Variables in Cross-Cultural Communication Study
\r\n\tWith the discovery of more unconventional heavier crude and alternative hydrocarbon sources, primary upgrading or cracking of the oil into lighter liquid fuel is critical. With increasing concern for environmental sustainability, the regulations on fuel specifications are becoming more stringent. Processing and treating crude oil into a cleaner oil with better quality is equally important. Hence, there has been a relentless and continuous effort to develop new crude upgrading and treating technologies, such as various catalytic systems for more economical and better system performance, as well as cleaner and higher-quality oil.
\r\n\r\n\tThis edited book aims to provide the reader with an overview of the state-of-the-art technologies of crude oil downstream processing which include the primary and secondary upgrading or treating processes covering desulfurization, denitrogenation, demetallation, and evidence-based developments in this area.
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Even when both parties speak the same language there can still be misunderstandings due to ethic and cultural differences. Over the last decade, there have been countless examples from the business sector that demonstrate how poor communication can lead to poor organizational performance. Understanding the impact of globalization on cross-culture communication is imperative for organizations seeking to create a competitive advantage in the global market. Recent economic challenges further highlight the need for organizations to develop the internal communication capacity necessary to control and monitor external threats. As society becomes more globally connected the ability to communicate across cultural boundaries has gained increasing prominence. Global businesses must understand how to communicate with employees and customers from different cultures in order to fulfill the organization’s mission and build value for stakeholders. The use of technology has had a profound impact on how businesses communicate globally and market their products and services. However, with the advancements in technology organizations must still be cognoscente of the culture nuisances that can potentially present obstacles in trying to increase profits and market share. According to Genevieve Hilton, “cultural proficiency doesn\'t mean memorizing every cultural nuance of every market. It\'s knowing when to listen, when to ask for help, and when—finally—to speak” [1].
For companies involved in global business operations the relationship of managers and subordinates in multinational firms is important. In research conducted by Thomas and Ravlin [2] it was found that participants to whom nationality was more important indicated lower perceptions of similarity with the manager, lower intentions to associate, and lower perceptions of managerial effectiveness. The results of the study strongly indicate that teaching members of different cultures to behave like each other is an ineffective approach to improving intercultural interactions in business settings [2]. Focus should be placed on using individual differences to create innovation. Training and development of individuals involved in intercultural interactions should involve more than simply promoting cultural adaptation
Communication is vital for businesses to effectively explain how their products and services differ from their competitors. Companies that are successfully able to communicate cross-culturally have a competitive advantage because they can devote more time and resources to conducting business and less time on internal and external communication issues [1]. Communication is necessary for individuals to express themselves and to fulfill basic needs. The same holds true for businesses, governments, and countries. Without the ability to communicate and understand each other, there would be chaos.Communication that is based on cultural understanding is more apt to prevent misunderstandings caused by personal biases and prejudices.
To illustrate the importance of communication on building relationships globally consider the example of the United States and South Korea. The relationship between these two countries is one built upon a rich history. In 1884, the United States government became the first foreign entity to purchase property in Korea [3]. Before this time no foreigner was permitted to live inside Seoul. Despite significant cultural differences, South Korea and the United States have been able to develop a communication process that other countries seek to replicate. The American Chamber of Commerce in Korea was established in 1953 with the chief purpose to promote the advancement of trade and commerce between Korea and the United States. Article II of the Chamber’s constitution outlines the following six objectives [4]:
To promote the development of commerce between the United States of America and Korea;
To promote measure calculated to benefit and protect the interests in Korea of member companies and citizens of the United States;
To represent, express, and give effect to the opinions of the Chamber business community of the United States regarding trade, commerce, finance, industry, and related questions;
To collect, evaluate, and disseminate among its members statistical and other information concerning commerce or other undertakings of interest to them;
To associate and cooperate with other organizations sharing mutual interests;
To do any and all other things incidental or related to the attainment of the above objectives.
When countries are able to exchange ideas and communicate in an open society everyone benefits. Kathleen Stephens, U.S. Ambassador to the Republic of Korea, summed it up by saying, “we must use our shared interests and values to compliment and transform each other’s growth” [5]. The main purpose of this study is to develop a cross-cultural communication model that can be applied by companies that communicate with employees from different cultures. The aim is to identify the steps that leaders of organizations competing in a global environment should consider when communicating to different cultures. This study uses a group a college students participating in the 2nd Korea America Student Conference (KASC) as the main research source for creating the model. KASC is supported by the International Student Conferences, a non-profit organization located in Washington, D.C., which sponsors student-run educational and cultural exchange programs for university students from the United States, Japan, and Korea [6]. The researchers used a mix of qualitative and quantitative research methodologies to generate the model. A comparative literature review is conducted and organizational examples of Samsung and Hyundai are considered to demonstrate the impact of globalization on cross-culture communication practices. The study also highlights the action research methodology that was employed to design the cross-culture communication model. In conclusion, the 4 C’s Global Leadership Model is introduced to demonstrate how organizational leaders can create innovation in a global environment by managing conflict, communication, creativity, and connectivity.
Research on cross-cultural communication often focuses on understanding how individual differences influence our ability to communicate with others. Since most individuals grow up within a single culture having to interact with others from a different culture or background can represent a challenge [7]. Exposure to different cultures affects our ability to communicate with others in a way that leads to positive outcomes. Fink, Neyer, and Kölling propose that researchers involved in cross-cultural studies should develop an understanding of the interrelations between cultural dimensions, cultural standards, and personality traits [7]. This increased awareness helps an individual to manage their own cross-cultural behavior as well as that of others.
Reza Najafbagy refers to co-orientation, the ability to familiarize all aspects of one’s own life in relation to someone of a different culture, as a primary component of intercultural communication [8]. Individuals that have experienced different cultures are more cognizant of how to alter their communication style so that others understand the information they are trying to transfer. Research conducted by Seak and Enderwick revealed the importance of providing cross-cultural communication and training skills for expatriates assigned to foreign locations in particularly, China, Hong Kong, Taiwan, Japan and South Korea [9]. Now more than ever global organizations must ensure that their employees have the skills required to communicate across cultural boundaries. Cross-cultural communication enhances innovation by allowing for collective problem solving and the open dissemination of information [10].
Cohen and Levinthal define the term absorptive capacity as an organization’s ability to identify and recognize the value of new external information, absorb it, and implement it into their business operations [11]. Organizations that understand the importance of external information are better able to use their core competencies to create a competitive advantage. Cross-culture communication enhances an organization’s absorptive capacity because it provides a new of perspective for satisfying the needs of stakeholders.Communication is the life-blood of organizations and must be allowed to flow throughout the entire organization. However, when information flows are random and there is no apparent directive of how to apply the outside knowledge the organization will not benefit [11]. Productivity decreases when organizational leaders are not able to communicate clear and concise expectations. Furthermore, conflict and tension arises when employees do not understand how their personal efforts contribute to the overall success of the organization.
Culture can affect how we perceive the actions of others. Ambassador Stephens gave the example of a “cheerleading group from North Korea that was participating in a sports competition in South Korea. When the group was heading to the venue one of the cheerleaders yelled for the bus stop. She then proceeds to get off the bus in the rain to get a poster of the North Korean leader that was getting wet. Why? Usually, 70% of Americans would say it was for show. 70% of Koreans would respond that the cheerleader really felt something” [5]. Our perception of others directly affects how we interpret their behavior and actions. Effective cross-culture communication requires that we base our perceptions on facts and not merely on personal biases and prejudices. The Global Leadership and Organizational Behavior Effectiveness Project (GLOBE) examined cultural values of organizational practices and leadership. The study focuses on identifying cultural influences on leadership and management practices. Some scholars believe that as society becomes more interconnected cultural differences will converge [12]. Even though some convergence may occur over time, countries will still maintain distinct cultural differences that will transcend technology and external influences.
Successful business leaders must be able to balance organizational objectives with external global challenges. As organizations become more interconnected the role of leaders in managing global teams is becoming increasingly important. Being able to navigate through different cultural nuances is a key skill for global leaders. Rabotin defines cultural intelligence as “the ability to interact with others from diverse cultural backgrounds, being aware of our cultural values that drive our attitudes, behaviors, and beliefs” [13]. Regardless of their geographic location leaders must be able to communicate across borders in order to create a competitive advantage and achieve results. It is clearly apparent that physical boundaries are increasingly becoming transparent. A global leader must be aware of their personal cultural biases and be willing to change their opinions by learning from those that are different from them [13].
