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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"159",leadTitle:null,fullTitle:"Superconductivity - Theory and Applications",title:"Superconductivity",subtitle:"Theory and Applications",reviewType:"peer-reviewed",abstract:'Superconductivity was discovered in 1911 by Kamerlingh Onnes. Since the discovery of an oxide superconductor with critical temperature (Tc) approximately equal to 35 K (by Bednorz and Muller 1986), there are a great number of laboratories all over the world involved in research of superconductors with high Tc values, the so-called "High-Tc superconductors". \nThis book contains 15 chapters reporting about interesting research about theoretical and experimental aspects of superconductivity. You will find here a great number of works about theories and properties of High-Tc superconductors (materials with Tc > 30 K). In a few chapters there are also discussions concerning low-Tc superconductors (Tc < 30 K). This book will certainly encourage further experimental and theoretical research in new theories and new superconducting materials.',isbn:null,printIsbn:"978-953-307-151-0",pdfIsbn:"978-953-51-4904-0",doi:"10.5772/684",price:139,priceEur:155,priceUsd:179,slug:"superconductivity-theory-and-applications",numberOfPages:360,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"bb0587d06c5516fc4e3c89818b9b17e6",bookSignature:"Adir Moyses Luiz",publishedDate:"July 18th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/159.jpg",numberOfDownloads:40539,numberOfWosCitations:37,numberOfCrossrefCitations:14,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:26,numberOfDimensionsCitationsByBook:4,hasAltmetrics:0,numberOfTotalCitations:77,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 7th 2010",dateEndSecondStepPublish:"November 4th 2010",dateEndThirdStepPublish:"February 9th 2011",dateEndFourthStepPublish:"April 10th 2011",dateEndFifthStepPublish:"June 24th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"10012",title:"Dr.",name:"Adir",middleName:"Moysés",surname:"Luiz",slug:"adir-luiz",fullName:"Adir Luiz",profilePictureURL:"https://mts.intechopen.com/storage/users/10012/images/1512_n.jpg",biography:"Adir Moysés Luiz is associate Professor at Instituto de Física at Federal University in Rio de Janeiro, (UFRJ) Brazil. He obtained doctor degree in Chemical Engineering (DSc.) at COPPE - “Universidade Federal do Rio de Janeiro”, (UFRJ) in Brazil. The year of degree obtaining is 1982. The research areas which were always in the focus of his activities are: thermodynamics, solid state physics, materials science and superconductivity. His present interests are: superconductivity, superconducting materials, applications of superconductivity, superconducting devices, microscopic theories of superconductivity. Until now, Dr. Luiz has published 44 articles in scientific journals, 23 books and 62 articles in annals of events. He has also published 22 presentations of events (different congresses, conference or lectures), translated 19 books, and participated in 43 graduate boards. 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It is the newest virtual reality technology. It usually uses 3D computer graphics technologies for visual presentation of the virtual world. The mixed reality can be created using the following technologies: augmented reality and augmented virtuality. Mixed and virtual reality, their applications, 3D computer graphics and related technologies in their actual stage are the content of this book. 3D-modeling in virtual reality, a stereoscopy, and 3D solids reconstruction are presented in the first part. 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He defended his Ph.D. in 1999 and habilitation thesis in the field of virtual reality and computer graphics in 2008. He is an associate professor at the Department of Computers and Informatics, Technical University of Košice. His scientific research focuses on computer graphics, parallel computing, virtual reality, and related technologies.",institutionString:"Technical University of Košice",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Technical University of Košice",institutionURL:null,country:{name:"Slovakia"}}}],coeditorOne:{id:"101330",title:"Dr.",name:"Dragan",middleName:"Mladen",surname:"Cvetković",slug:"dragan-cvetkovic",fullName:"Dragan Cvetković",profilePictureURL:"https://mts.intechopen.com/storage/users/101330/images/system/101330.jpg",biography:"Dragan Cvetković obtained a Ph.D. in Aeronautics from the Faculty of Mechanical Engineering, University of Belgrade, in 1997. To date, he has published sixty-five books, scripts, and practicums about computers and computer programs, aviation weapons, and flight mechanics. He has published many scientific papers as well. Dr. Cvetković became a full professor of Informatics and Computing at Singidunum University, Belgrade, in 2014. 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Our goal in this chapter is to describe how young children build from their natural curiosity about their world to having the skills for systematically observing, predicting, and understanding that world. We suggest that scientific reasoning is a specific type of intentional information seeking, one that shares basic reasoning mechanisms and motivation with other types of information seeking (Kuhn, 2011a). For example, curiosity is a critical motivational component that underlies information seeking (Jirout & Klahr, 2012), yet only in scientific reasoning is curiosity sated by deliberate data collection and formal analysis of evidence. In this way, scientific reasoning differs from other types of information seeking in that it requires additional cognitive resources as well as an integration of cultural tools. To that end, we provide an overview of how scientific reasoning emerges from the interaction between internal factors (e.g., cognitive and metacognitive development) and cultural and contextual factors.
The current state of empirical research on scientific reasoning presents seemingly contradictory conclusions. Young children are sometimes deemed “little scientists” because they appear to have abilities that are used in formal scientific reasoning (e.g., causal reasoning; Gopnik et al., 2004). At the same time, many studies show that older children (and sometimes adults) have difficulties with scientific reasoning. For example, children have difficulty in systematically designing controlled experiments, in drawing appropriate conclusions based on evidence, and in interpreting evidence (e.g., Croker, 2012; Chen & Klahr, 1999; Kuhn, 1989; Zimmerman, 2007).
In the following account, we suggest that despite the early emergence of many of the precursors of skilled scientific reasoning, its developmental trajectory is slow and requires instruction, support, and practice. In Section 2of the chapter, we discuss cognitive and metacognitive factors. We focus on two mechanisms that play a critical role in all cognitive processes (i.e., encoding and strategy acquisition/selection).
Effective scientific reasoning requires both deductive and inductive skills. Individuals must understand how to assess what is currently known or believed, develop testable questions, test hypotheses, and draw appropriate conclusions by coordinating empirical evidence and theory. Such reasoning also requires the ability to attend to information systematically and draw reasonable inferences from patterns that are observed. Further, it requires the ability to assess one’s reasoning at each stage in the process. Here, we describe some of the key issues in developing these cognitive and metacognitive scientific reasoning skills.
The main task for developmental researchers is to explain how children build on their intuitive curiosity about the world to become skilled scientific reasoners.
