Quantity and Transaction Value of Cross-border M&As in BRICS-T Countries between 1992 and 2016 (value in billion dollars).
\r\n\t
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Furthermore, firm-specific activities such as mergers & acquisitions (M&As), declaring loss, an important exports deal have positive or negative effect on the firms’ market value according to the nature of the event.
\nAim of the study is to investigate how economic crisis originated in developed countries have affected developing economies. Therefore, we examined the mergers & acquisitions activities of banking sector in BRICS-T countries. Lehman Brothers’ crash in September 2008 is assumed to be the trigger of the global financial crisis [1]. With this reasoning, we analyzed cumulative abnormal returns in precrisis (January 2004–September 2008) and postcrisis (November 2008–December 2013) periods to check if there are any differences between these periods. Brazil has negative mean cumulative abnormal returns in pre crisis period as well as the post crisis period. Russia has negative mean cumulative abnormal returns in precrisis period while mean cumulative abnormal returns for Russia are positive in postcrisis period. India has negative mean cumulative abnormal returns in precrisis period, which switches to positive in postcrisis period. China, in the opposite, has positive mean cumulative abnormal returns before crisis but negative mean cumulative abnormal returns after crisis. The mean cumulative abnormal returns in South Africa are positive in the precrisis period and negative in the post crisis period. Turkey has positive mean cumulative abnormal returns in both periods.
\nSection 2 introduces a literature review about M&As in BRICS-T countries in three different parts. First, literature about M&As during financial crisis is mentioned. Then, literature on abnormal returns in M&As during financial crisis is examined. Finally, financial overview of BRICS-T countries is investigated and shared. In Section 3, data and methodology are mentioned as well as the empirical results. The study is concluded with Section 4.
\nLiterature reviews has three sub-parts. In first section, M&As during financial crisis, particularly recent financial crisis, are mentioned. In second section, abnormal returns in cross-border M&As during financial crisis are investigated. In final section, there is a summary of financial overview of BRICS-T countries.
\nIt has been argued that M&As1 are closely related to the stock markets’ welfare. [2] suggests that M&A activities are not closely related to the business cycle but the state of the economy. In other words, if economy is in a good condition, stock markets have desired conditions for the firms to raise capital and grow their profitability [3]. On the other hand, in the opposite situation, that is, if economy is in a narrow condition, firms tend to be more conservative about M&A.
\nM&As are assumed to be a way of foreign direct investment2 (FDI) and they follow a wave path due to economic state [4]. M&A wave between 2003 and 2007 (precrisis period) indicated that cross-border M&As had increased compared to the recession periods. Especially in 2005, there had been a number of FDI flows to the developed countries, and the quantity and the value of M&As were the highest since 2000 [4]. In 2006, M&A activities started to rise in emerging countries. Until the second half of 2007, M&As continued increasing but after that they started to fall and got even worse in the first half of 2008 compared to 2007 [4].
\nCollapse of Lehman Brothers is assumed to be the trigger of financial crisis in September 2008 [1]. Hence, economic crisis caused a drastic fall in M&As.
\nOn the other hand, according to [5], financial crisis originated in developed countries in 2008 did not have the same large impact on emerging economies. The crisis emerged in United States, spread immediately to Europe but it only affected the specific regions and countries so harsh.
\nWhile some banks utilized M&A as an expansion strategy, some banks used it into their advantage during crisis. Banks in emerging economies such as China, Brazil and Russia acquired undervalued banks in developed countries as their prices in the stock markets fell [3]. Banks that are in healthy conditions in terms of capital and liquidity took the advantage of increasing their market share through M&As [6].
\nThere is a broad literature about generating abnormal returns3 through M&As. However, there are not many studies in investigating the abnormal returns during 2007–2008 crisis. In addition, the results of the studies are mixed.
\nThe research that does not include 2008 crisis is as follows. [7] examined 507 cross-border M&As between 1985 and 1998. They found negative and significant abnormal returns. In another study, [8] investigated cross-border M&A activities of 15 international banks between 1982 and 1987. They concluded that there had been negative and significant abnormal returns. In a single country study, [9] investigated M&As in U.S. between 1989 and 1999. He found that U.S. targets earn significantly positive abnormal returns while U.S. bidders’ wealth gains are insignificant. In another research conducted in the U.S., [10] used the data for bank-holding companies in United States between 1980 and 1990 in order to determine abnormal returns. Results revealed significantly negative abnormal returns. On the other hand, [11] found that there had been significant positive returns using 216 large publicly traded U.S. bank M&As between 1987 and 1999. In a cross-border study, [12] employed 73 cross-border banks M&As (from advanced economies to emerging economies) between 1998 and 2005. They found significant and positive abnormal returns. In Europe, [13] achieved the existence of positive abnormal returns for the shareholders of target banks cross-border M&As between 1989 and 1996. In another study in Europe, [14] found positive abnormal returns using the data from European banks between 1988 and 1997. [15] gathered the data for 98 large M&As in Europe between 1985 and 2000. They found that domestic M&As created positive returns. [16] suggested that value created would be larger if the target firm was in advanced economy using 425 cross-border M&As in India between 2000 and 2007.
\nIn order to investigate the effects of Asian crisis, [17] used a data of nine emerging countries namely Argentina, Brazil, Chile, Indonesia, Philippines, South Korea, and Thailand between 1988 and 2002. They concluded that acquirer firms show no significant difference in abnormal returns pre and postcrisis periods. On the other hand, [18] studied the M&As in eight East Asian countries between 1997 and 2003 in order to determine market reaction to M&As during Asian crisis. Their results showed that market reaction was negative in Indonesia, Malaysia, the Philippines, South Korea, and Thailand where the bank structure was less well settled.
\nThe results of the studies that investigate the effects of 2008 crisis are mixed. [19] utilized the M&A data in Europe between 2007 and 2010 to evaluate whether M&A differed in crisis period. They concluded that there were insignificant abnormal returns on the event date. On the other hand, abnormal returns were generated positively at the completions. However, [20] used 80 M&As in UK, USA, Canada, Germany, Japan, and France between 1999 and 2009 to determine stock returns of bidder firms. Abnormal returns precrisis and postcrisis period was not significantly different from zero. In another research, [3] examined 883 cross-border M&A deals in banking sector between 2004 and 2012. They concluded that only in M&As from emerging countries targeting developed countries, returns of the shareholders were significantly positive after the crisis. Finally, [21] gathered the M&A data for 20 emerging countries namely BRICS-T countries and Chile, Colombia, Czech Republic, Egypt, Hungary, Indonesia, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Taiwan and Thailand between 1997 and 2013. They concluded that M&As created positive abnormal returns. In addition, they found out that abnormal returns had increased after crisis for target firm’s stock.
\nIn conclusion, results of the studies are mixed and they change according to the period.
\nIn previous sections, it has been mentioned that there exists M&A waves. According to [22], there had been six M&A waves before the 2008 crisis, which are 1887–1907, 1919–1933, 1955–1975, 1980–1989, 1992–2002, and finally 2003–2007.