In research conducted by Choi and Chang it was noted that organizational culture directly impacts the attitudes and motivation of employees towards innovation [14]. The researchers identified three organizational factors of management support, resource availability, and support for learning as key enablers for innovation [14]. All three factors were found to be significantly correlated to implementation, however; only management support was determined to be a significant predictor of innovation [14]. This research supports the conclusion that when employees are fully engaged in the process of innovation success is more likely to be achieved. Organizations must have in place procedures that encourage innovation throughout the entire company. Every employee must feel vested in the company and continually seeking ways to improve processes.
A qualitative and quantitative research strategy consisting of group observations, interviews, and surveys were used to gather information. These methods are most appropriate because of the complexity involved in studying culture and communication from an individual perspective. By using these methods the researchers were able to ascertain underlying factors that are relevant to understanding the affects of culture on communication when two different groups interact with each other.
Participants were undergraduate and graduate students from South Korea and the United States selected to participate in the 2nd Korea America Student Conference (KASC). Selection to KASC is based on a competitive process. Participation in the study was completely voluntary and students were asked to complete a consent form. A total of 46 students attended KASC and 65% were female. 52% of the students resided in South Korea and 48% in the United States. Interestingly, 73% of all the students identified Korean as part of their ethnicity. 91% of the students were 19 to 23 years of age.
The researchers focused on collecting data from participants to assist in the development of a cross-cultural communication model. The researcher addressed the differences and similarities in communication strategies and the affect on building relationships. Previous research on culture has applied a field-based approach that allows for the exchange of information from multiple sources. The researchers followed a similar structure when participating in KASC over a two-week period.
The role of the researchers was that of consultants that sought to serve as a bridge between the participants and the administrators of the conference. It was important for the researcher to develop a level of trust with the participants quickly. To accomplish this, the researchers assisted staff with administrative tasks during the conference and shared in activities with the participants. The researchers used current programs established by KASC to expand opportunities to exchange information. For example, the researcher attended lectures, group discussions, and roundtable sessions.
The topics discussed during interviews included: a) reasons for participating in KASC, b) individual experiences and interactions with different cultures, c) reactions to changes that occurred during the conference, and d) discussion of how culture affects the communication between American and Korean students. During group observations the researchers focused on key words and phrases that were used by the students and categorized them according to relevant themes. Based on the words and phrases a concept map was created that helped to identify the predictors used in the study.
Five cultural measurements of acceptance, conflict, individualism, risk, and sharing were used to predict the country of residence. Table 1 defines the variables used in the study. The country of residence index was selected as the dependent variable because it is directly affected by culture.
Rigor and validity were addressed by continuously redefining the key issues identified by participants. Participant feedback was vital to this study and was used to form and shape the cross-cultural communication model. The survey that was developed for this study consisted of 23 items that were rated on five point Likert scale with 1=strongly disagree to 5=strongly agree. Each of the items assessed one of the cultural measurements of acceptance, conflict, individualism, risk, or sharing.
Acceptance | A measure of how important it is for an individual to be liked and accepted by others |
Conflict | A measure of an individual’s ability to work with those they do not agree with |
Individualism | An assessment of how independent an individual behaves in making decisions |
Risk | A measure of how open an individual is to trying new things and stepping outside of their comfort zone |
Sharing | A measure of how willing an individual is to share their opinions and feelings |
Country of residence | The country identified as the primary place of residence |
Variables in Cross-Cultural Communication Study
A multiple regression analysis was conducted to evaluate how well the five independent variables for culture predict the country of residence. The linear combination of culture measures was significantly related to the country of residence index,
1 | .715a | .512 | .368 | .471 |
a. Predictors: (Constant), Risk, Acceptance, Conflict, Individualism, Share; b. Dependent Variable: Residence |
Model Summary
The partial regression plots for conflict and sharing exhibit the best correlations for predicting the country of residence index. Table 3 presents indices to indicate the relative strength of the individual predictors. As expected all of the bivariate correlations between the country of residence index were positive.
Acceptance | .123 | -.140 |
Conflict | .556 | .558 |
Individualism | .243 | -.045 |
Share | .493 | .457 |
Risk | .021 | -.341 |
The Bivariate and Partial Correlations of the Predictors with the Country of Residence Index
The purpose of communication is to transfer ideas and knowledge from one entity to the other. The first step in communication is input; someone must say something that is received by someone else. The communication loop is successful when the receiver demonstrates that he or she understands what was being communicated. From an organizational perspective there are many barriers than can impede the flow of communication. These barriers include culture, technology, language, workforce, and environment. For the purpose of this model culture refers to the traditions and customs that are prevalent in the country where each company is located. These traditions and customs influence policies and procedures implemented by businesses. Technology is simply the use of mediums such as email, Internet, text messaging, and cell phones to communicate. When a company does not have experience using a particular technological medium to communicate it may rely on older methods that the other company views as inadequate. Language is what is spoken in the country where the company is located. If the languages of the two companies are different, then one company must learn the other’s language or a new language must be created. Workforce refers to the internal structure of the company, including employees, managers, and organizational leaders. Environment refers to the external forces that affect the company. For example, the economy can have an adverse impact on an organization and present an obstacle to cross-culture communication.
As illustrated in Figure 1, when these barriers are eliminated companies are able to experience innovation, reduced conflict, and better dissemination of information. J. Ku-Hyun (personal communication, July 20, 2009) stated “to be successful as a global corporation communication is critical.” Without communication organizations will cease to be. The challenge for organizations that must communicate cross-culturally is to ensure that their message is understood the way that it was intended. When communication barriers are not removed it is easy to make assumptions about what is being communicated. Our assumptions of what we thought was being communicated can be very different from the original message. Communication takes effort, it is much easier to sit back and simply assume what we think others are trying to tell us. To actively engage in communication takes time and energy. Organizations must be willing to invest the resources needed to support cross-culture communication.
Successful cross-cultural communication creates a dialogue, a continuous transfer of information. This exchange of information addresses our assumptions and clarifies points we do not understand. It also provides the opportunity for us to ask questions and confirm the information that was received. Having a dialogue reduces conflict because cultural misunderstandings can be dealt with when they arise. The dialogue only occurs when both parties agree to share information and ensure that the transfer of information is not blocked.
Cross-Culture Communication Model
To illustrate how companies can utilize the cross-culture communication model to improve business practices consider the examples of Samsung and Hyundai. Samsung is unique because of its focus on human resources and risk taking initiatives. The company was founded in 1938 and is the world’s largest conglomerate. Samsung is recognized as a global industry leader because of its inner capacity to take advantage of distinct initiatives (J. Ku-Hyun, personal communication, July 20, 2009). It hires a small percentage of non-Koreans inside Korea but employs a higher percentage off non-Koreans outside of Korea. The culture of the organization is very family centric. Decisions occur in a collective atmosphere that allows for communication at all levels of the organization. However, even when decisions are clearly communicated throughout the organization employees may not always show support.
The workforce can represent a barrier to cross-cultural communication when employees feel they are not valued. This presented an issue at Samsung. The expectation was that you stayed at job until your assignment was completed. However, with the increase of younger employees entering the workforce who had different expectations, Samsung had to make a change. Management made the decision to change the workday from five-to-nine to seven-to-four [15]. The reason for Samsung’s change was to get employees to be more team oriented and more productive. Changing the hours of the workday required employees to work together and it also required managers and supervisors to have a more active role in completing daily tasks. The seven-to-four schedule was a cultural shift that was instigated by new employees entering the company with a different outlook towards work and personal time. Prior to the change, employees were expected to stay on the job until it was completed. With the change the office closed at four and everything stopped. Now employees have to proactively plan their workday in order to ensure that everything is accomplished in a timely fashion.
Samsung has been able to become a global leader because of its commitment to the development of technological capabilities [16]. Management’s objective is to “develop technology capabilities for value creation in diverse business areas” that will ultimately support growth in global competition [16]. The focus on innovation requires the sharing of business practices and technology throughout the organization. Samsung must communicate with employees and customers simultaneously to ensure that company objectives are met.
J. Ku-Hyun (personal communication, July 20, 2009) reports that Samsung’s competitive advantages include 1) commitment to work that is translated into speed advantages, 2) highly skilled engineers and technicians, and 3) management talent and experience. Samsung has used these competitive advantages to enhance its relationship with customers and expand globally.
Hyundai Motor Company was formed in 1967 and has established itself as company that focuses on quality improvement and innovation [17]. From the small beginning in Seoul, Korea the company has now expanded to more than ten countries including the United States, Canada, India, China, Turkey, Russia, Malaysia, Sudan, Egypt, Indonesia, Iran, and Japan [17]. For this expansion to take place requires effective communication that is able to overcome cultural barriers and accomplish global management initiatives. Research conducted by Wright, Suh, and Leggett revealed that Hyundai’s achievement at globalization depended upon its ability to expand international sales as it gained experience in international markets [18]. The move from domestic to global production means Hyundai must operate in “unfamiliar and uncertain economic and cultural contexts” [18].
