More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
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Our breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
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“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
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Additionally, each book published by IntechOpen contains original content and research findings.
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We are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
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Simba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
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IntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\n
Since the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\n
Our breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n
“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\n
Additionally, each book published by IntechOpen contains original content and research findings.
\n\n
We are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n
\n\n
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Companies worldwide are already pivoting towards dynamic and reconfigurable production as a smarter way to build and make things. As such, this book discusses the next generation of manufacturing, which will involve the transformational convergence of intelligent machines, powerful computing and analytics, and unprecedented networking of people, products, and services.",isbn:"978-1-83962-649-4",printIsbn:"978-1-83881-087-0",pdfIsbn:"978-1-83962-650-0",doi:"10.5772/intechopen.90084",price:119,priceEur:129,priceUsd:155,slug:"smart-manufacturing-when-artificial-intelligence-meets-the-internet-of-things",numberOfPages:158,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"87004a19de13702d042f8ff96d454698",bookSignature:"Tan Yen Kheng",publishedDate:"January 14th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10150.jpg",numberOfDownloads:3314,numberOfWosCitations:0,numberOfCrossrefCitations:7,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:8,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:15,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 10th 2019",dateEndSecondStepPublish:"November 15th 2019",dateEndThirdStepPublish:"February 15th 2020",dateEndFourthStepPublish:"March 19th 2020",dateEndFifthStepPublish:"May 18th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!0,featuredMarkup:null,editors:[{id:"78857",title:"Dr.",name:"Tan Yen",middleName:null,surname:"Kheng",slug:"tan-yen-kheng",fullName:"Tan Yen Kheng",profilePictureURL:"https://mts.intechopen.com/storage/users/78857/images/system/78857.jpg",biography:"Dr. Tan Yen Kheng is the CEO and co-founder of Printed Power, a high-tech company headquartered in Singapore that develops edge computers for smarter buildings and manufacturing globally (China and the Association of Southeast Asian Nations [ASEAN]). The company empowers customers with sensor-end to application-end platform solutions to discover opportunities and capture value from actionable insights as well as co-create with domain experts using advanced artificial intelligence (AI)/machine learning (ML) tools. Dr. Tan is concurrently the associate editor of the IEEE Sensors Journal and the industrial chair of IEEE Singapore section. He was also chair/professor at the School of Electrical Engineering/Hanergy School of Renewable Energy at Beijing Jiaotong University (BJTU) where he built international partnership programs, delivered seminars and short courses, and performed research exchanges with research staff and students.",institutionString:"Printed Power LTD",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"6",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1356",title:"Manufacturing Engineering",slug:"technology-industrial-engineering-manufacturing-engineering"}],chapters:[{id:"73063",title:"Remote Management of Autonomous Factory",doi:"10.5772/intechopen.93527",slug:"remote-management-of-autonomous-factory",totalDownloads:384,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In today’s mass production era, the world is making things (products and systems) so quickly and systematically in huge volume. The demand for these products is very high and, at the same time, consumers are still in search for a need for making the production very personalized. Hence, the “one mold fits all” approach may not seem to be enough. The present approach is facing the lack of networking between the automation pyramid levels, that is, especially between enterprise resource planning (ERP) and manufacturing execution system (MES) layers and, in turn, communicating directly with the lower layers is not possible. This missing communication among the process equipment like machineries and field control systems like PLCs at the production shop floors implies that customization at the product layer for the consumer is still in progress in classical manufacturing. Mini-MES is a new concept being introduced here to solve the existing techniques reported in the literature and is followed by industry best practices. The novel mini-MES platform provides an avenue for the technology process level (the most bottom layer) to interplay interconnectivity and interoperability with its higher levels until the above pain points are addressed holistically. The chapter is going to focus mainly on the factory production of digital manufacturing and on describing the 3-Cs implementation plan, the enabling technology, and the achievable outcome ahead.",signatures:"Yen Kheng Tan and Felix George",downloadPdfUrl:"/chapter/pdf-download/73063",previewPdfUrl:"/chapter/pdf-preview/73063",authors:[{id:"78857",title:"Dr.",name:"Tan Yen",surname:"Kheng",slug:"tan-yen-kheng",fullName:"Tan Yen Kheng"},{id:"315449",title:"Mr.",name:"Felix",surname:"George",slug:"felix-george",fullName:"Felix George"}],corrections:null},{id:"72166",title:"Optimization of Manufacturing Production and Process",doi:"10.5772/intechopen.92304",slug:"optimization-of-manufacturing-production-and-process",totalDownloads:543,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter mainly introduces production processing optimization, especially for machining processing optimization on CNC. The sensor collects the original vibration data in time domain and converts them to the main feature vector using signal processing technologies, such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet packet in the 5G AI edge computing. Subsequently, the main feature will be sent for cloud computing using genetic programming, Space Vector Machine (SVM), etc. to obtain optimization results. The optimization parameters in this work include machining spindle rotation velocity, cutting speed, and cutting depth, while, the result is the optimized main spindle rotation speed range of CNC, which met machining roughness requirements. Finally, the relationship between vibration velocity and machining quality is further studied to optimize the three operational parameters.",signatures:"YinQuan Yu",downloadPdfUrl:"/chapter/pdf-download/72166",previewPdfUrl:"/chapter/pdf-preview/72166",authors:[{id:"313886",title:"Prof.",name:"Yinquan",surname:"Yu",slug:"yinquan-yu",fullName:"Yinquan Yu"}],corrections:null},{id:"72826",title:"Interlinking Industry 4.0 and Academia through Robotics and Automation: An Indian Perspective",doi:"10.5772/intechopen.92608",slug:"interlinking-industry-4-0-and-academia-through-robotics-and-automation-an-indian-perspective",totalDownloads:420,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Robots and automation systems are growing rapidly in the society globally with an annual global sales value of 16.5 billion USD in 2018 according to the International Federation of Robotics (IFR). They have found a commonplace not only in industries and service sector but also in households. This has attracted heavy investment by industries globally in the research and development of robotics and its applications. Understanding this rising trend in the industry and society, there is an obvious need for expertise and future workforce in robotics. According to the IFR report, India has recorded a growth of 39% in 2018 compared to the previous year in terms of annual robot installations. The annual report of the Confederation of Indian Industry (CII) in 2019 recommends robotics and automation as one of the prime areas of focus toward the development of national policies on Industry 4.0. One such interlinking initiative in robotics research and innovation has started at the Centre for Product Design and Manufacturing (CPDM) in the Indian Institute of Science (IISc). The project is designated under India’s first Industry 4.0-compliant Smart Factory R&D platform in a unique academic set-up. It aligns with the policies of Govt. of India to boost vision Industry 4.0 for India’s technological and economic transformation.",signatures:"Prajwal Prabhu, Subin Raj and Abhra Roy Chowdhury",downloadPdfUrl:"/chapter/pdf-download/72826",previewPdfUrl:"/chapter/pdf-preview/72826",authors:[{id:"175439",title:"Dr.",name:"Abhra Roy",surname:"Chowdhury",slug:"abhra-roy-chowdhury",fullName:"Abhra Roy Chowdhury"},{id:"321202",title:"Dr.",name:"Subin",surname:"Raj",slug:"subin-raj",fullName:"Subin Raj"},{id:"321203",title:"Dr.",name:"Prajwal",surname:"Prabhu",slug:"prajwal-prabhu",fullName:"Prajwal Prabhu"}],corrections:null},{id:"72044",title:"Applications of Machine Learning in Healthcare",doi:"10.5772/intechopen.92297",slug:"applications-of-machine-learning-in-healthcare",totalDownloads:997,totalCrossrefCites:5,totalDimensionsCites:5,hasAltmetrics:1,abstract:"Machine learning techniques in healthcare use the increasing amount of health data provided by the Internet of Things to improve patient outcomes. These techniques provide promising applications as well as significant challenges. The three main areas machine learning is applied to include medical imaging, natural language processing of medical documents, and genetic information. Many of these areas focus on diagnosis, detection, and prediction. A large infrastructure of medical devices currently generates data but a supporting infrastructure is oftentimes not in place to effectively utilize such data. The many different forms medical information exist in also creates some challenges in data formatting and can increase noise. We examine a brief history of machine learning, some basic knowledge regarding the techniques, and the current state of this technology in healthcare.",signatures:"Christopher Toh and James P. Brody",downloadPdfUrl:"/chapter/pdf-download/72044",previewPdfUrl:"/chapter/pdf-preview/72044",authors:[{id:"313941",title:"Ph.D. Student",name:"Christopher",surname:"Toh",slug:"christopher-toh",fullName:"Christopher Toh"},{id:"314005",title:"Dr.",name:"James",surname:"Brody",slug:"james-brody",fullName:"James Brody"}],corrections:null},{id:"72644",title:"Radio Systems and Computing at the Edge for IoT Sensor Nodes",doi:"10.5772/intechopen.92987",slug:"radio-systems-and-computing-at-the-edge-for-iot-sensor-nodes",totalDownloads:401,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Many Internet of Things (IoT) applications use wireless links to communicate data back. Wireless system performance limits data rates. This data rate limit is what ultimately drives the location of computing resources—on the edge or in the cloud. To understand the limits of performance, it is instructive to look at the evolution of cellular and other radio systems. The emphasis will be on the RF front-end architectures and requirements as well as the modulation schemes used. Wireless sensor nodes will often need to run off batteries and be low-cost, and this will constrain the choice of wireless communications system. Generally cheap and power efficient radio front ends will not support high data rates which will mean that more computing will need to move to the edge. We will look at some examples to understand the choice of radio system for communication. We will also consider the use of radio in the sensor itself with a radar sensor system.",signatures:"Malcolm H. 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The energy harvesting technologies can reduce or eliminate the need of batteries for edge devices by using super capacitors or rechargeable batteries to recharge them in the field. 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1. Introduction
The COVID-19 pandemic has triggered untold uncertainties in most global financial and commodity markets. In March 2020, stock price fell intensely, mortgage-backed securities and yield spread of corporate bonds surged significantly, and U.S. Treasury bonds which usually serve as a safe-haven, plunged [1, 2]. Specifically, the behavior of global stock market appears illogical in presence of the pandemic to many investors. For instance, S&P500, one of the mega-size stock markets experienced three phases of changes as the rate of COVID-19 infection worsens. In phase one, it recorded a high value on 19th February 2020, prior to the declaration of the outbreak as a pandemic by the World Health Organization (WHO). However, a surge in the spread of the virus, exacerbated by soaring death rates, caused panic and created a colossal urgency to accumulate cash balances, sparking a concurrent selloff in stocks [3]. Thus, in phase two, the S&P500 plummeted by 34% reaching its low on March 23, 2020. In the third phase, S&P500 rose by 30% on April 30, 2020, despite the lockdown orders initiated by many countries to curb the spread of the virus. In hindsight, stock markets have performed well, generally, because on the eve of the COVID-19 outbreak been pronounced a pandemic, the ratio of market capitalization to GDP was higher compared to its level in 2007, and a little higher than the maximum value during the dot-com bubble [4]. Analysts attribute the rebound of the stock market partly to various loose monetary policy and other interventions pursued by central banks [4], which instilled confidence into shareholders, lightening fears of the health crisis. Besides, country-specific characteristics such as structural economic fragility and “at-risk” population, also seem to have had little effect on stock market reactions to the pandemic [5].
As the pandemic intensifies, bond trading has also encountered challenges, regardless of the asset’s significance and essential role in the financial market system. In the face of the pandemic, the observed behavior of the bond market is similar to that of the global stock market. For instance, the U.S. Treasury bond and Canadian government bond markets, in phase one, witnessed an increase in demand for liquidity as investors embarked on a significant selloff [2]. In phase two, dealers curtailed the supply of liquidity, which deteriorated trading conditions. In the third phase, demand gradually reduced, due to some interventions introduced by the respective central banks of these giant economies.
In another development, commodity markets such as crude oil and the real estate markets equally experienced volatilities as the COVID-19 pandemic unfolds. On 20th April 2020, a barrel of West Texas Intermediate crude oil to be delivered in May recorded a negative price, implying sellers had to pay buyers [6]. Though the price for June also fell over a quarter on 27th April, it however remained a little above $12 a barrel. Crude oil market analysts attribute these fluctuations to a price-war between Russia and Saudi Arabia, which they claimed flooded the international market with crude oil and a slump in demand due to traveling and aviation restrictions imposed by countries following the pandemic [7].