\n\n\nTable 1\n shows the quantity and transaction value of cross-border M&As in BRICS-T countries between 2002 and 2016 [23]. In Brazil, cross-border M&As have value of $17 billion in 2003. It increased by 65% in 2004 and reached to $26 billion. In 2005, there is a decrease by 58% and the value is $15 billion. Then in 2006, there is a jump in the value and it has reached to $74 billion. In 2007, there is a fall by 72% in value. M&As have the peak value in 2008 during the precrisis period. There is a drastic fall in value in 2009 due to crisis. In 2010, M&As have the peak value in postcrisis period. It started declining afterwards. In Russia, the value of cross-border M&As is $35 billion in 2003, and in 2004, the value has declined by 72%. In 2005, the value has jumped to $63 billion and during the precrisis period, M&As have the peak value in 2007. In 2008, the value has decreased by 52% and in 2009 the decrease is 43%. Then, the value has been tripled in 2010. Cross-border M&As have their peak value in 2012 in postcrisis period. In India and China, cross-border M&As have the peak value in 2007 in precrisis period and in 2010 in postcrisis period. In India, there is a jump in cross-border M&A value in 2005 (4.5 times higher than 2004), and in China, there is a high increase in 2005 as well (6.25 times higher than 2004). In South Africa, values of M&As follow an increasing pattern until 2007. M&As have their peak value in 2007 in precrisis period and in 2009 in postcrisis period. Finally, in Turkey, M&As have their peak value in 2005 in precrisis period and 2012 in postcrisis period.
\n\n | Brazil | \nRussia | \nIndia | \nChina | \nSouth Africa | \nTurkey | \n||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Quantity | \nValue | \nQuantity | \nValue | \nQuantity | \nValue | \nQuantity | \nValue | \nQuantity | \nValue | \nQuantity | \nValue | \n|
1992–2002 | \n2627 | \n213 | \n1942 | \n35 | \nn/a | \nn/a | \n410 | \n9 | \n3541 | \n140 | \n587 | \n11 | \n
1999–2002 | \n\n | \n | \n | \n | 2578 | \n29 | \n\n | \n | \n | \n | \n | \n |
2003 | \n209 | \n17 | \n488 | \n35 | \n706 | \n6 | \n53 | \n2 | \n260 | \n11 | \n82 | \n1 | \n
2004 | \n269 | \n26 | \n398 | \n25 | \n762 | \n8 | \n101 | \n4 | \n241 | \n27 | \n64 | \n3 | \n
2005 | \n269 | \n15 | \n468 | \n63 | \n1251 | \n36 | \n96 | \n25 | \n244 | \n16 | \n120 | \n31 | \n
2006 | \n373 | \n74 | \n654 | \n52 | \n1446 | \n34 | \n117 | \n14 | \n338 | \n28 | \n167 | \n21 | \n
2007 | \n857 | \n53 | \n966 | \n159 | \n1504 | \n56 | \n210 | \n40 | \n289 | \n34 | \n238 | \n23 | \n
2008 | \n932 | \n105 | \n1718 | \n82 | \n1400 | \n49 | \n204 | \n21 | \n436 | \n26 | \n267 | \n19 | \n
2009 | \n497 | \n71 | \n3285 | \n36 | \n1293 | \n41 | \n245 | \n48 | \n369 | \n33 | \n183 | \n7 | \n
2010 | \n689 | \n160 | \n3684 | \n109 | \n1328 | \n60 | \n288 | \n53 | \n370 | \n27 | \n245 | \n23 | \n
2011 | \n816 | \n93 | \n3211 | \n88 | \n1042 | \n35 | \n266 | \n46 | \n356 | \n21 | \n269 | \n13 | \n
2012 | \n801 | \n69 | \n2532 | \n115 | \n1070 | \n37 | \n261 | \n38 | \n382 | \n16 | \n321 | \n24 | \n
2013 | \n612 | \n69 | \n2021 | \n67 | \n955 | \n32 | \n264 | \n48 | \n333 | \n11 | \n364 | \n19 | \n
2014 | \n559 | \n56 | \n1915 | \n18 | \n1084 | \n31 | \n302 | \n44 | \n402 | \n20 | \n363 | \n16 | \n
2015 | \n669 | \n50 | \n1819 | \n32 | \n1241 | \n51 | \n475 | \n68 | \n448 | \n45 | \n345 | \n18 | \n
2016 | \n613 | \n45 | \n1819 | \n40 | \n1271 | \n49 | \n671 | \n132 | \n418 | \n22 | \n226 | \n7 | \n
Quantity and Transaction Value of Cross-border M&As in BRICS-T Countries between 1992 and 2016 (value in billion dollars).
In this part, first, data, methodology, and the hypothesis are explained. Then empirical results are represented.
\nOur study uses daily market index returns, daily stock returns, and M&A announcement dates (event date) between January 2003–September 2008 and November 2008 and December 2013. We utilize the data from Bloomberg database for cross-border bank M&A activities in Brazil, Russia, India, China, South Africa, and Turkey. Our data consists of cross-border M&As with a transaction value over $100 million.
\nEvent study is employed for the analysis. Event studies aim to determine whether there are abnormal returns around the date an event is announced to the market. Abnormal returns are the returns that are less or more than normal returns when the related event is announced. These returns are usually related with the performance of the market index returns [24, 25]. The event is the M&A announcement date. There could be different event windows, which include the announcement date. In this study, we investigate abnormal returns for different event window lengths:
Two days before and two days after the event date (−2, +2)
A day before and a day after the event date (−1, +1)
The event day and a day after the event date (0, +1)
We choose market model in order to estimate market α4 and β5 over a prediction period, which is 128 days prior to and 9 days prior to event date, that is, (−128, −9). The market model is as follows:
\n\n\n
Then, abnormal return (AR) is calculated using predicted \n
\n\n
Later, average aggregate abnormal return (AAR) is calculated:
\nAfter that, by adding daily abnormal returns up, cumulative abnormal returns are obtained:
\nHere, \n
Finally, average aggregate cumulative abnormal return is calculated (AACAR):
\nand \n
This section introduces the empirical results. First, abnormal returns for the entire period are shown without separating the before/after crisis periods in Section 3.2.1. Then, abnormal returns for pre / post crisis periods are given in Sections 3.2.2 and 3.2.3, respectively.
\nThis part introduces the aggregate results, which means that abnormal returns of the M&A activities are included to the analysis without considering pre- and post-crisis periods. A total of 36 banks with M&A transaction values over $100 million are taken into consideration.
\n\n\nTable 2\n shows average aggregate daily abnormal returns two days before after the event date. The AARs before and on the announcement date are negative and significant at 5% while AARs are positive and significant at 5% significance level. The AARs increase through the event window. In other words, the AAR two days before the event day is −0.043, it is −0.040 on the day before the event day, and it is larger but still negative on the event day. One day after the event day, the AAR reaches to the largest value. There are excess returns on the M&As. On the second day, AAR decreases again.
\nEvent day\n1\n\n | \nAverage abnormal returns (%) (AAR) | \n
---|---|
−2 | \n−0.043 | \n
−1 | \n−0.040 | \n
0 | \n−0.029 | \n
1 | \n0.022 | \n
2 | \n0.001 | \n
AARs for the Related Event Associated with M&A Activities.
M&A announcement day.
\n\nTable 3\n shows aggregate CARs for the related event window. In 5-day event window (−2, +2), CAR is −0.042, and it is statistically significant at 5%. Then, in 3-day event window (−1, +1), CAR increases to −0.018, and this value is statistically significant at 5%. Finally, in 2-day event window, CAR increases to −0.007, and it is statistically significant at 5%.
\nEvent window\n1\n\n | \nAverage CAR (%) | \n
---|---|
−2, +2 | \n−0.042 | \n
−1, +1 | \n−0.018 | \n
0, +1 | \n−0.007 | \n
CARs for the Related Event Windows Associated with M&A Activities.
Time period that includes several days prior and after the event.
\n\nTable 4\n shows the distribution of 5-day CARs. The results show that there are negative abnormal returns in Brazil and Russia, while there are positive cumulative abnormal returns in China, India, South Africa, and Turkey. The results are significant at 5% significance level. In terms of 5-day CARs, Brazil has the lowest CAR among other countries, and it is followed by Russia. Although, there is positive CAR in India, South Africa, Turkey, and China have more CAR than India. CARs in South Africa and Turkey are very close. China has the largest CAR among these countries in 5-day event window.