Over the years Hyundai has learned valuable lessons on the importance of knowing and valuing the needs of its customers. In the early 1990s, Hyundai experienced problems when trying to expand production to Canada. The primary issue was that the company did not adapt the design of the Sonata for North American customers [18]. Consequently, the company lost market share to Toyota’s Camry and Honda’s Accord. Another important lesson was learned when Hyundai made the decision to start production in Turkey because of the lower cost for workers. Because of the lower wages management decided to use more hand-operated technology instead of the automated manufacturing processes utilized in its domestic plants. As a result, the production rate of the Turkish plant was 25% lower than that in Korea [18]. From these experiences Hyundai learned the importance of researching the culture of a country before making profound business decisions. Cross-culture communication involves adapting organizational policies to fit the context of where business transactions will occur.
Key to Hyundai’s success is its corporate philosophy that places the needs of customers as top priority in all business areas. This customer-oriented management style requires organizational leaders to be receptive to new ideas and to think outside the box. To encourage employees to expand their cultural awareness the company encourages three-month assignments to its overseas sites. For example, when the company was building a site in Alabama it allowed employees to visit and study the culture [19].The company promotes an employee backpack travel program around the globe where teams of three design their cultural experience. The teams compete for a company sponsorship of 15-day expeditions by writing what they hope to learn [19]. More than 47 teams have traveled to 70 countries, including Peru, Turkey, and Greece [19]. Employees that participate bring back what they learn and share it with their colleagues.
“Hyundai Motor Company is strengthening its position as a global brand, establishing local production systems on a global scale and supplying automobiles that meet the needs and tastes of customers in each specific region” [17]. During a visit to the Asan Plant located in Chungchungnam-do, Korea, it was very apparent of how the company is being innovative. The plant has a production capacity of 300,000 units and utilizes the latest in robotic technology to assemble vehicles (Tour Guide, personal communication, July 27, 2009). One unit is produced every 57 seconds and 100% of all the welding is completely automated. The Asan plant has 34,000 employees; the average annual salary is $50,000. The plant operates two ten-hour shifts and provides numerous incentives for employees that are innovative on the job.
The researchers proposed attending the 2nd Korea-America Student Conference in order to develop a relationship with participants that have a vested interest in global issues and to define the criteria for the proposed cross-culture communication model by using various qualitative methods. These objectives were accomplished and much insight gained into understanding how culture affects communication.
Samsung and Hyundai are only two examples of organizations that are effectively communicating cross-culturally. Both organizations have been able to learn from their past mistakes and create strategies that support their growth in the global market. The sharing of information makes it possible for other organizations to also benefit from the mistakes made by these organizations.
During one of the group observations a Korean participant stated, “A smile is a basic tool of communication” (personal communication, July 16, 2009). How true it is that a simple smile can break down communication barriers and build bridges of understanding. Cross-culture communication is not easy, especially when we are unfamiliar with the receiver of the information. Organizational leaders that have to communicate cross-culturally can use the following steps:
Develop clear and concise expectations for how the organization will accomplish its mission;
Ensure that employees understand their role in the organization;
Be willing to invest time to communicate;
Remember that communication is a two-way process, listen before you speak.
“Many misunderstanding have occurred, not only because of mistakes in the usage of words or expressions, but also because of the lack of goodwill and cultural knowledge” [8]. Mistakes are a normal part life and at times if we are not careful our mistakes can have lasting consequences. “We can make mistakes as long as we can correct mistakes. We can get feedback from the global market” [10]. The cross-culture communication model developed from this study provides a mechanism for obtaining feedback from the global market. The model identifies the barriers to cross-culture communication and summarizes the outcomes that can be achieved when these barriers are tackled.
The results of study verified that the five independent variables of acceptance, conflict, individualism, risk, and sharing could be used to predict country residence. A limitation to this study was the small sample size that used. In order to validate the validity and reliability of the study a larger sample size should be used in future studies. The participants of the study were also aware of the need to increase cultural awareness and displayed a desire to gain a deeper understanding of American and Korean relations. The study adds to the current body of knowledge on cross-culture communication by demonstrating the importance of culture in business settings.
Organizational cross-functionality or connectivity is essential to innovation because it brings together a diverse group of people from different functional backgrounds [20]. Management must take steps to ensure that cross-functionality does not create conflict and hinder communication within the team [20]. To effectively generate innovation the level of expertise and individual skill set of each team member must be ascertained. The innovation process is supported when members share a common vision and goal. Research indicates that cross-functional teams are more effective at new produce development that is valued by the customer [21].
Leadership is the foundation to cross-functionality because it provides the oversight and direction necessary for it to work. Leaders that are innovators are receptive to change and value feedback from those around them. They recognize that they cannot be successful unless those around them are successful. For innovative organizations it is necessary for management to develop innovative leaders. This is done by having in place recruitment strategies that target successful applicants that possess the skills necessary to the organization to the next level [22]. There should also be in place a well-developed talent-management process that identifies innovators, connects them to the mission of the organization, and provides the necessary internal resources for them to be successful.
The 4 C’s Global Leadership Model
A comparison of leadership strategies between Samsung and Hyundai provides the basis for developing the 4 C’s Global Leadership Model that can be employed by organizations to make certain that they have the right person for the job. The model consists of four key factors: communication, conflict, creativity, and connectivity. Being aware of what types of individuals make good global leaders allows the organization to develop HR policies to support recruitment and succession. By creating a pipeline of capable global leaders the organization is able to sustain innovation and change.
Leaders are the main link responsible for harnessing the ideas of employees to create innovation. They must also assess the development of their competitors and the needs of customers. Bringing together individuals from different cultural backgrounds will lead to conflict; however, this does not have to be perceived as a negative. The challenge for global leaders is to use the conflict as a benefit for the organization. Gehani and Gehani define conflict as simply the result of natural differences that occur between people from different backgrounds [23]. Different ideas and views lead to innovation and new products and services. From this perspective leaders should encourage healthy conflict. “Conflict between diverse groups of people can be used to drive the growth of their organizations” [23]. If there were no conflict to spark discussion there would be no innovation.
Communication helps to moderate the relationship between conflict and innovation. This fact is furthermore impacted by the complexity of competing in global environment. Leaders must be aware of the communication styles that are needed when working with multicultural teams. The use of the email, conference calls, and other technological innovations to communicate need to be addressed to ensure that all members are able to participate fully. When members feel they are not able to communicate openly innovation is hindered and the organization suffers.
As global competition continues to increase, successful organizations must evolve to meet the changing needs of consumers. Innovation provides the opportunity for organizations to think outside the box and “produce better product, faster, cheaper and more efficiently than competitors [24]. Creativity is directly linked to communication and innovation. Increased attention on innovation has caused organizational leaders to develop systems to manage the process and support the efforts of employees. According to Dooley & O’Sullivan being able to identify the correct process for implementing innovation will directly impact the success [24]. It is easier to implement innovation when the culture of the organization allows for employee feedback, planning, and evaluation.
Employees play a vital role in innovation. In research conducted by Barnett and Storey it was found that there was a strong connection between learning and innovation in organizations [25]. The researchers studied 220 employees at a manufacturing company, Tensator, located in the United Kingdom. Key to Tensator’s success is their ability to integrate succession planning with sustaining innovation [25]. Instead of focusing solely on keeping top management positions filled, the organization seeks to keep the pipeline of skilled laborers in amble supply to support innovation. Tensator follows a growth strategy that centers on “grow-your-own” [25]. This strategy requires the company to continually provide learning and development opportunities for employees to ensure that they remain at the top of their game.
Microsoft is applying a holistic approach to innovation in seeking to compete with Yahoo and Google in the development of search engines [26]. Management is aware they must do more than simply try to catch up with their competitors; they must redesign the way that search engines are viewed and utilized. The holistic approach is further supported by Porter in work done with organizations to help them improve their supply chains [27]. Organizations that use a holistic approach are able to obtain a broader view of how they fit into the global market. Instead of focusing on small segments of their business operations, attention is given to the entire process. This allows for the organization to implement innovation that will create value for customers.
Connectivity is defined as the ability to orchestrate organizational networks to move in the same direction in order to accomplish the company’s mission. Employees must feel contacted to the organization and understand how their individual effort contributes to the bottom line. Global leaders must look for opportunities to connect everyone within the organization to the overall goals and objectives. When employees understand the big picture and the direction the organization is taking innovation and change are supported.