A convergence of the uncertainties in the above global markets triggered an immediate deterioration of business environment with unintended negative consequences on commercial real estate markets. Demand for lease space slumped and continue to deteriorate as the pandemic unfolds due to the effects of social distancing and business closures across the globe. However, the impact appears to vary extensively across the real estate sectors. Whiles some sectors are severely and directly affected by the pandemic, others are less and indirectly affected. The performance of the stock market for Real Estate and Investment Trusts (REITs) reflects the differences in the degree of the uncertainties across different types of properties [8].
Though the individual global markets’ responses to the COVID-19 pandemic may seem somehow similar, albeit yet to be determined actual worldwide impact quantification, there is a strong likelihood of a potential lead–lag co-movement among the markets, which may be induced or heightened by major news of the health crisis. There is an astronomical increasing number of empirical studies towards the reactions of individual global markets to the COVID-19 crisis. In a short epistle, Krugman [4] disclosed that the “stock market is not the economy”, and that “connection between stock growth and the expansion of the real sector of the economy lies within loose and nonexistence”. Capelle-Blancard and Desroziers [5] confirmed various interventions by governments, as well as the believe that loose monetary policy and lockdown initiatives stimulated the rebound of stocks, they further agreed with Krugman [4] that there exists a loose relationship between market fundamentals and stock market uncertainties. Jefferson [7] on the other hand established that projections of future crude oil prices are uncertain, however, in the absence of supply-side shocks, oil prices are likely to rebound by the end of the third quarter of 2020. Goodell and Goutte [9] investigate co-movement of Bitcoin with levels of COVID-19 fatalities and show that the levels of COVID-19 deaths cause a rise in Bitcoin prices. However, the analyses from the previous studies have failed to examine coherences and lead–lag behavior among conventional global markets as they react to the COVID-19 crisis. Again, to the best of our knowledge, the existing studies have not analyzed the interrelationship structure and reactions of global markets to distinctive stages of the COVID-19 outbreak.
In response to the identified gaps, this study attempts to offer fresh insights as to whether major news items of COVID-19 influence the interdependence structure of international markets. We contribute to the existing literature in two-fold. First, we explore the degree of co-movement and lead–lag relationship among aggregate global stock index, commodities, and the REITs market using the cross-wavelet squared coherence and a rolling-window wavelet correlation (RWWC) technique. In addition to its ability to address issues of nonlinearity, (non-) economic shocks, regime shifts, and non-stationarities, the RWWC approach possesses time-varying attributes that makes it possible to measure the temporal variations of cross-market correlations over time and frequency domains [10], with implications for heterogeneous market actors. Second, we examine the influence of the COVID-19’s epidemic and pandemic stages on global market interrelatedness and determine whether international investors can hold positions in the markets to offset short-run investment losses during the crisis.
The remaining structure of the study is set as follows. Section 2 provides a review of the extant literature. Section 3 discusses the econometric techniques employed. Section 4 presents the results, while Section 5 provides conclusion and policy recommendations.
2. Literature review
Ramelli and Wagner [11] found a significant effect of world trade and global value chain on the value of corporations. Corporate bodies appeared profitable depending on the location of the epicenter of the pandemic. For instance, stocks of corporate bodies in China initially appeared risky as the pandemic unfolded while those in Europe were considered profitable. However, as the epicenter moved to Europe and America, investors perceived stocks of these regions to be unfavorable, causing the markets to behave feverishly. Investors were equally alarmed about the possibility of corporate bodies incurring high debts as well as the survival potentials of businesses with insufficient cash balances. Though the opportunity cost of holding cash balances appeared high, there was an increasing need to hold precautionary cash to soar the value of firms. Capelle-Blancard and Desroziers [5] revealed that investors in the stock markets were quick to respond to soaring cases of COVID-19, with advanced economies being highly affected. The authors also unearth that loose monetary and fiscal policies introduced by central banks and governments caused interest rates to fall, which moderated the fall in stock prices, making the market less responsive to the crisis. Baker et al. [12] argued that the stock market responded to the pandemic in a way that has never happened in history. The authors attributed this behavior to a host of factors such as governments’ restraints on economic activity and the introduction of social distancing measures in the presence of a service dominated economy. Ehrmann and Jansen [13] revealed the presence of significant co-movement between stock returns and national stock markets. Investors placed a high value on global news and the effects were moderated by large oscillations in global stock markets. However, investors turned to place less value on firm-specific news, which caused stock returns and national stocks market to move together. The authors observed that, this relationship was significant for stocks that are characterized by low co-movement with national market, resulting in a convergence of beta across stocks.
Haddad et al. [14] in studying the disruptions in debt markets as the pandemic unfolded made interesting revelations. The authors revealed that bonds that are usually regarded as safe havens recorded weighty losses which analysts found difficult to explain applying risk premium channel or standard default. Corporate bonds traded at a huge discount to their equivalent credit default swaps and this became worse for many safer bonds. Similarly, liquid bond exchange-traded funds witnessed a huge discount to their corresponding net asset value. These findings imply traders attempted to sell safer and high liquid securities to increase cash balances. However, these disruptions did not see the light of day as the market recovered in a matter of weeks. The authors attributed the fast recovery of the bonds market to the unparalleled measures the Fed introduced by purchasing corporate bonds instead of extending credit. Fontaine et al. [2] and Kargar et al. [15] found that the market for bonds evolved in three phases as the pandemic worsens, using two-year benchmark bonds for Canada. The first phase witnessed a sharp rise in the demand for cash balances, which traders did well to accommodate but at a higher cost. The second phase experienced a massive decrease in the supply of cash balances by dealers, leading to a huge deficit as demand for cash kept soaring. The third and final phase saw trading activity and price of cash balance stabilizing due to the interventions by central banks to assist the financial sector.
Regarding the crude oil market, the Arezki et al. [16] pointed out that net oil-exporting economies face a dual shock emanating from the health crisis and a fall in prices of crude oil. However, the shock from the pandemic turned to lead and influence the collapse in oil prices. This manifested itself through the traveling restrictions placed on the aviation industry, self-isolation, and social distancing and complete lockdowns measures introduced by governments around the globe [7]. Elsewhere, Barbosa et al. [17] revealed that the dual shock on net exporting countries negatively affected the financial and structural health of the oil sector in an unprecedented manner. Since the intensity and length of the health crisis are uncertain, the authors suggested net exporting countries should introduce fundamental intervention to reverse the trend to make the industry profitable again.
In the Real Estate and Investment Trusts market, Schnure [8] observed that the social distancing and lockdown measures caused almost all businesses in the global economy to shut down. There is a high probability that most of these businesses may find it very difficult to honor their rent in the near future, which will affect negatively on cash flow of property owners. Again, the authors explained that hikes in unemployment would cause unspeakably high rent default by households. Coibion et al. [18] found persistence in low inflation, heightened uncertainty, and lower mortgage rates as the pandemic worsened. The authors attributed this low consumer spending and collapse in demand for office space as employees work from home.
It is important to mention that the existing literature is yet to explain whether COVID-19 has influenced the interdependence structure of global markets. Besides, the influence of the crisis at the onset stage and its transition into a pandemic has rarely been explored. Thus, applying time-frequency estimation methods, this paper augments the literature by assessing the extent of co-movement and the direction of linkages (lead–lag relationship) among selected major global market, amid COVID-19 major news. Our study provides international investors with further insight as they seek to diversify their investment portfolios by purchasing securities that do not or less co-move to minimize losses under the heightened market periods of COVID-19.
3. Data and methods
The data for the empirical analysis and the econometric approach for the multidimensional dynamic correlation measure is explained in this section. As preliminary to the main analysis, static descriptive measures, and a correlation matrix based on a global measure, computed under different time samples are provided.
3.1 Data
Daily price levels of MSCI All Country World Index (MSCI ACWI), S&P GSCI Energy Index, S&P GSCI Non-Energy Index, and S&P Global Real Estate and Investment Trusts (SREITGUP), which spans from January 01, 2016, to August 17, 2020 (giving us 1165 realizations after cleaning and synchronization the timestamps) are considered and used as proxy for global markets1. The US dollar denominated price level datasets are sourced from the Bloomberg database terminal. The daily prices are converted to percentage log changes: ri,t=InPi,t/Pi,t−1×100, for i=1,…,4 and t=2,…,1165, where Pi,t and Pi,t−1 denote the close of day prices for global market i at day t and t−1 respectively. The time sample is foremost divided into pre-COVID-19 crisis (January 01, 2016, to December 07, 2020) and periods marking the onset and duration of COVID-19 (December 08, 2019, to August 17, 2020).
To establish the dynamic impact of the COVID-19 outbreak on the interrelationship structure of international markets and its implication thereon, the crisis period is further separated into epidemic (December 08, 2020, to March 10, 2020) and pandemic (March 11, 2020, to August 17, 2020) stages. The dating of these distinct periods is based on the announcements or timelines of the first patient who was reported to have developed symptoms of the Wuhan coronavirus (on December 08, 2019) and the subsequent declaration of the outbreak by WHO as a pandemic (on March 11, 2020)2.
Table 1 shows summary moment measures for the global markets during the crisis and non-crisis periods. Compared to the non-crisis, the average daily returns fairly decrease under the crisis period, with all markets recording low negative skewness values, indicative of a high tendency of reaping non-positive investment returns. Noticeably, the markets appear to be more volatile as we transition from the pre-crisis to the crisis period. For instance, the volatility measures for MSCI ACWI, S&P GSCI Energy, and the Global REITs (SREITGUP) increased almost three-fold between the two periods. Meaning, the COVID-19 crisis has ushered in periods of heightened uncertainty and created a high financial risk environment. Besides, the return distributions of the markets exhibit relatively more leptokurtic features under the crisis, hence giving rise to extreme return realizations.
Period I: Pre-COVID-19 crisis
Period II: COVID-19 crisis
Mean
Std Dev
Skew
Kurt
Mean
Std Dev
Skew
Kurt
MSCI ACWI
0.0359
0.6733
−0.7606
4.6379
0.0131
2.0655
−1.1728
7.2169
GSCI Energy
0.0486
1.8979
0.1638
3.2746
−0.2060
5.1673
−1.3398
9.8393
GSCI Non-Enr
0.0123
0.5844
−0.1489
0.7892
0.0019
0.7723
−0.7365
2.2106
SREITGUP
0.0137
0.6934
−0.4641
1.8341
−0.1105
2.7274
−1.6971
9.5034
Table 1.
Descriptive measures.
Results of the Pearson’s unconditional correlations for the market pairs are presented in Table 2. Except for MSCI ACWI-Global REITs (with correlation measure of 0.5598), low positive correlations (ranging from 0.1450 to 0.3668) characterizes the pairs before the coming into being of the crisis. However, all the pairwise correlations increased in magnitude from low positive to moderately low positive values (0.3764 to 0.5813), with a peaked measure of 0.8962 for MSCI ACWI-Global REITs. These static-based measures signal the influence of the COVID-19 crisis on cross-market relationship, which seems to be in line with the literature that suggests that financial markets tend to move closely together (i.e., increased correlation or co-movement intensity) during turmoil or crisis. Yet, estimations that are more robust are undertaken to validate these early detections.
Period I: Pre-COVID-19 crisis
Period II: COVID-19 crisis
(a)
(b)
(c)
(d)
(a)
(b)
(c)
(d)
(a) MSCI ACWI
1
1
(b) GSCI Energy
0.3668
1
0.4671
1
(c) GSCI Non-Enr
0.2921
0.2511
1
0.5813
0.4340
1
(d) SREITGUP
0.5598
0.1608
0.1450
1
0.8962
0.3764
0.5599
1
Table 2.
Pearson correlation matrix.
3.2 RWWC methodology
To examine the interrelationship structure between the daily percentage log changes of MSCI ACWI, S&P GSCI Energy Index, S&P GSCI Non-Energy Index, and S&P Global REITs Index across time and frequency, we employed a dynamic correlation version of the wavelet correlation approach by Gençay et al. [19]. The rolling-window wavelet correlation (RWWC) method, which was introduced by Ranta [20, 21] has recently gained traction in the economics and financial literature, perhaps due to its ability to unearth the temporal variations of the wavelet correlation for distinct time series, by incorporating a dynamic measure under a multidimensional setting. Using the Maximal Overlap Discrete Wavelet Transformation (simply, MODWT) methodology (see, [22, 23, 24]; etc.) and following Gençay et al. [19], we express the MODWT-based unbiased estimator of the wavelet correlation for pairs of the market series, Xt and Yt for scale λj as:
where, γ∼XYλj represent the unbiased estimator of the wavelet covariance for the wavelet constituents W∼X,j,t and W∼Y,j,t involving the pair of distinct series, and σX2λj∼ and σY2λj∼ denote unbiased measures of the wavelet variances for X and Y at scale λj.