\nName of the country | \nCAR (−2, +2) (%) | \n
---|---|
Brazil | \n−0.287 | \n
China | \n0.017 | \n
India | \n0.001 | \n
Russia | \n−0.006 | \n
South Africa | \n0.013 | \n
Turkey | \n0.014 | \n
Distribution of 5-day CARs (−2, +2) in BRICS-T countries.
\n\nTable 5\n shows the distribution of 3-day CARs. Brazil, Russia, and India have negative CARs while China, South Africa, and Turkey have positive CARs. The results are significant at 5% significance level. In 3-day event window, Brazil has the least CAR among other countries and it is followed by India and Russia. While India has slightly positive CAR in 5-day event window, it has negative CAR in 3-day event window. South Africa and Turkey have positive CAR in 3-day event window as well as the 5-day event window. Turkey has the largest CAR among other countries in 3-day event window.
\nName of the country | \nCAR (−1, +1) (%) | \n
---|---|
Brazil | \n−0.129 | \n
China | \n0.007 | \n
India | \n−0.012 | \n
Russia | \n−0.011 | \n
South Africa | \n0.012 | \n
Turkey | \n0.026 | \n
Distribution of 3-day CARs (−1, +1) in BRICS-T countries.
\n\nTable 6\n shows the distribution of 2-day CARs. Brazil, Russia, and India have negative CARs while China, South Africa, and Turkey have positive CARs. The results are significant at 5% significance level. In 2-day event window, Brazil has the least CAR and it is followed by Russia and India. Turkey has the largest CAR among other countries in 2-day event window as well as 2-day event window.
\nName of the country | \nCAR (0, +1) (%) | \n
---|---|
Brazil | \n−0.071 | \n
China | \n0.011 | \n
India | \n−0.009 | \n
Russia | \n−0.015 | \n
South Africa | \n0.014 | \n
Turkey | \n0.026 | \n
Distribution of 2-day CARs (0, +1) in BRICS-T countries.
\n\nTable 7\n shows the distribution of mean CARs. On an average, Brazil, India, and Russia have negative cumulative abnormal returns and China, South Africa, and Turkey have positive cumulative abnormal returns between 2003 and 2013 for banking industry. The results are significant at 5% significance level.
\nName of the country | \nMean CAR (%) | \n
---|---|
Brazil | \n−0.162 | \n
China | \n0.012 | \n
India | \n−0.007 | \n
Russia | \n−0.011 | \n
South Africa | \n0.013 | \n
Turkey | \n0.022 | \n
Distribution of mean CARs in BRICS-T countries.
This section introduces the abnormal return analysis results of M&As in banking sector during the pre-crisis period, that is, between September 2003 and November 2008. In this manner, 22 banks M&As with a M&A transaction value more than $100 million have been investigated.
\n\n\nTable 8\n shows AARs for pre-crisis period. There are negative AARs before and on the event date. However, there are positive abnormal returns after the announcement date. The results are significant at 5% significance level. Two days before the announcement day AAR is −0.057 and 1 day before the event day it increases to −0.051. On the announcement day, AAR increases to −0.037 and 1 day after the event day, it turns to positive and has its peak value. In other words, the AARs follow an increasing path until 1 day after the announcement day. Two days after the announcement date, it decreases but it is still positive. This figure is very similar to the aggregate case in Section 3.2.1.
\nEvent day | \nAverage abnormal returns (%) | \n
---|---|
−2 | \n−0.057 | \n
−1 | \n−0.051 | \n
0 | \n−0.037 | \n
1 | \n0.032 | \n
2 | \n0.004 | \n
AARs for the Related Event Windows Related to M&A Activities Before Crisis (2003-2008/9).
\n\nTable 9\n shows CARs for 5-day, 3-day, and 2-day event windows. There are negative cumulative abnormal returns in precrisis period. The values tend to increase as the event window gets narrower to the event date. The results are significant at 5% significance level.
\nEvent window | \nAverage CAR (%) | \n
---|---|
−2, +2 | \n−0.053 | \n
−1, +1 | \n−0.019 | \n
0, +1 | \n−0.006 | \n
CARs for the Related Event Windows in Response to M&A Activities Before Crisis (2003-2008/9).
\n\nTable 10\n shows 5-day CARs in BRICS-T countries. In Brazil, India, and Russia, CARs are negative and significant at 5% level. In China, South Africa, and Turkey, CARs are positive and significant at 5% level. Brazil has the least CAR and it is followed by India and Russia in 5-day event window. China has the largest CAR in 5-day event window in precrisis period and it is followed by South Africa and Turkey. Note that Turkey had the largest CAR in the aggregate case.
\nName of the country | \nCAR (−2, +2) (%) | \n
---|---|
Brazil | \n−0.368 | \n
China | \n0.064 | \n
India | \n−0.025 | \n
Russia | \n−0.039 | \n
South Africa | \n0.041 | \n
Turkey | \n0,009 | \n
Distribution of 5-day CARs (−2, +2) in BRICS-T countries before crises (2003–2008/2009).
\n\nTable 11\n shows the distribution of 3-day cumulative abnormal returns in BRICS-T countries in precrisis period. In Brazil, India, and Russia, CARs are negative and significant at 5% level. In China, South Africa, and Turkey, cumulative abnormal returns are positive and significant at 5% level. Brazil has the least CAR in 3-day event window during the precrisis period. However, the CAR value has increased with respect to 5-day event window. Russia and India follow Brazil and their CAR values have decreased compared to 5-day event window. China still has the largest CAR but the values have decreased in 3-day event window. CARs in South Africa and Turkey have increased in 3-day event window
\nName of the country | \nCAR (−1, +1) (%) | \n
---|---|
Brazil | \n−0.157 | \n
China | \n0.057 | \n
India | \n−0.035 | \n
Russia | \n−0.044 | \n
South Africa | \n0.047 | \n
Turkey | \n0.018 | \n
Distribution of 3-day CARs (−1, +1) in BRICS-T countries before crises (2003–2008/2009).
\n\nTable 12\n shows the distribution of CARs in two-day event window. Brazil, India, and Russia have negative abnormal returns while China, South Africa, and Turkey have positive abnormal returns. The results are significant at 5% significance level. Brazil has the least CAR in 2-day event window. This value of CAR in 2-day event window is larger than the value of CAR in 3-day event window. CAR for Russia in 2-day event window is less than CAR in 3-day event window and CAR for India in 3-day event window is larger than CAR in 2-day event window. China still has the largest CAR in 2-day event window and the value has increased compared to the 3-day event window. CAR in South Africa has increased while CAR in Turkey has decreased in 2-day event window with respect to 3-day event window.
\nName of the country | \nCAR (0, +1) (%) | \n
---|---|
Brazil | \n−0.077 | \n
China | \n0.067 | \n
India | \n−0.026 | \n
Russia | \n−0.061 | \n
South Africa | \n0.051 | \n
Turkey | \n0.013 | \n
Distribution of 2-day CARs (0, +1) in BRICS-T countries before crises (2003–2008/2009).
\n\nTable 13\n shows the distribution of mean CARs in BRICS-T countries for precrisis period. Accordingly, Brazil, India, and Russia generates negative and statistically significant abnormal returns while China, South Africa, and Turkey obtains positive cumulative abnormal returns between September 2003 and September 2008 for banking industry for M&A transactions with a value more than $100 million. Brazil has the least mean CAR and it is followed by Russia and India while China has the largest mean CAR and South Africa and Turkey follow it.
