*Edited by Doaa Hashad*

Gene therapy is becoming a promising technology for the management of many human diseases. Hereditary and acquired disorders can both be tackled using the technique of gene therapy. This book provides detailed, up-to-date topics addressing basic principles of gene therapy and discussing some of the challenges encountered by scientists in developing this relatively novel technology. The development of new and efficient gene transfer vectors is of utmost importance in the progress of the field of gene therapy. Both viral and non-viral vectors are extensively discussed. A detailed chapter elaborates the problem of host immune rejection of transplanted donor cells or engineered tissue that can be avoided using the encapsulation of transgenic cells, thus avoiding the use of drugs that achieve immunosuppression.

Photo by sheelamohanachandran / DolLarPC

Gene Therapy - Principles and Challenges

Gene Therapy

Principles and Challenges

*Edited by Doaa Hashad*


**5. Conclusions and Future Works**

The development of improved installation and maintenance procedures for technologies that harness energy from ocean currents is a promising research field that needs to be studied in detail in order to achieve a successful future commercialization of these technologies. In this paper, we have explained the procedures for manual and automated installation and maintenance maneuvers for gravity-based first generation TECs, and have also carried out an economic-financial evaluation in order to highlight the merits of automated installation and maintenance maneuvers for tidal energy technologies. Some of the potential benefits that the use of these maneuvers could provide are the following: (i) an increase in the competitiveness of these technologies; (ii) a reduction in human resources and the number and duration of installation and maintenance operations; (iii) the use of cheaper general purpose ships instead of high cost special vessels for maintenance operations; and (iv) a higher project profitability, among others. We have conducted a numerical case study of a TEF in the Alderney Race (UK). This farm comprises 42 TECs of 1.2 MW, and the study was carried out with the objective of determining the economic-financial viability of the project when employing either automated or manual installation and maintenance maneuvers. After applying the economic-financial procedure explained throughout this paper to the case study, we attained results indicating that the project would, in both cases, obtain a good profitability and can consequently recommend investment from both accounting and financial points of view, since the benefits produced from the first year are good. We have additionally discovered that the profitability is even greater when employing devices capable of performing automated maneuvers. The aforementioned results also indicate that the variables that have the greatest influence on the profitability of the project are, in the case of both types of maneuvers, the AEP, the price of energy and the interest rate. Finally, having attained the results of the case study, we can conclude that the economic-financial methodology employed is useful as regards modeling different renewable energy projects, as is also the case of the manual and automated installation and maintenance procedures for first generation TECs presented in this work. This methodology additionally has an advantage in that it could, with minor adaptations, be used for other kinds of offshore renewable energy projects and TEC designs. Our future research will be focused on the application of this model to other offshore renewable energy projects.

**Author Contributions:** E.S., R.M. and J.A.S. conceived, designed and performed the proposed methodology and the case study. Additionally, E.S., R.M. and J.A.S. analyzed the data and participated in writing the paper.

**Funding:** This research has been supported by the Spanish Ministerio de Economía y Competitividad under Research Grants DPI2014-53499-R.

**Conflicts of Interest:** The authors declare no conflicts of interest.

*Energies* **2019**, *12*, 2464


**3.3 Regulatory T cells**

The role of Treg cells in *Leishmania* infection is still being elucidated, although though they have been shown in rodent models to be involved in disease pathology and parasite persistence depending on the experimental model used. CD4+ CD25+ Treg cells have been shown to suppress CD4+ T cell activity in *L. major*-infected C57BL/6 mice, thereby favoring parasite persistence [98, 113, 114]. Treg cells influence both primary and secondary infections with *L. major*, as they render otherwise non-susceptible mice susceptible to infection [115]. However, their activity may also be dependent on the infecting *Leishmania* species. For example, Treg cells play a protective role during infection with New World *Leishmania* species, such as *L. amazonensis* [95, 116]. Transferring Treg cells from an *L. amazonensis*-infected mouse to a naïve mouse prior to infection with *L. amazonensis* reduced the development of lesions suggesting that they may also contribute to the control of immunopathogenic responses [116]. Understanding how Treg cells are involved in human *Leishmania*-infections is still being explored, with evidence so far suggesting that these cells play a role at the infection site and contributing directly to parasite persistent as the main source of IL-10 production [95, 98].

#### **3.4 B cells and antibodies**

The function of B cells in CL has not conclusively been shown. During the initial *Leishmania* infection, antibody production by B cells themselves are not believed to play a role, in controlling parasites as *Leishmania* are intracellular. However, some studies indicate that B cells may regulate both protective and pathogenic immune responses during *Leishmania* infection, depending on the infecting species and model used. Production of *L. major* antibodies was shown to be important for DCs to phagocytose parasites, as the absence of antibodies by B cells resulted in larger lesions in B cell−/− mice, higher parasite load, low production of IFNγ and a decreased cell-mediated immune response [117]. Moreover, IgG−/− BALB/c mice infected with *L. major* resulted in larger lesions and higher parasite load compared to IgG+ BALB/c mice [118]. In contrast, a study using a BALB/c mice deficient in IgM transmembrane domain (μMT), thereby lacking mature B cells, observed that these mice were resistant to *L. major* infection [119]. Other studies using BALB/c mice lacking IL-4Rα expression specifically on B cells, mbicreIL-4Rα−/lox BALB/c mice, resulted in a protective host immunity [29, 119, 120].

There is still a lot of knowledge to gain on B cells' function and whether they play a part in protection or pathology during infection with *Leishmania* parasites.

### **4. Persistent** *Leishmania* **infection and emulating concomitant immunity**

Naturally and experimental infection with cutaneous *Leishmania* species is controlled following the development of an adaptive TH1 immune response. After induction of this response, parasite numbers decline in infected tissues, lesions heal and lifelong immunity against the infecting *Leishmania* species is gained [121]. Though recovery from cutaneous disease has been reached, a small number of *Leishmania* parasites normally remain indefinitely in the host at the initial site of infection; known as persistent parasites [122, 123]. These parasites play an important role in maintaining protective immunity in the event of reinfection by providing a constant source of *Leishmania* antigen for immune stimulation [121, 124]. Both mice and humans who recover from CL maintain chronic subclinical infection at the lesion site and have been shown to be highly resistant to second challenge through sandfly transmitted infections [125]. Though, the immune response is unable to clear the primary infection, the immune system can facilitate concomitant immunity by IFNγ secreting CD4+ TH1 cells [126]. However, reactivation of disease causing infection has been documented for leishmaniasis when the immune system is no longer able to control this low level chronic parasite infection [127, 128]. This is frequently observed when persistently infected individuals become immunosuppressed, such as during infection with the human immunodeficiency virus (HIV) [127, 129].