We specify the MODWT-based unbiased estimator of the wavelet variance as:
σ∼X2λj=1N∼j∑t=Lj−1N−1W∼j,t2E2
where, w∼j,t represent the jth-level MODWT wavelet constituents for market variable X, Lj=2j−1L−1+1 give the length of the scale λj wavelet filter, and N∼j=N−Lj+1 present the number of wavelet constituents unaffected by the boundary. Next, an expression for the computation of a random interval that captures non-spurious wavelet correlations [21] and offers an approximate 1001−2p% confidence interval is deduced from the extended work of Whitcher et al. [25]:
where, hρ∼XY=tanh−1ρ∼XY describes Fisher’s z-transformation and Nj∼ remains the number of wavelet constituents that correspond to a particular scale.
To provide short-term investment solutions for traders during the ongoing COVID-19 crisis, we considered to perform our analysis at the lower frequency bands or investment horizons, thus, the 2 ∼ 4 day band (D1: intraweek), 4 ∼ 8 day band (D2: weekly), and the 8 ∼ 16 day band (D3: fortnight), which are associated respectively with scales λj of the MODWT time-scale decomposition, where j=1,…,3. We computed the RWWC using a 100-day window size, which we rolled forward one day (or datapoint) at a time, and centred around time t. With a sample size of 1165, we obtained a total of (1165 minus 100 = 1065) windows. We later varied the window size to 120 (or half-year) to verify the sensitivity of the results to the choice of window length. The two window sizes, w=100 and w=120 truncated on June 04, 2020, and May 20, 2020, respectively, giving us relatively less information losses compared to using higher window sizes. On the other hand, selecting very low window lengths plays down on the power of the test, therefore our choices are not misplaced.
4. Empirical results
We begin the main analysis with results from the cross-wavelet squared coherence method of Grinsted et al. [26]. Under this well-known technique, the estimator of interdependence is rooted on a continuous wavelet transform (see, [9, 21, 27]; etc., for detailed explanation) rather than the discrete wavelet transform. Besides, unlike the RWWC, the wavelet coherence does not incorporate a dynamic measure in its computation. Therefore, taken as a precursor to the RWWC analysis, we initially gleaned the direction and nature of the interdependence structure that emerges for the market pairs using the wavelet coherence plots shown in Appendix B. A general observation of Appendix B reveals that the arrows are mostly pointing to the right, implying the series are positively correlated, with patches of varying significant coherences predating the year 2020. Observably, the post-2020 period recorded a high degree of coherences for pairs involving stock market-Global REITs and stock market-energy commodities, which witnessed long stretches of white contours over the frequency bands. These detected peaks in coherences conform to the contagion effect literature that projects high co-movements (or increased correlation intensity) for financial markets during and/or after the occurrences of turmoils or major crises. This finding is in line with the findings of Polanco-Martínez et al. [24] who reported strong correlation (0.56–0.87) among global financial markets during episodes of heightened economic crises, particularly during the 2008 financial meltdown.
Specifically, in Appendix B, we first focus on the behavior of the global stock market and energy commodity pair. As noted, a significantly strong positive correlation could be gleaned between the 16 and 128 trading day bands, with purloins of co-movement within the intraweek to fortnight trading frequencies. The strongest level of coherences falls within March and May 2020, where the equity market clearly leads energy commodity. This post-2020 co-movement pattern could perhaps be attributed to the lockdown measures, which brought global transportation to a temporary halt, hence the lagging of global energy commodity in its interrelationship with stock market. This finding confirms those of Nguyen et al. [28] who established strong evidence of co-movement (ranging from 0.62–0.89) between stock and energy markets at the peak of the global financial crisis 2007–2009. Similar visibly strong coherences, which intensifies at the beginning of 2020 and beyond, conspicuously grows from the intraweek to half-year trading day bands for the stock market and Global REITs pair. It is also important to stress that the coherences between stock market-Global REITs stretch over longer periods than what could be witnessed for the other global market pairs. These zones of strong correlations are suggestive of contagion impact resulting from the imposition of measures, implemented by world governments and their central banks to avert meltdown of the global financial markets.
With respect to stock market and non-energy commodity in Appendix B, we notice isles of significantly segmented zones of coherences, which mark periods, before, during, and after the outbreak of COVID-19. Coherences between energy and non-energy commodities appear generally weak, with few moderately low correlations concentrated between frequency bands of 8–32, and fairly distributed across time. Similarly, besides the 32–128 trading day bands of the opening months of 2020, coherences between energy commodity and global REITs are equally weak. Finally, we observe patches of moderately low significant co-movements between non-energy commodity and Global REITs, which appears mounded within the medium-to-long-run frequencies (16–128) with weak coherences below the fortnight band, coupled with a nonhomogeneous lead–lag relationship.
Our RWWC analysis in Figure 1, drawn from a dynamic version of the discrete wavelet transform is initially estimated using a 100-day window length using Eq. (3). The estimations from the dynamic approach reveal thought-provoking findings that may be hardly discernible with static or global measures. The horizontal axis of Figure 1 depicts timelines (or time intervals) and the vertical axis represents frequency bands or investment horizons categorized into D1 (intraweek), D2 (weekly), and D3 (fortnight)3. A glance from the RWWC results in Figure 1 shows that the market pairs are predominantly characterized by weak to moderately low positive correlations, with few abrupt zones of inverse correlations that completely disappear during the uncertain periods ushered in by the global outbreak of COVID-19. This latter finding signals a negligible or unlikely opportunity of benefiting from any of the market assets as a safety net tool or instrument to hedge against short-term losses of an international investment portfolio. Generally, except for stock market and the Global REITs pair, we observe fairly low but steadily increasing correlations for the markets over the frequency bands before the onset of COVID-19.
Figure 1.
A 100-day RWWC for pairs of selected global markets. Note: the strength of the dynamic wavelet correlation for the pairs is displayed by the heat map colors, which ranges from weak to high (thus, from blue, cyan, green, white, yellow, orange, to red respectively), where red (blue) denotes highest (lowest) wavelet correlation coefficients within a 95% confidence interval (refer to the web version of the article for color representation).
Particularly in Figure 1, we notice vast yellow to less warm orange regions with scores of white and green patches before the dating of the COVID-19 crisis for markets pairs of non-energy and Global REITs on one side, and energy versus Global REITs at the other side. Similar colors from the correlation heatmap could also be somehow advance to describe interrelationship behavior for the pairs involving energy and non-energy commodities, as well as stock market and (non-) energy commodity. These pre-COVID-19 co-movement patterns mark an era that is generally dominated by moderately fewer interactive markets, except for the stock market and Global REITs pair, which exhibited moderately high interactions. Besides, the latter market pair witnessed strong co-movement across the trading frequencies in the second to the third quarter of 2016, a period which coincides with the UK’s referendum on June 23, 2016, to leave European Union. Perhaps, the uncertainties induced by the referendum accounted for such high cross-market interactions4.
As viewed from Figure 1, the onset and the distinct phases of COVID-19 has had cause to alter the correlation patterns of the market pairs. For instance, the RWWC measure becomes strong (warm orange to reddish heatmap colors) during the epidemic phase of COVID-19 for stock market-energy commodity and energy commodity-Global REITs, only to reverse to moderately low correlations, as observed under the pre-COVID-19 period, thereby signaling a temporal effect of the crisis on the market pairs. The remaining markets recorded increasing co-movements in the early period of the epidemic, however, these intensifies and peaks in the latter part of the epidemic, and subsequently overflows into the pandemic period. Our finding confirms that of Samadi et al. [29] who provided strong evidence to the effect that energy market exhibited low co-movement (0.36) during the pre-Ccovid-19 episode, which later heightened (0.88) during the pandemic era. The results presuppose that COVID-19 has exerted varying influences on the relatedness of global markets, and as a result decreased, to a large extent, the tendency of reaping diversification gains. These strong co-movements could be a consequence of looming heightened financial instabilities, compelling central banks to implement loose monetary and financial measures to curtail the effect of the crisis.
From the above context, it would be non-advantageous to hold a position in pairs of these global markets during the ongoing crisis, more importantly, for the stock market and Global REITs. As the evidence suggests, the latter pair react more to global shocks or crises, perhaps, owing to the reason that both markets are subjected to similar circumstances, hence, in periods where stock prices plummet, REITs are not overly immune to the perils of falling stock prices.
To ensure the robustness of our results we conduct a sensitivity test by increasing the rolling-window size to 120-days and report the results in Appendix A, estimated using Eq. (3). Conspicuously, we observe similar trends for the market pairs, and therefore conclude that our results are invariant with the size of the rolling-window.
5. Conclusions
This study investigates the behavior of global markets amid the onset and the different stages of COVID-19. We represent the first period as pre-COVID-19 (January 1, 2016, to December 07, 2019) – this period is characterized by high financial market growth and stability; the second period as an epidemic (December 07, 2020, to March 10, 2020), which is considered as relatively high volatility in a specific country (China) or region; and the last as a pandemic stage (March 11, 2020, to August 17, 2020), branded as a period of heightened global markets instability. Four selected international markets, namely MSCI AWCI, S&P GSCI Energy, S&P GSCI Non-energy, and S&P Global REITs are used for the analysis. We accomplished the goal of the study by applying both static and dynamic measures to ascertain the extent to which COVID-19 has influence the interrelationship structure of global markets.
Overall, we detect that COVID-19, through its different stages has generally affected the relatedness patterns of global markets. Thus, co-movements of the markets become stronger during the heightened periods of COVID-19’s epidemic and pandemic, and as a result, erodes, to a greater extent, the likelihood of diversification benefits. These increases in co-movement are attributed to the loose monetary and financial measures as well as stringent interventions imposed central banks and government, worldwide, as panic remediations to curtail global economic meltdown.
In conclusion, hitherto the general observation that global markets comove during episodes of heightened crisis, our study provides a strong evidence that these correlations are not just strong during the entire period but stronger at the peak of the crisis or pandemic stage (March 11, 2020, to August 17, 2020, in the case of COVID-19). This situation is likely to vary from country to country due to the degree of aggressive interventions and restrictions introduced by respective central banks and governments. The findings of this study indicate that health crisis (COVID-19) can have important implications for global markets through some transmission channels. It is thus important that policy-makers, through research, begin to identify these important channels and fashion both institutional and regulatory policies to address them. Future research can widen the scope to cover relevant aspects of this study by asking the following questions; what accounts for the differences in global markets or countries with dissimilar reactions to COVID-19 pandemic? Can these differences in reaction across global markets and countries be attributed to different approaches to the conduct of monetary policy or institutional characteristics? Can the differences in responses of global markets be due to approaches adopted by various countries in handling the pandemic? Even though COVID-19 health crisis has been pronounced a global pandemic, its negative impact has not been equally distributed, leading to dissimilar responses across countries. By asking and addressing the above questions using different techniques, researchers will produce findings with a strong heterogeneity.
Acknowledgments
The authors acknowledge the efforts of Mia Vulovic. We are also grateful to the editor and all prospective reviewers. As the usual caveat goes, all views and errors/omissions are that of authors, and do not in any way represent the positions of our institutions.
Conflict of interest
The authors declare no conflict of interest.
Appendix A: A 120-day RWWC for pairs of selected global markets.
Note: the strength of the dynamic wavelet correlation for the pairs is displayed by the heat map colors, which ranges from weak to high (thus, from blue, cyan, green, white, yellow, orange, to red respectively), where red (blue) denotes highest (lowest) wavelet correlation coefficients within a 95% confidence interval (refer to the web version of the article for color representation).
Appendix B: Cross-wavelet squared coherence with phase difference for selected pairs of global markets.
Note: In the wavelet coherence plots, the time interval (01.04.2016–2017.08.2020) is represented by the horizontal axis, while the vertical axis gives the frequencies (ranging from a 2-day to 128-day or half-year band). The degree of coherence is described by the heatmap, where warmer greenish to reddish colors denote medium-to-high interrelationship, and the light to deep blue indicates weak to uncorrelated markets. The 5% statistically significant coherence is displayed within the zones bounded by the white contours, and also confined to the “cone of influence” (the bell-shaped region), beyond that, coherence estimates become spurious. The direction of coherence is detected through the phase arrows, where the left and right black arrows denote that the two market series are out-of-phase (negative correlation or opposite movement) and in-phase (positive correlation or same direction of movement) respectively. Down pointing arrows put the first series as a leader; upward arrows mean the second is leading; right and down means the first series is leading; right and up suggest the first is lagging; the first series lags when the arrows point left and down, and leads the second series when the directional arrows point left and up. In all our plots, the first and second series correspond to the positions of the figure caption.