The 4 C’s Global Leadership Model is designed to provide a basis for organizational leaders to use in mapping out strategies for working globally with multicultural teams. The business environment is continually evolving and global leaders must persistently develop new organization strategies to meet global challenges. Although, Samsung and Hyundai both compete in different industries there are parallels that can be gleamed from how they communicate and inspire innovation from their employees. The Cross-Culture Communication Model and 4 C’s Global Leadership Model are tools designed to assist organizational leaders to compete in the ever-changing global environment.
The core aspects of global leadership critical to leading innovation and change are vision, communication, and responsibility. In 2008 a group of scholars and business leaders identified twenty-five factors that are important for managers in implementing innovation [28]. Key among them was that management must have a clear vision for the organization. The organizational vision provides a roadmap for employees by defining what the organization hopes to accomplish.Communication is important because it allows for the exchange and refinement of ideas. Effective communication requires that organizations not become bogged down with hierarchal thinking that can typically slow down the communication process. Large organizations must operate like small organizations and be able to respond quickly to organizational and market changes [28].
Successful organizations must focus on goals that are socially responsible [28]. Innovation is not just creating the coolest new gadget but it is creating the coolest new gadget that serves the environment in a sustainable manner. As organizations become more global the focus on corporate social responsibility increases. Organizations can no longer operate within a silo. The actions of one organization can affect many others. According to Westlund it is no longer sufficient for organizations only to make a profit and comply with the law [29]. They must also be socially responsible and give something back to the global community that they serve.
Response surface methodology (RSM) is an intersection of the experimental design, the objective optimization and the statistical modeling. RSM aims to identify the adequate mathematical model with the optimal selection of the influence factors to predict the responsive factor and to obtain the extremum of the model under the constrained numerical intervals or categorical levels of the influence factors. The objectives are achieved through designing and conducting a series of experiments to collect the necessary amount of experimental data to approximate the mathematical model.
RSM was originally proposed and described in [1] and some other papers are subsequently published to contribute to the development of the RSM [2, 3]. Review papers began to appear starting from [4] that summarized the utilization of RSM in the chemical and processing fields. It is followed by a few more recent review papers in the application of the physical and science area [5, 6] and the biometric area [7] and some books in the related subjects as well [8, 9]. RSM has quickly gained popularity in empirical modeling in physical experiments to replace or complement the traditional presumed approach [10] where the theoretical knowledge of the experimental systems are available since its appearing. It is as well utilized in modeling the numerical experiments together with the simulation-based methods such as the finite element analysis in the application of the design optimization [11]. RSM contains three skeletal concepts including the estimation of the mathematical model or function, the design of experiments (DoE) and the validation and representation of the postulated mathematical function. Those three steps are likely to be insufficient to conduct only once and will require iteration in practice to achieve a satisfactory result [4].
The most commonly used postulation for the mathematical model is first-order or higher-order polynomials [12, 13]. Despite the wide implementation in chemistry and chemical engineering, it is inevitable that under certain circumstances, the polynomials are inadequate to approximate the underlying RMS functions (e.g. non-linear systems). Especially with the rapid development of information technology in the recent few decades, the utilization of RMS has been spreading to cover many other fields such as civil [14], advanced manufacturing [15, 16], and biomedical engineering [17, 18] and agricultural and food science [19, 20]. Experimental data has become much easier to collect, process and cache, parallel to which is the emergence of machine learning.
Machine learning (ML) is a process of the model building using experimental data or past experience in order to solve a given problem [21, 22]. It enhances the RMS by fitting a rather wide range of approximation models to achieve the responsive surface function. The whole process of ML model construction innately cooperates with the model estimation and model validation stages of the RSM [22]. Additionally, many ML models intrinsically select the most significant influence factors during the model construction process (e.g. the LASSO [23], the GLM and decision trees based models [24, 25]). This nature of the ML models will help to reduce the chance for attempting different DoE to identify the most appropriate influence factor combination and therefore contribute to diminishing the repetitiveness of the three-step cycle and reducing the total experimental runs and cost. Indeed, in cases where the experimental data is extremely expensive or difficult to obtain such as those in the biochemistry field [18, 19], the polynomial approximation and the corresponding DoE methods such as the full or partial factorial design [26], central composite design [27] and the Taguchi’s experimental designs [26, 28] are still of utmost importance.
While the ML methods may not be suitable in certain scenarios where expensive time and financial cost are associated with the physical experiments, some techniques are still worth to be explored and utilized to replace or complement the traditional polynomial approach. The objective of the chapter is to introduce some of the linear and non-linear ML models to estimate the responsive surface function under the fair assumption of a reasonable cost and easiness in obtaining enough amount of experimental data. The book chapter is divided into 4 sections, the following section describing the frequently used ML models in detail, Section 3 implementing an engineering example to demonstrate the advantages of those ML models in comparison to the traditional polynomial postulation and Section 4 summarizing the content in the chapter and discussing further research direction.
Before diving into the various ML models to estimate the responsive surface functions, it is worthy to comprehend the overall model construction and assessment process of the ML approach. Similar to the cyclic three-concept of the RSM, ML also contains an iterative process to reach a satisfactory estimation result.
ML requires the pre-acquisition of a good amount of experimental data (i.e., the number of samples should be more than the number of coefficients to be estimated. The more samples, the better). A model is constructed using part of the data (training set) and assessed on the remaining data (test set). This technique will help to eliminate the risk of the overfitting problem (the model predicts superior on the current data, yet inferior on unseen data), and therefore to ensure the reliability of the constructed model even when new experimental data becoming available.
In addition to the train-test split technique, ML also employs the cross-validation technique to further assist in preventing the overfitting issue and simultaneously help to select the hyper-parameters (parameters that requires pre-definition by the researcher). When deploying the cross-validation technique, the training set is divided into multiple folds of smaller sets. One of the fold is held as the validation set to assess the prediction power of the constructed model and the rest is utilized to construct the model. The process of model building and validation repeats until each sub-fold having been used as a validation set. The final goodness-of-fitness of the model going through the cross-validation technique is computed as the average of each performance values in the loop.
The following part of the section will introduce a few commonly used regression ML models (learning a function mapping from the influence factors to the responsive factor based on available influence-response pair data) as the responsive surface function:
the advanced linear regression models (the least absolute shrinkage and selection operator model and the generalized linear model).
the tree-based models (decision trees, random forest and the gradient boosting decision trees).
a basic type of the Neutral Nets, i.e. the multiple layer perceptrons and
support vector regression.
RSM favors the low-order polynomial as the postulation of the mathematical function where the coefficients of the polynomial are estimated by finding the optimal solution to minimize the sum of squared error of the observed response values and the predicted response values. In ML term, the traditional RSM approach to approximate the responsive surface function is referred to as the ordinary least squared model (OLS).
Take the first-order polynomial as an example [29], i.e.
where
where the
LASSO is an upgraded version of the OLS as it allows influence factors selection during the coefficients estimation process and generalization (a technique to avoid overfitting) [23]. LASSO is also a linear regression model yet differs from the OLS by adding an extra regularization term to the loss function to realize the two additional functions [31], i.e. the optimization problem then becomes:
where
Another useful linear ML model is the generalized linear model (GLM) which allows estimating the response factor when the residuals of the responses do not follow a Gaussian distribution, e.g. the response factor is always positive, or constant value changes in the influence factors leads to exponential value varying other than constant varying of the response factor. In this case, GLM can be utilized to approximate the responsive surface function. It elevates the OLS by differing in two aspects, the predicted value of the response factor is linked to an inverse function of the linear combination of influence factors, i.e.
where
Examples of the unit deviance of the EDM are given in Table 1 [33].
Distribution | Response domain | Unit deviance |
---|---|---|
Gaussian | ||
Poisson | ||
Gamma | ||
Inverse Gaussian |
The unit deviance functions for response factors following various distributions.
The 1st-order polynomial assumption is made for purpose of simplifying the concept to introduce the above linear ML models. The intersection and quadratic terms of a higher-order polynomial can be easily computed and added to by performing a transformation on the 1st-order polynomial. The linear regression models described above are still suitable to estimate the coefficients of the transformed influence factors to form a higher-order polynomial responsive surface function.
As mentioned, non-linearity may exist between the influence factors and as such, the polynomials will become inadequate to approximate the mapping from the influence factors to the responsive factor. In this case, the non-linear ML models will become distinctive.
Decision trees (DT), instead of the linear regression models described in Section 2.2, is a non-parametric ML model (models defined without coefficients). The problem is to build a model to predict the values of the responsive factor by means of defining a series of decision principles deduced from the training data [29].