Currently, vaccine programs have been unsuccessful to emulate the protective responses mediated by concomitant immunity as observed during subclinical infections with persistent parasites (reviewed in [130]). Similar, a sterile cure whereby the parasites are completely eliminated has not been achieved without consequently the loss of long-term immunity [131].

#### *Protective and Pathogenic Immune Responses to Cutaneous Leishmaniasis DOI: http://dx.doi.org/10.5772/intechopen.101160*

In the past the leishmanization live vaccine practice was employed by inoculating virulent *Leishmania* parasites into individuals, however this has since fallen out of practice due to safety concerns regarding development of non-healing lesions [132]. Since then, vaccine-candidates have failed to provide protection against natural exposure even though they demonstrate protective cell-mediated immunity in rodent models. It is thought that this is due to differences in experimental delivery versus the natural route of infection via the bite of a sandfly. Other challenges are observed when using whole killed parasites or subunit protein vaccine candidates only short-term protection in rodent models has been observed [123, 131].

The difference in protective immunity induced following natural infection and inoculation of whole killed parasites is not fully understood but it has been hypothesized that there is a difference in the immunologic memory responses, which is influenced by the presence of live versus killed parasites. Moreover, the adjuvant dose-quantity tested to date may not be sufficient to generate a memory T cell population [123, 128]. It is possible that vaccines utilizing live-attenuated parasites will most closely mimic natural infection, potentially providing long-term protection against infection and disease [131].

Recently, vector-associated factors have been identified to have an important impact on challenge models in vaccine-mediated immunity [130]. Following needle versus infected sandfly challenge in mice showed that various protein/adjuvant-based vaccines provided intermediate protection against needle challenge whereas sandfly challenge failed to provide protection. Despite generating antigen-specific TH1 immune responses prior to and following challenge, vaccines failed to protect against infected sandfly challenge [125, 133]. The sandfly vector challenge model clearly emphasizes important factors induced by the sandfly, such as the impact of recruited inflammatory cells and immune-mediated host cell activation by the vector.


**4.6 Clustering coefficient**

The clustering coefficient indicator of the physical environment is measured by applying the equation of the clustering Eq. (9), and the major two components to run the equation are the observed number of links among the destinations and the possible number of links. However, there was no clear explanation about how to measure these two components in the reviewed literature; therefore, the criteria to measure these two components are designed by this study. In this regard, the first component of this equation is the observed links between destinations. It considered 5 min as the maximum walking distance between two destinations, which is a 200-m length. Thus, each destination has a potential relationship with all other destination in the 200-m radius. The reason for such an assumption is because, if the distance between every two destinations is not a complete journey for the walker but rather a sub-journey, then the minimum distances mean a better relationship. Based on this criterion, each destination was defined as a center and a straight line was drawn to all other adjacent destinations in the 200-m radius. The required information was elicited from the cadastral maps with the assistance of AutoCAD 3D map software; in this regard, the shapefile maps were generated to both the links and the destinations on the 400-m radius and 600-m radius scales. From this, the layers were added into QGIS software. The resulting total number of links was considered the observed links (numerator). The second component is the possible number of links, even if they did not exist, between the destinations; for this purpose, the equation used was: the number of possible links = (n2 − n)/2 (denominator), Eq. (9), where n is the total number of destinations. Thus, two independent variables resulted from this indicator, on the 400-m radius and 600-m radius scales. The two variables were labeled as ClsCofS1, ClsCofS2 (**Table 1**). Thereafter, on the 400-m radius scale, the clustering coefficient variable CICS1, indicated that the Al-Saymmar and Al-Abassya neighborhoods were identical, at 0.05 for each. Meanwhile, Al-Mugawlen was slightly different at 0.04. Moreover, on the 600-m radius scale, the clustering coefficient variable CICS2, indicated that the Al-Saymmar and Al-Abassya neighborhoods are identical at 0.04 for each. Finally, Al-Mugawlen was slightly different at 0.03 (**Table 2**).

#### **4.7 Edges assessment**

The method to assess the quality of street edges was adopted the frontage quality index (SFOMD) [50]: p.108. It depends on a Likert scale of seven points, starting with (1), which is the lowest score of the assessment, and ending with (7), which

**379**

**Table 3.**

*SFOMD index, analysis of the edges.*

**4.8 Enclosure ratio**

*Approaching Urban Design through the Analysis of Structural Differences within Three…*

is the highest score. The application of the method depends on observations is conducted by a specialist team and criteria based sampling of the urban tissue. The computation of the overall index concerning the quality of the area was adopted from Gehl [51] and Hershberger and Clements [52], which combines the five indicators by totaling their raw scores. Therefore, the minimum score of the process is five points which represents the poorest quality streets, whereas, the highest score is 35 points, which represents the best possible quality. Thus, one independent variable was developed in terms of the edges assessment, namely: The Frontage quality index (SFOMDS1) on the 400-m radius scale (**Table 1**). Moreover, the principle of sampling the streets is an important issue to avoid bias and to validate the generalization of the results, therefore, this study depends on the selection of three streets segments based on the hierarchal level of the street. From the hierarchical street levels, they define the main street, a connector street, and a cul-de-sac from each case study. The information of the survey was transferred into Excel-sheets for the purpose of analysis. The individual survey sheets were summarized (**Table 3**). Accordingly, three variables were developed from this analysis, which were: the Frontage quality index of the main street (SFMODS1M), the Frontage quality index of the Connecting street (SFMODS1C), and the Frontage quality index of the Col-