A mechanistic account of the development of scientific reasoning includes information about the processes by which this change occurs, and how these processes lead to change over time (Klahr, 2001). Mechanisms can be described at varying levels (e.g., neurological, cognitive, interpersonal) and over different time scales. For example, neurological mechanisms (e.g., inhibition) operate at millisecond time scales (Burlea, Vidala, Tandonneta, & Hasbroucq, 2004) while learning mechanisms may operate over the course of minutes (e.g., inhibiting irrelevant information during problem solving; Becker, 2010). Many of the cognitive processes and mechanisms that account for learning and for problem solving across a variety of domains are important to the development of scientific reasoning skills and science knowledge acquisition. Many cognitive mechanisms have been identified as underlying scientific reasoning and other high-level cognition (e.g., analogy, statistical learning, categorization, imitation, inhibition; Goswami, 2008). However, due to space limitations we focus on what we argue are the two most critical mechanisms –
Encoding is the process of representing information and its context in memory as a result of attention to stimuli (Chen, 2007; Siegler, 1989). As such, it is a central mechanism in scientific reasoning because we must represent information before we can reason about it, and the quality and process of representation can affect reasoning. Importantly, there are significant developmental changes in the ability to encode the relevant features that will lead to sound reasoning and problem solving (Siegler, 1983; 1985). Encoding abilities improve with the acquisition of
Effective encoding is dependent on directing attention to
Strategies are sequences of procedural actions used to achieve a goal (Siegler, 1996). In the context of scientific reasoning, strategies are the steps that guide children from their initial state (e.g., a question about the effects of weight and distance in balancing a scale) to a goal state (e.g., understanding the nature of the relationship between variables). We will briefly examine two components of strategy development:
New strategies are added to the repertoire of possible strategies through discovery, instruction, or other social interactions (Chen, 2007; Gauvain, 2001; Siegler, 1996). There is evidence that children can discover strategies on their own (Chen, 2007). Children often discover new strategies when they experience an insight into a new way of solving a familiar problem. For example, 10- and 11-year-olds discovered new strategies for evaluating causal relations between variables in a computerized task only after creating different cars (e.g., comparing the effects of engine size) and testing them (Schauble, 1990). Similarly, when asked to determine the cause of a chemical reaction, children discovered new experimentation strategies only after several weeks (Kuhn & Phelps, 1982). Over time, existing strategies may be modified to reduce time and complexity of implementation (e.g., eliminating redundant steps in a problem solving sequence; Klahr, 1984). For example, determining causal relations among variables requires more time when experimentation is unsystematic. In order to identify which variables resulted in the fastest car, children often constructed up to 25 cars, whereas an adult scientist identified the fastest car after constructing only seven cars (Schauble, 1990).
Children also gain new strategies through social interaction, by being explicitly taught a strategy, imitating a strategy, or by collaborating in problem solving (Gauvain, 2001). For example, when a parent asks a child questions about events in a photograph, the parent evokes memories of the event and helps to structure the child’s understanding of the depicted event, a process called conversational remembering (Middleton, 1997). Conversational remembering improves children’s recall of events and often leads to children spontaneously using this strategy. Parent conversations about event structures improved children’s memory for these structures; for example, questions about a child’s day at school help to structure this event and improved recall (Nelson, 1996). Children also learn new strategies by solving problems cooperatively with adults. In a sorting task, preschool children were more likely to improve their classification strategies after working with their mothers (Freund, 1990). Further, children who worked with their parents on a hypothesis-testing task were more likely to identify causal variables than children who worked alone because parents helped children construct valid experiments, keep data records, and repeat experiments (Gleason & Schauble, 2000).
Children also acquire strategies by interacting with an adult modeling a novel strategy. Middle-school children acquired a reading comprehension strategy (e.g., anticipating the ending of a story) after seeing it modeled by their teacher (Palinscar, Brown, & Campione, 1993). Additionally, children can acquire new strategies from interactions with other children. Monitoring other children during problem solving improves a child’s understanding of the task and appears to improve how they evaluate their own performance (Brownell & Carriger, 1991). Elementary school children who collaborated with other students to solve the balance-scale task outperformed students who worked alone (Pine & Messer, 1998). Ten-year-olds working in dyads were more likely to discuss their strategies than children working alone and these discussions were associated with generating better hypotheses than children working alone (Teasley, 1995).
More than one strategy may be useful for solving a problem, which requires a means to select among candidate strategies. One suggestion is that this process occurs by adaptive selection. In adaptive selection, strategies that match features of the problem are candidates for selection. One component of selection is that newer strategies tend to have a slightly higher priority for use when compared to older strategies (Siegler, 1996). Successful selection is made on the basis of the effectiveness of the strategy and its cost (e.g., speed), and children tend to choose the fastest, most accurate strategy available (i.e., the most adaptive strategy).
Cognitive mechanisms provide the basic investigation and inferential tools used in scientific reasoning. The ability to reason about knowledge and the means for obtaining and evaluating knowledge provide powerful tools that augment children’s reasoning.
Sodian, Zaitchik, and Carey (1991) argue that two basic skills related to early metacognitive acquisitions are needed for scientific reasoning. First, children need to understand that inferences can be drawn from evidence. The theory of mind literature (e.g., Wellman, Cross, & Watson, 2001) suggests that it is not until the age of 4 that children understand that beliefs and knowledge are based on perceptual experience (i.e., evidence). As noted earlier, experimental work demonstrates that preschoolers can use evidence to make judgments about simple causal relationships (Gopnik, Sobel, Schulz, & Glymour, 2001; Schulz & Bonawitz, 2007; Schulz & Gopnik, 2004; Schulz, Gopnik,& Glymour, 2007). Similarly, several classic studies show that children as young as 6 can succeed in simple scientific reasoning tasks. Children between 6 and 9 can discriminate between a conclusive and an inclusive test of a simple hypothesis (Sodian et al., 1991). Children as young as 5 can form a causal hypothesis based on a pattern of evidence, and even 4-year-olds seem to understand some of the principles of causal reasoning (Ruffman, Perner, Olson, & Doherty, 1993).
Second, according to Sodian et al. (1991), children need to understand that inference is itself a mechanism with which further knowledge can be acquired. Four-year-olds base their knowledge on perceptual experiences, whereas 6-year-olds understand that the testimony of others can also be used in making inferences (Sodian & Wimmer, 1987). Other research suggests that children younger than 6 can make inferences based on testimony, but in very limited circumstances (Koenig, Clément, & Harris, 2004). These findings may explain why, by the age of 6, children are able to succeed on
Research with older children, however, has revealed that 8- to 12-year-olds have limitations in their abilities to (a) generate unconfounded experiments, (b) disconfirm hypotheses, (c) keep accurate and systematic records, and (d) evaluate evidence (Klahr, Fay, & Dunbar, 1993; Kuhn, Garcia-Mila, Zohar, & Andersen, 1995; Schauble, 1990, 1996; Zimmerman, Raghavan, & Sartoris, 2003). For example, Schauble (1990) presented children aged 9-11 with a computerized task in which they had to determine which of five factors affect the speed of racing cars. Children often varied several factors at once (only 22% of the experiments were classified as valid) and they often drew conclusions consistent with belief rather than the evidence generated. They used a positive test strategy, testing variables believed to influence speed (e.g., engine size) and not testing those believed to be non-causal (e.g., color). Some children recorded features without outcomes, or outcomes without features, but most wrote down nothing at all, relying on memory for details of experiments carried out over an eight-week period.
Although the performance differences between younger and older children may be interpreted as potentially contradictory, the differing cognitive and metacognitive demands of tasks used to study scientific reasoning at different ages may account for some of the disconnect in conclusions. Even though the simple tasks given to preschoolers and young children require them to understand evidence as a source of knowledge, such tasks require the cognitive abilities of induction and pattern recognition, but only limited metacognitive abilities. In contrast, the tasks used to study the development of scientific reasoning in older children (and adults) are more demanding and focused on hypothetico-deductive reasoning; they include more variables, involve more complex causal structures, require varying levels of domain knowledge, and are negotiated across much longer time scales. Moreover, the tasks given to older children and adults involve the acquisition, selection, and coordination of investigation strategies, combining background knowledge with empirical evidence. The results of investigation activities are then used in the acquisition, selection, and coordinationof evidence evaluation and inference strategies. With respect to encoding, increases in task complexity require attending to more information and making judgments about which features are relevant. This encoding happens in the context of prior knowledge and, in many cases, it is also necessary to inhibit prior knowledge (Zimmerman & Croker, in press).