\n',keywords:"global markets, COVID-19 outbreak, co-movement, RWWC, portfolio diversification, JEL classification: C22, G15",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/74536.pdf",chapterXML:"https://mts.intechopen.com/source/xml/74536.xml",downloadPdfUrl:"/chapter/pdf-download/74536",previewPdfUrl:"/chapter/pdf-preview/74536",totalDownloads:409,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:1,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:50,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"June 19th 2020",dateReviewed:"November 22nd 2020",datePrePublished:"December 22nd 2020",datePublished:"February 24th 2021",dateFinished:"December 22nd 2020",readingETA:"0",abstract:"The COVID-19 pandemic has in its short existence caused economic downturn and affected global markets. As would be expected, the occurrences of global crises or shocks often heighten uncertainties in international markets and increase correlations among them. Yet, not much is known of the actual impacts of COVID-19 on the behavior of global markets. This piece attempts to investigate whether the COVID-19 crisis has had any impact on the interrelationship structure of international markets using the cross-wavelet squared coherence and a dynamic wavelet correlation technique. It emerges that co-movements of the pairwise series become stronger (0.70–0.89) during the heightened periods labeled as epidemic and pandemic phases of COVID-19, than that of the periods that mark the pre-COVID-19 era (−0.49–0.36), hence announcing the influence of the crisis and eroding prospect of benefiting from a hedge instrument and/or a diversifier. Again, we observe that stock market-Global REITs have been the most influenced pair, showing significantly peaked co-movements (0.63–0.87) during the distinct phases of COVID-19. We attribute these developments to the loose monetary and financial measures implemented by central banks of the world. The findings hold important implications for economic and financial actors regarding diversification, hedging, and investment risk management.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/74536",risUrl:"/chapter/ris/74536",book:{id:"10065",slug:"wavelet-theory"},signatures:"Maurice Omane-Adjepong, Imhotep Paul Alagidede and John Bosco Dramani",authors:[{id:"324897",title:"Ph.D. Student",name:"Maurice",middleName:null,surname:"Omane-Adjepong",fullName:"Maurice Omane-Adjepong",slug:"maurice-omane-adjepong",email:"omane.maurice@gcuc.edu.gh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"327959",title:"Prof.",name:"Imhotep",middleName:null,surname:"Paul Alagidede",fullName:"Imhotep Paul Alagidede",slug:"imhotep-paul-alagidede",email:"imhotep.alagidede@wits.ac.za",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of the Witwatersrand",institutionURL:null,country:{name:"South Africa"}}},{id:"335781",title:"Dr.",name:"John",middleName:null,surname:"Dramani Bosco",fullName:"John Dramani Bosco",slug:"john-dramani-bosco",email:"boscodramani@yahoo.co.uk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Kwame Nkrumah University of Science and Technology",institutionURL:null,country:{name:"Ghana"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Literature review",level:"1"},{id:"sec_3",title:"3. Data and methods",level:"1"},{id:"sec_3_2",title:"3.1 Data",level:"2"},{id:"sec_4_2",title:"3.2 RWWC methodology",level:"2"},{id:"sec_6",title:"4. Empirical results",level:"1"},{id:"sec_7",title:"5. Conclusions",level:"1"},{id:"sec_8",title:"Acknowledgments",level:"1"},{id:"sec_11",title:"Conflict of interest",level:"1"},{id:"sec_9",title:"",level:"1"}],chapterReferences:[{id:"B1",body:'Aramonte, S., & Avalos, F., 2020. The recent distress in corporate bond markets: cues from ETFs (No. 6). Bank for International Settlements'},{id:"B2",body:'Fontaine, J.S., Ford, H., & Walton, A., 2020. COVID-19 and bond market liquidity: Alert, isolation and recovery. Staff Analytical Note 2020–2014'},{id:"B3",body:'Shiller, R., & Malkiel, B., 2020. Does Covid-19 prove the stock market is inefficient? Pairagraph. Available online at: https://www.pairagraph.com/dialogue /c93c449006c344ce94e6e2e8fbe7aba3. (Accessed August 05, 2020)'},{id:"B4",body:'Krugman, P., 2020. Crashing Economy, Rising Stocks: What’s Going On? New York Times. Available online at: https://www.nytimes.com/2020/04/30/opinion /economy-stock-market-coronavirus.html. (Accessed August 30, 2020)'},{id:"B5",body:'Capelle-Blancard, G., & Desroziers, A., 2020. The stock market is not the economy? Insights from the COVID-19 crisis. Insights from the COVID-19 Crisis. CEPR Covid Economics'},{id:"B6",body:'The Economist. 2020. Oil and commodity prices are where they were 160 years ago. The Economist Magazine. Available online at: https://www.economist.com/ graphic-detail/2020/04/27/oil-and-commodity-prices-are-where-they-were-160-years-ago. (Accessed August 15, 2020)'},{id:"B7",body:'Jefferson, M., 2020. A crude future? COVID-19s challenges for oil demand, supply and prices. Energy Research & Social Science, 68, p.101669. https://doi.org/ 10.1016/j.erss.2020.101669'},{id:"B8",body:'Schnure, C., 2020. The outlook for REITs during the COVID-19 crisis. REIT Newsletter for Advisors. Available online at: https://www.reit.com/news/blog/market-commentary/outlook-reits-during-covid-19-crisis. (Accessed August 10, 2020)'},{id:"B9",body:'Goodell, J.W., & Goutte, S., 2020. Co-movement of COVID-19 and bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, p.101625. https://doi.org/10.1016/j.frl.2020.101625'},{id:"B10",body:'Omane-Adjepong, M., & Alagidede, I. P., 2020. Dynamic linkages and economic role of leading cryptocurrencies in an emerging market. Asia-Pacific Financial Markets https://doi.org/10.1007/s10690-020-09306-4'},{id:"B11",body:'Ramelli, S., & Wagner, A.F., 2020. Feverish stock price reactions to COVID-19. Swiss Finance Institute Research Paper (20–12)'},{id:"B12",body:'Baker, S.R., Bloom, N., Davis, S.J., Kost, K.J., Sammon, M.C., & Viratyosin, T., 2020. The unprecedented stock market impact of COVID-19 (No. w26945). National Bureau of Economic Research'},{id:"B13",body:'Ehrmann, M., & Jansen, D.J., 2020. Stock Return comovement when investors are distracted: More, and more homogeneous. CEPR Discussion Paper No. DP14713'},{id:"B14",body:'Haddad, V., Moreira, A. & Muir, T., 2020. When selling becomes viral: Disruptions in debt markets in the covid-19 crisis and the fed’s response (No. w27168). National Bureau of Economic Research'},{id:"B15",body:'Kargar, M., Lester, B., Lindsay, D., Liu, S., Weill, P.O., & Zúñiga, D., 2020. Corporate bond liquidity during the COVID-19 Crisis. NBER Working Paper No. 27355'},{id:"B16",body:'Arezki, R., Fan, R.Y., & Nguyen, H., 2020. Covid-19 and oil price collapse: Coping with a dual shock in the Gulf cooperation council. ERF Policy Brief No. 52'},{id:"B17",body:'Barbosa, F., Bresciani, G., Graham, P., Nyquist, S., & Yanosek, K., 2020. Oil and Gas after COVID-19: The Day of Reckoning or a New Age of Opportunity? McKinsey and Company'},{id:"B18",body:'Coibion, O., Gorodnichenko, Y., & Weber, M., 2020. The cost of the Covid-19 crisis: lockdowns, macroeconomic expectations, and consumer spending. NBER Working Paper No. 27141'},{id:"B19",body:'Gençay, R., Selçuk, F., & Whitcher, B., 2002. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press'},{id:"B20",body:'Ranta, M., 2010. Wavelet multiresolution analysis of financial time series. Ph.D Thesis, University of Vaasa, Finland Acta Wasaensia'},{id:"B21",body:'Ranta, M., 2013. Contagion among major world markets: A wavelet approach. International Journal of Managerial Finance, 9(2): 133–149. https://doi.org/ 10.1108/17439131311307556'},{id:"B22",body:'Fernández-Macho, J., 2012. Wavelet multiple correlation and cross-correlation: A multiscale analysis of Eurozone stock markets. Physica A: Statistical Mechanics and its Applications, 391, 1097–1104. https://doi.org/10.1016/j.physa.2011.11.002'},{id:"B23",body:'Percival, D.B., & Walden, A.T., 2006. Wavelet methods for Time Series Analysis. Cambridge University Press, 2006'},{id:"B24",body:'Polanco-Martínez, J.M., Fernández-Macho, J., Neumann, M.B., & Faria, S.H., 2018. A pre-crisis vs. crisis analysis of peripheral EU stock markets by means of wavelet transform and a nonlinear causality test. Physica A: Statistical Mechanics and its Applications, 490, 1211–1227. http://dx.doi.org/10.1016/ j.physa.2017.08.065'},{id:"B25",body:'Whitcher, B., Guttorp, P., & Percival, D.B., 2000. Wavelet analysis of covariance with application to atmospheric time series. Journal of Geophysical Research, 105(D11): 941–962. https://doi.org/10.1029/2000JD900110'},{id:"B26",body:'Grinsted, A., Moore, J.C., & Jevrejeva, S., 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11, 561–566. https://doi.org/10.5194/npg-11-561-2004'},{id:"B27",body:'Omane-Adjepong, M., & Dramani, J.B., 2018. On the dynamic effects of global commodities on stock market blocks in Africa. Applied Economics Letters, 25(11): 800–805. https://doi.org/10.1080/13504851.2017.1368978'},{id:"B28",body:'Nguyen, T.T.H., Naeem, M.A., Balli, F., Balli, H.O., & Vo, X.V., 2020. Time-frequency comovement among green bonds, stocks, commodities, clean energy, and conventional bonds. Finance Research Letters. (In-Press). https://doi.org/10.1016/j.frl.2020.101739'},{id:"B29",body:'Samadi, A.H., Owjimehr, S., and Halaf, Z.N., 2020. The Cross-Impact between Financial Markets, Covid-19 Pandemic, and Economic Sanctions: The Case of Iran. Journal of Policy Modeling. Article in Press'}],footnotes:[{id:"fn1",explanation:"International bonds, bills, and the currency markets are beyond our scope, perhaps, they could be considered in future studies."},{id:"fn2",explanation:"For the applicable COVID-19 timelines, refer to: https://www.weforum.org/agenda/2020/04/coronavirus-spread-covid19-pandemic-timeline-milestones/ (Accessed on July 29, 2020)."},{id:"fn3",explanation:"For recent applications, readers may refer the works by Polanco-Martíneza et al., (2018); Omane-Adjepong and Alagidede [10]; Nguyen et al., (2020); etc."},{id:"fn4",explanation:"It is imperative knowing that the weight composition of MSCI ACWI and SREITGUP captures several developed markets in Europe."}],contributors:[{corresp:"yes",contributorFullName:"Maurice Omane-Adjepong",address:"omane.maurice@gcuc.edu.gh",affiliation:'
Wits Business School, University of the Witwatersrand, 2 St. David’s Place, Parktown, South Africa
'},{corresp:null,contributorFullName:"Imhotep Paul Alagidede",address:null,affiliation:'
Wits Business School, University of the Witwatersrand, 2 St. David’s Place, Parktown, South Africa
Economics Department, Kwame Nkrumah University of Science and Technology, Ghana
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1. Introduction
Since the early 19th century, indentation technique has been extensively used for characterizing the mechanical properties of vast range of materials. In general, the indentation test is known to measure the hardness of materials. In conventional techniques, the mean contact pressure (MCP) upon indenting a specimen surface is evaluated. This is done on the basis of the residual area measured from the image of the indent impression and the known value of the applied load. The quantitative parameter, thus evaluated, represents the material’s response against deformation. In fact, MCP measured at the fully developed plastic zone is known as hardness [1]. With the progress in the technology and its incorporation in the experimental setup, instrumented indentation technique, particularly ‘nanoindentation’ has been evolved to assess various mechanical as well as metallurgical properties of a range of materials [2, 3, 4]. This includes characterizing elastic moduli, residual stress, creep properties, dislocation density, strain rate sensitivity etc. [5, 6, 7, 8, 9, 10, 11, 12]. Among all these developments, the potential of the nanoindentation technique in generating the indention stress (σind) – indentation strain (εind) curve is the most recent one and it is explained in detail in this present chapter [2, 3].