The SFOMD index in Al-Saymmar neighborhood demonstrated the lowest levels of frontage quality, at 21, 20, and 15 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. The SFOMD index in Al-Mugawlen neighborhood demonstrated moderate levels of frontage quality, at 27, 23, and 22 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. Finally, the SFOMD index in Al-Abassya neighborhood demonstrated the highest levels of frontage quality, at 30, 27, and 27 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively, (**Tables 2** and **3**). Therefore, the value of the SFOMD index increased in parallel with the increased grid structure among the street typologies. For example, the red level of the SFOMD index, brought about different scores across the three neighborhoods, 21, 27, and 30, for Al-Saymmar,

The enclosure ratio was measured for the sampled streets in **Figures 6–8**, Thus,

three independent variables resulted from applying the enclosure indicator on the 400-m radius scale; these were coded as EnRBS1M, EnRBS1C and EnRBS1CS

*DOI: http://dx.doi.org/10.5772/intechopen.87221*

de-sac (SFMODS1CS) (**Table 1**).

Al-Mugawlen, and Al-Abassya respectively (**Table 2**).

*Approaching Urban Design through the Analysis of Structural Differences within Three… DOI: http://dx.doi.org/10.5772/intechopen.87221*

#### **Table 3.**

*Sustainability in Urban Planning and Design*

**4.6 Clustering coefficient**

indicator. Thus, two independent variables were addressed by the PRDR, which were labeled: PRDRS1, PRDRS2 (**Table 1**). Then, the specified PRDR equation Eq. (8) was utilized to compute the indicators, which must be ≤1. A value of 1 represents an optimum relationship that has identical aerial and real distances; whereas, the smaller ratio illustrates that the real route is longer than the aerial distance. In other words, the street network route distance between the two points relates the user's departure station to the location of a contextual destination; thus, the shorter distance indicates the more accessible destination. The PRDR for the 16 destinations of each case study were averaged to determine how well the destinations of each case study are served by the street network. The PRDRS1 on the 400-m radius scale slightly differed among the three neighborhoods, at 0.73, 0.77, and 0.72 for Al-Saymmar, Al-Mugawlen, and Al-Abassya neighborhoods, respectively. Also, it illustrated similar differences for the 600-m radius scale, at 0.76, 0.79, and 0.76 for Al-Saymmar, Al-Mugawlen, and Al-Abassya neighborhoods, respectively (**Table 2**).

The clustering coefficient indicator of the physical environment is measured by applying the equation of the clustering Eq. (9), and the major two components to run the equation are the observed number of links among the destinations and the possible number of links. However, there was no clear explanation about how to measure these two components in the reviewed literature; therefore, the criteria to measure these two components are designed by this study. In this regard, the first component of this equation is the observed links between destinations. It considered 5 min as the maximum walking distance between two destinations, which is a 200-m length. Thus, each destination has a potential relationship with all other destination in the 200-m radius. The reason for such an assumption is because, if the distance between every two destinations is not a complete journey for the walker but rather a sub-journey, then the minimum distances mean a better relationship. Based on this criterion, each destination was defined as a center and a straight line was drawn to all other adjacent destinations in the 200-m radius. The required information was elicited from the cadastral maps with the assistance of AutoCAD 3D map software; in this regard, the shapefile maps were generated to both the links and the destinations on the 400-m radius and 600-m radius scales. From this, the layers were added into QGIS software. The resulting total number of links was considered the observed links (numerator). The second component is the possible number of links, even if they did not exist, between the destinations; for this purpose, the equation used was: the number of possible links = (n2 − n)/2 (denominator), Eq. (9), where n is the total number of destinations. Thus, two independent variables resulted from this indicator, on the 400-m radius and 600-m radius scales. The two variables were labeled as ClsCofS1, ClsCofS2 (**Table 1**). Thereafter, on the 400-m radius scale, the clustering coefficient variable CICS1, indicated that the Al-Saymmar and Al-Abassya neighborhoods were identical, at 0.05 for each. Meanwhile, Al-Mugawlen was slightly different at 0.04. Moreover, on the 600-m radius scale, the clustering coefficient variable CICS2, indicated that the Al-Saymmar and Al-Abassya neighborhoods are identical at 0.04 for each. Finally,

**378**

**4.7 Edges assessment**

Al-Mugawlen was slightly different at 0.03 (**Table 2**).

The method to assess the quality of street edges was adopted the frontage quality index (SFOMD) [50]: p.108. It depends on a Likert scale of seven points, starting with (1), which is the lowest score of the assessment, and ending with (7), which

*SFOMD index, analysis of the edges.*

is the highest score. The application of the method depends on observations is conducted by a specialist team and criteria based sampling of the urban tissue. The computation of the overall index concerning the quality of the area was adopted from Gehl [51] and Hershberger and Clements [52], which combines the five indicators by totaling their raw scores. Therefore, the minimum score of the process is five points which represents the poorest quality streets, whereas, the highest score is 35 points, which represents the best possible quality. Thus, one independent variable was developed in terms of the edges assessment, namely: The Frontage quality index (SFOMDS1) on the 400-m radius scale (**Table 1**). Moreover, the principle of sampling the streets is an important issue to avoid bias and to validate the generalization of the results, therefore, this study depends on the selection of three streets segments based on the hierarchal level of the street. From the hierarchical street levels, they define the main street, a connector street, and a cul-de-sac from each case study. The information of the survey was transferred into Excel-sheets for the purpose of analysis. The individual survey sheets were summarized (**Table 3**). Accordingly, three variables were developed from this analysis, which were: the Frontage quality index of the main street (SFMODS1M), the Frontage quality index of the Connecting street (SFMODS1C), and the Frontage quality index of the Colde-sac (SFMODS1CS) (**Table 1**).

The SFOMD index in Al-Saymmar neighborhood demonstrated the lowest levels of frontage quality, at 21, 20, and 15 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. The SFOMD index in Al-Mugawlen neighborhood demonstrated moderate levels of frontage quality, at 27, 23, and 22 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. Finally, the SFOMD index in Al-Abassya neighborhood demonstrated the highest levels of frontage quality, at 30, 27, and 27 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively, (**Tables 2** and **3**). Therefore, the value of the SFOMD index increased in parallel with the increased grid structure among the street typologies. For example, the red level of the SFOMD index, brought about different scores across the three neighborhoods, 21, 27, and 30, for Al-Saymmar, Al-Mugawlen, and Al-Abassya respectively (**Table 2**).