Sodian and Bullock (2008) also argue that mature scientific reasoning involves the metastrategic process of being able to think explicitly about hypotheses and evidence, and that this skill is not fully mastered until adolescence at the very earliest. According to Amsel et al. (2008), metacognitive competence is important for hypothetical reasoning. These conclusions are consistent with Kuhn’s (1989, 2005, 2011a) argument that the defining feature of scientific thinking is the set of cognitive and metacognitive skills involved in differentiating and coordinating theory and evidence. Kuhn argues that the effective coordination of theory and evidence depends on three metacognitive abilities: (a) The ability to encode and represent evidence and theory separately, so that relations between them can be recognized; (b) the ability to treat theories as independent objects of thought (i.e., rather than a representation of “the way things are”); and (c) the ability to recognize that theories can be false, setting aside the acceptance of a theory so evidence can be assessed to determine the veridicality of a theory. When we consider these cognitive and metacognitive abilities in the larger social context, it is clear that skills that are highly valued by the scientific community may be at odds with the cultural and intuitive views of the individual reasoner (Lemke, 2001). Thus, it often takes time for conceptual change to occur; evidence is not just evaluated in the context of the science investigation and science classroom, but within personal and community values. Conceptual change also takes place in the context of an individual’s personal epistemology, which can undergo developmental transitions (e.g., Sandoval, 2005).
Returning to the encoding and retrieval of information relevant to scientific reasoning tasks, many studies demonstrate that both children and adults are not always aware of their memory limitations while engaged in investigation tasks (e.g., Carey, Evans, Honda, Jay, & Unger, 1989; Dunbar & Klahr, 1989; Garcia-Mila & Andersen, 2007; Gleason & Schauble, 2000; Siegler & Liebert, 1975; Trafton & Trickett, 2001). Kanari and Millar (2004) found that children differentially recorded the results of experiments, depending on familiarity or strength of prior beliefs. For example, 10- to 14-year-olds recorded more data points when experimenting with unfamiliar items (e.g., using a force-meter to determine the factors affecting the force produced by the weight and surface area of boxes) than with familiar items (e.g., using a stopwatch to experiment with pendulums). Overall, children are less likely than adults to record experimental designs and outcomes, or to review notes they do keep, despite task demands that clearly necessitate a reliance on external memory aids.
Children are often asked to judge their memory abilities, and memory plays an important role in scientific reasoning. Children’s understanding of memory as a fallible process develops over middle childhood (Jaswal & Dodson, 2009; Kreuzer, Leonard, & Flavell, 1975). Young children view all strategies on memory tasks as equally effective, whereas 8- to 10-year-olds start to discriminate between strategies, and 12-year-olds know which strategies work best (Justice, 1986; Schneider, 1986). The development of metamemory continues through adolescence (Schneider, 2008), so there may not be a particular age that memory and metamemory limitations are no longer a consideration for children and adolescents engaged in complex scientific reasoning tasks. However, it seems likely that metamemory limitations are more profound for children under 10-12 years.
Likewise, the acquisition of other metacognitive and metastrategic skills is a gradual process. Early strategies for coordinating theory and evidence are replaced with better ones, but there is not a stage-like change from using an older strategy to a newer one. Multiple strategies are concurrently available so the process of change is very much like Siegler’s (1996) overlapping waves model (Kuhn et al., 1995). However,
Similarly, Zohar and Peled (2008) focused instruction in the control-of-variables strategy (CVS) on metastrategic competence. Fifth-graders were given a computerized task in which they had to determine the effects of five variables on seed germination. Students in the control group were taught about seed germination, and students in the experimental group were given a metastrategic knowledge intervention over several sessions. The intervention consisted of describing CVS, discussing when it should be used, and discussing what features of a task indicate that CVS should be used. A second computerized task on potato growth was used to assess near transfer. A physical task in which participants had to determine which factors affect the distance a ball will roll was used to assess far transfer. The experimental group showed gains on both the strategic and the metastrategic level. The latter was measured by asking participants to explain what they had done. These gains were still apparent on the near and far transfer tasks when they were administered three months later. Moreover, low-academic achievers showed the largest gains. It is clear from these studies that although meta-level competencies may not develop routinely, they can certainly be learned via explicit instruction.
Metacognitive abilities are necessary precursors to sophisticated scientific thinking, and represent one of the ways in which children, adults, and professional scientists differ. In order for children’s behavior to go beyond demonstrating the correctness of one’s existing beliefs (e.g., Dunbar & Klahr, 1989) it is necessary for meta-level competencies to be developed and practiced (Kuhn, 2005). With metacognitive control over the processes involved, children (and adults) can change what they believe based on evidence and, in doing so, are aware not only that they are changing a belief, but also know
Much of the existing laboratory work on the development of scientific thinking has not
Our goal in this section is to re-examine our two focal mechanisms (i.e., encoding and strategy) and show how the development of these cognitive acquisitions and metastrategic control of them are facilitated by both the social and physical environment.
Children must learn to encode effectively, by knowing what information is critical to pay attention to. They do so in part with the aid of their teachers, parents, and peers. Once school begins, teachers play a clear role in children’s cognitive development. An ongoing debate in the field of science education concerns the relative value of having children learn and discover how the world works on their own (often called “discovery learning”) and having an instructor guide the learning more directly (often called “direct instruction”). Different researchers interpret these labels in divergent ways, which adds fuel to the debate (see e.g., Bonawitz et al., 2011; Hmelo-Silver, Duncan, & Chinn, 2007; Kirshner, Sweller, & Clark, 2006; Klahr, 2010; Mayer, 2004; Schmidt, Loyens, van Gog, & Paas, 2007). Regardless of definitions, though, this issue illustrates the core idea that learning takes place in a social context, with guidance that varies from minimal to didactic.
Specifically, this debate is about the ideal role for adults in helping children to encode information. In direct instruction, there is a clear role for a teacher, often actively pointing out effective examples as compared to ineffective ones, or directly teaching a strategy to apply to new examples. And, indeed, there is evidence that more direct guidance to test variables systematically can help students in learning, particularly in the ability to apply their knowledge to new contexts (e.g., Klahr & Nigam, 2004; Lorch et al., 2010; Strand-Cary & Klahr, 2008). There is also evidence that scaffolded discovery learning can be effective (e.g., Alfieri, Brooks, Adrich, & Tenenbaum, 2011). Those who argue for discovery learning often do so because they note that pedagogical approaches commonly labeled as “discovery learning,” such as problem-based learning and inquiry learning, are in fact highly scaffolded, providing students with a structure in which to explore (Alfieri et al., 2011; Hmelo-Silver et al., 2007; Schmidt et al., 2007). Even in microgenetic studies in which children are described as engaged in “self-directed learning,” researchers ask participants questions along that way that serve as prompts, hints, dialogue, and scaffolds that facilitate learning (Klahr & Carver, 1995). What there appears to be little evidence for is “pure discovery learning” in which students are given little or no guidance and expected to discover rules of problem solving or other skills on their own (Alfieri et al., 2011; Mayer, 2004). Thus, it is clear that formal education includes a critical role for a teacher to scaffold children’s scientific reasoning.
A common goal in science education is to correct the many misconceptions students bring to the classroom. Chinn and Malhotra (2002) examined the role of encoding evidence, interpreting evidence, generalization, and retention as possible impediments to correcting misconceptions. Over four experiments, they concluded that the key difficulty faced by children is in making accurate observations or properly encoding evidence that does not match prior beliefs. However, interventions involving an explanation of what scientists expected to happen (and why) were very effective in mediating conceptual change when encountering counterintuitive evidence. That is, with scaffolds, children made observations independent of theory, and changed their beliefs based on observed evidence. For example, the initial belief that a thermometer placed inside a sweater would display a higher temperature than a thermometer outside a sweater was revised after seeing evidence that disconfirmed this belief and hearing a scientist’s explanation that the temperature would be the same unless there was something warm inside the sweater. Instructional supports can play a crucial role in improving the encoding and observational skills required for reasoning about science.