\nName of the country | \nMean CAR (%) | \n
---|---|
Brazil | \n−0.201 | \n
China | \n0.062 | \n
India | \n−0.029 | \n
Russia | \n−0.048 | \n
South Africa | \n0.046 | \n
Turkey | \n0.014 | \n
Distribution of mean CARs in BRICS-T countries before crises (2003–2008/2009).
This section introduces abnormal returns in M&As in the banking industry during the postcrisis period, that is, between November 2008 and December 2013. In this manner, 14 bank M&As with a M&A transaction value more than $100 million have been investigated.
\n\n\nTable 14\n shows the AARs for the related event window in postcrisis period. Two days before the announcement date, AAR is positive; 1 day before the announcement date AARs is negative; and on the event date, AAR is positive. One-day and 2-day after the event date, AARs are slightly negative. The results are significant at 5% significance level.
\nEvent day | \nAverage abnormal returns (%) | \n
---|---|
−2 | \n0.002 | \n
−1 | \n−0.002 | \n
0 | \n0.001 | \n
1 | \n−0.001 | \n
2 | \n−0.001 | \n
AAR for the Related Event Windows in Response to M&A Activities After Crisis (2008/11-2013).
\n\nTable 15\n shows CARs for the related event windows in postcrisis period. In 5-day event window, CAR is positive and in 3-day and 2-day event windows cumulative abnormal returns are negative. Average CAR has the largest value in 5-day event window, the least value in 3-day event window. The results are significant at 5% significance level.
\nEvent window | \nAverage CAR (%) | \n
---|---|
−2, +2 | \n0.002 | \n
−1, +1 | \n−0.004 | \n
0, +1 | \n−0.001 | \n
CARs for the Related Event Windows in Response to M&A Activities After Crisis (2008/11-2013).
\n\nTable 16\n shows distribution of the 5-day CARs in BRICS-T countries in postcrisis period. Brazil has the least CAR among other countries. South Africa and China follow Brazil. Note that South Africa had positive CAR in precrisis period. Russia has the largest CAR and it is followed by India and Turkey. Another remarkable points are that India had negative CAR in precrisis period and China had the largest positive CAR in precrisis period
\nName of the country | \nCAR (−2, +2) (%) | \n
---|---|
Brazil | \n−0.045 | \n
China | \n−0.007 | \n
India | \n0.026 | \n
Russia | \n0.050 | \n
South Africa | \n−0.044 | \n
Turkey | \n0.019 | \n
Distribution of 5-day CARs (−2, +2) in BRICS-T countries after crises (2008/2011–2013).
\n\nTable 17\n shows the distribution of 3-day CARs in BRICS-T countries in postcrisis period. South Africa now has the least CAR among other countries in 3-day event window. CAR value in Brazil does not change compared to the 5-day CAR but the CAR value in South Africa has decreased. CAR values in China and India have also decreased while CAR in Turkey has increased. Russia has the largest CAR among other countries and the value has increased.
\nName of the country | \nCAR (−1, +1) (%) | \n
---|---|
Brazil | \n−0.045 | \n
China | \n−0.018 | \n
India | \n0.010 | \n
Russia | \n0.055 | \n
South Africa | \n−0.059 | \n
Turkey | \n0.036 | \n
Distribution of three-day CARs (−1, +1) in BRICS-T countries after crises (2008/2011–2013).
\n\nTable 18\n shows the 2-day CARs in BRICS-T countries during postcrisis period. The figure is similar to the 3-day CAR case. South Africa has the least CAR and its value has not changed. The CAR value in China has increased slightly. CARs in India have decreased while the CARs in Russia and Turkey have increased.
\nName of the country | \nCAR (0, +1) (%) | \n
---|---|
Brazil | \n−0.052 | \n
China | \n−0.017 | \n
India | \n0.008 | \n
Russia | \n0.076 | \n
South Africa | \n−0.059 | \n
Turkey | \n0.042 | \n
Distribution of 2-day CARs (0, +1) in BRICS-T countries after crises (2008/2011–2013).
\n\nTable 19\n shows the distribution of mean CARs in BRICS-T countries in postcrisis period for M&A transactions with a value more than $100 million. Brazil, China, and South Africa have negative and statistically significant mean CARs while India, Russia, and Turkey have positive and statistically significant mean CARs. South Africa has the least mean CAR and Brazil and China follow it. Russia has the largest CAR and Turkey and India follow Russia.
\nName of the country | \nMean CAR (%) | \n
---|---|
Brazil | \n−0.047 | \n
China | \n−0.014 | \n
India | \n0.015 | \n
Russia | \n0.063 | \n
South Africa | \n−0.054 | \n
Turkey | \n0.032 | \n
Distribution of mean CARs in BRICS-T countries after crises (2008/2011–2013).
Economic activities have direct impact on firms operating in a country and M&A activities have a close relationship with the economic welfare. If stock markets have desired conditions, there are more M&A activities. When there is an economic recession, firms are more conservative about M&A activities.
\nAlthough many researchers have worked on the abnormal returns during M&As, there are only a few studies on capturing abnormal returns of M&As during financial crisis. In this manner, we investigate M&A activities with a transaction value more than $100 million in banking industry in BRICS-T countries before and after the financial crisis in 2008. Studies have shown that positive abnormal returns are generated after 2008 crisis in the emerging markets [3, 21].
\nAccording to our results, in precrisis period, Brazil has the least mean CAR with a value −0.201 among BRICS-T countries. Russia and India follow Brazil with CAR values −0.048 and −0.029, respectively. China has the largest mean CAR value, that is, 0.062. South Africa and Turkey follow Russia with mean CARs 0.046 and 0.014, respectively. In postcrisis period, now, South Africa has the least mean CAR among BRICS-T countries. Note that mean CAR in South Africa is positive before crisis and it is negative after crisis. Brazil still has negative mean CAR in postcrisis period with an increased value compared to precrisis period. China has negative mean CAR value, which is −0.014 in post crisis period. Mean CAR in China is positive in precrisis period and it is negative in postcrisis period and this figure is similar to South Africa case. Russia has the largest and positive mean CAR in postcrisis period. The value of mean CAR in Russia is negative in precrisis period and it is positive in postcrisis period with a value 0.063. India has positive mean CAR in postcrisis period with a value 0.015, while it is negative in precrisis period. Mean CAR in Turkey remains positive mean in postcrisis period and the value is increased to 0.032. In conclusion, Russia has negative mean CAR in precrisis period while mean CAR for Russia is positive in postcrisis period. This result is compatible with [3, 21]. India has negative mean CAR in precrisis period, which switches to positive in postcrisis period, which is compatible with previous research [21]. China, in the opposite, has positive mean CAR in precrisis period but negative mean CAR in postcrisis period. The mean CAR in South Africa is positive in the precrisis period and negative in the post crisis period. These results are in line with the previous research. This is due to the sample and the period differences. Turkey has positive mean cumulative abnormal returns in both pre- and postcrisis periods. Mean CARs are higher in the postcrisis period. This result supports the previous research [21].
\nIn further, we might conclude that 2008 crisis had a significant effect on M&As (over $100 million) in BRICS-T countries. Consequently, abnormal return analysis would give precious results for investigating M&As in the emerging financial markets.
\nTerpenes are chemical molecules synthesized from isoprene, 2-methyl-1,3 butadiene which are polymerized, thus obtaining one of nature’s most diversified families of secondary metabolites.