#### **4.8 Enclosure ratio**

The enclosure ratio was measured for the sampled streets in **Figures 6–8**, Thus, three independent variables resulted from applying the enclosure indicator on the 400-m radius scale; these were coded as EnRBS1M, EnRBS1C and EnRBS1CS

**Figure 6.** *Al-Saymmar edges assessment.*

**Figure 7.** *Al-Mugawlen edges assessment.*

**381**

*p* < 0.001, *R*2

*Approaching Urban Design through the Analysis of Structural Differences within Three…*

the individuals. The measured attributes of the physical environment were tested in terms of their predictability for the walking minutes. Thus, their variables are considered predictors (X), which need to be tested in terms of their predictability for the walking minutes (Y). In other words, this analysis tests whether the walking outcome variables can be individually predicted by the measured attributes of the physical environment. For such a purpose, the hierarchal regression analysis was chosen because of its flexibility to enter predictors in a split block with extra predictors. Moreover, the *p*-value (<0.05) indicates the significance of the models,

explain the variance of the outcomes. The hierarchical regression analyses were run to test the predictability of the physical environment indicators, which were individually tested with the walking minutes in one model for each indicator to determine the effect significance of the predictor on the outcome variable (*p*-value)

The Block density, the predictability of the model was significant (*F*(173, 1) =

the variance of the total walking minutes. Also, the test shows that the higher walking behavior outcome score was associated with higher block densities (*b* = 11.817, *p* < 0.001). In term of Housing Units density, the predictability of the model was

explain 13.2% of the variances of the total walking minutes. It was showed that the higher scores for walking behavior outcomes were associated with higher housing

the singular model was able to explain 6.1% of the variances of the total walking minutes. Furthermore, the higher walking behavior outcome scores were associated with a higher diversity of all commercial land uses (*b* = 2.478, *p* < 0.001). Regarding the commercial land use without parking, wholesale, and workshops variable on a 400-m radius scale, the predictability of the model was significant

10.4% of the variances of the total walking minutes. Furthermore, the higher scores of walking behavior outcomes were associated with the higher diversity of commercial land use without parking, wholesale, and workshops (*b* = 2.390, *p* < 0.001). In term of the diversity of the non-residential land use on a 400-m radius scale, the predictability of the model was significant with the total walk-

to explain 7.5% of the variances of the total walking minutes. Also, the higher of walking behavior outcomes scores were associated with a lower diversity of non-

Regarding the connectivity indicators; the node (streets intersections) intensity on both scales 400-m and 600-m radius, the predictability of the model was

9.7% of the variances of the total walking minutes on the two scales, respectively. However, the higher walking behavior outcome scores were associated with the higher node intensities only on the 400-m radius scale in terms of the total walking minutes (*b* = 0.573, *p* < 0.001). In term of the street intensity on 400-m and 600-m radius scales, the predictability of the two models was significant for the total

= 0.097), respectively. Thus, the models were able to explain 13.5 and

Diversity of all commercial land use on a 400-m radius scale; the predictability

). Moreover, the SPSS software was utilized for the conducting all the

= 0.089). Thus, the singular model was able to explain 8.9% of

= 0.104). Thus, the model was able to explain

) explain the potential of the predictors to

= 0.132). Thus, the model was able to

= 0.075). Thus, the model was able

= 0.135) and (*F*(173, 1) = 18.678,

= 0.061). Thus,

*DOI: http://dx.doi.org/10.5772/intechopen.87221*

while the determination coefficient (*R*2

required statistical analysis in this study.

significant (*F*(173, 1) = 26.231, *p* < 0.001, *R*2

ing minutes (*F*(173, 1) = 13.947, *p* < 0.001, *R*2

residential land use (*b* = −3.390, *p* < 0.001).

significant (*F*(173, 1) = 26.940, *p* < 0.001, *R*2

of the model was significant (*F*(173, 1) = 11.145, *p* < 0.001, *R*2

units densities (*b* = 0.040, *p* < 0.001).

(*F*(173, 1) = 20.176, *p* < .001, *R*2

and the (*R*2

**5.1 Findings**

16.989, *p* < 0.001, *R*2

**Figure 8.** *Al-Abassya edges assessment.*

(**Table 1**). The variables required to apply the indicator are the width of the street and the heights of the adjacent buildings, which were measured directly in this study. Then, the function of the indicator was applied to the sections of all the sampled streets segments. In terms of the main streets (EnRBS1M), the three case studies have a broadly similar value of enclosure ratios, at 2.9, 2.7, and 2.7, for Al-Saymmar, Al-Mugawlen, and Al-Abassya, respectively. In terms of the Connecting streets with the green level of betweenness (EnRBS1C), the highest score was noted with the Al-Mugawlen neighborhood (1.7); Al-Abassya showed a moderate enclosure ratio level (1.3), and the lowest level was noticed within Al-Saymmar (1). In terms of the local streets and the blue level of betweenness (EnRBS1CS), the highest score was noted within the Al-Mugawlen neighborhood (1.7); Al-Saymmar showed a moderate enclosure ratio level (1.1), and the lowest level was noticed with Al-Saymmar (0.93) (**Table 2**).


**Author details**

Hulya Bukulmez

Address all correspondence to<EMAIL_ADDRESS>
Department of Pediatrics, Division of Pediatric Rheumatology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, Ohio, United States


**APPROACHES TO DISASTER MANAGEMENT - EXAMINING THE IMPLICATIONS OF HAZARDS, EMERGENCIES AND DISASTERS**

Edited by **John Tiefenbacher**

#### **Approaches to Disaster Management - Examining the Implications of Hazards, Emergencies and Disasters**

http://dx.doi.org/10.5772/3355 Edited by John Tiefenbacher

#### **Contributors**

Diane Brand, Hugh Nicholson, McIntosh, Outi Niininen, C. Emdad Haque, Mohammed S Uddin, Sima Ajami, Mario Beruvides, Andrea Jackman, Thomas Allen, Stephen Sanchagrin, George McLeod, Thomas Glade, Roxana Liliana Ciurean, Dagmar Schroeter, Martin Bryant, Penny Allan, Paul Houser

#### **© The Editor(s) and the Author(s) 2013**

The moral rights of the and the author(s) have been asserted.