In laboratory studies of reasoning, there is direct evidence of the role of adult scaffolding. Butler and Markman (2012a) demonstrate that in complex tasks in which children need to find and use evidence, causal verbal framing (i.e., asking whether one event caused another) led young children to more effectively extract patterns from scenes they observed, which in turn led to more effective reasoning. In further work demonstrating the value of adult scaffolding in children’s encoding, Butler and Markman (2012b) found that by age 4, children are much more likely to explore and make inductive inferences when adults intentionally try to teach something than when they are shown an “accidental” effect.
As discussed earlier in this chapter, learning which strategies are available and useful is a fundamental part of developing scientific thinking skills. Much research has looked at the role of adults in teaching strategies to children in both formal (i.e., school) and informal settings (e.g., museums, home; Fender & Crowley, 2007; Tenenbaum, Rappolt-Schlichtmann, & Zanger, 2004).
A central task in scientific reasoning involves the ability to design controlled experiments. Chen and Klahr (1999) found that directly instructing 7- to 10-year-old children in the strategies for designing unconfounded experiments led to learning in a short time frame. More impressively, the effectiveness of the training was shown seven months later, when older students given the strategy training were much better at correctly distinguishing confounded and unconfounded designs than those not explicitly trained in the strategy. In another study exploring the role of scaffolded strategy instruction, Kuhn and Dean (2005) worked with sixth graders on a task to evaluate the contribution of different factors to earthquake risk. All students given the suggestion to focus attention on just one variable were able to design unconfounded experiments, compared to only 11% in the control group given their typical science instruction. This ability to design unconfounded experiments increased the number of valid inferences in the intervention group, both immediately and three months later. Extended engagement alone resulted in minimal progress, confirming that even minor prompts and suggestions represent potentially powerful scaffolds. In yet another example, when taught to control variables either with or without metacognitive supports, 11-year-old children learned more when guided in thinking about how to approach each problem and evaluate the outcome (Dejonckheere, Van de Keere, & Tallir, 2011). Slightly younger children did not benefit from the same manipulation, but 4- to 6-year-olds given an adapted version of the metacognitive instruction were able to reason more effectively about simpler physical science tasks than those who had no metacognitive supports (Dejonckheere, Van de Keere, & Mestdagh, 2010).
Clearly, even with the number of studies that have focused on individual cognition, a picture is beginning to emerge to illustrate the importance of social and cultural factors in the development of scientific reasoning. Many of the studies we describe highlight that even “controlled laboratory studies” are actually scientific reasoning in context. To illustrate, early work by Siegler and Liebert (1975) includes both an instructional context (a control condition plus two types of instruction:
The sociocultural perspective highlights the role that language, speech, symbols, signs, number systems, objects, and tools play in individual cognitive development (Lemke, 2001). As highlighted in previous examples, adult and peer collaboration, dialogue, and other elements of the social environment are important mediators. In this section, we highlight some of the verbal, visual, and numerical elements of the physical context that support the emergence of scientific reasoning.
Most studies of scientific reasoning include some type of
Panel A illustrates the type of pictorial support that accompanies the verbal description of a hypothesis-testing task (from
Various elements of
Although language, symbols, and number systems are used as canonical examples of cultural tools and resources within the socio-cultural tradition (Lemke, 2001), recent advances in
Computer simulations allow visualization of phenomena that are not directly observable in the classroom (e.g., atomic structure, planetary motion). Other advantages include that they are less prone to measurement error in apparatus set up, and that they can be programmed to record all actions taken (and their latencies). Moreover, many systems include a scaffolded method for participants to keep and consult records and notes. Importantly, there is evidence that simulated environments provide the same advantages as isomorphic “hands on” apparatus (Klahr, Triona, & Williams, 2007; Triona & Klahr, 2007).
New lines of research are taking advantage of advances in computing and intelligent computer systems. Kuhn (2011b) recently examined how to facilitate reasoning about multivariable causality, and the problems associated with the visualization of outcomes resulting from multiple causes (e.g., the causes for different cancer rates by geographical area). Participants had access to software that produces a visual display of data points that represent main effects and their interactions. Similarly, Klahr and colleagues (Siler, Mowery, Magaro, Willows, & Klahr, 2010) have developed an intelligent tutor to teach experimentation strategies (see Figure 1, panel C). The use of intelligent tutors provides the unique opportunity of personally tailored learning and feedback experiences, dependent on each student’s pattern of errors. This immediate feedback can be particularly useful in helping develop metacognitive skills (e.g., Roll, Alaven, McLaren, & Koedinger, 2011) and facilitate effective student collaboration (Diziol, Walker, Rummel, & Koedinger, 2010).
Tweney, Doherty, and Mynatt (1981) noted some time ago that most tasks used to study scientific thinking were artificial because real investigations require
Determining the developmental trajectory of scientific reasoning has been challenging, in part because
There is far more to a complete account of scientific reasoning than has been discussed here, including other cognitive mechanisms such as formal hypothesis testing, retrieval, and other reasoning processes. There are also relevant non-cognitive factors such as motivation, disposition, personality, argumentation skills, and personal epistemology, to name a few (see Feist, 2006). These additional considerations do not detract from our assertion that encoding and strategy use are critical to the development of scientific reasoning, and that we must consider cognitive and metacognitive skills within a social and physical context when seeking to understand the development of scientific reasoning. Scientific knowledge acquisition and, importantly, scientific knowledge
All authors contributed equally to the manuscript. The authors thank Eric Amsel, Deanna Kuhn, and Jamie Jirout for comments on a previous version of this chapter.
Spinocerebellar ataxia (SCA), which is included in spinocerebellar degeneration (SCD), is a genetically heterogeneous group of autosomal dominantly inherited progressive disorders [1]. Cerebellar atrophy is the most prominent clinical feature of this condition and is accompanied by spinal cord and sequential brain stem and basal ganglion damage. Therefore, coordinated movement of the eyes, head, trunk, and extremities is impaired. Therefore, the activities of daily living (ADL) and participation in social activities are limited, and the quality of life (QOL) is undisputedly impaired in these patients [2].
The effects of medication and surgery in this clinical setting depend on the cause of ataxia and the extent of neuronal damage [3, 4]; however, there is no rational effective treatment for SCA and it is difficult to slow the progression of the disease. Rehabilitation [5, 6], including physical therapy [7, 8], aimed at improving/maintaining motor function, ADL, and QOL [5] is an important intervention for patients with SCA. Here we provide a narrative review of physical rehabilitation for SCA.
For the clinical diagnosis of cerebellar ataxia, specific blood studies and magnetic resonance imaging (MRI) have been performed [9]. Furthermore, genetic techniques improve the diagnosis of degenerative cerebellar ataxia [10]. Although the details of the findings of these genetic and blood studies are beyond the scope of this review of rehabilitation, cerebellar atrophy and cerebellar motor deficits are traditionally common observations in patients with degenerative cerebellar ataxia [9]. Furthermore, recently, the absence of motor cerebellar symptoms has also been recognized as being important for rehabilitation [11].
The cerebellum is the motor-control system in humans [12]. Clinically, the oculomotor deficit, speech deficits, ataxia in the trunk and extremities, balance disorder, and gait disturbance are the targets of rehabilitation in SCA [9, 13]. The possible underlying pathogenetic mechanisms include distorted timing, abnormal sensory acquisition, impaired sensory motor synchronization, impaired triggering of corticomotor excitability, and abnormal visuokinesthetic cerebro-cerebellar interactions [13].