The chemical diversity of terpenes is determined by the polymerization capacity of isoprene; because of this their classification is linked to the addition of five carbons to the basic molecular unit. The biosynthesis of the chemical precursors of isoprene, dimethylallyl pyrophosphate (DMAPP) and isopentenyl pyrophosphate (IPP) is produced by two diversified metabolic routes, the mevalonate route (MEV) and the 2C-Methyl-D-erythirol-4-phosphate (MEP) route [1].
DMAPP and IPP are hemiterpenes and are responsible of forming the various subclasses of compounds that make up the terpenes. Additionally, these isoprene polymers can be linear or can form rings and adhere to their structure oxygen and nitrogen atoms. The approximate number of known terpenes is close to 55,000 compounds [2].
Traditionally they are classified as [3]:
Hemiterpenes. These are constituted by five carbon atoms and are the basic units of the terpenes, the best-known example is 2-methyl-1,3 butadiene or isoprene.
Monoterpenes. These are constituted by 10 carbon atoms, resulting from the union of two units of isoprene, which are abundant in essential oils. Some important substances are: pinene, myrcene, limonene, thujene, etc.
Sesquiterpenes. These are formed by 15 carbon atoms, which are the result of the junction of three units of isoprene, some examples are: bisabolene, zingiberene, germacrene, caryophyllene, etc.
Diterpenes. These are formed by 20 carbon atoms or four units of isoprene; some important compounds are retinol, taxol and phytol.
Triterpenes. Squalene and several phytosterols such as sitosterol stand out among the terpenes containing 30 carbon atoms or six units of isoprene.
Tetraterpenes. These are constituted by 40 carbon atoms and eight units of isoprene, many of them are dyes like carotenes, among these the most important are carotene, lycopene and bixin.
Polyterpenes. These are composed of more than 40 carbon atoms; they are often found in gums and latex of various plant species.
Essential oils are common secondary metabolites in vegetables. From 10 to 200 compounds can be found in an essential oil, and their main characteristic is their ability to evaporate at room temperature. The chemical variability in an oil is significant; however, its components can be classified into three large groups (Figure 1).
Main molecules of essential oils.
Terpenes are the majority group, being monoterpenes and sesquiterpenes the most abundant. These can be present as hydrocarbons, consisting of carbon and hydrogen, or can have various functional groups such as alcohols, thiols, aldehydes, ketones, and ethers.
The second group of importance is aromatic compounds, many of them with an important biological activity such as derivatives of cinnamaldehyde, thymol, anethole or carvacrol.
There is a third miscellaneous group in a lower proportion that groups various molecules such as hydrocarbons, aldehydes, ketones, esters, etc. Examples of these substances are isovaleraldehyde or dodecanal.
Essential oils are usually found in low concentrations in plant organisms, ranging from 0.1 to 1%. They can exceed this value as is the case of clove oil with up to 10%, and are present in all plant organs and leaves:
The extraction processes are diverse, depending on the part of the plant used; the simplest and most widespread is the extraction by distillation with steam current, which does not require expensive equipment. Other methods are mechanical extraction used mainly to obtain oil from citrus pericarps, extraction using solvents which is useful when components can be affected by high temperatures and extraction using a supercritical CO2 current, which does not need high temperatures while maintaining the chemistry of molecules, but it is very expensive to implement.
About 4000 species have been investigated by their ability to produce essential oils, but only about 30 are marketed massively globally; their main use is intended for the cosmetic industry and aromatherapy, although several of the compounds from essences could be valuable to the pharmaceutical industry. There are certainly still species whose essential oils have not been analyzed in their chemical composition or in their bioactivity, which could be interesting as a source of new secondary metabolites.
Since they are volatile metabolites, their low boiling points make it possible to have them as steam in a remarkably simple way; for this reason the ideal analysis is gas chromatography with GC/MS mass spectrometry.
The use of capillary columns has made it possible to have defined separations in essential oils that exceed 100 compounds, usually chromatographic separation is made in nonpolar columns with 95% dimethylpolysiloxane, due to the fact that several components of an essential oil contain polar groups such as hydroxyl (OH); the realization of these components using columns of intermediate polarity has been made. Both assays result in a complete chemical inquiry of molecules and are complementary. The correct structural elucidation is performed by combining several analyses such as comparison with spectrum databases and the theoretical and experimental determination of the retention rates of the compounds. For this purpose, there are databases, being the most used the “Identification of essential oil components by gas chomatography/mass spectrometry,” with approximately 4000 compounds from essential oils [4].
The GC/MS technique is limited in the fact that it is ineffective in evaluating stereoisomers, in such cases it is necessary to use chiral columns or techniques such as nuclear magnetic resonance imaging.
A more thorough investigation of the chemical identity of the molecules of an essential oil can be done with an equipment that couples gas chromatography with spectrophotometric techniques, such as nuclear magnetic resonance imaging and infrared spectroscopy. It is also possible to analyze NMR or IR spectra in previously isolated molecules by column or thin layer chromatography [5].
Several monoterpenes have a diverse and useful biological activity for treating diseases and ailments; some have valuable aromatic characteristics in cosmetics and perfumery. Those molecules that have relevant information and studies are analyzed to verify their use as phytotherapeutic elements (Figure 2).
Monoterpene molecules with therapeutic importance.
Pinenes. These have alpha and beta isomers; their formula is C10H10 and they are common in essential oils from conifers, although they can be found in many other species such as rosemary and lavender [6, 7, 8]; oils with high concentrations of pinenes generally have antimicrobial activity [8, 9]. Traditionally many plants containing pinene-rich essential oils are used in respiratory system disease [9].
1–8 cineol (Eucalyptol). Oxygenated monoterpene has a C10H18O formula whose functional group is an ether that is present in many varieties of eucalyptus. Among its most noteworthy properties are analgesic, anti-inflammatory and antimicrobial [10]. Plants with eucalyptol-rich essential oils are used for expectorant and decongestant properties of the respiratory system [11].
Limonene. It is a monoterpene whose formula is C10H16; it has two optical isomers R-limonene or D- limonene and S-limonene or L-limonene, which stand out by the insecticide [12, 13] and antimicrobial properties [14].
Myrcene. It is a monoterpene whose formula is C10H16; it is the main component of
Linalool. It is a hydroxylated monoterpene with C10H18O formula, its pleasant aroma makes it widely used in perfumery. Its action on the central nervous system is evidenced by its sedative, anxiolytic, analgesic and anti-inflammatory properties [19, 20]. Its antimicrobial and antioxidant properties have been evaluated with good results [21, 22].
Citral. It is an oxygenated monoterpene containing a group of aldehyde; its formula is C10H16O. There are two isomers known as neral (cis isomer) and geranial (trans isomer) [23], which are abundant in species such as
Camphor. It is an oxygenated monoterpene whose functional group is a ketone; its formula is C10H16O and it is present in two optical isomers R and S, which are abundant in the species
Menthol. It is a hydroxylated monoterpene, with a C10H20O formula, which has seven isomers that are very common in mint varieties such as Peppermint. It is one of the most used compounds in the food, cosmetic, pharmaceutical industries, and pesticides, among others. Its aromatic properties are very well known [30]; however, its most noticeable and known effect is that of analgesia at the topical level [31, 32].
Terpineol. It is a hydroxylated monoterpene with a C10H18O formula. It is known by having five isomers (α, β, γ, δ and 4-terpineol) [33], which are abundant in the essential oil of tea tree (
Citronellol. It is a hydroxylated monoterpene with a C10H20O formula. There are two enantiomers (+)-citronellol and (−)-citronellol [36]. The first is quite common in citronella oil, and the second is abundant in rose oil [37], which is used in perfumery. Its properties are insecticide [38], analgesic and anti-inflammatory [39], and antioxidant [40].