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Violations are liable to prosecution under the governing Copyright Law.

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#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2013 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

Legal deposit, Croatia: National and University Library in Zagreb

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
Approaches to Disaster Management - Examining the Implications of Hazards, Emergencies and Disasters Edited by John Tiefenbacher

p. cm. ISBN 978-953-51-1093-4 eBook (PDF) ISBN 978-953-51-5140-1


**Author details**

Peyman Pourafshary\* and Nikoo Moradpour School of Mining and Geosciences, Nazarbayev University, Nur-Sultan, Kazakhstan

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Hybrid EOR Methods Utilizing Low-Salinity Water DOI: http://dx.doi.org/10.5772/intechopen.88056*


**About the Special Issue Editor**

**Thurmon Lockhart** is Professor in the Biomedical Engineering and Biological Design program in the School of Biological Health and Systems Engineering at Arizona State University, Tempe, AZ. He is also Adjunct Professor at the Barrow Neurological Institute, Research Affiliate at the Mayo Clinic College of Medicine, Division of Endocrinology, and Guest Professor at Ghent University in Belgium. His research focuses on the identification of injury mechanisms and the quantification of sensorimotor deficits and movement disorders associated with aging and neurological disorders on fall accidents. His academic grounding in biomechanical modeling, nonlinear dynamics, human postural control, gait mechanics, and wearable biosensor design underscore a fundamental capacity to provide unique clinical solutions to injury prevention utilizing both engineering and biomedical principles. He has translated research findings into practice by reaching a significant number of external organizations and individuals. His outreach efforts have impacted several organizations, including UPS, the US Navy, Los Alamos National Security, the DOE, GE, and BP. In recognition of these scientific achievements, Prof. Lockhart and co-workers were awarded the Alexander C. Williams, Jr., Design Award from the Human Factors and Ergonomics Society in 2008. His research was recently featured on the PBS NOVA ScienceNow and Good Morning America programs and in the Fortune, AgingWell, Men's Health, and Discover magazines.


Kampon Kaeoket

*Faculty of Veterinary Science, Semen laboratory, Department of Clinical Science and Public Health, Mahidol University Thailand*

#### **1. Introduction**

Artificial insemination (AI) is one of the first reproductive biotechnologies has been established and developed in the pig production system. In most case, liquid stored semen or fresh semen is used for AI in commercial swine herds (Wagner and Thibier, 2000). The use of FT boar semen for AI is limited due to the low fertility outcomes compared to extended fresh semen (Johnson et al., 2000; Wagner and Thibier, 2000). The first success of boar semen cryopreservation was reported in 1956 (Polge, 1956) and the first pregnancy was achieved with FT boar semen using surgical insemination in 1970 (Polge, 1970). Currently, the attempt to develop the boar semen cryopreservation technique is ongoing. Nevertheless, the success of boar semen cryopreservation is relatively variable because the factors responsible for the cryosurvival of boar spermatozoa have not been entirely elucidated.

Cryopreservation of boar semen is useful for preservation of genetic resources, improve the genetic progress and enhance the transportation of genetic material across countries (Almlid and Hofmo, 1996; Johnson, 1998).  Unfortunately, the advancement of sperm cryopreservation in pigs is slow, partly due to the pig producer is satisfied with the liquid stored semen and low conception rate and litter size remain the major problems when using FT boar semen (Eriksson et al., 2002.). Under field conditions, low fertility is still obtained even using FT boar semen with a sufficient motility and number of spermatozoa for insemination (Johnson et al., 2000, Eriksson et al., 2002).

The use of frozen-thawed (FT) boar semen has been developed for artificial insemination (AI) in pig long time ago in Europe and USA (Larsson and Einarsson, 1976). In Thailand, few studies on boar semen cryopreservation have been established (Buranaamnuay et al., 2006 a,b). However, a great variation on the survival rate of post-thawed spermatozoa are obtained, due to the lack of biological background concerning the cryopreservation technique (Buranaamnuay et al., 2006 a,b).  In addition, it was found that the supplement of extended boar semen with 5 mM of cysteine improved the viability and functional status of

Nowadays, there are 2 techniques for cryopreservation in boar semen, traditional nitrogen method and controlled rate freezing method. Verheyen (1993) reported a significantly better post-thaw sperm outcome when computer controlled rate freezing was used compared to non-controlled rate freezing. In human, it has been reported that controlled rate freezer method provided significant superior post-thaw sperm motility, viability, and cryosurvival rate, compared with traditional nitrogen method (Petyim and Choavaratana, 2006). However, the study of Thalchil (1981) did not confirm the different outcome of these 2 methods. Besides breed-specific fertility, data from field trials found that the mean motility of frozen–thawed semen between Norwegian Landrace and Duroc boars was difference. In the different breeds of boars found the differences in membrane lipid composition, can neither explain the major differences in post-thaw survival and fertility between breeds

Major limitation of frozen-thawed semen (FT-boar semen) have been observed, i.e. low conception rate and low litter size after AI (Johnson et al., 2000; Buranaamnuay et al., 2006). The relatively low fertility of FT-boar is associated with many factors. It has been reported that reactive oxygen species (ROS) generation, induced by the cryopreservation process, can be responsible for mammalian sperm damage (Griveau and Le Lannou, 1997) ROS production has been associated with reduction of sperm motility and decreased capacity for sperm–oocyte fusion. Spermatozoa are sensitive to lipid peroxidation due to their high content of polyunsaturated fatty acids, and are unable of resynthesizing their membrane components, although this may not be the sole mechanism by which sperm function is impaired by ROS. Many studies have shown that the supplementation of antioxidants in extenders improved the qualities of both fresh boar semen (Bamba and Cran, 1992; Funahashi and Sano, 2005) and frozen boar semen (Breininger et al., 2005; Gadea et al., 2005; Pena et al., 2003; Roca et al., 2004; Roca et al., 2005). Earlier studies showed that the supplementation of some antioxidant such as, water soluble Vitamin E 200 µM to semen extenders for freezing boar spermatozoa reduced post-thaw ROS generation and improved sperm motility and viability (Pena et al., 2003). Funahashi and Sano (2005) reported that supplementation extended boar semen with glutathione or *L-*cysteine of 5 mM improved the viability and functional status of boar spermatozoa during liquid storage at 10 C for at least 14 day. A recent report (Gadea et al., 2005) demonstrated that supplementation with 1 mM of reduced glutathione to freezing media resulted in a protective effect on sperm function.

