess Fas Ligand (CD95L) that can trigger the apoptosis of infiltrating inflammatory and immune cells that express Fas (CD95). [20] Keratinocytes themselves can also trigger T-cell apoptosis by the release of the cytokine TNF-α, which on binding the TNF-receptor-1 of T-cells triggers their apoptosis. Loss or impairment of either of these two mechanisms to induce T-cell apoptosis may have a role in the pathogenesis of OLP.

integrity is maintained by the keratinocytes. Apoptosis of the keratinocytes by the CD8+ cytotoxic T-cells results in the loss of the maintenance function by performed the keratinocytes, leading to disruption of the basement membrane, thereby allowing the non-specific T-cell to infiltrate the epithelial cell layers. [14, 23] The disruption of the basement membrane also leads to apoptosis of the keratinocytes, due to the loss basement-membrane derived signal to prevent their apoptosis, and so on. This ongoing, self-perpetuating cycle may explain the chronicity of

Oral Lichen Planus

155

http://dx.doi.org/10.5772/56482

**Matrix Metalloprotienases (MMPs):** MMP-9 concentrations have been found to be increased in culture supernatants taken from patients with OLP compared with normal controls. [25] MMP-9 is one member of a family of some 20 MMPs identified to date, which are all zinccontaining proteinases. [26] MMP-9 (together with MMP-2) are gelatinases that cleave collagen IV. Other MMPs can cleave collagen IV and laminin. The MMPs are inhibited by tissue inhibitors of metalloproteinases (TIMPs) that form stable complexes with MMPs or pro (precursor) MMP's. [27] T-cells release activators of MMP-9, resulting in disruption of the

**Chemokine (C-C Motif) Ligand 5 (CCL5 (RANTES)):** CCL5 is a key chemokine released by various cells including, activated T-lymphocytes, oral keratinocytes and mast cells and has a critical role in the recruitment of various immune and inflammatory cells, including lympho‐ cytes, monocytes, eosinophils, basophils and mast cells. CCR1, CCR3 to CCR5, and CCR9 and CCR10 are key cell surface receptors for CCL5 and have also been identified in lichen planus. [28] CCL5 attracts mast cells which degranulate, releasing TNF-α and chymase, which in turn up regulates OLP lesional T-cell release of CCL5, leading again to the development of a self-

**Mast Cell Activation and Degranulation:** Mast cells are not only increased in numbers, but most are degranulated in OLP (compared with normal tissues). [14, 23, 28] Mast cell degra‐ nulation results in the release of variety of pre-inflammatory mediators, including TNF-α, chymase and typtase. TNF-α can up-regulate endothelial cell adhesion molecules required for the lymphocytes to adhere to luminal surfaces of blood vessels and their subsequent extrava‐ sation. Chymase, a mast cell protease, activates MMP-9, so contributing to basement mem‐ brane disruption. Both chymase and TNF-α can stimulate CCL5 secretion by lesional T-

Genetic factors clearly must have a role in the pathogenesis of OLP. Recently, the identification of genetic polymorphisms of cytokine/receptor gene loci has been shown to act as clear-cut genetic risk factors for a number of autoimmune diseases. [29] Polymorphism of several cytokines has been shown to be associated with the clinical presentation of LP. [30] Genetic polymorphisms of the first intron of the promoter gene of interferon-γ and development of oral lesions of LP and an association between the –308A TNF-α allele and the development of cutaneous lesions of LP. [30] The occurrence of OLP has also been linked to MHC class II allele

perpetuating cycle, that further contributes to the chronicity of OLP.

lymphocytes, which in turn can trigger further mast cell degranulation. [28]

OLP.

basement membrane.

**7. Genetic factors**

Langerhans cells may also contribute to the loss of self-tolerance. Langerhans cells phagocytose the apoptotic bodies and debris of basal keratinocytes, but in doing so, may process and present to the CD4+ T helper cells a self-antigen derived from the remains of the basal keratinocyte. In turn, this may activate self-reactive CD4+ T cells that differentiate into Th1 or Th2 phenotypes and promote cell- or antibody-mediated autoimmune reactions against basal keratinocytes, including the stimulation of the cytotoxic T cells against the basal keratinocytes. [14]