In nanoindentation, the associated high-resolution depth sensing technique aids to estimate the depth or size of the deformation zone. The process records the continuous response of indentation load (P) in the range of μN vs. indentation depth (h) in the magnitude of nm. The P-h curve obtained therein helps to assess the various properties of the studied materials. Unlike the conventional technique, in instrumented nanoindentation, hardness is estimated by using indirect measurement of projected contact area from P-h curve and the known geometry of the indenter tip. Similarly, elastic modulus of the material is estimated using the slope of the unloading segment in the P-h response of materials [13]. This method of analysis has been used for various scientific studies to characterize the localized mechanical properties of the samples in sub-micron scale. In fact, this revolutionary modification in the assessment methodology through nanoindentation has opened up a wide range of studies to extract the different relevant mechanical properties of materials on a small-scale.
One of the breakthroughs is the capability of this technique in generating the σind - εind response of a material of interest [3, 14, 15, 16]. This novel and recent development plays a significant role in understanding the localized deformation capability of materials system. This is particularly because stress – strain characteristics can provide an insight into the elastic – plastic mechanisms of the materials, as per the conventional notion. In fact, estimation of localized stress – strain characteristics of a material through nanoindentation can even be a substitute for typically used small-scale characterization techniques for instance, micro-pillar compression [17, 18]. Nevertheless, nanoindentation is further beneficial owing to its easier sample preparation, simplicity in experimental execution, and non-destructive nature. This technique therefore has enormous potential for evaluation of small-scale mechanical properties of materials with minimal effort.
Considering this, the present chapter is dedicated to provide a reasonable understanding for generating σind - εind data from the P-h curve of nanoindentation. To develop a more conceptual idea for a new reader, the importance of indenter tip geometry in activating different deformation modes within the indented volume are discussed at the first hand. Subsequently, the basic relationships for the indentation, the method of analysis and generation of protocol for obtaining the σind - εind curve will be discussed.
2. Role of indenter configuration
It is noteworthy that both the uni-axial tensile/compression test as well as the indentation technique are capable to assess the stress–strain characteristics of a material, however, with usually different size-scale of samples along with varying stress-states. The former provides an understanding for the degree of bond stretching induced elastic deformation and dislocation mediated plastic/permanent deformation in the material. To obtain such desired information, the strain-induced into the material should be controlled in such a way that, the material’s response reflects the gradual activation and transition from the elastic to the plastic deformation. This is realized in uni-axial deformation without any strain gradient in the specimen, at least macroscopically.
In contrary to that, upon indentation, presence multi-axial state of stress exists beneath the indenter tip. Moreover, the constraint nature of deformation induces strain gradient within the deformation volume. Hence, for assessing the elastic–plastic activity within the deformation zone, the indentation tests need to be specially designed to produce a smooth strain distribution (or gradient) along with its gradual increment. To maintain that, indenter tip geometry needs to be carefully chosen to reflect the σind - εind characteristics from the localized region. In this regard, the most suitable indenter configuration is spherical tip (or sphero-conical indenter).
Before getting into the details about the configuration of the spherical indenter tip and its importance for σind - εind generation, the reader needs to develop a comprehensive idea about the different type of indenter tips that are used in general. For the same, the geometrical aspect of indenter configuration is briefed here. From a geometrical point of view, indenters are classified into two: (i) geometrically similar indenters (GSI) and (ii) non-geometrically similar indenter (N-GSI) [4].
The most commonly used sharp pyramidal indenter such as four-sided Vickers (for micro-and macro-indentation) and three-sided Berkovich indenters (for nanoindentation) comes under the category of GSI. On the other hand, the spherical indenter falls under the category of N-GSI. The major difference in deformation characteristics experienced by a specimen surface, by indenting with any of these two categories of indenter tips can better be appreciated from Figure 1. Schematic representations in Figure 1(a-c) show the deformation modes activated in traditional elastic–plastic material while increasing the indentation load/depth, using GSI. The mathematical relation for geometrical similarity originates from the ratio of the contact radius (ac) to the maximum depth of indentation (hmax). For GSI, ac-i/hmax-i = ac-j/hmax-j = ac-k/hmax-k = constant. The subscript, i, j, k signify increasing level of h. Nevertheless, this constant ratio of ac/hmax ensures that the size of the deformation zone of indentation varies uniformly irrespective of the depth of penetration. This helps to estimate the property of the subjected material independent of the applied indentation load/depth. Nevertheless, owing to the sharp nature of the Vickers and Berkovich indenter, the strain-induced within the indentation volume is large enough to generate significant plastic deformation [1]. In that case, dislocation activity is always the dominant mechanism within the deformation volume beneath the indenter tip, irrespective of the change in depth of indentation, as apparent from Figure 1. This assists to precisely measure the hardness of a material independent of the indentation load, in the theoretical sense. However, it is realized that GSI is not adequate to assess the elastic deformation response of the indented material. In fact, while using conventional Vickers and Berkovich indenters, occurrence of prominent dislocation activity within the deformation volume negates any influence of elastic activity therein. This acts as the limitation of the most commonly using Vickers and Berkovich indenter for generating the stress–strain curve.
Figure 1.
Schematic illustration of the indentation behavior associated with traditional elastic-plastic metallic using (a-c) sharp geometrically similar indenter and (d-e) spherical non-geometrically similar indenter at various indentation depths.
On the other hand, a completely different deformation response is being experienced, while the specimen surface is indented using N-GSI (spherical tip) with increasing indentation load/depth. Figure 1(d-f), illustrate the deformation scenario within the indentation volume, in such case. It is evident from the figure that, nature of deformation is entirely different in comparison to that for GSI. This difference originates from the non-geometrical similarity of the indenter. In case, the specimen surface is indented with a N-GSI, ac-i/hmax-i < ac-j/hmax-j < ac-k/hmax-k. This essentially means with the progress of the indentation, increment in the contact radius becomes more pronounced with respect to the depth of penetration. Such movement of indenter within the material surface gradually increases the induced strain/stress into the material. Also, the blunt nature of the indenter assists in generating a smooth stress field within the indentation volume, specifically as compared to GSI. As a net effect, spherical indenter facilitates a gradual activation of elastic to the plastic deformation mechanism. This potential for gradual instigation of the deformation mechanism similar to that observed in case of uni-axial test, is exploited for σind - εind generation from nanoindentation.
Nevertheless, the most crucial part in this regard is the data analysis procedure that is necessary to convert the indentation P-h response into a reliable σind - εind curve. There have been numerous attempts to obtain a stress–strain curve from traditional indentation as well as instrumented one. In the process, the protocol for generation of indentation stress–strain curve has undergone various alterations, to precisely correlate the materials’ property. In the next section, we have briefed the different approaches adopted to appreciate the σind - εind behavior of a material. This will help to understand the scientific developments that has been materialized on this particular topic, so far.
3. Evolution of σind - εind generation protocols
The concept for the generation of σind - εind curve from indentation is introduced by Tabor in the 1950s. Tabor has measured the MCP on the specimen indented with a spherical tip to estimate the stress that is induced in the process [1]. The most crucial part, however, is the estimation of εind. Tabor defined εind by the relation (d/D), where d is the diameter of the residual impression and D is the diameter of the indenter tip. Here d is measured using the traditional approach, i.e., by imaging of residual impression after unloading. The general trend of σind - εind characteristics of materials, generated following Tabor’s protocol, resembles well with that evaluated through traditional uniaxial compression test [1]. However, this method of analysis accounts for only single σind - εind data from an indentation. So, it means that several indentation tests with different indentation parameters are necessary to be pursued, to obtain a continuous σind - εind curve for a material, making the process cumbersome.
Nevertheless, Tabor’s approach revealed the potential of the indentation technique and instigated more studies to develop a state-of-the-art protocol for generating σind - εind curve of a material. In this regard, automation through the instrumented indentation has opened up enormous possibilities to generate the σind - εind curve using a single indentation. In turn, the localized deformation behavior of a material can be precisely obtained. First among all is the Field and Swan approach [19]. They have proposed to incorporate multiple partial unload segments during each indentation. Here, the P-h responses obtained for each particular segments are used to measure the corresponding σind and εind values. The strain, on the other hand, is estimated using the relation a/Ri, where Ri represents the radius of the indenter tip. As per Field and Swan approach, the deformation associated in each unloading segment is assumed to be purely elastic. Correspondingly, the classical Hertzian elastic relationship (explained in the next section by Eq. (1)) is applied on those P-h responses to assess the contact radius, a. From the measured a value, contact area (Ac) is estimated instead of residual impression-based analysis in Tabor’s protocol.
The Field and Swan approach has much significance in the present scenario, owing to its implementation of the Hertzian contact mechanics theory. Nevertheless, interpretation of indentation strain as per both Tabor’s as well as Field and Swan approaches has been questioned for its integrity with the fundamental concept of strain. In general, strain is defined as the ratio of change in length to the initial length in a region of deformation considered. However, this fundamental relationship is not met in both these above-mentioned approaches.
In order to overcome this fundamental lacking, various studies have been conducted to formulate an adequate relationship for the εind. Among those attempts, the protocol developed by Kalidindi and Pathak has succeeded in defining εind as per the most basic concept of strain [16]. The present chapter is extensively covering the formulation and implementation of Kalidindi and Pathak protocol for the generation of σind - εind curve for a material subjected to nanoindentation. This protocol is essentially formulated based on classical Hertzian theory, which is explained below.
4. Contact mechanics for spherical tip-based indentation
Contact mechanics theory introduced by Hertz has provided a fundamental basis for the indentation technique [20]. Classical Hertzian theory predicts the elastic responses of frictionless contact between two different bodies of dissimilar geometries (with varying properties) in contact. This theory is formulated based on the assumption that material is homogenous and isotropic. In the present scenario of indentation using spherical indenter, the Hertzian theory for elastic contact between the sphere (indenter) and elastic half-space (specimen surface) is used for the formulation of σind - εind generation. In the indentation aspect, the material of interest is considered as an elastic half-space by following the criteria that indenter tip radius (Ri) should be at least ten times smaller than the horizontal dimensions of the sample [21].
As explained in previous Section 2 (see Figure 1(d-f)), indentation using spherical indenter tip facilitates the gradual activation of elastic to plastic mechanisms in the material. Therefore, for the sake of understanding, the overall deformation scenario can be categorized into (i) fully elastic and (ii) plastic following the initial elastic section. The schematic representation of these two modes of deformation and their corresponding P-h response is showed in Figure 2. In the first case, material recovers all the depth it penetrated upon the indentation (see Figure 2(a) and (c)). In the second case, some amount of permanent deformation is existing within the indentation volume (see Figure 2(b) and (d)). Hertz has provided the basis for the elastic deformation associated in two former cases using the relation below,
Figure 2.
Schematic representation of indentation of behavior of material in (a) fully elastic condition and in the pressure of (b) plastic deformation. Corresponding indentation load vs. indentation depth responses of materials are shown in (c) and (d).
P=43EeffReff12hr32E1
1Eeff=1−νs2Es+1−νi2Ei,1Reff=1Ri−1RsE2
Here P is the applied load, hr is the recoverable depth, Reff is the effective tip radius and Eeff is the effective elastic moduli. All the characteristic terms mentioned here can be appreciated from Figure 2(b). The terms hmax and hp in Figure 2 represent the maximum depth of indentation at Pmax and recurring plastic depth of indentation post-unloading (P is zero), respectively. In the Hertzian relation, the role of elastic deformation on the two mating parts is assessed using Eeff. The value of Eeff accommodates the elastic deformation associated with the hard indenter and soft sample. Eeff during the indentation is estimated using the relation (2). Similarly, Reff takes into account the influence of plastic activity on the overall deformation. It is related to the indenter tip radius (Ri) and the radius of curvature of the sample (Rs) upon the indentation. Reff of the sample is estimated using the relation (2).
All these relations derived by Hertz has laid the foundation for the formulation of σind - εind data from the nanoindentation P-h response. This is explained in details in the following section.
5. Defining the indentation stress and indentation strain
It is well understood from Section 3 that Tabor’s and Field and Swan’s protocols do not suffice to define the εind precisely. Nevertheless, Kalidindi and Pathak have defined the σind and εind by considering the size of the deformation zone formed beneath the indenter and correlated it with the fundamental Hertzian relationship [16]. This protocol has succeeded in producing comprehensive σind - εind data from the nanoindentation experiments (explained in Section 6).