A DT model is built in a top-down manner with each split node partitioning the influence factors into a subgroup and the process eventually reaches a value of the responsive factor [24]. The more important the split node, the higher the node in the tree. The common criteria to minimize as to decide the orders for future split nodes are mean squared error, Poisson deviance and the mean absolute error [34]. Assuming to use the Poisson deviance, suppose the data at the node
where
In comparison to the linear regression models, DT can fit non-linear systems due to the nature of how it is constructed. Besides, since the importance of each influence factor is computed during the tree construction process, it also helps to select the most significant influencers as a procedure of the feature selection and thus to improve the prediction accuracy of the resulted model. However, there are hidden drawbacks when implementing the DT [34]:
Overfitting problem, i.e. an over-complex DT is built, resulting in good prediction in existing data but poor prediction in unseen data.
Unstable model, i.e. slight variation in the data can lead to a completely different DT.
In order to address the disadvantages of the DT and to estimate a satisfactory model, ensemble techniques are explored. The ensemble technique combines the prediction of multiple base estimators to achieve reduced variance and enhanced robustness over a single meta-estimator. DT is a common model used as a type of base estimator to form the final meta-estimator. The DT used in an ensemble model is usually simple-structured by limiting the maximum depth of the trees or the maximum leaf nodes of the trees and as a consequence to ease the overfitting issue of using a single DT model.
Two types of ensemble trees, the random forests and the GBDT are introduced here.
Random forest constructs multiple DT with each DT built from a subset drawn with replacement from the training dataset and uses the averaged prediction of the individual DT as the final prediction for the meta-estimator [29]. GBDT builds a sequence of DT with each preceding DT attempting to eliminate the error of the current sequential DT model and uses a weighted sum of the predictions generated by the sequentially built DT to produce the final prediction [29]. More details regarding the two models can be found in [35, 36].
Neutral-nets models are a group of models originally inspired by the biological neural networks and are able to learn a complex function mapping from the influence factors to the responsive factor. Neural-nets models have popularized since their development due to their accuracy in predicting without knowing the underlying relationship between the influencers and the responders and therefore massive descendent models have been published recently. In this section, the first generation and the most fundamental neural-nest model, the multiple layer perceptrons (MLP) is presented [37].
MLP builds a non-linear function approximation to map the set of influence factors to the responsive factor using the training data. Between the influence factors and the final response factor, there may exist one or multiple non-linear layers, as illustrated in Figure 1 [29].
Structure of a multiple layer perceptrons (MLP) model.
Each circle in Figure 1 is a neuron. The leftmost layer is the input layer that consists of the neurons representing the influence factors. Each neuron in the hidden layer is a weighted linear summation of the neurons in the previous layer followed by a non-linear transformation by applying an activation function. The value of the response factor is given by the neuron in the output layer after receiving the values from the last hidden layer and transforming the values using the linear summation and an appropriate activation function. For the MLP regression model, the activation function in the last step is an identity function, i.e. no activation function is applied in the last step. Similar to the advanced linear models, MLP employs the sum of the squared error loss and an additional
MLP utilizes the stochastic gradient descent (SGD) to update the coefficients based on the gradient of the regularization term and the loss function at each iteration in order to obtain the optimal values of the coefficients [38], i.e.
where
Support Vector Regression (SVR) was developed in the 1990s [39], a decade late than the surge of the neutral-nets [40]. Unlike the Neutral-nets, which are result-oriented ‘black-box’ models, the SVR has well-defined underpinned theoretical properties.
Support vectors are the data points from the training set that have a direct determining impact on the optimum location of the decision surface (a hyperplane separating one class of data points from anther) [41]. The SVR outstands when there is a limited number of experimental data, in particular, when the number of samples in the training set is less than the number of influence factors, as the SVR fit a mathematical model by utilizing a subset of the training samples to decide the decision surface. It enhances the linear approximation models by means of fitting non-linear properties in the data as during the model construction process, the influence factors can go through a pre-define non-linear kernel function and as such to realize a non-linear transformation to obtain the response [29].
The procedure to obtain the predicted response values using SVR is similar to the MLP and is illustrated in Figure 2. The support vectors are first transformed using and a map
The architecture of the support vector regression (SVR) to obtain a prediction.
Assuming for the 1st-order polynomial approximation (as represented in Eq. (1)), SVR searches for the coefficients by minimizing the inner product of the coefficients, i.e.
subjected to the condition of limiting the prediction error into a certain threshold, i.e.
with constrains
This minimization problem can be resolved by finding the solution of an equivalent Langrangian Dual problem, i.e. finding the
subjected to the conditions
The traditional RSM method and the introduced ML models above will be employed to approximate the underlying mathematical model for a set of mechanical engineering data used in [42].
In the manufacturing processes process, the machine vibration severity level is a critical index to indicate the status of the machine tool and the finish of the cutting material. If a high severity level occurs, the manufacturing process is likely going wrong such as the occurrence of chatter or breakage of a machine tool. However, it is not always loss-effective to have the corresponding devices installed and human technicians in-place to monitor the vibration severity continuously. Instead, a numerical approximation can be explored by collecting machining data, which is easier and less expensive to access.
The experimental dataset contains 56519 samples and 74 variables collected from the sensors installed on the shop floor machine and the central controller computer [42]. To simplify the case to a single response factor problem, the severity variables along different directions measured on various ranges are compacted together using their standardized
A piece of program code is written in Python language and utilizes a package named Scikit-learn [29] to realize the discussed ML models. The main objective here is to demonstrate the advantages of ML in estimating the response surface function to predict the values of the response factor with higher accuracy compared with the traditional RSM polynomial approximation.
First of all, the dataset is standardized to its unit variance form in the same manner as to compute the single severity column in Section 3.1, i.e., using the formula:
The standardized dataset (containing the 56512 samples data) is divided into a training set and a held-out test set with a 7/3 ratio. The training dataset is further split into three subfolders with each subfolder utilized as a validation set to estimate the prediction power of the model constructed using the remainder two subfolders to adopt the cross-validation technique described in Section 2.1. Employment of the cross-validation technique will further prevent the over-fitting problem and as such to reassure the robustness of the estimated model and better prediction on unseen data. The training set is used to estimate and select the optimal responsive function and the held-out test set is used to exam the prediction accuracy and the reliability of the fitted RSM function on unseen data. The goodness-of-fitness of the estimated function on the training set and the prediction accuracy of the function on unseen data is measured using three different measuring metrics and the outcomes are shown in the cross-validation results and the held-out test set results sections respectively in Table 2.
Metric name/model type | Linear models | Non-linear models | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Model name | OLS/RSM | LASSO | GLM | RF | GBDT | MLP | SVR | |||
Polynomial order | 1st | 2nd | 1st | 2nd | 1st | 2nd | N/A | |||
Cross-validation Results | ||||||||||
EV | 0.697 | 0.841 | 0.700 | 0.877 | 0.700 | 0.865 | 0.923 | 0.924 | 0.905 | 0.901 |
MAPE | 31.4% | 88.4% | 31.4% | 19.7% | 30.2% | 17.8% | 13.5% | 13.0% | 15.3% | 14.9% |
RMSE | 0.660 | 1.62 | 0.656 | 0.630 | 0.656 | 0.660 | 0.333 | 0.331 | 0.372 | 0.352 |
Held-out Test Set Results | ||||||||||
EV | 0.699 | −1.08 | 0.699 | 0.878 | 0.699 | 0.865 | 0.925 | 0.926 | 0.908 | 0.900 |
MAPE | 31.4% | 160% | 31.5% | 19.7% | 30.3% | 18.0% | 13.2% | 12.9% | 14.6% | 14.3% |
RMSE | 0.657 | 7.74 | 0.658 | 0.609 | 0.657 | 0.639 | 0.328 | 0.325 | 0.367 | 0.348 |
Experimental results of applying the traditional RSM method and the ML methods on a set of engineering data.
The three measuring metrics evaluate the model performance from different statistical perspectives to ensure a solid conclusion to be reached. The statistical meanings and the computation equations for the three measuring metrics are given below [43]:
The explained variance (EV) measures the proportion to which a mathematical model accounts for the variance of a given data set and is computed as
The mean absolute percentage error (MAPE) measures the percentage difference between the actual and the predicted response factor values and is defined as:
The best possible MAPE value is 0% when every single predicted value matching the actual one. The greater the score, the worse the model performs.
The rooted mean squared error (RMSE) measures the actual differences between the actual value and the predicted value from the model and is defined by:
The smaller the value, the better the model performs.