Several molecules with interesting properties can be found in C15 sesquiterpene (Figure 3). From a therapeutic view, there is evidence that validates its biological activity, highlighting anti-inflammatory, analgesic and anticancer trials.
Sesquiterpene molecules with therapeutic purposes.
Bisabolol. These are isomers, out of which stand (−)-α-Bisabolol, (−)-epi-α-Bisabolol, (+)-α-Bisabolol and (+)-epi-α-Bisabolol which are abundant in the species
β-Caryophyllene. It has a C15H24 formula. It is one of the most abundant sesquiterpenes in essential oils. Various bioactivity studies have been carried out in this molecule with good results, such as analgesic [45, 46], anti-inflammatory [47] and anticancer [48].
Chamazulene. With a C14H16 formula, it is a molecule derived from the sesquiterpene matricina, which is one of the few aromatic molecules that have a blue coloration. It is found in
Caryophyllene oxide. It is an oxygenated sesquiterpene with a C15H24O formula; it has properties similar to those of caryophyllene, such as analgesic and anti-inflammatory [53].
Germacrene. It belongs to the sesquiterpenes family, and it has three double links in its structure. There are five types of germacrenes: A, B, C, D, E. Recent studies mention its antioxidant potential [5, 54].
Artemisinic acid. It has a C15H22O2 formula, and it is one of the most interesting sesquiterpenes for health due to its antimalarial properties [55]. It is abundant in the species
Patchoulene. It is a sesquiterpene with a C15H24 formula. It is common to find its isomers α, β, α, and δ in essential oils. It is attributed to various types of bioactivity, the most relevant being those found in β-patchoulene as anti-inflammatory [57], antigastritis [58, 59], and cosmetic [60].
Humulene. Also known as α-caryophyllene, its formula is C15H24. It is named after the essential oil of the species
Bergamotene. It is a sesquiterpene with a C15H24 formula. It has four isomers α-cis, β-cis, α-trans and β-trans. It is found in several citric species such as
Farnesene. It has a C15H24 formula. It is a molecule found in several essential oils, and it is a precursor to many other sesquiterpenes since its open-chain structure and its 4-double bonds contribute to this action, as well as in the possibility of having a wide variety of isomers between geometrics and stereoisomers. Its cytotoxic and genotoxic [68], insecticide [69] and neuroprotective effects [70, 71] have been evaluated.
Eudesmol. Hydroxylated sesquiterpene with a C15H26O formula is a very interesting molecule by the multiple positive bioactivity assays, highlighting antimicrobial and antifungal [72], anticancer [73, 74] and antiangiogenic [75].
Most of the terpenes present in essential oils have some degree of toxicity, which is not detected when consuming aromatic species directly because in most cases the oil yield is low. Many commonly used essential oil components are potentially dermal irritating with restrictions on application concentrations [76, 77]. There are also some terpenes whose toxicity is much more dangerous, such as pulegone which causes liver damage and seizures [78], and thujone that can cause dementia by being neurotoxic [79].
This brief review has shown the chemical and biological importance of low molecular weight and volatile terpenes. For this reason, components of secondary metabolites are known as essential oils. The abundance of these molecules is much higher than the one presented in this chapter, since the information presented covers those whose scientific evidence and industrial importance are references in this family of metabolites. There is still much research to be carried out on the hundreds of molecules from which there is still little or no information. There are still aromatic species whose essential oils have not yet been described and that could be a source of new monoterpenes and sesquiterpenes that are beneficial to humans.
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\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{regionId:"4",sort:"featured,name"},profiles:[{id:"58592",title:"Dr.",name:"Arun",middleName:null,surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/58592/images/1664_n.jpg",biography:"Arun K. Shanker is serving as a Principal Scientist (Plant Physiology) with the Indian Council of Agricultural Research (ICAR) at the Central Research Institute for Dryland Agriculture in Hyderabad, India. He is working with the ICAR as a full time researcher since 1993 and has since earned his Advanced degree in Crop Physiology while in service. He has been awarded the prestigious Member of the Royal Society of Chemistry (MRSC), by the Royal Society of Chemistry, London in 2015. Presently he is working on systems biology approach to study the mechanism of abiotic stress tolerance in crops. His main focus now is to unravel the mechanism of drought and heat stress response in plants to tackle climate change related threats in agriculture.",institutionString:null,institution:{name:"Indian Council of Agricultural Research",country:{name:"India"}}},{id:"4782",title:"Prof.",name:"Bishnu",middleName:"P",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4782/images/system/4782.jpg",biography:"Bishnu P. 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First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Kenshi Saho",authors:[{id:"209334",title:"Associate Prof.",name:"Kenshi",middleName:null,surname:"Saho",slug:"kenshi-saho",fullName:"Kenshi Saho"}]},{id:"57977",doi:"10.5772/intechopen.71900",title:"Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics",slug:"unscented-kalman-filter-for-state-and-parameter-estimation-in-vehicle-dynamics",totalDownloads:1962,totalCrossrefCites:10,totalDimensionsCites:11,abstract:"Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Mark Wielitzka, Alexander Busch, Matthias Dagen and Tobias\nOrtmaier",authors:[{id:"122290",title:"Dr.",name:"Tobias",middleName:null,surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",middleName:null,surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"202801",title:"M.Sc.",name:"Matthias",middleName:null,surname:"Dagen",slug:"matthias-dagen",fullName:"Matthias Dagen"},{id:"222868",title:"MSc.",name:"Alexander",middleName:null,surname:"Busch",slug:"alexander-busch",fullName:"Alexander Busch"}]},{id:"57804",doi:"10.5772/intechopen.71138",title:"Consensus-Based Distributed Filtering for GNSS",slug:"consensus-based-distributed-filtering-for-gnss",totalDownloads:1377,totalCrossrefCites:4,totalDimensionsCites:5,abstract:"Kalman filtering in its distributed information form is reviewed and applied to a network of receivers tracking Global Navigation Satellite Systems (GNSS). We show, by employing consensus-based data-fusion rules between GNSS receivers, how the consensus-based Kalman filter (CKF) of individual receivers can deliver GNSS parameter solutions that have a comparable precision performance as their network-derived, fusion center dependent counterparts. This is relevant as in the near future the proliferation of low-cost receivers will give rise to a significant increase in the number of GNSS users. With the CKF or other distributed filtering techniques, GNSS users can therefore achieve high-precision solutions without the need of relying on a centralized computing center.