Book Chapters / Κεφάλαια Βιβλίου

This chapter explores the potential of dynamic statistics software for supporting the teaching and learning of the Common Core Standards for Mathematics. It shares the experiences from a teaching experiment that implemented a data-driven approach to mathematics instruction using the dynamic data-visualization software InspireData © (Hancock, 2006), an educational package specifically designed to meet the learning needs of students in the middle and high school grades (Grades 4-12). We report on how a group of Grade 4 (about 9-year-old) students used the affordances provided by the dynamic learning environment to gather, analyze, and interpret data, and to draw data-based conclusions and inferences. The role of the technological tool in scaffolding and extending these young students' stochastical and mathematical reasoning is discussed.

Methods from game theory and mechanism design have been proven to be a powerful mathematical tool in order to understand, control, and efficiently design dynamic, complex networks, such as the Internet. Game theory provides a good starting point for computer scientists to understand selfish rational behavior of complex networks with many agents. Such a scenario is readily modeled using game theory techniques, in which players with potentially different goals participate under a common setting with well prescribed interactions. The Nash equilibrium stands out as the predominant concept of rationality in noncooperative settings. Thus, game theory and its notions of equilibria provide a rich framework for modeling the behavior of selfish agents in these kinds of distributed and networked environments and offering mechanisms to achieve efficient and desirable global outcomes despite selfish behavior. The most important algorithmic solutions and advances achieved through game theory are reviewed.

The chapter synthesizes extant interdisciplinary literature, by putting together a combination of relationship management theories as well as banking, economics, and finance theories, and blends this with findings from an ethnographic research platform to discuss the critical variables in the development of Bank-SME relationships. In addition, the chapter considers the effect of the recent economic crisis on the Bank-SME relationship. It can be seen that few banks looked inside their relationship with their SME customers as a means of redressing the crisis' effect and this has detrimental effects on their longterm performance. As a consequence, the chapter proposes recommendations so as to reduce the crisis negative impact. Moreover, it highlights that the new developments in the technological environment, i.e. social media, can be used to strengthen the Bank-SME relationship's success and is especially pertinent in such times of financial duress as it can enhance the communication mode of the dyad.

The notion that breast cancer is not a single disease but many – that there is considerable heterogeneity among different tumors – is not new, having been observed by physicians for decades. But with the advent of powerful analytical and computational tools, systems biology has provided a fascinating view of the underlying mutations, biology, and networks that drive the process of breast tumorigenesis. The information derived from analyzing these networks is also proving to be clinically useful and is beginning to be incorporated into the standard clinical management of breast cancer patients – to determine prognosis and guide the choice of therapeutics, for example. However, these advances are complicated by the heterogeneity of cancer cell phenotypes that exist within a single tumor, which drives metastasis, resistance to treatment, and, eventually, recurrence. Systems biology offers a strong tool for investigating both levels of heterogeneity to comprehensively define, and ultimately attack, the aberrant molecular networks governing breast cancer cells. Epidemiology Breast cancer is a staggering public health problem. The American Cancer Society expects that over 232,000 cases of invasive breast cancer will be diagnosed in 2014, accounting for the largest number of cancer cases in women (ACS, 2014). Nearly 40,000 deaths are predicted to occur, which rank it the second leading cause of cancer deaths in women, behind only lung cancer. Fortunately, the number of newly diagnosed cases has decreased since 2000, largely as a result of a reduction in number of post-menopausal women on hormone replacement therapy, which has been strongly linked to the development of breast cancer (Rossouw et al., 2002). Death rates have also decreased in recent years and this has been attributed to improvements in screening and early detection with mammography as well as better treatment options (ACS, 2012). However, racial disparities do exist as African American women have a lower incidence rate but an increased chance of death (ACS, 2011). While this may reflect socioeconomic factors and access to health care, there also seems to be a biological difference since African American women are more likely to be diagnosed with aggressive cancers (Amend et al., 2006; Stead et al., 2009). Additionally, over 62,000 cases of carcinoma in situ are expected in 2014 (ACS, 2014). These are non-invasive neoplasms and likely represent a pre-malignant stage in the progression toward invasive breast cancer (Burstein et al., 2004).

Metastasis is the cause of almost all cancer-related deaths. It is an extremely complex, multistep process defined as the spreading of cancer cells from their primary site to distant tissues. Once metastasis occurs it causes catastrophic damage to the critical organs, which is ultimately detrimental to patients. Collective efforts of many scientists have revealed the underlying molecular mechanisms of metastasis by a considerable extent, but there is still a colossal job to be undertaken by researchers to solve this life-threatening health problem. In this chapter, metastasis is explained by focusing on underlying molecular pathways. We define the steps that a cancer cell needs to climb in order to metastasize and discuss the significant molecular actors aberrantly regulated during this process. First, we outline how these molecules are deregulated in cancer cells in order to circumvent natural barriers against metastasis. Then, we give a molecular explanation on why some cancer types metastasize to certain organs. Lastly, we look into recent therapeutic trials, which involve targeting of pathways in the metastatic cascade (Figure 18.1).