As per this novel approach, eq. (1) is rearranged by incorporating the following relations:
σind=Pπa2;σind=Eeffεind;εind=43πhra≈hr2.4aE3
a=ReffhrE4
The indentation strain defined using the above relationship satisfies the general definition of strain. This can be better appreciated from Figure 3(a). In the figure, the dashed spherical shaped region beneath the indenter tip schematically shows the actual size of the deformation zone upon indentation. Based on the εind defined from Hertzian relation, the length of the deformation zone beneath the indenter tip at Pmax is noted to be ∼2.4a. Interestingly, a simulative study on the prediction of indentation behavior strongly agrees with this relation for tungsten and aluminium [16]. This has validated the new definition of εind, which is derived without any alteration of the fundamental Hertzian relation. This novel protocol is remarkably different yet comprehensive with respect to the other discussed approaches. This is primarily because it basically takes into account the actual size of the deformation zone during the indentation, rather than simply estimating the εind data using the concept of variation in indent impression.
Figure 3.
(a) Schematic representation of the deformation behaviour associated with indentation. Figure highlights the actual deformation zone of indentation and the idealized deformation zone of indentation. (b) Schematic representation of surface irregularities on a sample.
Furthermore, this novel protocol has provided a reasonable basis for the analogical comparison of indentation behavior using spherical indenter and uniaxial compression test. The overall nature of the material response upon nanoindentation can be considered as the replication of compressing up to a depth of hmax on a cylindrical sample of height 2.4a and radius a. To visualize it clearly, the idealized deformation zone of indentation and actual deformation of indentation is schematically shown in Figure 3(a). The shape of the actual deformation zone formed is schematically showed as spherical. The reader should be aware that, in reality, owing to the anisotropy in material’s properties, the actual shape of the deformation zone of indentation can be slightly different from this schematic representation. It is also noteworthy that with slight alternation in relation (4), hmax can be used instead of hr in the numerator to accommodate the plastic activity [15]. This whole theoretical concept has paved the way for generating σind - εind curve from the P-h signal in nanoindentation. To realize it in a practical scenario, the reader has to understand the necessary steps to follow for obtaining a reliable output.
6. Theoretical conceptualization to experimental execution
As mentioned in Section 1, nanoindentation typically generates a P-h response and its characteristics define the mechanical property of the material indented. Compared to any other characterization technique, particularly, the most commonly used uni-axial tests, the size of the active deformation region for nanoindentation is extremely small. Therefore, proper measures are necessary at every steps right from the precise sample preparation to the careful data analysis to obtain reliable data.
6.1 Sample preparation
The existence of an artefacts such as scratches or the presence of foreign particles on the surface can influence the P-h signal and thereby the generated σind - εind data. The poorly polished samples create a scratch on the surface, the depth of which can be in hundreds of nanometres. Data recorded from such a region will certainly influence the overall σind - εind characteristics and consequently alter the assessment of the true properties of the material. This can be visualized and understood from the schematic representation in Figure 3(b). In the figure, red coloured triangular shape and yellow coloured circular shape reveal the presence of sample surface roughness and foreign particles respectively. As per the indentation sequence, the indenter will first acquire the data from those artefacts and move to the bulk of the sample. So, actual material which is supposed to show the pure elastic response initially, is now influenced by the presence of sample surface artefacts. As a net effect, the P-h response from the bulk sample is influenced by the surface roughness/foreign particle. Hence, the assessed properties are certainly different from the true ones [1]. In case of conventional uniaxial tests, such misinterpretation of results can be obtained in case a specimen slips upon loading, or even when elastic properties are estimated from a tensile experiment, without attaching an extensometer to the test specimen.
To avoid such issues, well-polished, smooth, flat and plane-parallel specimen should be subjected to nanoindentation. The necessary steps to achieve such artefact free surface vary with the material of interest. However, colloidal silica polish for few hours (minimum 3 h) after the conventional polish using silicon carbide paper with decreasing mesh size and diamond polish is prescribed for metallic specimens, to attain a reasonably good surface condition for the σind - εind generation. Depending on the surface characteristics of the material, electropolishing may also appear to be a better option to minimize the artefacts on the sample surface.
6.2 Conversion of experimental P-h data to effective P-h data
It is noted that theoretical predictions and the experimental outcome may result to some disparities in case of the nanoindentation test. In this regard, it is noteworthy that proper data analysis plays a key role in the generation of σind - εind curve. It is highlighted in the previous section (Section 6.1) that nanoindentation experiments mandate extremely good quality surface finish. Nevertheless, obtaining the required surface finish is difficult in practice. A proper data correction route on the experimentally obtained P-h curve, on the other hand, can negate the role of artefacts on the σind - εind analysis. This step is crucial to compute a reliable stress–strain curve. For the same, effective initial contact point between the indenter tip and the specimen surface is estimated following the “zero-point correction” (ZPC). In fact, ZPC deals with discarding the data points which are influenced by unavoidable surface irregularities. In turn, the effective contact point is determined on the basis of Hertzian theory which reciprocates the material behavior. According to the type of nanoindentation instrument used, Kalidindi and Pathak have proposed two different approaches for the data correction using ZPC. One is for nanoindenter with (a) Contact Stiffness Mode, CSM (or Dynamic Mechanical Analysis, DMA) and another for (b) Non-Contact Stiffness Mode, N-CSM [22]. These two modes are slightly different in the method of experimentation.
6.2.1 CSM mode or DMA mode
In CSM or DMA mode, harmonic force is imposed in the loading and unloading segment during the indentation. This is highlighted at the inset (a) of Figure 4. It can be hypothetically viewed as if the specimen undergoes multiple indentations with minimal depth scale (2 to 4 nm) while conducting a single indentation. Displacement responses corresponding to these harmonic forces are recorded throughout the indentation. These assist in assessing the variation in contact stiffness, S (ordPdh) upon the indentation. Precise determination of S from each steps of CSM leads to estimate the continuous variation in the related properties of materials with increasing h, for example, hardness and elastic modulus changes [22].
Figure 4.
Indentation load vs. indentation depth response generated using nanoindentation. Insets in the figure highlight the method of indentation in CSM mode and N-CSM mode.
In the present scenario, the continuously varying S, hr, and P are obtained from the CSM mode of the nanoindenter and these signals are used for ZPC. For the same, the Hertzian relation (Eq. (1)) for elastic contact is rearranged into the following relationship,
P−23hrS=−23h∗S+P∗E5
Here P* and h* denote the effective indentation load and depth respectively. A linear regression analysis on relation (5) helps to trace the P* and h* values through the slope (−23h∗) and y-intercept (P*). Once the P* and h* are established, the experimentally generated P-h signal has to be corrected for obtaining an ‘effective P-h curve’, which is devoid of any influence from the surface artefacts [16].
6.2.2 Non-CSM mode
In N-CSM mode, indentation is performed without harmonic force. This is also highlighted in inset (b) of Figure 4. In this particular case, ZPC is performed by recasting the Hertzian equation as per the relation below (derived from Eq. (1),
hr−h∗=kP−P∗,k=341Eeff1√ReffE6
In the above relationship, the k value is constant in the elastic segment [15]. It is worth reiterating here that within the elastic segment, continuously varying h equates with hr whereas Reff to Ri (explained in the subsequent Section 6.3). Also, prior understanding of elastic moduli of the material makes the calculation much easier. Essentially, regression analysis on the initial elastic segment of experimentally obtained data helps to calculate the values of P* and h* and thereby the effective P-h data is estimated.
6.2.3 Selection of data segment
The above-mentioned data correction procedures for nanoindenter with CSM or N-CSM mode, ideally has to be performed on the initial elastic segment of the P-h segment. Such elastic segment dwells within few nanometers, in reality. The exact value of this elastic segment however varies with the sharpness (or bluntness) of the indenter tip and the associated variation in strain gradient [2]. The question here is how to precisely choose a segment in the P-h curve which can be used for the data correction using Eqs. (5) and (6). This can be realized through the iteration process on the initial segment with a different depth limit. For instance, for nanoindentation with hmax of 250 nm, regression analysis has to be performed in initial segments with indentation depth of 10 nm (or any other limit) to higher. By doing so, the accurate point of transition from elastic to plastic (data limit) can be approximately finalized based on the continuity nature observed in the effective P-h curve as well as the corresponding σind - εind curve (explained in next Section 6.3).
6.3 Conversion of effective P-h curve to σind - εind curve
As explained in Section 5, the Hertzian relation has provided a basis to obtain σind - εind curve from the P-h response. Once the effective P-h response is computed using the steps mentioned in Section 6.2, Eqs. (3) and (4) are used for obtaining the corresponding σind - εind values. In this conversion process, estimating the continuously varying a is important for calculating the continuous evolution in the σind and εind values. It is particularly evident from Eq. (4) that, a is the main characterizing parameter to obtain the σind and εind values.
Prior to going through further details, the physical significance of a and the mechanisms behind its alteration during indentation are explained through Figure 5. The figure schematically shows the indentation behavior of different materials with different extents of elastic–plastic activities. Sample-1 with green color indicates the material with full elastic recovery. Sample-2 (orange) and sample-3 (blue) exhibit the indentation behavior of two materials with different degrees of plastic activities along with elastic deformation. In a fully elastic material (sample-1), the indented surface recovers the whole depth upon the complete removal of load. Thereby Rs attains infinity in this case (see Figure 5). So, Reff = Ri for material with full depth recovery (see equation (2)). Similarly, owing to the full recovery, continuously recording h signal can equate with the depth recovery (hr). In short, Reff = Ri and h = hr within the elastic regime of material upon indentation.
Figure 5.
Schematic representation of the nature of deformation volume beneath the indenter tip for materials with three different degree of elastic-plastic property. Green line shows the sample with full depth recovery. The orange and light blue colors reveal the indentation behaviour of samples with different shares of elastic and plastic activities.
But, once the dislocation mediated plastic activity is instigated, Rs attains a finite value. The orange and light blue colors in Figure 5 reveal the formation of finite values of Rs in the materials due to the occurrence of plastic deformation. In these two cases, Reff is no longer equal to Ri. It is reported that Reff is significantly larger than Ri once plastic deformation initiates in the material. Almost a 100-fold increment in the Reff is reported with presence of plastic activity in aluminum sample [3].
All these physical changes are also related to hr after the unloading. While comparing sample-2 and sample-3, depth recovery is noted to be higher for the former. Correspondingly, Rs in the material also changes. It is apparent from the Figure 5 that Rs-1 > Rs-2 and hr-1 > hr-2. As a net effect of change in Reff and hr, contact between the indenter and sample deflects. This is reciprocated in the changes in a (a1 > a2). In conclusion, all three parameters are correlated which are primarily controlled by the share of elastic–plastic activities within the material of interest. Eq. (4) derived by Hertz relates all these physical phenomena and in the present scenario, it is utilized to estimate σind-εind curve using relation (3).
6.3.1 σind-εindfromCSM nanoindenter
Estimation of a from nanoindentation using CSM mode is straight forward. The interrelation between S and a are derived from Eqs. (1) and (4) according to the Hertzian theory, as shown below:
dPdh=2EeffReff1/2he1/2=2Eeffaa=S2EeffE7
The analytical significance of this mathematical derivation lies in the fact that unlike Tabor’s approach, this expression (eq. (7)) enables to assess the nature of deformation inside the material without a visual inspection of residual impression.
In the data analysis, once the evolving values of a are established using eq. (7), the final σind - εind curve is generated from Eq. (3). Figure 6(a) shows σind - εind curves obtained before and after ZPC on experimental P-h data. Interestingly, in this novel protocol, elastic moduli measured from the loading and unloading segments of the σind - εind curve are noted to resemble each other [16]. This observation has validated the new definition for εind as well as the novel protocol for reliably assessing the mechanical property via σind - εind curve.
Figure 6.
(a) σind−εind curve obtained before and after the zero-point correction. (b) Schematic representation of the P-h responses with multiple unloading segments for generating σind−εind curve in N-CSM measurement nanoindenter.