Cryopreservation of boar semen need to be developed for AI in the pig industry due to a number of reasons including preservation of a good genetic resource, increase genetic improvement, distribution of genetic lines across countries and reduce boar transportation

the chilled boar spermatozoa (Funahashi and Sano, 2005).

**3. Cryopreservation of animal spermatozoa**

**2. History of cryopreservation**

(Waterhouse et al., 2006).

thawing process (Eriksson and Rodrigrez-Martinez, 2000; Córdova-Izquierdo et al., 2006) and the supplement of some additives to the semen extender (Peña et al., 2003, Gadea et al., 2004; Roca et al., 2004, 2005).

Boar semen differs in several aspects from the semen of other domestic animals, for instance, the semen is produced in a large volume and highly sensitive to cold shock, the viability of the sperm cells is dramatically reduced when expose to temperatures below 15 C (Gilmore et al., 1996). Therefore, the manipulation of boar semen requires special consideration during cryopreservation process (Johnson et al., 2000). Many factors that should be concerned for the boar semen cryopreservation included composition of diluents, type and concentration of cryoprotective agent, equilibration time, cooling rate and thawing procedure.

The relatively low fertility of FT boar semen is associated with many factors including a highly sensitive plasma membrane of boar spermatozoa against the changing in temperature during cooling, freezing and thawing process (Holt, 2000; Watson, 2000). This problem is related to the lipid composition of the sperm plasma membrane. The plasma membrane of the boar spermatozoa contains a high level of polyunsaturated fatty acids (PUFAs) i.e., docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA), and had a low cholesterol to phospholipids ratio. DPA and DHA are dominant fatty acids in the plasma membrane of boar spermatozoa (Johnson et al., 1969).

During cryopreservation, PUFAs decrease dramatically due to lipid peroxidation. This is initiated when the spermatozoa is attacked by reactive oxygen species (ROS) (De Lamirande and Gagnon, 1992; Sikka et al., 1995). In mammals, the major sources of ROS formation include leucocyte, defective and dead spermatozoa (Aitken et al., 1994; Silva, 2006). The excessive ROS formation influence sperm motility, mid-piece abnormalities and spermoocyte fusion (Chatterjee et al., 2001; Agarwal et al., 2005).

The supplement of antioxidant compounds and some fatty acid to the semen extender, to minimize ROS formation and protect the plasma membrane function, have been used in many species (Peña et al., 2003; Gadea et al., 2004; Roca et al., 2004; 2005; Maldjian et al., 2005). It has been demonstrated that the proportion of DHA was significantly higher in the semen diluted with an extender supplemented with n-3 enriched hen egg yolks compared with the semen diluted with normal hen egg yolks (Maldjian et al., 2005). However, no study has been demonstrated clearly whether or not the supplement of DHA could improve the quality of the boar spermatozoa after cryopresevation. Rooke et al. (2001) found that DHA supplement in the boar feed increase progressive motility and normal acrosome and decrease abnormal spermatozoa. Recently, Kaeoket et al. (2008) reported that the supplement of DHA-enriched fish oil improved the FT boar semen quality. It has been shown that the supplement of cryoprotective agents (e.g., glycerol and Equex®), cholesterol analogue (Zeng and Terada, 2001) and antioxidants (e.g., Vitamin E, alpha-tocopherol, glutathione, taurine, cysteine, butylated hydroxytoluene, superoxide dismutase and catalase) in the semen extenders does improve the freezing ability of spermatozoa of many species such as stallion (Aurich et al., 1997; Ball et al., 2001), bull (Beconi et al., 1993; Bilodeau et al., 2001), ram (Uysal and Bucak, 2007; Bucak et al., 2007), avian (Donoghue and Donoghue, 1997), boar (Cerolini et al., 2000) and some wildlife (Leibo and Songsasen, 2000). Studies have demonstrated that the supplement of alpha-tocopherol (Peña et al., 2003), butylated hydroxytoluene (Roca et al., 2004), superoxide dismutase and catalase (Roca et al.,


**Author details**

Corneliu Octavian Dumitru1 \*, Gottfried Schwarz1 , Fabien Castel2 , Jose Lorenzo3 and Mihai Datcu1,4

1 German Aerospace Center (DLR), Remote Sensing Technology Institute, Wessling, Germany

2 Atos Integration S.A.S (ATOS FR), Toulouse, France

3 Atos Spain S.A. (ATOS ES), Madrid, Spain

4 Politehnica University of Bucharest (UPB), Bucharest, Romania

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[14] Datcu M, Dumitru CO, Schwarz G, Castel F, Lorenzo J. Data Science Workflows for the CANDELA Project. 2019. Available from: https://www. bigdatafromspace2019.org [Accessed:

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[18] DIAS Platform. 2019. Available from: https://www.copernicus.eu/ news/upcoming-copernicus-dataand-information-access-services-dias

[19] Dumitru C, Schwarz G, Datcu M. Land cover semantic annotation derived from high-resolution SAR images. The IEEE Journal of Selected Topics in Applied Earth Observations and Remote

[20] Earth Observation image Librarian (EOLib). 2019. Available from: http:// wiki.services.eoportal.org/tiki-index. php?page=EOLib [Accessed: April 2019]

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*Artificial Intelligence Data Science Methodology for Earth Observation DOI: http://dx.doi.org/10.5772/intechopen.86886*