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Amir Khodabandeh, Peter J.G. Teunissen and Safoora Zaminpardaz",authors:[{id:"116970",title:"Prof.",name:"Peter",middleName:null,surname:"Teunissen",slug:"peter-teunissen",fullName:"Peter Teunissen"},{id:"210691",title:"Dr.",name:"Amir",middleName:null,surname:"Khodabandeh",slug:"amir-khodabandeh",fullName:"Amir Khodabandeh"},{id:"210714",title:"Dr.",name:"Safoora",middleName:null,surname:"Zaminpardaz",slug:"safoora-zaminpardaz",fullName:"Safoora Zaminpardaz"}]},{id:"57455",doi:"10.5772/intechopen.71205",title:"Kalman Filter Models for the Prediction of Individualised Thermal Work Strain",slug:"kalman-filter-models-for-the-prediction-of-individualised-thermal-work-strain",totalDownloads:1205,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, the main aim of the present study was to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained ‘online’ via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up the time update equation depending only on the initial value of Tc and the latest value of the exogenous variables. Both models were trained and validated using data from laboratory- and outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKF models provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKF model (overall root mean square error, RMSE = 0.31°C) compared to the EKF model (RMSE = 0.45°C).",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Jia Guo, Ying Chen, Weiping Priscilla Fan, Si Hui Maureen Lee,\nJunxian Ong, Poh Ling Tan, Yu Li Lydia Law, Kai Wei Jason Lee and\nKok-Yong Seng",authors:[{id:"171298",title:"Dr.",name:"Kok-Yong",middleName:null,surname:"Seng",slug:"kok-yong-seng",fullName:"Kok-Yong Seng"},{id:"209402",title:"Dr.",name:"Ying",middleName:null,surname:"Chen",slug:"ying-chen",fullName:"Ying Chen"},{id:"209404",title:"Dr.",name:"Jia",middleName:null,surname:"Guo",slug:"jia-guo",fullName:"Jia Guo"},{id:"220688",title:"Ms.",name:"Weiping Priscilla",middleName:null,surname:"Fan",slug:"weiping-priscilla-fan",fullName:"Weiping Priscilla Fan"},{id:"220689",title:"Ms.",name:"Si Hui Maureen",middleName:null,surname:"Lee",slug:"si-hui-maureen-lee",fullName:"Si Hui Maureen Lee"},{id:"220690",title:"Mr.",name:"Junxian",middleName:null,surname:"Ong",slug:"junxian-ong",fullName:"Junxian Ong"},{id:"220691",title:"Ms.",name:"Poh Ling",middleName:null,surname:"Tan",slug:"poh-ling-tan",fullName:"Poh Ling Tan"},{id:"220692",title:"Ms.",name:"Yu Li Lydia",middleName:null,surname:"Law",slug:"yu-li-lydia-law",fullName:"Yu Li Lydia Law"},{id:"220693",title:"Dr.",name:"Kai Wei Jason",middleName:null,surname:"Lee",slug:"kai-wei-jason-lee",fullName:"Kai Wei Jason Lee"}]},{id:"57768",doi:"10.5772/intechopen.71874",title:"Kalman Filters for Parameter Estimation of Nonstationary Signals",slug:"kalman-filters-for-parameter-estimation-of-nonstationary-signals",totalDownloads:1544,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"An adaptive Taylor-Kalman filter with PSO tuning for tracking nonstationary signal parameters in a noisy environment with primary focus on time-varying power signals has been presented in this piece of work. In order to deal with the dynamic envelope of the power signal, second-order Taylor expansion has been used such that the Taylor coefficients are updated with the PSO-tuned Taylor-Kalman Filter algorithm. In addition to this, for fast convergence, a self-adaptive particle swarm optimization technique has been used for obtaining the optimal values of model and measurement error covariances of the Kalman filter. The proposed algorithm is linear and therefore has less computational burden, which is easier to be implemented on a hardware platform like DSP processor or FPGA. The proposed PSO-tuned Taylor-Kalman filter exhibits robust tracking capabilities even under changing signal dynamics, immune to critical noise conditions, harmonic contaminations, and also reveals excellent convergence properties.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Sarita Nanda",authors:[{id:"209587",title:"Dr.",name:"Sarita",middleName:null,surname:"Nanda",slug:"sarita-nanda",fullName:"Sarita Nanda"}]}],mostDownloadedChaptersLast30Days:[{id:"57673",title:"Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design",slug:"kalman-filter-for-moving-object-tracking-performance-analysis-and-filter-design",totalDownloads:3149,totalCrossrefCites:13,totalDimensionsCites:21,abstract:"This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Kenshi Saho",authors:[{id:"209334",title:"Associate Prof.",name:"Kenshi",middleName:null,surname:"Saho",slug:"kenshi-saho",fullName:"Kenshi Saho"}]},{id:"57977",title:"Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics",slug:"unscented-kalman-filter-for-state-and-parameter-estimation-in-vehicle-dynamics",totalDownloads:1962,totalCrossrefCites:10,totalDimensionsCites:11,abstract:"Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Mark Wielitzka, Alexander Busch, Matthias Dagen and Tobias\nOrtmaier",authors:[{id:"122290",title:"Dr.",name:"Tobias",middleName:null,surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",middleName:null,surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"202801",title:"M.Sc.",name:"Matthias",middleName:null,surname:"Dagen",slug:"matthias-dagen",fullName:"Matthias Dagen"},{id:"222868",title:"MSc.",name:"Alexander",middleName:null,surname:"Busch",slug:"alexander-busch",fullName:"Alexander Busch"}]},{id:"57455",title:"Kalman Filter Models for the Prediction of Individualised Thermal Work Strain",slug:"kalman-filter-models-for-the-prediction-of-individualised-thermal-work-strain",totalDownloads:1205,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, the main aim of the present study was to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained ‘online’ via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up the time update equation depending only on the initial value of Tc and the latest value of the exogenous variables. Both models were trained and validated using data from laboratory- and outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKF models provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKF model (overall root mean square error, RMSE = 0.31°C) compared to the EKF model (RMSE = 0.45°C).",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Jia Guo, Ying Chen, Weiping Priscilla Fan, Si Hui Maureen Lee,\nJunxian Ong, Poh Ling Tan, Yu Li Lydia Law, Kai Wei Jason Lee and\nKok-Yong Seng",authors:[{id:"171298",title:"Dr.",name:"Kok-Yong",middleName:null,surname:"Seng",slug:"kok-yong-seng",fullName:"Kok-Yong Seng"},{id:"209402",title:"Dr.",name:"Ying",middleName:null,surname:"Chen",slug:"ying-chen",fullName:"Ying Chen"},{id:"209404",title:"Dr.",name:"Jia",middleName:null,surname:"Guo",slug:"jia-guo",fullName:"Jia Guo"},{id:"220688",title:"Ms.",name:"Weiping Priscilla",middleName:null,surname:"Fan",slug:"weiping-priscilla-fan",fullName:"Weiping Priscilla Fan"},{id:"220689",title:"Ms.",name:"Si Hui Maureen",middleName:null,surname:"Lee",slug:"si-hui-maureen-lee",fullName:"Si Hui Maureen Lee"},{id:"220690",title:"Mr.",name:"Junxian",middleName:null,surname:"Ong",slug:"junxian-ong",fullName:"Junxian Ong"},{id:"220691",title:"Ms.",name:"Poh Ling",middleName:null,surname:"Tan",slug:"poh-ling-tan",fullName:"Poh Ling Tan"},{id:"220692",title:"Ms.",name:"Yu Li Lydia",middleName:null,surname:"Law",slug:"yu-li-lydia-law",fullName:"Yu Li Lydia Law"},{id:"220693",title:"Dr.",name:"Kai Wei Jason",middleName:null,surname:"Lee",slug:"kai-wei-jason-lee",fullName:"Kai Wei Jason Lee"}]},{id:"57692",title:"Applications of Kalman Filters for Coherent Optical Communication Systems",slug:"applications-of-kalman-filters-for-coherent-optical-communication-systems",totalDownloads:1480,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In this chapter, we review various applications of Kalman filtering for coherent optical communication systems. First, we briefly discuss the principles of Kalman filter and its variations including extended Kalman filter (EKF) and adaptive Kalman filter (AKF). Later on, we illustrate the applicability of Kalman filters for joint tracking of several optical transmission impairments, simultaneously, by formulating the state space model (SSM) and detailing the principles. A detailed methodology is presented for the joint tracking of linear and nonlinear phase noise along with amplitude noise using EKF. Also, approaches to enhance the performance obtained by EKF by combining with other existing digital signal processing (DSP) techniques are presented. Frequency and phase offset estimation using a two stage linear Kalman filter (LKF)/EKF is also discussed. A cascaded structure of LKF and EKF by splitting the SSM to jointly mitigate the effects of polarization, phase and amplitude noise is also presented. The numerical analysis concludes that the Kalman filter based approaches outperform the conventional methods with better tracking capability and faster convergence besides offering more feasibility for real-time implementations.