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Energy Efficiency in Meat Processing

Energy conservation plays a vital role towards sustainable development of meat processing. Energy costs for many meat plants represent the fourth highest operational cost. In meat processing, moderate levels of both electrical and thermal energy are consumed in wide range of processes and applications. However, energy efficiency improvement in the meat processing industry have been a focus to incr

Kreativ etnografi

Bidraget behandlar hur etnografi kan bedrivas i en mer kreativ riktning och vikten av att bryta mot inarbetade procedurer och normer inom forskning.

Cancer gene expression data analysis using rough based symmetrical clustering

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based

Convergence Rates and Fluctuations for the Stokes–Brinkman Equations as Homogenization Limit in Perforated Domains

We study the homogenization of the Dirichlet problem for the Stokes equations in R3 perforated by m spherical particles. We assume the positions and velocities of the particles to be identically and independently distributed random variables. In the critical regime, when the radii of the particles are of order m-1, the homogenization limit u is given as the solution to the Brinkman equations. We p

The validity of students’ conceptions of differentiability and continuity

University students’ conceptions of differentiability, continuity and relationsbetween these concepts were studied to reveal their choices of representationsand their strategies to justify their relational claims. Questionnaires and interviews were used to collect data. The results were analysed and categorizedthrough a framework based on understanding as connections between concepts,and a theory

Uncertainty quantification for physics-informed deep learning

The development of physics-informed deep learning is radically changing compu-tational science and engineering, allowing for an effective integration ofphysics-based and datadriven modeling. Deep learning provides a powerful tool forthe discovery of governing dynamics underneath data and enables nonlinear model-reduction. A Bayesian viewpoint of deep learning is discussed in this chapter towards t