6.3.2 σind-εindfrom non-CSM nanoindenter
As compared to the CSM mode, experimentation and method of analysis is different in case of N-CSM mode of nanoindentation. In N-CSM mode, multiple unloading segments are introduced into the indentation test for measuring the evolution in a and thereby the continuous variation in σind and εind values. This is similar to Field and Swan approach in terms of experimentation. Figure 6(b) schematically shows the P-h curve obtained after the multiple unloading. Once the effective P-h curve is generated by employing ZPC, following Section 6.2, the evolving values of a are estimated from each segment. For the same, Reff value is estimated by fitting the unloading response using the modified Hertz relation as mentioned below,
hr=hmax−hp=kP2/3E8
here k is a function of Reff and Eeff (see relation (6)). Eeff can be traced from the prior understanding of elastic moduli of sample or from the initial elastic segment in the P-h curve [15]. So, from the understanding of k value of the respective alloys and the recorded value of hp with reduction in indentation load in the unloading segments, Reff is estimated by fitting using the relation (8). Once Reff is established, a can be determined from relation (4) and in turn σind - εind curve can be generated using Eq. (3). It is also important to note here that, number of data points in the resultant σind - εind curve depends on the number of unloading segments provided in the experiment.
7. Protocol for σind - εind generation in pseudoelastic shape memory alloys
Previous sections have elaborated the potential of the nanoindentation technique in appreciating the σind - εind characteristics of traditional elastic–plastic metallic systems. In a further extension, Sujith and Sen have revealed the capability of nanoindentation in assessing the unique pseudoelastic (or superelastic) properties of shape memory alloys (SMA) via σind - εind curve [2, 6]. This recent development has succeeded in the producing the specialized stress - strain characteristics of the pseudoelastic NiTi system using most commonly used N-CSM nanoindenter.
It is noteworthy at this point that as compared to the traditional elastic–plastic metallic alloys, pseudoelastic system is different owing to the occurrence of reversible stress-induced martensitic transformation (SIMT). In pseudoelastic alloys (some examples of metallic systems are NiTi, Cu-Al-Zn, Cu-Al-Ni, Ni-Ti-Fe, Fe-Mn-Si, Fe-Mn-Si-Co-Ni), parent austenitic phase transforms to product martensitic phase upon the application of stress and it reverts to the previous austenite with the release of stress. Owing to this reversible SIMT along with usual elastic deformation in the parent and product phase, the NiTi system in pseudoelastic state shows (8–10) % of recoverable strain. This is also reflected as a unique characteristic in the conventional uni-axial stress - strain curve. Hence, evaluating such unique property using nanoindentation requires special attention in terms of (a) optimizing indentation parameters as well as (b) tailored σind - εind generation protocol. This investigation by Sujith and Sen is the first of its kind to consider spherical indenter tips with varying Ri as well as Pmax levels with the aim to identify the optimum combination to precisely evaluate localized pseudoelasticity in SMA through nanoindentation. Following steps are briefed:
7.1 Optimizing indentation parameters
For optimizing the indentation parameters, a detailed analysis is performed on the P-h curve obtained from various indenter configuration (Ri of 10 μm, 20 μm and 50 μm) as well as Pmax (1 mN to 7 mN). Details of the experiments and analysis procedures are reported elsewhere [2]. However, the key observations in this method of analysis are mentioned here.
Optimization of indentation parameters is performed based on the close scrutiny of the experimentally generated P-h curve using Hertzian theoretical prediction and the understanding of the pseudoelastic behavior in the alloy system. Figure 7(a) shows the method of analysis performed on the P-h curve. The black solid and the red dashed curves in Figure 7(a) show the experimental results and Hertzian theoretical predication of indentation response, respectively. Using this comparison, overall deformation mode in the indentation is parted into different sections. Correspondingly, the depth of indentation, specifically influenced by pseudoelasticity is assessed. This can be even better appreciated from Figure 7(b). Physical variation associated with indentation volume of NiTi sample using Ri of 10 μm and 20 μm are schematically (in two halves) shown in Figure 7(b). The region influenced by reversible SIMT is highlighted as green color in the schematics. This novel method of analysis is performed using a range of combination of indentation parameter. The most adequate combination to assess the pseudoelasticity is identified based on the share of reversible SIMT activity and the overall depth recoverability (minimum 90% depth recovery). Based on this systematic analysis, spherical indenter with Ri of 20 μm and Pmax of 5 mN is noted to be most suitable combination for appreciating pseudoelasticity devoid of the influence of dominant plasticity, in NiTi system.
Figure 7.
(a) The P-h response of pseudoelastic NiTi system at its optimized nanoindentation parameter condition (Ri=20μm and Pmax=5mN). Red dotted curve shows the theoretical prediction of P-h response and the green dotted region infers the region that is dominantly influenced by reversible SIMT in the NiTi alloy. Ptr in the graph highlights the indentation load at which SIMT initiates in the material. (b) Schematic representation of share of different deformation mechanisms within the nanoindentation volume for pseudoelastic NiTi system indented using Ri of 10 μm and 20 μm. (c) σind−εind curve corresponding to the P-h response (Figure 7(a)). Here, σtr is the transformation stress to initiate SIMT [2].
7.2 σind - εind protocol
Considering the extremely high depth recoverability (≥ 90%) of pseudoelastic NiTi system, following assumption is used while generating the corresponding σind - εind curves,
Reff=Riandhr=hE9
Section 6.3 has already mentioned about the validity of this assumption when material shows full depth recovery. In the present scenario, same assumption is used with depth recovery limit of 90% of the hmax. This assists in converting the P-h response into σind - εind curve using Eq. (3), while employing relations (9) in it. Essentially, this new protocol defined the εind and a using following relation,
εind≈h2.4aa=RihE10
Prior to conversation of P-h results into σind - εind curve, ZPC is performed following Section 6.2.2. Figure 7(c) shows the σind - εind curve that is generated from the P-h response of NiTi system. Interestingly the curve has shown the signature trends of pseudoelastic system like sudden changes in the transformation strength, plateau strain, significant recovery etc. The transformation strength (σtr) of the NiTi system estimated from the nanoindentation resembles reasonably well with that derived from uni-axial compression test [2]. This has validated the present protocol for future analysis on smart characteristics of NiTi based shape memory alloys.
8. Closure
The present chapter elucidates the vast potential of nanoindentation technique to develop insights about the localized stress–strain characteristics of materials. Nevertheless, to achieve the σind - εind curve, experiments need to be carefully designed. Also, post indention analysis should be meticulously performed to obtain the reliable data. Blunt spherical indenter tip is primarily necessary to activate elastic and plastic mechanisms sequentially in the material and thereby to estimate the σind - εind curve. On the other hand, different post-indentation analysis has to be adopted based on the mode of nanoindenter and the material of interest to compute the indentation stress–strain data. Validation of the protocols are also discussed for pseudoelastic material systems. The detailed explanation provided in the present chapter based on the physical mechanism associated with different alloy system upon indentation and further data analysis can pave the way for future usage of this method of analysis in various studies.
\n',keywords:"nanoindentation, stress-strain curve, small-scale, plasticity, NiTi",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/77158.pdf",chapterXML:"https://mts.intechopen.com/source/xml/77158.xml",downloadPdfUrl:"/chapter/pdf-download/77158",previewPdfUrl:"/chapter/pdf-preview/77158",totalDownloads:186,totalViews:0,totalCrossrefCites:1,dateSubmitted:"February 10th 2021",dateReviewed:"May 20th 2021",datePrePublished:"June 12th 2021",datePublished:null,dateFinished:"June 12th 2021",readingETA:"0",abstract:"Nanoindentation is a widely used state of the art facility to precisely and conveniently evaluate the mechanical properties of a wide group of materials. Along with the determination of elastic modulus and hardness of materials, this chapter particularly aims to explore the possibilities to assess the corresponding stress–strain characteristics of elastic–plastic materials and most importantly unique pseudoelastic materials. The suitability of continuous stiffness measurement (CSM) based nanoindenter systems along with the adaptability of the instrument without CSM for precisely evaluating the deformation behavior of specialized materials is discussed in details. In this regard, the roll of indenter tip geometry and size is greatly emphasized. The recent research in the field is reviewed thoroughly and the updated protocol generated is illustrated.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/77158",risUrl:"/chapter/ris/77158",signatures:"Indrani Sen and S. Sujith Kumar",book:{id:"10943",type:"book",title:"Elasticity of Materials",subtitle:null,fullTitle:"Elasticity of Materials",slug:null,publishedDate:null,bookSignature:"Associate Prof. Gülşen Akın Evingür and Dr. Önder Pekcan",coverURL:"https://cdn.intechopen.com/books/images_new/10943.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-961-0",printIsbn:"978-1-83969-960-3",pdfIsbn:"978-1-83969-962-7",isAvailableForWebshopOrdering:!0,editors:[{id:"180256",title:"Associate Prof.",name:"Gülşen",middleName:null,surname:"Akın Evingür",slug:"gulsen-akin-evingur",fullName:"Gülşen Akın Evingür"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Role of indenter configuration",level:"1"},{id:"sec_3",title:"3. Evolution of σind - εind generation protocols",level:"1"},{id:"sec_4",title:"4. Contact mechanics for spherical tip-based indentation",level:"1"},{id:"sec_5",title:"5. Defining the indentation stress and indentation strain",level:"1"},{id:"sec_6",title:"6. Theoretical conceptualization to experimental execution",level:"1"},{id:"sec_6_2",title:"6.1 Sample preparation",level:"2"},{id:"sec_7_2",title:"6.2 Conversion of experimental P-h data to effective P-h data",level:"2"},{id:"sec_7_3",title:"6.2.1 CSM mode or DMA mode",level:"3"},{id:"sec_8_3",title:"6.2.2 Non-CSM mode",level:"3"},{id:"sec_9_3",title:"6.2.3 Selection of data segment",level:"3"},{id:"sec_11_2",title:"6.3 Conversion of effective P-h curve to σind - εind curve",level:"2"},{id:"sec_11_3",title:"6.3.1 σind-εind from CSM nanoindenter",level:"3"},{id:"sec_12_3",title:"6.3.2 σind-εind from non-CSM nanoindenter",level:"3"},{id:"sec_15",title:"7. Protocol for σind - εind generation in pseudoelastic shape memory alloys",level:"1"},{id:"sec_15_2",title:"7.1 Optimizing indentation parameters",level:"2"},{id:"sec_16_2",title:"7.2 σind - εind protocol",level:"2"},{id:"sec_18",title:"8. Closure",level:"1"}],chapterReferences:[{id:"B1",body:'D. Tabor, The Hardness of Metals, Oxford University Press, 1951.'},{id:"B2",body:'S. Kumar S, I.A. Kumar, L. Marandi, I. Sen, Assessment of small-scale deformation characteristics and stress-strain behavior of NiTi based shape memory alloy using nanoindentation, Acta Mater. 16375 (2020) 1–2. https://doi.org/10.1016/j.actamat.2020.09.080.'},{id:"B3",body:'S. Pathak, S.R. Kalidindi, Spherical nanoindentation stress-strain curves, Mater. Sci. Eng. R Reports. 91 (2015) 1–36. https://doi.org/10.1016/j.mser.2015.02.001.'},{id:"B4",body:'Antony C. fisher Cripps, Nanoindentation, Springer International Publishing, 2011.'},{id:"B5",body:'G.M. Pharr, An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments, J. Mater. Res. 7 (1992) 1564–1583. https://doi.org/10.1557/JMR.1992.1564.'},{id:"B6",body:'S. Kumar S, L. Marandi, V.K. Balla, S. Bysakh, D. Piorunek, G. Eggeler, M. Das, I. Sen, Microstructure – Property correlations for additively manufactured NiTi based shape memory alloys, Materialia. 8 (2019) 100456. https://doi.org/10.1016/j.mtla.2019.100456.'},{id:"B7",body:'J. Hu, W. Zhang, G. Peng, T. Zhang, Y. Zhang, Nanoindentation deformation of refine-grained AZ31 magnesium alloy: Indentation size effect, pop-in effect and creep behavior, Mater. Sci. Eng. A. 725 (2018) 522–529. https://doi.org/10.1016/j.msea.2018.03.104.'},{id:"B8",body:'I.C. Choi, B.G. Yoo, Y.J. Kim, J. Il Jang, Indentation creep revisited, J. Mater. Res. 27 (2012) 3–11. https://doi.org/10.1557/jmr.2011.213.'},{id:"B9",body:'S. Graça, R. Colaço, P.A. Carvalho, R. Vilar, Determination of dislocation density from hardness measurements in metals, Mater. Lett. 62 (2008) 3812–3814. https://doi.org/10.1016/j.matlet.2008.04.072.'},{id:"B10",body:'C.P. Frick, T.W. Lang, K. Spark, K. Gall, Stress-induced martensitic transformations and shape memory at nanometer scales, Acta Mater. 54 (2006) 2223–2234. https://doi.org/10.1016/j.actamat.2006.01.030.'},{id:"B11",body:'S. Sujith Kumar, I. Sen, A Comparative Study on Deformation Behaviour of Superelastic NiTi with Traditional Elastic–Plastic Alloys in Sub-micron Scale, Trans. Indian Inst. Met. (2021). https://doi.org/10.1007/s12666-021-02207-8.'},{id:"B12",body:'K. Jacob, D. Yadav, S. Dixit, A. Hohenwarter, B.N. Jaya, High pressure torsion processing of maraging steel 250: Microstructure and mechanical behaviour evolution, Mater. Sci. Eng. A. 802 (2021) 140665. https://doi.org/10.1016/j.msea.2020.140665.'