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The methodological discourse of mixed-methods research offers general procedures to combine quantitative and qualitative methods for investigating complex fields of research such as higher education. However, integrating different methods still poses considerable challenges. To move beyond general recommendations for mixed-methods research, this chapter proposes to discuss methodological issues with respect to a particular research domain. Taking current studies on the transition to higher education as an example, the authors first provide an overview of the potentials and limitations of quantitative and qualitative methods in the research domain. Second, they show the need for a conceptual framework grounded in the theory of the research object to guide the integration of different methods and findings. Finally, an example study that investigates transition with regard to the interplay of the individual student and the institutional context serves to illustrate the guiding role of theory. The framework integrates different theoretical perspectives on transition, informs the selection of the research methods, and defines the nexus of the two strands that constitute the mixed-methods design. As the interplay of individual and context is of concern for teaching and learning in general, the example presented may be fruitful for the wider field of higher education research.
Pelvic floor dysfunction (PFD) is characterized by the failure of the levator ani (LA) muscle to maintain the pelvic hiatus, resulting in the descent of the pelvic organs below the pubococcygeal line. This chapter adopts the modified Humphrey material model to consider the effect of the muscle fiber on passive stretching of the LA muscle. The deformation of the LA muscle subjected to intra-abdominal pressure during Valsalva maneuver is compared with the magnetic resonance imaging (MRI) examination of a nulliparous female. Numerical result shows that the fiber-based Humphrey model simulates the muscle behavior better than isotropic constitutive models. Greater posterior movement of the LA muscle widens the levator hiatus due to lack of support from the anococcygeal ligament and the perineal structure as a consequence of birth-related injury and aging. Old and multiparous females with uncontrolled urogenital and rectal hiatus tend to develop PFDs such as prolapse and incontinence.
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
In this paper we propose a stochastic programming method to analyse limit and shakedown of structures under uncertainty condition of strength. Based on the duality theory, the shakedown load multiplier formulated by the kinematic theorem is proved actually to be the dual form of the shakedown load multiplier formulated by static theorem. In this investigation a dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit and the shakedown limit. The edge-based smoothed finite element method (ES-FEM) with three-node linear triangular elements is used for structural analysis.
The coupling of charged molecules, nanoparticles, and more generally, inorganic/organic nanohybrids with semiconductor field-effect devices based on an electrolyte–insulator–semiconductor (EIS) system represents a very promising strategy for the active tuning of electrochemical properties of these devices and, thus, opening new opportunities for label-free biosensing by the intrinsic charge of molecules. The simplest field-effect sensor is a capacitive EIS sensor, which represents a (bio-)chemically sensitive capacitor. In this chapter, selected examples of recent developments in the field of label-free biosensing using nanomaterial-modified capacitive EIS sensors are summarized. In the first part, we present applications of EIS sensors modified with negatively charged gold nanoparticles for the label-free electrostatic detection of positively charged small proteins and macromolecules, for monitoring the layer-by-layer formation of oppositely charged polyelectrolyte (PE) multilayers as well as for the development of an enzyme-based biomolecular logic gate. In the second part, examples of a label-free detection by means of EIS sensors modified with a positively charged weak PE layer are demonstrated. These include electrical detection of on-chip and in-solution hybridized DNA (deoxyribonucleic acid) as well as an EIS sensor with pH-responsive weak PE/enzyme multilayers for enhanced field-effect biosensing.
Bio-feedstocks
(2011)