The functions that realize the traditional RSM linear model (i.e. OLS), and the six ML models, including the LASSO, the GLM, the random forests (RF), the GBDT, the MLP and the SVR) in the Sciket-Learn package [29] are implemented and utilized to estimate the underlying mathematical function that maps the 63 influence factors to the target responsive factor for the case study dataset. The technique that generates the 2nd order polynomial terms is also deployed to obtain the polynomial estimation of the traditional RSM function. The goodness-of-fitness and the prediction accuracy of the estimated responsive function using the RSM model or each of the ML models are displayed in the corresponding column labeled with the name of the model in Table 2. Particularly, for linear models, both 1st-order and 2nd-order polynomial terms have been attempted.
Furthermore, in order to investigate whether the existing experimental data is enough to achieve a robust and accurate responsive surface function, the learning curve, which determines the cross-validation training and validation accuracy scores under different training sample sizes, is also drawn for each of the models and shown in Figure 3. More specifically, using a proportion of the training data to perform the cross-validation technique described in Section 2.1. The mean, the minimum and the maximum of the cross-validation results will be shown on the learning curve for each of the subset used.
The learning curves obtained using the traditional RSM method (OLS) and the ML models (LASSO, GLM, RF, GBDT, MLP, and SVR).
The scores in Table 2 display the cross-validation accuracy and the prediction accuracy on a held-out test set of the RSM and the ML models assessed under three different measuring metrics, the explained variance (EV), the mean absolute percentage error (MAPE) and the rooted mean squared error (RMSE).
Results in Table 2 has demonstrated that assuming for 1st-order polynomial, the performance of the tradition RSM method, i.e. OLS, is equally mediocre with the other two linear ML models, LASSO and GLM, as all of them produce similar testing results under each of the measuring metrics. This indicates that a 1st-order polynomial assumption may be not enough to include all information between and within the influence factors, which has been validated by the improved model performance for 2nd-order polynomial approximation using a corresponding ML linear model and using the non-linear approximation models.
Under the 2nd-order polynomial postulation, the two linear ML learning models, LASSO and GLM greatly surpass the RSM method as the two models allow influencer selection during model construction and as such to eliminate the influence factors that interferes with estimating precise coefficients. Therefore, more accurate polynomial coefficients are estimated; Besides, both LASSO and GLM perform better under the 2nd-order polynomial assumption than under the 1st-order assumption. The two points imply that some intersection terms of the 2nd-polynomials are redundant and intrusive while the others are necessary to be taken into account. Estimating the coefficients of the intrusive influencers can be a bottleneck for the traditional RSM approximation method as the 2nd-order polynomial approximation using OLS has seen obvious chaos with unreasonably large prediction error.
The table has also demonstrated that the non-linear ML models (RF, GBDT, MLP and SVR) outperform the linear models with or without the intersection and quadratic terms as the measuring metrics have leapt when switching from the linear models to the non-linear ones. Within all the non-linear ML models, the GBDT has exceeded all the others though the performance advantage is relatively small (<10%). In comparison with the traditional RSM method (1st-order polynomial), GBDT has seen a significant improvement (about 50%) on prediction accuracy measured using each of the metrics.
The scores obtained on the held-out test set are almost equal with those achieved through the cross-validation stage for all experimented ML models. The consistency in the metric values reassures the reliability of the constructed model and the prediction accuracy of the model on future unseen data.
Figure 3 depicted the learning curve for each of the model described above trained using a proportion of
Diving into the detail of an individual subplot in Figure 3, the red points represent the mean of the training scores while the green ones represent the mean of the validation scores of the cross-validation stage under the training sets with 5 different sizes. The shadowed interval shows the distance between the minimum and maximum of either the training scores (red shadow) or the validation scores (green shadow) under each subset. In cases where the scores are close, the interval can be invisible. The x-axis represents the proportion of the original training set used and the y–axis represents the prediction scores of the trained model. Here, the explained variance is used. The model used to produce the scores is shown on the top of each subplot.
Looking at OLS approximation with the 1st order polynomial terms (the 1st plot on the left), the model is apparently over-fitted when the training set is small. With the increase of the training data size, the validation scores improve but the training scores decrease and the two come to parallel with each other when all training data is used. For the OLS approximation with the 2nd order polynomial terms (the 1st plot on the right), the model is always over-fitted, increasing the training samples does not seem to improve the problem.
For the LASSO approximation with the 1st order polynomial terms (the 2nd plot on the left) and with the 2nd order polynomial terms (the 2nd plot on the right), both of the models improve on predicting the validation set when the overall training set is up-scaled. However, the training scores decrease at the same time.
The same trend applies to the GLM approximations (as shown in the two plots on the 3rd line).
The RF model and the GBDT model have displayed a more satisfying pattern. As shown in the two plots on the 4th line, the validation scores increase or remain as a constant together with the increase of the validation scores when the training samples grow. Even when all training samples are used, the validation score is still lower than the training score. This indicates that there is a chance for a better-fitted model if more experimental data is to be used.
For the MLP model and the SVR model (as shown in two plots on the last line), the training scores are climbing after experienced an inflection point when 0.01 proportion of the training data is used and the validation scores continue to rise with more data involved. Though the training and the validation scores are neck and neck when the whole training set is used, both of training and the validation scores still get space for improvement.
Considering the general law that the testing scores will not exceed the training scores and the actual scores shown in the plots, we can conclude that all of the RF, GBDT, MLP and SVR models have the potential to train a more accurate responsive surface function if more experimental data becoming available in the future. The other models have already reached their limitations using the current training set and will see little improvement with larger training data.
A finite element method such as the Monte Carlo simulation can be applied in two ways to complement the RSM study, either to assist in obtaining a better responsive surface function estimation or to achieve the extremums of the existing function.
Instead of collecting more experimental data, synthetic data can be generated using the known knowledge of value intervals, categorical levels or distributions of the influence factors obtained from the existing data. These synthetic data can be populated in pairs of inputs and responses and then be used as a supplement of the current training set aiming to train a more accurate responsive surface function (i.e., to train new RF, GBDT, MLP and SVR models using the up-scaled data). Alternatively, generating the inputs data solely to feed into the obtained mathematical model to attain a corresponding response. Then, picking the pair of influence factors and responsive factor values leading to the global minimum or maximum as the extremums. The simulation technique will not be further discussed here in this book chapter.
In summary, this book chapter has introduced and discussed
two linear ML models (LASSO, GLM) and four non-linear ML models (random forests, GBDT, MLP, and SVR) as alternatives to estimate the responsive surface function.
The two linear models use the same optimization function as the traditional RSM method (i.e., OLS in the ML term) to estimate the optimal coefficients of the assumed polynomial yet exceed the OLS by adding an extra regularization term to help to eliminate the redundant and intrusive influencer factors. The improvement can greatly save the efforts on attempting different combinations of influence factors (especially with the higher-order polynomial terms) and solving the optimization function repetitively.
The non-linear ML models can pick up the non-linearity across the influence factors that cannot be modeled by the linear models and therefore lead to more precise prediction accuracy.
The advantages of using the ML approached models have been demonstrated by the mechanical engineering case study in Section 3.
Results in Table 2 has shown that all the ML models outperform the traditional RSM polynomial approach. The non-linear models have produced dramatically improved prediction accuracy. In particular, the GDBT model has shown to exceed the OLS for about 50% on prediction accuracy under each of the measuring metrics.
The investigation on the size of the experimental data, i.e. the learning curves in Figure 3, has shown that the four non-linear ML models are capable to produce a model with higher accuracy if more training data becoming available.
Last yet not least, the simulation technique has been introduced. This technique is essential in either the physical-based or the computer-based experiments to assist in further improving the estimation of the responsive surface function or obtaining the extremums of the current function.