Oculomotor deficits cause deoptimized vision. The vestibulo-ocular reflex and smooth pursuit [14] partially depend on motor prediction in static and dynamic movement and contribute to dynamic gazing [15]; moreover, the cerebellum contributes to the trainability of eye-head coordinated movements [16].
Abnormal excitability and modulation in the motor cortex and corticospinal tract causes a voluntary contraction deficit in [17, 18]. Cerebellar stimulation modulates the motor-evoked potential induced by transcranial magnetic stimulation (TMS) of the primary motor cortex [19, 20, 21]; however, this modulation is absent in patients with SCA [22, 23]. Furthermore, the cortical silent period, which reflects the excitability of the inhibitory GABAergic neural circuit in the primary motor cortex, is abnormal in these patients [24, 25, 26, 27, 28, 29], and this cerebellar effect on the cortical silent period is characteristic of the healthy population [30]. Before muscle contraction for movement, the corticospinal excitability increases in healthy individuals; in contrast, this facilitation is insufficient in SCA [31]. In addition, in patients with SCA, muscle tones are decreased [11] and the spinal reflex excitability is facilitated by cerebellar stimulation [32, 33, 34]. The long latency spinal reflex, which is correlated with the cortical circuit, is disturbed in SCA [35]. Although this functional cerebellum-spine connection may contribute to the preparation for muscle contraction, there is insufficient evidence that these connections contribute to motor control in healthy and cerebellar ataxia populations.
In simple movements, such as extension of the elbow, coordinated activity of the biceps and triceps is needed. For ballistic elbow-extension movement practice, the triphasic muscle agonist and antagonist contraction patterns contribute to the smooth movement, but under/overshooting appears during the uncoordinated contraction pattern of patients with SCA [36, 37]. Furthermore, this contraction pattern may be obtained by temporal electrical stimulation in these individuals [37].
The cerebellar internal model contributes to predictable/online/offline motor control and motor learning/adaptation [38]. The symptoms associated with motor learning do not appear at the onset of the cerebral atrophy [39], because several brain areas, i.e., the prefrontal cortex, primary motor cortex, and basal ganglia, compensate for cerebellar function in early-stage SCA [5, 6, 39]. Recently, the motor learning deficit at the early stage of the disorder was reportedly detected using an adaptation task [40]. Therefore, the assessment of the capacity for motor learning may be important to strategize the interventions that are concretely described in the following sections.
Representative nonataxia symptoms include hyperreflexia, areflexia, extensor plantar, spasticity, paresis, muscle atrophy, fasciculations, myoclonus, rigidity, chorea/dyskinesia, dystonia, resting tremor, sensory symptoms, urinary dysfunction, cognitive impairment, and brain stem oculomotor signs [41]. The Inventory of NonAtaxia Symptoms (INAS) [41] is used to estimate these nonataxia symptoms. The appearance of these symptoms depends on the type of SCA [41].
We should conduct assessment to detect the degree of motor dysfunction and consider more effective intervention of physical rehabilitation. The first, the imaging technology such as MRI provides us with structural information about the atrophic areas of the brain associated with the disease. We described about neuroimaging technique in Section 3.1. The next, we can use some outcome measurement to estimate the motor dysfunction and verification in the physical rehabilitation. Then, we introduce the representable outcome measures for physical rehabilitation in SCA in Section 3.2. However, we had not established method to estimate the remaining of motor learning ability, which is one of the most important factors to predict the effect of physical rehabilitation. Therefore, we propose the possible assessment of motor learning ability in Section 3.3.
Neuroimaging is a technique that is used to visualize the structural and functional activities of the brain. MRI measurements, such as diffusion tensor imaging and surface-based morphometry, visualize the brain structures. Functional activity imaging is achieved using fMRI and NIRS, which are indicators of cerebral blood flow, and electroencephalogram (EEG) and magnetoencephalography, which are indicators of electrical activity. Positron emission tomography and single-photon emission computed tomography with nuclear tracers are also used in this setting. The application of neuroimaging in the rehabilitation of cerebellar disorders includes voxel-based lesion symptom mapping in patients with stroke, to investigate the recovery of upper arm reach [42] and walking ability [43] depending on the lesion site.
Although conventional MRI [44] is widely used for the neuroimaging of spinocerebellar degeneration, to obtain diagnostic findings, few studies have used neuroimaging as a guideline or outcome of rehabilitation. The lack of reports in this context hampers the quantification of cerebellar degeneration in SCA and its correlation with motor dysfunctions. In terms of measurement techniques, the cerebellum exhibits a much tighter folding compared with the cerebral cortex, with individual cortical sheets with a thickness of 1–2 mm and a sheet area of 1500–2000 cm2, compared with a sheet area of 2200 cm2 with a thickness of 1.5–4 mm in the cerebral cortex. Therefore, the typical 2–4 mm3 spatial resolution of neuroimaging techniques is insufficient to capture local cerebellar changes. Patient factors include the difficulty in limiting the brain regions involved in movement disorders to the cerebellum, because the degenerative regions in SCD extend beyond this structure to multiple brain regions [45].
Among the neuroimaging modalities, the role of voxel-based morphometry (VBM) is notable in SCA rehabilitation. VBM is a statistical analysis of the entire brain in voxel units (1 mm3) that is used to identify the behavioral patterns and related brain morphological characteristics of patients [46]. Burciu et al. assessed the degree of cerebellar atrophy concerning motor and learning functions using VBM to evaluate brain structure changes after 2 weeks of balance training in patients with SCD; these authors reported the association between an increased volume of the dorsal premotor cortex and increased balance ability [47]. Matsgi et al. reported an association between VBM and neurophysiological markers in cerebellar brain inhibition (CBI), with atrophy of the dentate nucleus at VBM observed in cases of pure cerebellar ataxia that did not show CBI [48]. Bando et al. reported a correlation between adaptive learning ability and gray matter volume of the cerebellar IV-VII lobules and the supramarginal gyrus in a prismatic adaptation task in SCA [49]. Thus, VBM may be a biomarker to explain motor dysfunction in patients with SCA.
Conversely, VBM is not an ideal tool to show a causal relationship between brain structural changes and behavioral differences. As a solution to this problem, we can propose a combination of VBM and neurostimulation [50], as neurostimulation of the brain regions associated with the behavioral patterns obtained by VBM and the observation of behavioral changes before and after stimulation allow us to examine brain degeneration sites and behavior.
Gait disturbance is a major symptom of the cerebellar pathology in SCA [51]. The functional ambulation categories (FAC) is useful for the comprehensive assessment of walking ability; the FAC assesses gait for about 15 m and climbing stairs and classifies gait levels into 6 levels [52]. The FAC is also used in the exercise program created by Research Committee for Ataxia Disease (Research team under the jurisdiction of the Ministry of Health, Labour and Welfare in Japan, http://ataxia.umin.ne.jp/rehabilitation/).
The quantitative assessment of cerebellar ataxia is very important in clinical practice. The International Cooperative Ataxia Rating Scale (ICARS) has been used as a quantitative assessment of ataxia symptoms. However, it has been noted that the test reliability of the eye movement items is low [53]. The Scale for Assessment and Rating of Ataxia (SARA) is an 8-item performance-based scale that yields a total score of 0–40 (most severe ataxia). The minimal detectable change (MDC) for individual score difference from the baseline to the 1-year follow-up in SARA was <3.5 (n = 171; SCA1, n = 43; SCA2, n = 61; SCA3, n = 37; and SCA6, n = 30; mean age, 50.9 ± 13.5 years; mean disease duration, 11.8 ± 5.6 years) [54]. SARA does not include an eye movement section. Schmahmann et al. noted the importance of assessing oculomotor abnormalities and developed the Brief Ataxia Rating Scale, a modification of ICARS [55]. Each SCA genotype exhibits specific symptoms [56]. Therefore, these assessments should be used differently for different symptoms. However, one feature that is consistent among these assessments is that the scoring range is large and does not allow the assessment of minute symptom changes. Honda et al. developed a system to measure the evaluation of SARA using a depth sensor [57]. Using this system, the degree of ataxia can be measured numerically. In addition, because the system is inexpensive, it can be installed at the patient’s home, making it a useful tool for telemedicine.