This chapter aims at identifying the main design and operation constraints, that smart environments are expected to experience within a 5G wireless/mobile network and how these constraints can be addressed using cognitive radio networks. This chapter first provides a general description of 5G wireless/mobile networks and stresses their role in the future wireless communications with emphasis given on smart environments. Then, the smart environments are presented based on their architecture characteristic and the applications associated with their operation. In addition, an overview of various current standards related to IoT applications is presented followed by the concept of cognitive radio networks and the available experimental platforms stressing the benefits of employing this technology in the future 5G wireless/mobile networks. Finally, the research challenges associated with integrating 5G wireless/mobile networks and IoT are outlined.

In this chapter, we argue for the importance of embedding computer programming into existing mathematics curricula through the use of educational games apps. We illustrate the apps' opportunities by showing how programming was incorporated into a task undertaken by a group of 10-11 year old students. This was their first experience of computer programming at the primary level. The results of this study contribute to understanding a) the development of students' reasoning about mathematical concepts and procedures throughout the participants' engagement with A.L.E.X. app, and b) students' articulated impressions about educational games apps with mathematical content, including students' acknowledgment of the pedagogical role that an iPad could play.

The accomplishment of a manufacturing company's objectives is strongly connected to the efficient solution of scheduling problems that are faced in the production environment. Numerous methods for the solution of these problems have been published. However, very few of them have been adopted by manufacturing companies. This chapter suggests that the basic reason behind this imbalance is the inadequate representation of the scheduling process when designing decision support systems. Hence, the algorithms that are designed and included in these systems might not reflect the problems that actually have to be solved. The relevance of algorithmic design can be improved by using a more complete representation of the scheduling process, which would be highly relevant for increasing the adoption rate of new support systems. The main contribution of the chapter concerns the development of a theoretical framework for the design of scheduling decision support systems. This framework is based on an interdisciplinary approach that integrates insights from cognitive psychology, computer science, and operations management. The use of this framework implies that the design of a decision support system should start with an examination of the human, organizational, and technical characteristics of the scheduling situation that has to be supported. This information can be obtained and analyzed using appropriate methodologies such as hierarchical task analysis, cognitive task analysis and cognitive work analysis as well as other methodologies, such as interviews, observations, context diagrams, and data flow diagrams. The designer of the decision support system can then match the results of the analysis to the guidelines of the theoretical framework and proceed accordingly.

This chapter discusses the insights developed for designing scheduling algorithms according to three design projects where algorithms have been developed. The choice of applications covers a broad spectrum. The methods used are from three different fields, namely combinatorial optimization, genetic (evolutionary) algorithms, and mathematical optimization. The application areas differ also in terms of the role of a human user of the algorithm. Some of these algorithms have been developed without detailed study of the competences of the perceived users. Others have examined humans when performing the scheduling tasks manually, but have not considered the change in cognitive load if the process of planning changes due to the new algorithm and computerized support. Although none of the design projects fulfils all criteria developed in the framework of Chap. 12, we show that the framework helps to assess the design projects and the resulting algorithms, and to identify the main weaknesses in these applications. Finally, we show how they can be addressed in future. The three application areas are: 1. Decision support for shunting yard scheduling using a network flow heuristic. 2. An evolutionary multi-objective decision tool for job-shop scheduling. 3. Group sequencing. A predictive-reactive scheduling method for job-shop scheduling.

Atomic force microscope (AFM) is a powerful and invaluable tool for imaging and probing the mechanical properties of biological samples at the nanometric scale. The importance of nano-scale characterization and nanomechanics of soft biological tissues is becoming widely appreciated, and AFM offers unique advantages in this direction. In this chapter, we describe the procedure to collect data sets (imaging and mechanical properties measurement) of collagen gels and tumor tissues. We provide step-by-step instructions throughout the procedure, from sample preparation to cantilever calibration, data acquisition, analysis, and visualization, using two commercial AFM systems (PicoPlus and Cypher ES) and software that accompanied the AFM systems and/or are freeware available (WSxM, AtomicJ). Our protocols are written specifically for these two systems and the mentioned software; however, most of the general concepts can be readily translated to other AFM systems and software.

Micro-firms are vitally important for the economy of most countries, not only developing economies, but also advanced industrial economies, as shown by the relevant statistics. For example, ‘In 2007, 89% of firms in the United States (U.S.) had less than 20 employees and 79% had less than 10' (Cunningham et al., 2014). According to Eurostat (2015), 29.2% (i.e. about 30%) of the EU employees work in micro-enterprises (<10 employees), while about 20% are employed in small firms (<50 employees). In other words, about half of the European workforce is employed in micro- and small firms. The average contribution for the period 2008-2013 of micro-firms to value added of the euro area economy was 22% (ECB, 2013).

We present the IMPRESS software system; a tool that can support the first responders in cases of disaster management and resources allocation and the optimization of response to large-scale emergencies. This is a multi-level architecture system that has desktop and mobile interfaces, supporting the decision makers with different modules. Every module has been designed in a way that the data can be unified, no matter the source, and the related calculation engines are providing optimized information for both the users in the incident management centre and on the field. In this article IMPRESS system components are presented, among the validation and optimization activities during the demonstrations implemented in Palermo, Italy (field test exercise), Podgorica, Montenegro (field test exercise) and Sofia, Bulgaria (table top exercise).