Despite the case study is to predict the vibration severity of the manufacturing machine, the utilization of the ML methods in the RSM can be extended to solve many other engineering problems such as (but not limited) to predict the machine tool life, to estimate the reliability of a structural material or to optimize a bioengineering process where appropriate.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
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The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
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\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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This chapter focuses on source and their potential role, antimicrobial mechanism in food preservation, and current knowledge on the subject.",book:{id:"7261",slug:"active-antimicrobial-food-packaging",title:"Active Antimicrobial Food Packaging",fullTitle:"Active Antimicrobial Food Packaging"},signatures:"Farhan Saeed, Muhammad Afzaal, Tabussam Tufail and Aftab Ahmad",authors:[{id:"192244",title:"Dr.",name:"Farhan",middleName:null,surname:"Saeed",slug:"farhan-saeed",fullName:"Farhan Saeed"},{id:"232885",title:"Dr.",name:"Aftab",middleName:null,surname:"Ahmed",slug:"aftab-ahmed",fullName:"Aftab Ahmed"},{id:"245894",title:"Dr.",name:"Muhammad",middleName:null,surname:"Afzaal",slug:"muhammad-afzaal",fullName:"Muhammad Afzaal"},{id:"255994",title:"Mr.",name:"Tabussam",middleName:null,surname:"Tufail",slug:"tabussam-tufail",fullName:"Tabussam Tufail"}]},{id:"55599",doi:"10.5772/intechopen.69301",title:"Nutritional, Bioactive and Physicochemical Characteristics of Different Beetroot Formulations",slug:"nutritional-bioactive-and-physicochemical-characteristics-of-different-beetroot-formulations",totalDownloads:3891,totalCrossrefCites:12,totalDimensionsCites:24,abstract:"Beetroot possesses high nutritional value and is considered one of the main dietary sources of nitrate. Nitrate has increasingly attracted the interest of the scientific community regarding new physiological, nutritional and therapeutic approaches with beneficial effects on the cardiovascular system. These effects can be explained by the possible effect of dietary nitrate in stimulating nitric oxide synthesis. Dietary nitrate can be reduced to nitrite in the oral cavity, which is then decomposed to nitric oxide and other bioactive nitrogen oxides in the stomach. Beetroot administration can be conducted by several types of formulations, in order to provide a convenient and alternative source of dietary beetroot, such as beetroot juice or beetroot chips and powder. The challenge in providing a product which, in addition to being rich in nitrate, is attractive and easy to administer, while also being microbiologically safe, is increased by the limited scientific information available concerning the nutritional aspects of beetroot formulations. In this chapter, a brief review on the efficiency of different beetroot formulations on health indicators is conducted, emphasizing the effects following the intake of nitrate-enriched beetroot gel. The metabolic and hemodynamic effects of beetroot formulations in healthy and non-healthy volunteers are also discussed.",book:{id:"5766",slug:"food-additives",title:"Food Additives",fullTitle:"Food Additives"},signatures:"Diego dos S. Baião, Davi V.T. da Silva, Eduardo M. Del Aguila and\nVânia M. Flosi Paschoalin",authors:[{id:"97533",title:"Dr.",name:"Vania",middleName:null,surname:"Paschoalin",slug:"vania-paschoalin",fullName:"Vania Paschoalin"}]},{id:"56718",doi:"10.5772/intechopen.70197",title:"Natural Antimicrobials, their Sources and Food Safety",slug:"natural-antimicrobials-their-sources-and-food-safety",totalDownloads:3990,totalCrossrefCites:9,totalDimensionsCites:23,abstract:"With consumer awareness about food safety and quality, there is a high demand for the preservative (synthetic)-free foods and use of natural products as preservatives. Natural antimicrobials from different sources are used to preserve food from spoilage and pathogenic microorganisms. Plants (herbs and spices, fruits and vegetables, seeds and leaves) are the main source of antimicrobials and contain many essential oils that have preservation effect against different microorganisms. Mainly, herb and spices contain many essential oils and the examples include rosemary, sage, basil, oregano, thyme, cardamom, and clove. These essential oils are very effective against many pathogenic and spoilage microorganisms like Salmonella, Escherichia coli, Listeria monocytogenes, Campylobacter spp., and Staphylococcus aureus and help to increase their quality and shelf stability. These antimicrobial compounds are also used in combination with edible food coatings and inhibit the ability of microorganisms to grow on the surface of food and food products.",book:{id:"5766",slug:"food-additives",title:"Food Additives",fullTitle:"Food Additives"},signatures:"Muhammad Sajid Arshad and Syeda Ayesha Batool",authors:[{id:"192998",title:"Dr.",name:"Muhammad Sajid",middleName:null,surname:"Arshad",slug:"muhammad-sajid-arshad",fullName:"Muhammad Sajid Arshad"},{id:"209272",title:"Ms.",name:"Syeda Ayesha",middleName:null,surname:"Batool",slug:"syeda-ayesha-batool",fullName:"Syeda Ayesha Batool"}]},{id:"56355",doi:"10.5772/intechopen.70090",title:"Food Preservatives from Plants",slug:"food-preservatives-from-plants",totalDownloads:3041,totalCrossrefCites:1,totalDimensionsCites:15,abstract:"It has long been shown that phytochemicals protect plants against viruses, bacteria, fungi and herbivores, but only relatively recently we have learnt that they are also critical in protecting humans against diseases. A significant amount of medicinal plants is consumed by humans. As food‐related products, they additionally improve human health and general well‐being. This chapter deals with plant‐derived food preservatives. Particular attention has been paid to the following berry fruits: cranberry (Vaccinium macrocarpon), bilberry (Vaccinium myrtillus), black currant (Ribes nigrum), elderberry (Sambucus nigra), cornelian cherry (Cornus mas) and açaí (Euterpe oleracea), as well as the following herbs and spices: peppermint (Mentha piperita), basil (Ocimum basilicum), rosemary (Rosmarinus officinalis), thyme (Thymus vulgaris), nettle (Urtica dioica), cinnamon (Cinnamomum zeylanicum) bark, cloves (Syzygium aromaticum) and licorice (Glycyrrhiza glabra) as alternative sources of natural antimicrobial and antibiofilm agents with potential use in food industry. Moreover, we present an overview of the most recent information on the positive effect of bioactive compounds of these plants on human health. This chapter is a collection of essential and valuable information for food producers willing to use plant‐derived bioactive substances for ensuring the microbiological safety of products.",book:{id:"5766",slug:"food-additives",title:"Food Additives",fullTitle:"Food Additives"},signatures:"Hubert Antolak and Dorota Kregiel",authors:[{id:"179443",title:"Associate Prof.",name:"Dorota",middleName:null,surname:"Kregiel",slug:"dorota-kregiel",fullName:"Dorota Kregiel"},{id:"197451",title:"MSc.",name:"Hubert",middleName:null,surname:"Antolak",slug:"hubert-antolak",fullName:"Hubert Antolak"}]},{id:"55776",doi:"10.5772/intechopen.69284",title:"Waste Degradation and Utilization by Lactic Acid Bacteria: Use of Lactic Acid Bacteria in Production of Food Additives, Bioenergy and Biogas",slug:"waste-degradation-and-utilization-by-lactic-acid-bacteria-use-of-lactic-acid-bacteria-in-production-",totalDownloads:3114,totalCrossrefCites:6,totalDimensionsCites:14,abstract:"Lactic acid bacteria (LAB) are one of the most well-studied bacterial groups known from ancient times. These valuable microorganisms are used in numerous areas, especially food industry and medicine. LAB produce a wide range of compounds for food upgrading. Moreover, LAB can find special applications like generation of bioenergy not affecting the surrounding environment. The article considers physiological and biochemical processes determining valuable characteristics of the bacteria, potential applications of LAB and their products, especially in food industry and bioenergy sector, and discusses LAB potential contribution into solution of waste disposal problem.",book:{id:"5766",slug:"food-additives",title:"Food Additives",fullTitle:"Food Additives"},signatures:"Galina Novik, Olga Meerovskaya and Victoria Savich",authors:[{id:"101157",title:"Dr.",name:"Galina",middleName:null,surname:"Novik",slug:"galina-novik",fullName:"Galina Novik"},{id:"174332",title:"MSc.",name:"Victoria",middleName:null,surname:"Savich",slug:"victoria-savich",fullName:"Victoria Savich"},{id:"197033",title:"Dr.",name:"Olga",middleName:null,surname:"Meerovskaya",slug:"olga-meerovskaya",fullName:"Olga Meerovskaya"}]}],mostDownloadedChaptersLast30Days:[{id:"56317",title:"Food Additives and Processing Aids used in Breadmaking",slug:"food-additives-and-processing-aids-used-in-breadmaking",totalDownloads:3779,totalCrossrefCites:8,totalDimensionsCites:10,abstract:"The main classes of additives used in breadmaking are: (i) oxidants/reductants; (ii) emulsifiers; (iii) hydrocolloids; and (iv) preservatives. The main processing aids used are enzymes. Historically, market trends have developed from the use of ingredients in greater quantities - to obtain specific effects in bread (such as fat for crumb softness) - to the use of additives at much lower levels (max. 1%) and, more recently, to enzymes which are used in parts per million (ppm). According to many regulations, enzymes do not need to be declared on the label of the final product, attending the “clean label” trend. We will describe the food additives used under each class, individually describing their mode of action and effects on dough rheology, during the breadmaking process, and on product quality. We will also describe the main enzymes currently used, dividing them according to the substrate they act on (gluten, starch, lipids, non-starch polysaccharides or NSPS), individually describing their mode of action and effects on dough rheology, during the breadmaking process, and on product quality. Legal aspects will also be addressed. We will conclude with future trends in the use of additives and processing aids in breadmaking.",book:{id:"5766",slug:"food-additives",title:"Food Additives",fullTitle:"Food Additives"},signatures:"Luis Carlos Gioia, José Ricardo Ganancio and Caroline Joy Steel",authors:[{id:"196530",title:"Prof.",name:"Caroline",middleName:"Joy",surname:"Steel",slug:"caroline-steel",fullName:"Caroline Steel"},{id:"197499",title:"BSc.",name:"Luis Carlos",middleName:null,surname:"Gioia Jr.",slug:"luis-carlos-gioia-jr.",fullName:"Luis Carlos Gioia Jr."