The balance dysfunction in SCA has a significant impact on QOL [58]. The Berg Balance Scale and the Timed Up and Go test are widely used to assess balance dysfunction in SCA [59]. However, despite their widespread use, these assessments have not been examined for reliability and validity in SCA. Kondo et al. examined the test reliability of the Balance Evaluation Systems Test (BESTest) [60]. The BESTest is a multitask balance assessment tool that was developed to identify specific postural control problems (i.e., biomechanical constraints, stability limits, anticipatory postural adjustments, postural responses, sensory orientation, dynamic balance during gait, and cognitive effects) [61]. The MDC for an individual score difference from the baseline to the 4-week follow-up in BESTest was <8.7 (n = 20; SCA3, n = 4; SCA6, n = 9; SCA31, n = 7; mean age, 63.7 ± 10.1 years; age at onset, 53.9 ± 10.5 years; baseline SARA, 9.9 ± 3.5) [61]. Many types of balance function measures have been reported. However, BESTest is the only scale that is considered to have absolute reliability in SCA.
Gait speed is often used as an outcome of intervention studies in SCA [62, 63]. However, some changes in the gait pattern (e.g., base of support and gait speed) most likely reflect cerebellar-unspecific, compensatory strategies, and a high spatiotemporal gait variability appears to be a distinctive feature of ataxic gait [58, 64]. The Gait Variability Index (GVI) is a measure of gait variability that has been examined regarding reliability and validity [65]. The MDC for an individual score difference from day 1 to day 2 in GVI was <8.6 (Friedreich’s ataxia, n = 81; baseline ICARS, 70.4 ± 7.9) [65]. It has been suggested that gait instability in SCA are characterized by a stronger effect of balance-related impairments of cerebellar control during slow walking and a stronger effect of impaired intra-limb coordination during fast walking [58]. Therefore, in clinical practice, it is necessary to evaluate not only the optimal gait speed, but also slow walking and fast walking, to extract the characteristics of gait instability.
The cerebellum has the ability to compensate for tissue damage and loss of function. This is called the cerebellar reserve [6]. Mitoma et al. suggested that this is important for motor rehabilitation at a time when the cerebellar reserve is functioning [6]. Motor rehabilitation in the early stages may maintain and improve the cerebellar reserve [66, 67]. Therefore, it is important to assess this parameter.
Cerebellar ataxia is the main symptom of SCA. Ataxia symptoms may represent a compensation for predictive control using feedback control [6]. Predictive control requires a mechanism called internal model [38]. The internal model is constantly updated by motor learning [68]. In turn, motor learning is one of the most important functions of the cerebellum. Thus, a measure of motor learning ability may be useful as an assessment of the cerebellar reserve.
Prism adaptation (PA) is widely used as an assessment of motor learning ability in patients with SCA [40, 69]. The basic procedure of PA is shown in Figure 1. First, at the “baseline,” the task is performed without a prism lens. Subsequently, the prism lens is introduced and the task is performed. In the initial phase, the lens is set off to either the left or right side of the target, but the error is corrected as the number of repetitions increases. This period is called the “initial error correction phase.” Thereafter, a spatial realignment phase is performed under the prism lens. The purpose of this phase is to gather visuospatial information including the errors. Next, the prism is removed and an “after-effect phase” is performed. If the spatial information is being re-learned, errors are generated in the opposite direction to the initial error correction phase. Recently, Hashimoto et al. developed the Adaptability Index (AI), which is a composite index computed from several parameters measured PA (Figure 2). The clinical efficacy of the AI in discriminating patients with SCA from healthy individuals has been demonstrated [70]. Furthermore, Bando et al. found that a reduced AI was correlated with gray matter atrophy in the cerebellum in the SCA group [49]. In particular, the right lobule VI and the left Crus I showed the most robust correlation. These cerebellar regions are consistent with the correlates of PA detected in previous human and nonhuman primate studies [71, 72]. AI is considered as a motor learning index that reflects the cerebellar reserve (in this case, the degree of cerebellar atrophy).
Overview of prism adaptation. The ordinate shows the finger-touch error represented from the target to the touch point. Three phases are generally used: (1) absence of a prism lens (prism off), (2) presence of a prism lens (prism on), and (3) absence of a prism lens (prism off).
Calculation of the adaptability index (AI). The AI is calculated as follows: AI = a × b × c, where “a” is the adaptation index defined as the probability of correct touches in the last 10 trials of the spatial realignment phase 1, “b” is the retention index defined as the probability of incorrect touches in the initial 5 trials of the after-effect phase, and “c” is the extinction index designated as the probability of correct touches in the last 10 trials of the spatial realignment phase 2.
PA can be implemented using a simple system. In addition, it takes only 20 min to complete a PA. Reaching tasks can be performed even in the period during which the patient is unable to walk, and the fact that the PA can be assessed continuously over a long period is an advantage. However, only cross-sectional studies have been conducted in previous reports [40, 49, 69, 70, 73, 74]. Future studies need to be designed to examine long-term changes and intervention effects.
The targets of rehabilitation in cerebellar ataxia are mainly disability in ADL, gait, and motor dysfunction. Therefore, GAS, FIM, 10-m walking test, TCA, SARA, ICARS, and BESTest are used as important outcomes in rehabilitation. The most important strategies of rehabilitation for cerebellar ataxia including SCA consists in balance training (see Section 4.3), gait training (see Section 4.2), and muscle strengthening training using a high-intensity program (see Section 4.1). Further, optional possible interventions are using assistive technology (see Section 4.4) and neuromodulation technique (see Section 4.5).
Rehabilitation methods for cerebellar ataxia have been reported [75]. The most important strategy is the increase in the intensity of physical training, such as balancing, gait, and strength [76]. Several systematic reviews [77, 78, 79] and narrative reviews [3, 75, 80, 81] introduced and recommended intensive physical therapy for cerebellar ataxia in patients with SCA. Miyai et al. [62] reported that physical and occupational therapies of 2 h × 5 days +1 h × 2 days per week for 4 weeks were applied to inpatients and improved the SARA score and gait speed; however, the effect was carried over only up to 12 weeks after the training, and had disappeared at 24 weeks [62]. Conversely, Ilg et al. reported that intensive coordinative physiotherapy delivered over 4 weeks improved motor performance in degenerative cerebellar ataxia in a study with an intraindividual control design [63].
An outpatient rehabilitation program for 6 weeks applied to 19 participants with Friedreich’s ataxia improved the motor domain item in the FIM score and Friedreich’s Ataxia Impact Scale, but the posthome program could not maintain the effect [82]. Therefore, this finding indicates that continuous outpatient rehabilitation programs are important for maintaining the ADL in patients with Friedreich’s ataxia. Additional large-scale studies are needed to investigate the long-term effect of outpatient rehabilitation programs and identify the characteristics of patients who respond to treatment. Therefore, the development of optimal individual programs is important to obtain the effect of training, regardless of the inpatient, outpatient, or home-self-training setting [83]. The semi-order program of the Research Committee for Ataxia Disease (Research team under the jurisdiction of the Ministry of Health, Labour and Welfare in Japan, http://ataxia.umin.ne.jp/rehabilitation/) can be used for this purpose.