Author details

Syed Ali Raza Naqvi1 \*, Sana Nadeem1 , Sana Komal1 , Syed Ali Asad Naqvi2 , Muhammad Samee Mubarik3 , Sajid Yaqub Qureshi3 , Shahzad Ahmad3 , Ali Abbas1 , Muhammad Zahid4 , Naeem-Ul-Haq Khan1 , Syed Shujat Raza1 and Nosheen Aslam5

1 Department of Chemistry, Government College University, Faisalabad, Pakistan

2 Department of Geography, Government College University, Faisalabad, Pakistan

3 Department of Wild and Fisheries, Government College University, Faisalabad, Pakistan

4 Department of Chemistry, University of Agriculture, Faisalabad, Pakistan

5 Department of Biochemistry, Government College University, Faisalabad, Pakistan

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**Advanced Materials for Clothing and Textile Engineering**

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**1. Introduction**

Cluster analysis [1] is a robust tool for exploring the underlining structures in data and grouping them with similar objects called clusters. Cluster analysis found applications in different fields ranging from the main task of data mining applications [2] such as scientific data exploration, spatial database applications, web analysis, marketing, medical diagnostics, computational biology, etc., to statistical data analysis that is used in many fields including machine

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

learning, pattern recognition [3], image analysis [4], information retrieval [5], and bioinformatics [6]. There are different algorithms related to neural networks; the most popular are K-means, the self-organizing map (SOM), neural gas (NG), and growing neural gas (GNG) [7, 8].

It adapts the reference vectors (prototype vectors) "*wi*

, *wi*1

(*x*, *w*) is the ranking index associated with each weight *wi*

for *k* = 1, 2, …,*N* − 1; ‖*x* − *wj*‖ < ‖*x* − *wik*‖, where *wik*

tors *wci*(*i* 0 , *i* 1 , …,*i*

*wi*0

*wi*1

*k i*

where:

*h*(.,.

*hλ*( *k i*

for *hλ*

of the position ranking (*wi*0

is second adjacent to *x*

The updating step of adjusting *wi*

extent is represented by {*ε*(*t*) = *εi*

(*v*, *w*) ∈ [0, 1]: considers the *wi*

∆*wi* = *ε*(*t*).*hλ*(*ki*

is adjacent to *x*.

ment within the network. NG not just adapts the winner vector for a specific input vector as a single-layered soft competitive learning neural network, but also updates the residual reference vectors according to the input vector nearness using a soft-max updating rule [21]. The main advantages of NG network [22] are: (1) lower distortion error than other clustering algorithms (k-means, maximum-entropy and SOM), (2) faster assemblage due to low distor-

The NG algorithm is represented by the dependence of updating strengths for *c* reference vec-

, …,*wik*) of the reference vectors *wci*

(*x*, *w*)).(*x* − *wi*

*ε*(*t*) ∈ [0, 1]: the learning rate (step size) that characterize the total range of the variation. This

(*k*) ∈ [0, 1], the exponential form exp(−*k*/*λ*) was proposed [22] to obtain the best extensive

*λ*: finds the number of reference vectors that significantly change their positions in the updat-

The NG algorithm is widely related to the structure of fuzzy clustering methods [23]. So, NG

all the reference vectors *wi* (*i* = 1, 2, …,*c*)instead of using *uij* (2 ≤ *i* ≤ *c*, 1 ≤ *j* ≤ *N*) in FCM algorithm. This algorithm is based on solving a cost function using iterative methods plus the familiarity with linear optimization methods, essentially the gradient descent method and Newton's

∫*P*(*x*) *hλ*(*ki*

*i*

within the input extent.

ing steps and usually individually decrease with the iteration step *t* as: *λ*(*t*) = *λi*

2*C*(*λ*) ∑ *i*=1 *c*

): deterministic function with some regularity condition imposed on it.

. (*εf* /*εi*) *t*/(Max\_ter)

result compared to other options like the Gaussian function.

method. Therefore, the NG cost function to optimize [22] is:

of repetitions and the repetition step respectively.

used the uncertainty of the relationship value (*hλ*(*k*

*Eng* = \_\_\_\_\_ 1

*N*−1) on their position ranking. If the input vector is presented by *x*, the definition

Performance Assessment of Unsupervised Clustering Algorithms Combined MDL Index

.

according to a Hebb-like learning rule is given by:

will be:

is the reference vector, which has *k* vectors *wj*

), *i* = 1, 2, …,*c* (1)

. (*λf* /*λi*) *t*/(Max\_iter) .

(*x*, *w*)))/(*C* (*λ*))to set each input vector "*x*" to

(*x*, *w*))‖*x* − *wi*‖2 (2)

}, so Max\_iter, so and *t* denote the maximum number

.

177

tion errors, (3) submission a stochastic gradient descent on a specific energy surface.

" without any fixed topological arrange-

http://dx.doi.org/10.5772/intechopen.74506

The goal of this work is to present a comparison among neural gas (NG), growing neural gas (GNG), and robust growing neural gas (RGNG) approaches that are related to neural networks, as well as design a new simulation tool for the purpose of education and scientific research using unsupervised learning methods. Due to the difficulty in introducing these algorithms in literature, the three techniques have been presented using a simple graphical user interface (GUI) model. Alziarjawey et al. [9] introduced an application of Matlab GUI in the medical field using the ECG signal for heart rate monitoring and PQRST detection. They introduced another application by developing a software package based on GUI, which consists of two modules using many important methods derived from linear algebra [10]. Aljobouri et al. [11] designed an educational tool for biosignal processing and medical imaging using a GUI package. The user friendly package explained in this work can be used easily by: choosing any method, changing the predefined parameters for each algorithm and comparing the results. Hence, it can be used without any programming knowledge. The interested reader may find more technical details in our previous reports and publications [12, 13].

The current study is organized as follows: Section 2 provides the unsupervised clustering algorithms. Case studies are described in Section 3. Sections 4 and 5 present the experimental implementation on the synthetic dataset and clustering package design, respectively. Finally, Section 6 concludes the paper and introduces future work.