},{id:"197500",title:"BSc.",name:"José Ricardo",middleName:null,surname:"Crepaldi Ganancio",slug:"jose-ricardo-crepaldi-ganancio",fullName:"José Ricardo Crepaldi Ganancio"}]},{id:"60470",title:"Contamination, Prevention and Control of Listeria monocytogenes in Food Processing and Food Service Environments",slug:"contamination-prevention-and-control-of-listeria-monocytogenes-in-food-processing-and-food-service-e",totalDownloads:2090,totalCrossrefCites:1,totalDimensionsCites:6,abstract:"This chapter reviews issues related to the occurrence and growth of Listeria monocytogenes in food processing and food service environments. L. monocytogenes is a food-borne pathogen with the capacity to contaminate raw or minimally processed foods such as chilled ready-to-eat (RTE) foods. The consumption of food contaminated with L. monocytogenes can result in a disease known as listeriosis among vulnerable groups of people such as pregnant women and fetuses, newborns, adults between the ages of 65 and 75, and people with weakened immune systems. L. monocytogenes is ubiquitous and has been isolated from soil, vegetation, sewage, water, animal feed, fresh and frozen meat including poultry, slaughterhouse wastes and the feces of healthy animals and humans. The bacterium is both acid tolerant and salt tolerant. It is able to grow at refrigerator temperature, and is therefore often associated with the consumption of raw or minimally processed and often chilled RTE foods. L. monocytogenes is able to form biofilms on food processing and preparation surfaces, which protects it from antimicrobial action. Continuous education of vulnerable groups regarding food safety will increase their awareness of the importance of practicing safer food handling practices such as hand washing and safe storage of RTE foods as a means to prevent listeriosis.",book:{id:"6648",slug:"listeria-monocytogenes",title:"Listeria Monocytogenes",fullTitle:"Listeria Monocytogenes"},signatures:"Frederick Tawi Tabit",authors:[{id:"229896",title:"Dr.",name:"Frederick Tawi",middleName:null,surname:"Tabit",slug:"frederick-tawi-tabit",fullName:"Frederick Tawi Tabit"}]},{id:"56718",title:"Natural Antimicrobials, their Sources and Food Safety",slug:"natural-antimicrobials-their-sources-and-food-safety",totalDownloads:3985,totalCrossrefCites:9,totalDimensionsCites:23,abstract:"With consumer awareness about food safety and quality, there is a high demand for the preservative (synthetic)-free foods and use of natural products as preservatives. Natural antimicrobials from different sources are used to preserve food from spoilage and pathogenic microorganisms. Plants (herbs and spices, fruits and vegetables, seeds and leaves) are the main source of antimicrobials and contain many essential oils that have preservation effect against different microorganisms. Mainly, herb and spices contain many essential oils and the examples include rosemary, sage, basil, oregano, thyme, cardamom, and clove. These essential oils are very effective against many pathogenic and spoilage microorganisms like Salmonella, Escherichia coli, Listeria monocytogenes, Campylobacter spp., and Staphylococcus aureus and help to increase their quality and shelf stability. These antimicrobial compounds are also used in combination with edible food coatings and inhibit the ability of microorganisms to grow on the surface of food and food products.",book:{id:"5766",slug:"food-additives",title:"Food Additives",fullTitle:"Food Additives"},signatures:"Muhammad Sajid Arshad and Syeda Ayesha Batool",authors:[{id:"192998",title:"Dr.",name:"Muhammad Sajid",middleName:null,surname:"Arshad",slug:"muhammad-sajid-arshad",fullName:"Muhammad Sajid Arshad"},{id:"209272",title:"Ms.",name:"Syeda Ayesha",middleName:null,surname:"Batool",slug:"syeda-ayesha-batool",fullName:"Syeda Ayesha Batool"}]},{id:"77442",title:"Fermentation of Cocoa Beans",slug:"fermentation-of-cocoa-beans",totalDownloads:421,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Cocoa bean fermentation is a spontaneous process driven by an ordered microbial succession of a wide range of yeasts, lactic acid and acetic acid bacteria, some aerobic sporeforming bacteria and various species of filamentous fungi. The process of cocoa fermentation is a very important step for developing chocolate flavor precursors which are attributable to the metabolism of succession microbial. The microbial ecology of cocoa has been studied in much of the world. In Venezuela, studies have been carried out with Criollo, Forastero, and Trinitario cocoa, fermented under various conditions, the results obtained coinciding with the reported scientific information. Fermentation must be associated with the type of cocoa available, carried out knowing the final processing and derivative (paste, butter, powder). The results shown in this chapter correspond to investigations carried out with cocoa from three locations in Venezuela. The quantification, identification, isolation, functionality of the most representative microbiota involved in the fermentation of these grains was sought. This to give possible answers to the fermentation times and improvement of the commercial quality. Likewise, generate greater interest on the part of the producers in carrying out the fermentation.",book:{id:"9709",slug:"fermentation-processes-benefits-and-risks",title:"Fermentation",fullTitle:"Fermentation - Processes, Benefits and Risks"},signatures:"Romel E. Guzmán-Alvarez and José G. Márquez-Ramos",authors:[{id:"238233",title:"Dr.",name:"Romel",middleName:null,surname:"E. Guzmán-Alvarez",slug:"romel-e.-guzman-alvarez",fullName:"Romel E. Guzmán-Alvarez"},{id:"269154",title:"Dr.",name:"José",middleName:null,surname:"G. Márquez-Ramos",slug:"jose-g.-marquez-ramos",fullName:"José G. Márquez-Ramos"}]},{id:"63469",title:"Use of Natural Antimicrobial Agents: A Safe Preservation Approach",slug:"use-of-natural-antimicrobial-agents-a-safe-preservation-approach",totalDownloads:2882,totalCrossrefCites:15,totalDimensionsCites:31,abstract:"Microorganism contamination at various stages of food chain is one of the major causes for food spoilage that ultimately leads to food waste, increasing food insecurity issues and substantial economic losses. Various synthetic chemical preservatives are being used to control microbial food spoilage and to extend product shelf life. Researchers and consumers are discouraging the use of synthetic preservatives due to their negative health impacts. Naturally occurring antimicrobials have gained attention among researchers and food manufacturer due to their safety and nontoxic status. Natural preservatives are easy to obtain from plants, animals and microbes. These naturally occurring antimicrobial agents can be isolated from indigenous sources using various advanced techniques. Natural preservatives such as nisin, essential oils, and natamycin have effective potential against spoilage and pathogenic microorganisms. The regulations regarding the use of these naturally occurring preservatives are not well defined in some developing countries. This chapter focuses on source and their potential role, antimicrobial mechanism in food preservation, and current knowledge on the subject.",book:{id:"7261",slug:"active-antimicrobial-food-packaging",title:"Active Antimicrobial Food Packaging",fullTitle:"Active Antimicrobial Food Packaging"},signatures:"Farhan Saeed, Muhammad Afzaal, Tabussam Tufail and Aftab Ahmad",authors:[{id:"192244",title:"Dr.",name:"Farhan",middleName:null,surname:"Saeed",slug:"farhan-saeed",fullName:"Farhan Saeed"},{id:"232885",title:"Dr.",name:"Aftab",middleName:null,surname:"Ahmed",slug:"aftab-ahmed",fullName:"Aftab Ahmed"},{id:"245894",title:"Dr.",name:"Muhammad",middleName:null,surname:"Afzaal",slug:"muhammad-afzaal",fullName:"Muhammad Afzaal"},{id:"255994",title:"Mr.",name:"Tabussam",middleName:null,surname:"Tufail",slug:"tabussam-tufail",fullName:"Tabussam Tufail"}]}],onlineFirstChaptersFilter:{topicId:"389",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. By addressing hot topics in veterinary sciences, we aim to gather authoritative texts within each issue of this series, providing in-depth overviews and analysis for graduates, academics, and practitioners and foreseeing a deeper understanding of the subject. Forthcoming texts, written and edited by experienced researchers from both industry and academia, will also discuss scientific challenges faced today in Veterinary Medicine and Science. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. 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Science",value:19,count:5}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:3},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:1},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],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. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:287,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. 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. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. 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. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/192730",hash:"",query:{},params:{id:"192730"},fullPath:"/profiles/192730",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()