Subsequently, the continuity of the intensive training is an important factor, because degradation in physical function was reported. Therefore, approaches aimed at upkeeping these programs in a way that suits the patients are needed. For example, exergames contribute to the practice of exercise at home. In the future, tele-rehabilitation systems [84] should be tested for the improvement (or maintenance) of the function and continuity of exercise.
Gait training has been reported to improve spatiotemporal gait parameters (cadence, step length/width, gait speed, etc.) [85, 86, 87], complex gait (Timed Up and Go test, Dynamic Gait Index) [85], independence (FAC) [86], ataxia (SARA) [88], and adaptive locomotor adjustments (ALA) [88]. Patients with SCA exhibit problems other than the gait disturbance itself, i.e., stiffening of the body in an attempt to avoid the occurrence of gait disturbances. Therefore, it is important to focus on gait disturbances and increasing the number of walking patterns when considering gait training in a person with SCA.
Disturbances of gait are the core features of SCA [89, 90, 91, 92], thus leading to a risk of falling down [93]. Patients with cerebellar ataxia walk with a reduced walking speed and cadence, as well as reduced step length, stride length, and swing phase; increased walking base width, stride time, step time, stance phase, and double limb support phase; and increased variability of step length, stride length, and stride time [94]. These items are affected by both balance-related impairments and deficits related to limb control and intra-limb coordination [95]. We believe that balance training and coordination training are key to the improvement of gait disturbances. Regarding the details of balance training, please refer to the Section 4.3.
In addition, stiffening of the body leads to a decrease in the number of walking patterns; as a result, ALA deteriorates [96, 97]. ALA implies that obstacle avoidance is achieved by modifying basic walking patterns in response to obstacle properties, e.g., a sloping road, stepping over an obstacle, or dynamically changing the spaces created by pedestrians in a hallway. In persons with SCA, feelings of anxiety as a result of the frequent experience of falls, as well as deficits related to limb control by ataxia, could negatively affect their ALA because of increased muscular co-contractions and reduced joint movements [98]. We will describe the approaches to improve ALA in the next paragraph.
The proposals for gait training are as follows: gait training without or with a treadmill. First, in gait training without a treadmill, we refer the reader to Section VI of the BESTest as gait adaptability training [61]. Section VI of the BESTest consists of a 7-item scale: (1) Gait Natural, (2) Change Speed, (3) Head Turns, (4) Pivot Turn, (5) Obstacles, (6) “Get Up & Go” Test, and (7) Cognitive Task “Get Up & Go” Test, aimed at evaluating the stability of the gait. These elements are important to improve ALA. As an example of gait training, persons with SCA are asked to walk while making an effort to change their walking speed according to therapist’s instructions to engage is “fast (or slow)” walking as fast (or slow) as possible. If patients need assistance when walking, you might want to change the walking speed with the support of a therapist.
Second, gait training using a treadmill has advantages in that patients can practice a relatively large amount of gait training over a short period and the therapists can control the speed and incline easily. Gait training using a treadmill has been reported as a potentially promising tool for improving ALA in a person with SCA [88], as well as gait disturbances in a person with Parkinson’s disease [99, 100]. It has been reported that variability was increased during slow and fast walking, but was normal during the preferred walking speed in a person with cerebellar ataxia [101]. Another study reported that, in ataxia, walking at the preferred speed minimizes the gait abnormalities, and the analysis of gait at a wide range of speeds is recommended [94]. For this reason, when using a treadmill in gait training, we suggest that walking be practiced at the speed at which the gait disturbance increases (i.e., slow or fast walking speed) for specific patients. When the fear of falling increases, the use of a harness is recommended, to provide a safe environment for gait without the fear of falling.
It is important to improve the balance ability and ALA during gait training in a person with SCA. Gait training is a relatively easy method; however, it is left to the therapist’s discretion and experience. By changing the task itself or adjusting the difficulty level of the task, gait training may be able to overcome the limited walking patterns of these patients.
All patients with SCA will develop balance difficulties during the course of the disease. Balance is essential for mobility, and is very important for QOL. Although there is no effective pharmacological treatment for decreasing the ataxia or slowing disease progression, physical therapy plays an important role in controlling ataxia and improving or maintaining function through training [76]. In general, the physical therapy programs for degenerative cerebellar ataxia are based on intensive static and dynamic balance and coordination training. There is some evidence that such therapeutic training programs alleviate the ataxic symptoms and improve functional activities in a person with cerebellar ataxia [63, 78, 102]. In these patients, the disease progressively damages the cerebellar structure that plays a crucial role in motor learning [103]; however, these studies have indicated that it is necessary for highly repetitive balance training for balance impairment in SCA. For this reason, highly repetitive balance training in patients with SCA should be the focus of future studies.
More concretely, balance training exercises in early stages of the disease, i.e., ambulation, include the following categories: (1) static balance training, (2) dynamic balance training, and (3) coordination training (Figure 3). In addition, combining a dual task with balance training improves balance and reduces the number of falls in individuals with cerebellar ataxia [104].
National Center of Neurology and Psychiatry (NCNP) balance training program. This balance training program was devised through consultations with patients with SCA, medical doctors, and therapists at the NCNP in Japan. In the advanced stage of SCA, it is recommended to perform the programs indicated by an asterisk.
Moreover, it is important to provide support for these approaches and make them a habit of exercising. For instance, if the patients with SCA have no habit of exercising, they should start with a small number of exercises (i.e., the minimum necessary) to get used to exercising, followed by the gradual increase in the number of exercises. If the patients with SCA have a habit of exercising, the therapist should teach them to adjust the exercise load (e.g., exercise more slowly and/or provide a small base of support). It is also important to adopt balance training that can be enjoyed, e.g., video games [105] and Tai Chi [106], as a means of continuing balance training.
In advanced stages of the disease (i.e., no ambulation), it is necessary to perform balance training under safe conditions (e.g., prone, supine, crawl, and sitting positions), to prevent the decrease in physical activity. Even in advanced stages, it has been reported that a person with degenerative ataxia may benefit from balance training [107]. In addition, it is necessary to focus on ADL and living infrastructure at this stage. If a patient with SCA requires assistance during transfer, engaging in repetitive transfer training with assistance and/or modification of the living infrastructure (e.g., installation of handrails) are necessary.
Focusing on highly repetitive balance training in patients with SCA might preserve the balance function. There is no scientific basis for the number of balance training exercises that are necessary to achieve this goal; however, we would like to recommend engaging in 30 repetitions at least per balance training session. Furthermore, the balance training must be designed to provide a significant challenge to the person’s balance. If a person with SCA wants to preserve the balance function, they have to continue engaging in repetitive balance training, “use it or lose it.” However, few studies have reported the effect of gait and balance training in persons with SCA. Therefore, further studies are needed to clarify the clinical effectiveness of gait and/or balance training.
In recent years, various technologies have been used in the assessment of and treatment based on rehabilitation, as well as to support daily life in patients with SCD. Curara, a wearable robotic system, assists both hip and knee movements and supports the wearer’s rhythmic gait using a synchronization control based on a central pattern generator [108]. Gait support using the curara system has been reported to improve gait smoothness in patients with SCD [109]. In addition to these findings, a recent study addressed the effects of robotic gait training combined with noninvasive brain stimulation. This report showed that robot gait training using Lokomat-Pro in combination with cerebellar tDCS improved the functional scores on SARA, especially the scores on the subitems of gait, stance, sitting, and heel-shin slide compared with robot gait training alone [110]. Thus, hybrid training using robots and noninvasive brain stimulation will be applied to the rehabilitation treatment of patients with SCD in the future.