**2.5 Effect of Vn on sperm penetration of the cumulus oophorus**

*In vitro* matured COCs were randomly assigned to 4 groups (3 replicates). Half of the oocytes (2 groups) were denuded by vortexing (cumulus denuded or CD) and the other half (2 groups) were kept cumulus-enclosed (CE). Both CD and CE oocytes were fertilized under control conditions (in standard fertilization medium) and in the presence of 500 nM Vn (Tanghe *et al.* 2004*b*). The oocytes were co-incubated with sperm at a concentration of 106 sp mL-1 for 20 h (39°C; 5% CO2). Each group contained 76 to 100 oocytes. Prior to fixation the presumed zygotes were vortexed to remove excess spermatozoa. After staining, the oocytes were evaluated for fertilization and polyspermy.


*4.2. Performance Analysis*

As mentioned above, the CRLB traces out a lower bound for minimum possible variance that an unbiased estimator can achieve. Next, we will analyze the efficiency of the proposed solution by comparing its covariance matrix with the benchmark, i.e., CRLB. For derivation simplicity, we would compare their inverse, rather than directly compare the two separately. The CRLB has been presented

in (33). By invoking the matrix inversion lemma [32] to (33) and using the definitions of **X** and **Y**, we have after mathematical simplifications,

$$\mathbf{CRLB}\_{\mathbf{c}} \left(\mathbf{u}^{o}\right)^{-1} = \left(\frac{\partial \mathbf{r}^{o}}{\partial \mathbf{u}^{o}}\right)^{\mathrm{T}} \mathbf{Q}\_{\mathbf{r}}^{-1} \left(\frac{\partial \mathbf{r}^{o}}{\partial \mathbf{u}^{o}}\right) - \left(\frac{\partial \mathbf{r}^{o}}{\partial \mathbf{u}^{o}}\right)^{\mathrm{T}} \mathbf{Q}\_{\mathbf{r}}^{-1} \left(\frac{\partial \mathbf{r}^{o}}{\partial \mathbf{s}^{o}}\right) \widetilde{\mathbf{Z}}^{-1} \left(\frac{\partial \mathbf{r}^{o}}{\partial \mathbf{s}^{o}}\right)^{\mathrm{T}} \mathbf{Q}\_{\mathbf{r}}^{-1} \left(\frac{\partial \mathbf{r}^{o}}{\partial \mathbf{u}^{o}}\right) \tag{83}$$

where the expression of **Z** has been given in (32).

On the other hand, using (82), (81), (69), (68) and (52) successively, we can reformulate the inverse of cov(**u**) as

$$\text{cov}(\mathbf{u})^{-1} = \mathbf{G}\_3^T \mathbf{Q}\_\mathbf{r}^{-1} \mathbf{G}\_3 - \mathbf{G}\_3^T \mathbf{Q}\_\mathbf{r}^{-1} \mathbf{G}\_4 \mathbf{Z}^{-1} \mathbf{G}\_4^T \mathbf{Q}\_\mathbf{r}^{-1} \mathbf{G}\_3 \tag{84}$$

where **G**3 = **B**−1 1 **G**1**B**−1 2 **G**2, **G**4 = **B**−1 1 **D**1, and **¯ Z** = **Q**−1 **s** + **G**T 4**Q**−1 **r G**4 + **G**T 0 (**GcQcG**T **c** + **Qr**c) −1 **G**0.

Comparing (83) with (84), we observe that **CRLB**c(**u**o) −1 and cov(**u**) −1 are identical in structure. Next, we proceed to prove their equivalency under the following conditions:

(C1) **Δs**t,*m***c**o *k* − **s**o t,*m*, **Δs**t,*m***s**o t,*m* − **s**o r,*n*, **Δs**r,*n***c**o *k* − **s**o r,*n*, **Δs**r,*n***s**o t,*m* − **s**o r,*n*, and **Δc***k***c**o *k* − **s**o t,*m*, **Δc***k***c**o *k* − **s**o r,*n*, for *k* = 1, 2, ... ,*K*, *m* = 1, 2, ... , *M* and *n* = 1, 2, ... , *N*;

(C2) **Δ***rm*,*n***u**o − **s**o t,*m*, **Δ***rm*,*n***u**o − **s**o r,*n*, **Δ***rm*,*n***s**o t,*m* − **s**o r,*n*, and **Δs**t,*m* − **Δs**ˆt,*m* **u**o − **s**o t,*m*, **Δs**r,*n* − **Δs**ˆr,*n***u**o − **s**o r,*n* for *m* = 1, 2, ... , *M* and *n* = 1, 2, ... , *N*;

The condition C1 implies the transmitter/receiver position error and the calibration target position error are negligibly small compared with the range between the calibration target and the transmitter/receiver. The condition C2 implies the BR measurement noise and the error in the refined transmitter/receiver position are negligibly small compared to the range between the calibration target and the transmitter/receiver. Using the conditions C1 and C2, we obtain, after some involved algebraic manipulations, that

$$\mathbf{G}\_3 = \frac{\partial \mathbf{r}^0}{\partial \mathbf{u}^0}, \mathbf{G}\_4 = -\frac{\partial \mathbf{r}^0}{\partial \mathbf{s}^0}, \mathbf{G}\_0 = -\left(\frac{\partial \mathbf{r}\_c^0}{\partial \mathbf{s}^0}\right), \mathbf{G}\_\mathbf{c} = -\left(\frac{\partial \mathbf{r}\_c^0}{\partial \mathbf{c}^0}\right) \tag{85}$$

By this point, we can draw the conclusion that

$$\text{cov}(\mathbf{u})^{-1} = \mathbf{CRLB}\_{\mathbf{c}}(\mathbf{u}^{\diamond})^{-1} \tag{86}$$

That is, the proposed solution accomplishes the CRLB accuracy if the two conditions C1 and C2 are satisfied. In reality, localization scenarios, which satisfy the conditions C1 and C2, are not rare. These two conditions can be satisfied if the unknown target and the calibration targets are far from the transmitters and receivers, if not these conditions can still be satisfied if the BR measurement noise and the transmitter/receiver/calibration target position errors are sufficiently small.


Author details

Mostafa Kheshti\* and Lei Ding

\*Address all correspondence to<EMAIL_ADDRESS>
Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Ministry of Education, Jinan, China