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Lalitha Pakala and Bernhard Schmauss",authors:[{id:"210340",title:"M.Sc.",name:"Lalitha",middleName:null,surname:"Pakala",slug:"lalitha-pakala",fullName:"Lalitha Pakala"},{id:"210654",title:"Prof.",name:"Bernhard",middleName:null,surname:"Schmauss",slug:"bernhard-schmauss",fullName:"Bernhard Schmauss"}]},{id:"58292",title:"Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems",slug:"sensitivity-based-adaptive-srukf-for-state-parameter-and-covariance-estimation-on-mechatronic-system",totalDownloads:1345,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Since the initial developments in the state-space theory in the 1950s and 1960s, the state estimation has become an extensively researched and applied discipline. All systems that can be modelled mathematically are candidates for state estimators. The state estimators reconstruct the states that represent internal conditions and status of a system at a specific instant of time using a mathematical model and the information received from the system sensors. Moreover, the estimator can be extended for system parameter estimation. The resulting Kalman filter (KF) derivatives for state and parameter estimation also require knowledge about the noise statistics of measurements and the uncertainties of the system model. These are often unknown, and an inaccurate parameterization may lead to decreased filter performance or even divergence. Additionally, insufficient system excitation can cause parameter estimation drifts. In this chapter, a sensitivity-based adaptive square-root unscented KF (SRUKF) is presented. This filter combines a SRUKF and the recursive prediction-error method to estimate system states, parameters and covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts, while the system is not sufficiently excited. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint state and parameter UKF.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Mauro Hernán Riva, Mark Wielitzka and Tobias Ortmaier",authors:[{id:"122290",title:"Dr.",name:"Tobias",middleName:null,surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",middleName:null,surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"210832",title:"M.Sc.",name:"Mauro",middleName:"Hernán",surname:"Riva",slug:"mauro-riva",fullName:"Mauro Riva"}]}],onlineFirstChaptersFilter:{topicId:"1401",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:99,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:288,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. By addressing hot topics in veterinary sciences, we aim to gather authoritative texts within each issue of this series, providing in-depth overviews and analysis for graduates, academics, and practitioners and foreseeing a deeper understanding of the subject. Forthcoming texts, written and edited by experienced researchers from both industry and academia, will also discuss scientific challenges faced today in Veterinary Medicine and Science. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:8,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. She has over 160 Scientific Publications in International Journals and Conferences and she is the author of 5 books on Innovation and Decision Making in Industrial Applications and Engineering.",institutionString:null,institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"179628",title:"Prof.",name:"Dima",middleName:null,surname:"Jamali",slug:"dima-jamali",fullName:"Dima Jamali",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSAIlQAO/Profile_Picture_2022-03-07T08:52:23.jpg",institutionString:null,institution:{name:"University of Sharjah",institutionURL:null,country:{name:"United Arab Emirates"}}},{id:"170206",title:"Prof.",name:"Dr. Orhan",middleName:null,surname:"Özçatalbaş",slug:"dr.-orhan-ozcatalbas",fullName:"Dr. Orhan Özçatalbaş",profilePictureURL:"https://mts.intechopen.com/storage/users/170206/images/system/170206.png",institutionString:null,institution:{name:"Akdeniz University",institutionURL:null,country:{name:"Turkey"}}},{id:"250347",title:"Associate Prof.",name:"Isaac",middleName:null,surname:"Oluwatayo",slug:"isaac-oluwatayo",fullName:"Isaac Oluwatayo",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRVIVQA4/Profile_Picture_2022-03-17T13:25:32.jpg",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},{id:"141386",title:"Prof.",name:"Jesús",middleName:null,surname:"López-Rodríguez",slug:"jesus-lopez-rodriguez",fullName:"Jesús López-Rodríguez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRBNIQA4/Profile_Picture_2022-03-21T08:24:16.jpg",institutionString:null,institution:{name:"University of A Coruña",institutionURL:null,country:{name:"Spain"}}},{id:"208657",title:"Dr.",name:"Mara",middleName:null,surname:"Del Baldo",slug:"mara-del-baldo",fullName:"Mara Del Baldo",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLMUQA4/Profile_Picture_2022-05-18T08:19:24.png",institutionString:"University of Urbino Carlo Bo",institution:null}]},{id:"92",title:"Health and Wellbeing",coverUrl:"https://cdn.intechopen.com/series_topics/covers/92.jpg",editor:{id:"348225",title:"Prof.",name:"Ann",middleName:null,surname:"Hemingway",slug:"ann-hemingway",fullName:"Ann Hemingway",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035LZFoQAO/Profile_Picture_2022-04-11T14:55:40.jpg",biography:"Professor Hemingway is a public health researcher, Bournemouth University, undertaking international and UK research focused on reducing inequalities in health outcomes for marginalised and excluded populations and more recently focused on equine assisted interventions.",institutionString:null,institution:{name:"Bournemouth University",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"169536",title:"Dr.",name:"David",middleName:null,surname:"Claborn",slug:"david-claborn",fullName:"David Claborn",profilePictureURL:"https://mts.intechopen.com/storage/users/169536/images/system/169536.jpeg",institutionString:null,institution:{name:"Missouri State University",institutionURL:null,country:{name:"United States of America"}}},{id:"248594",title:"Ph.D.",name:"Jasneth",middleName:null,surname:"Mullings",slug:"jasneth-mullings",fullName:"Jasneth Mullings",profilePictureURL:"https://mts.intechopen.com/storage/users/248594/images/system/248594.jpeg",institutionString:"The University Of The West Indies - Mona Campus, Jamaica",institution:null},{id:"331299",title:"Prof.",name:"Pei-Shan",middleName:null,surname:"Liao",slug:"pei-shan-liao",fullName:"Pei-Shan Liao",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000032Fh2FQAS/Profile_Picture_2022-03-18T09:39:41.jpg",institutionString:"Research Center for Humanities and Social Sciences, Academia Sinica, Taiwan",institution:null}]},{id:"93",title:"Inclusivity and Social Equity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/93.jpg",editor:{id:"210060",title:"Prof. Dr.",name:"Ebba",middleName:null,surname:"Ossiannilsson",slug:"ebba-ossiannilsson",fullName:"Ebba Ossiannilsson",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6LkBQAU/Profile_Picture_2022-02-28T13:31:48.png",biography:'Professor Dr. Ebba Ossiannilsson is an independent researcher, expert, consultant, quality auditor and influencer in the fields of open, flexible online and distance learning (OFDL) and the "new normal". Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.',institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null,editorialBoard:[{id:"320585",title:"Ph.D.",name:"Deborah",middleName:null,surname:"Young",slug:"deborah-young",fullName:"Deborah Young",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002vZLcTQAW/Profile_Picture_2022-05-10T08:30:47.jpg",institutionString:"Empowering Communities Globally",institution:null},{id:"348038",title:"Associate Prof.",name:"Feyza",middleName:null,surname:"Bhatti",slug:"feyza-bhatti",fullName:"Feyza Bhatti",profilePictureURL:"https://mts.intechopen.com/storage/users/348038/images/system/348038.jpg",institutionString:"Girne American University",institution:{name:"Girne American University",institutionURL:null,country:{name:"Cyprus"}}},{id:"128665",title:"Prof.",name:"Man-Chung",middleName:null,surname:"Chiu",slug:"man-chung-chiu",fullName:"Man-Chung Chiu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bR9OrQAK/Profile_Picture_2022-03-09T08:36:59.JPG",institutionString:null,institution:{name:"Beijing Normal University",institutionURL:null,country:{name:"China"}}}]},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. 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