Accordingly, the use of walking aids is a complementary method for balance and gait impairment. In general, walking aids such as canes and walkers improve postural stability, but their improper use increases the risk of falling [111]. Because the manipulation of a cane requires coordinated upper limb movements [112], patients with SCD who have upper limb ataxia are likely to experience difficulty in using a cane. Conversely, because a walker does not require much coordinated movement of the upper limbs, technology-based walkers are being developed. Recently, a smart walker for mobility assistance and monitoring system aid, ASBGo, was developed and reported to improve gait parameters and postural stability in patients with SCA [113, 114]. In addition to technology, some studies on walking assistance using dogs and handkerchiefs have also been reported. Walking with a rehabilitation dog that has been specifically trained for goal-directed interventions or with an assistance dog that helps people with physical disability and mobility impairments has been reported to improve balance while walking in patients with SCD [115]. Furthermore, the handkerchief-guided gait, in which the patient with SCD walks along with the caregiver while maintaining light tension on a handkerchief by pulling lightly, has been shown to decrease body swaying and increase stride length and gait velocity during walking [116].
Moreover, technology is also being used as a tool to assess ataxia in patients with SCD living at home. Most of them represent attempts to evaluate SARA, which is a typical measure of ataxia, at home. In recent years, a technology aimed at objectively evaluating the speech, upper and lower limb, balance, and gait functions using wearable inertial sensors and a Kinect camera was developed, which makes it possible to discriminate between normal and abnormal functions and to detect ataxia at an early stage [117]. In addition, SaraHome has been developed to allow the remote evaluation of SARA items using Kinect and Leap Motion Controller [118]. Moreover, a spoon equipped with an inertial sensor, called Ataxia Instrumented Measurement-Spoon, has been developed, which allows the evaluation of upper limb function in ataxia while eating with a spoon [119, 120, 121]. Because SCD is an intractable neurological disease, it is difficult for many patients to leave their houses. Therefore, the contribution of technology to home-based rehabilitation is expected to increase in the future if a low-cost and easy method of assessing ataxia at home is established using the technologies and products of daily living described above.
Regarding the support of ADL, BMI studies have been reported. Patients with severe SCA often have difficulty in communicating because of language impairment. The application of BMI using event-related potentials and frequency bands of EEG is being investigated as a solution to this problem. The operational accuracy of BMI using P300 for event-related potentials was 82.9% in patients with SCA, which was similar to the accuracy observed in healthy subjects (83.2%) [122]. There are also reports of BMI manipulation in patients with SCD using the EEG frequency band associated with motor imagery [123]. BMI has a wide range of applications in diseases of the central nervous system, such as communication tools, transportation, and life support, and is expected to contribute to the QOL of patients with SCD.
Neuromodulation via noninvasive brain stimulation (NIBS) is a potential method for the treatment of cerebellar ataxia [19, 124]. A previous systematic review [125] reported the effectiveness of cerebellar neuromodulation using the TMS technique of transcranial direct current stimulation (tDCS). The SARA and ICARS scores in patients with SCA3, multiple system atrophy, and postlesion ataxia, as assessed using real cerebellar rTMS (1 Hz), were significantly lower than those detected in the sham stimulation group [125]. Furthermore, no harmful side effects were noted [125]. Cerebellar rTMS can modulate the plasticity of the vestibular reflex [16, 126]; therefore, cerebellar rTMS has potential for application in balance training to enhance vestibular contributions.
A single session of anodal cerebellar tDCS (2 mA, 20 min) significantly improved SARA, ICARS, 9-hole-peg test, and 8-m walking test scores [127]. Furthermore, combined anodal cerebellar tDCS and cathodal spinal DCS (5 days/week, 2 weeks) improved SARA score, ICARS score, 9-peg test, and 8-m walking time in patients with degenerative cerebellar ataxia [128]. There is insufficient evidence regarding whether simultaneous stimulation is more effective than single stimulation [129]; however, it is possible that this intervention method will produce improvements. Based on these findings, which were gleaned from small-sample studies, we suggest that a neuromodulation montage will improve the ataxia, balance, and gait ability. Therefore, we should perform further studies using a larger population.
Individualized physical rehabilitation programs for patients with SCA may improve/maintain their motor function, balance, gait ability, and ADL. In particular, the intensity and continuity of gait and balance training need to be considered to achieve effectiveness. Furthermore, several technologies, such as depth sensors, robotics, and NIBS, have contributed to the development of methods for the assessment and treatment of motor dysfunction in individuals with SCA. We should continue to study populations suffering from dysfunction caused by SCA.
This work was supported by Shijonawate Gakuen University and JSPS KAKENHI (Grant Number 20 K11298).
The authors declare no conflict of interest.
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\n\nQUALITY CONTENT
\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
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In many filter applications after the initial transients, the gain matrix K tends to a constant during the steady state, which points to design the filter based on constant gains alone. Such a constant gain Kalman filter (CGKF) can be designed by minimising any suitable cost function. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. Though CGKF results may not be too close to those of RRR, they are acceptable. It accepts extremely simple models and the gains are robust in handling similar scenarios. In this chapter, we provide examples of applying the CGKF by ancient Indian astronomers, parameter estimation of spring, mass and damper system, airplane real flight test data, ballistic rocket, re-entry of space object and the evolution of space debris.",book:{id:"7466",slug:"introduction-and-implementations-of-the-kalman-filter",title:"Introduction and Implementations of the Kalman Filter",fullTitle:"Introduction and Implementations of the Kalman Filter"},signatures:"Mudambi R. 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LabVIEW also includes different applications such as serial device control, data analysis, data presentation, data storage and communication over the internet. Analysis library; It includes versatile and useful functions such as signal generation, signal processing filters, Windows statistics and regressions, linear algebra and array arithmetic. Due to the graphical nature of LabVIEW, it is an innate data presentation package. You can view the data in any form you want. Chart, graph and user-defined graph are among the output options that can be used. As a scientist or an engineer, you frequently measure physical changes such as temperature, pressure, time, mass, electric current, light intensity, radioactivity etc. You generally need to analyze and present the data. When you have large amounts of data, you need to use software to analyze and present the data. LabVIEW makes these actions easy for you. Because LabVIEW includes hundreds of built-in and add-on functions you need that make it easy to create a user-friendly interface. 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Owing that this kind of images contains the information of interest in a codified form, processing them is of main relevance and has been a widely studied topic for many years. Several mathematical tools have been used to analyze fringe patterns, from the classic Fourier analysis to regularization methods. Some methods based on wavelet theory have been proposed for this purpose in the last years and have evidenced virtues to consider them as a good alternative for fringe pattern analysis. 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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. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. 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He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. He is especially interested in the genetic differentiation pattern and speciation process that correlate to the flashing pattern and mating behavior of some fireflies in Japan. He then worked for Olympus Corporation, a Japanese manufacturer of optics and imaging products, where he was involved in the development of luminescence technology and produced a bioluminescence microscope that is currently being used for gene expression analysis in chronobiology, neurobiology, and developmental biology. 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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. 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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:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{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:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{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:'"Politechnica" University Timişoara',institution:null},{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:{name:"Tecnalia",country:{name:"Spain"}}},{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. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. 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Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",slug:"anca-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",slug:"attilio-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",slug:"yanfei-(jacob)-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"}}}]},onlineFirstChapters:{paginationCount:7,paginationItems:[{id:"82777",title:"Sustainability and Social Investment: Community Microhydropower Systems in the Dominican Republic",doi:"10.5772/intechopen.105995",signatures:"Michela Izzo, Alberto Sánchez and Rafael Fonseca",slug:"sustainability-and-social-investment-community-microhydropower-systems-in-the-dominican-republic",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}},{id:"82387",title:"Kept Promises? 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