},{id:"B13",body:'W.C. Oliver, G.M. Pharr, Measurement of hardness and elastic modulus by instrumented indentation: Advances in understanding and refinements to methodology, J. Mater. Res. 19 (2004) 3–20. https://doi.org/10.1557/jmr.2004.19.1.3.'},{id:"B14",body:'S. Pathak, D. Stojakovic, S.R. Kalidindi, Measurement of the local mechanical properties in polycrystalline samples using spherical nanoindentation and orientation imaging microscopy, Acta Mater. 57 (2009) 3020–3028. https://doi.org/10.1016/j.actamat.2009.03.008.'},{id:"B15",body:'S. Pathak, J. Shaffer, S.R. Kalidindi, Determination of an effective zero-point and extraction of indentation stress-strain curves without the continuous stiffness measurement signal, Scr. Mater. 60 (2009) 439–442. https://doi.org/10.1016/j.scriptamat.2008.11.028.'},{id:"B16",body:'S.R. Kalidindi, S. Pathak, Determination of the effective zero-point and the extraction of spherical nanoindentation stress-strain curves, Acta Mater. 56 (2008) 3523–3532. https://doi.org/10.1016/j.actamat.2008.03.036.'},{id:"B17",body:'N.G. Mathews, A.K. Saxena, C. Kirchlechner, G. Dehm, B.N. Jaya, Effect of size and domain orientation on strength of Barium Titanate, Scr. Mater. 182 (2020) 68–73. https://doi.org/10.1016/j.scriptamat.2020.02.039.'},{id:"B18",body:'G. Dehm, B.N. Jaya, R. Raghavan, C. Kirchlechner, Overview on micro- and nanomechanical testing: New insights in interface plasticity and fracture at small length scales, Acta Mater. 142 (2018) 248–282. https://doi.org/10.1016/j.actamat.2017.06.019.'},{id:"B19",body:'J.S. Field, M. V. Swain, A simple predictivity model for spherical indentation, J. Mater. Res. 8 (1993) 297–306. https://doi.org/10.1557/JMR.1993.0297.'},{id:"B20",body:'S.K. Kang, Y.C. Kim, Y.H. Lee, J.Y. Kim, D. Kwon, Hertz elastic contact in spherical nanoindentation considering infinitesimal deformation of indenter, Tech. Proc. 2012 NSTI Nanotechnol. Conf. Expo, NSTI-Nanotech 2012. 1 (2012) 132–135.'},{id:"B21",body:'B.C. Maji, M. Krishnan, The effect of microstructure on the shape recovery of a Fe-Mn-Si-Cr-Ni stainless steel shape memory alloy, Scr. Mater. 48 (2003) 71–77. https://doi.org/10.1016/S1359-6462(02)00348-2.'},{id:"B22",body:'X. Li, B. Bhushan, A review of nanoindentation continuous stiffness measurement technique and its applications, Mater. Charact. 48 (2002) 11–36. https://doi.org/10.1016/S1044-5803(02)00192-4.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Indrani Sen",address:"indrani.sen@metal.iitkgp.ac.in",affiliation:'
Department of Metallurgical and Materials Engineering, Indian Institute of Technology, India
Department of Metallurgical and Materials Engineering, Indian Institute of Technology, India
Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology, India
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This has not been solved even for convex polygons. This case has intrinsic interest in view of the connection of polygons with the geometry of the universal Teichmüller space and approximation theory. This survey extends our previous survey of 2005 and presents the new approaches and recent essential progress in this field of geometric complex analysis, having various important applications. Another new topic concerns quasireflections across finite collections of quasiintervals.",signatures:"Samuel L. Krushkal",authors:[{id:"316576",title:"Prof.",name:"Samuel",surname:"Krushkal",fullName:"Samuel Krushkal",slug:"samuel-krushkal",email:"krushkal@math.biu.ac.il"}],book:{id:"8760",title:"Structure Topology and Symplectic Geometry",slug:"structure-topology-and-symplectic-geometry",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"92883",title:"Prof.",name:"Paul",surname:"Bracken",slug:"paul-bracken",fullName:"Paul Bracken",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/92883/images/system/92883.jpg",biography:"Professor Paul Bracken is currently a Professor in the Department of Mathematics, at the University of Texas RGV in Edinburg, TX. He obtained his BSc degree from the University of Toronto and holds a Ph.D. from the University of Waterloo in Canada. His research interests include mathematical problems from the area of quantum mechanics and quantum field theory, differential geometry, a study of partial differential equations as well as their overlap with other problems in physics. He has published more than 180 papers in journals and books and has given many talks at different levels over the years. This is the seventh volume he has worked on with IntechOpen publishers.",institutionString:"The University of Texas Rio Grande Valley",institution:{name:"The University of Texas Rio Grande Valley",institutionURL:null,country:{name:"United States of America"}}},{id:"151307",title:"Dr.",name:"Min",surname:"Lei",slug:"min-lei",fullName:"Min Lei",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/151307/images/system/151307.jpg",biography:"Dr. Min Lei has worked at Shanghai Jiaotong University as an associate professor since 2004. From February 2003 to February 2004, she worked as a research fellow in the Department of Mechanical Engineering, Nanyang University of Technology, Singapore. From 2000 to 2002, she worked as a postdoctoral fellow in the Institute of Mechanical and Electrical Control, School of Mechanical and Power Engineering, Shanghai Jiaotong University. She received a doctoral degree from the Department of Biomedical Engineering, Shanghai Jiaotong University, in 2000. Dr. Lei has long been engaged in biomedical signal processing, modern signal processing, pattern recognition and classification, and nonlinear dynamics.",institutionString:"Shanghai Jiao Tong University",institution:{name:"Shanghai Jiao Tong University",institutionURL:null,country:{name:"China"}}},{id:"308593",title:"Dr.",name:"Jananeeswari",surname:"Narayanamoorthy",slug:"jananeeswari-narayanamoorthy",fullName:"Jananeeswari Narayanamoorthy",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"313608",title:"Prof.",name:"Cenap",surname:"Özel",slug:"cenap-ozel",fullName:"Cenap Özel",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"King Abdulaziz University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"313610",title:"MSc.",name:"Mesfer",surname:"Al Qahtani",slug:"mesfer-al-qahtani",fullName:"Mesfer Al Qahtani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"King Abdulaziz University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"313865",title:"Associate Prof.",name:"Yoritaka",surname:"Iwata",slug:"yoritaka-iwata",fullName:"Yoritaka Iwata",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Kansai University",institutionURL:null,country:{name:"Japan"}}},{id:"314607",title:"Dr.",name:"Ying-Qiu",surname:"Gu",slug:"ying-qiu-gu",fullName:"Ying-Qiu Gu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Fudan University",institutionURL:null,country:{name:"China"}}},{id:"319565",title:"Mr.",name:"Karthic",surname:"Mohan",slug:"karthic-mohan",fullName:"Karthic Mohan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"open-access-funding",title:"Open Access Funding",intro:"
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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. 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She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. 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Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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\r\n\tIn general, the harsher the environmental conditions in an ecosystem, the lower the biodiversity. Changes in the environment caused by human activity accelerate the impoverishment of biodiversity.
\r\n
\r\n\tBiodiversity refers to “the variability of living organisms from any source, including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; it includes diversity within each species, between species, and that of ecosystems”.
\r\n
\r\n\tBiodiversity provides food security and constitutes a gene pool for biotechnology, especially in the field of agriculture and medicine, and promotes the development of ecotourism.
\r\n
\r\n\tCurrently, biologists admit that we are witnessing the first phases of the seventh mass extinction caused by human intervention. It is estimated that the current rate of extinction is between a hundred and a thousand times faster than it was when man first appeared. The disappearance of species is caused not only by an accelerated rate of extinction, but also by a decrease in the rate of emergence of new species as human activities degrade the natural environment. The conservation of biological diversity is "a common concern of humanity" and an integral part of the development process. Its objectives are “the conservation of biological diversity, the sustainable use of its components, and the fair and equitable sharing of the benefits resulting from the use of genetic resources”.
\r\n
\r\n\tThe following are the main causes of biodiversity loss:
\r\n
\r\n\t• The destruction of natural habitats to expand urban and agricultural areas and to obtain timber, minerals and other natural resources.
\r\n
\r\n\t• The introduction of alien species into a habitat, whether intentionally or unintentionally which has an impact on the fauna and flora of the area, and as a result, they are reduced or become extinct.
\r\n
\r\n\t• Pollution from industrial and agricultural products, which devastate the fauna and flora, especially those in fresh water.
\r\n
\r\n\t• Global warming, which is seen as a threat to biological diversity, and will become increasingly important in the future.
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\r\n\tThe environment is subject to severe anthropic effects. Among them are those associated with pollution, resource extraction and overexploitation, loss of biodiversity, soil degradation, disorderly land occupation and planning, and many others. These anthropic effects could potentially be caused by any inadequate management of the environment. However, ecosystems have a resilience that makes them react to disturbances which mitigate the negative effects. It is critical to understand how ecosystems, natural and anthropized, including urban environments, respond to actions that have a negative influence and how they are managed. It is also important to establish when the limits marked by the resilience and the breaking point are achieved and when no return is possible. The main focus for the chapters is to cover the subjects such as understanding how the environment resilience works, the mechanisms involved, and how to manage them in order to improve our interactions with the environment and promote the use of adequate management practices such as those outlined in the United Nations’ Sustainable Development Goals.
\r\n\tPollution is caused by a wide variety of human activities and occurs in diverse forms, for example biological, chemical, et cetera. In recent years, significant efforts have been made to ensure that the environment is clean, that rigorous rules are implemented, and old laws are updated to reduce the risks towards humans and ecosystems. However, rapid industrialization and the need for more cultivable sources or habitable lands, for an increasing population, as well as fewer alternatives for waste disposal, make the pollution control tasks more challenging. Therefore, this topic will focus on assessing and managing environmental pollution. It will cover various subjects, including risk assessment due to the pollution of ecosystems, transport and fate of pollutants, restoration or remediation of polluted matrices, and efforts towards sustainable solutions to minimize environmental pollution.
\r\n\tWater is not only a crucial substance needed for biological life on Earth, but it is also a basic requirement for the existence and development of the human society. Owing to the importance of water to life on Earth, early researchers conducted numerous studies and analyses on the liquid form of water from the perspectives of chemistry, physics, earth science, and biology, and concluded that Earth is a "water polo". Water covers approximately 71% of Earth's surface. However, 97.2% of this water is seawater, 21.5% is icebergs and glaciers, and only 0.65% is freshwater that can be used directly by humans. As a result, the amount of water reserves available for human consumption is limited. The development, utilization, and protection of freshwater resources has become the focus of water science research for the continued improvement of human livelihoods and society.
\r\n
\r\n\tWater exists as solid, liquid, and gas within Earth’s atmosphere, lithosphere, and biosphere. Liquid water is used for a variety of purposes besides drinking, including power generation, ecology, landscaping, and shipping. Because water is involved in various environmental hydrological processes as well as numerous aspects of the economy and human society, the study of various phenomena in the hydrosphere, the laws governing their occurrence and development, the relationship between the hydrosphere and other spheres of Earth, and the relationship between water and social development, are all part of water science. Knowledge systems for water science are improving continuously. Water science has become a specialized field concerned with the identification of its physical, chemical, and biological properties. In addition, it reveals the laws of water distribution, movement, and circulation, and proposes methods and tools for water development, utilization, planning, management, and protection. Currently, the field of water science covers research related to topics such as hydrology, water resources and water environment. It also includes research on water related issues such as safety, engineering, economy, law, culture, information, and education.
",coverUrl:"https://cdn.intechopen.com/series_topics/covers/41.jpg",keywords:"Water, Water resources, Freshwater, Hydrological processes, Utilization, Protection"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. 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He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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