Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running

BACKGROUND: Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level. METHODS: We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG. RESULTS: We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given. CONCLUSIONS: Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described

Editors’ Introduction: An Overview of the Educational Administration and Leadership Curriculum: Traditions of Islamic Educational Administration and Leadership in Higher Education

This chapter provides an overview of several topics relevant to constructing an approach to teaching educational administration and leadership in Muslim countries. First, it places the topic in the context of the changing nature and critiques of the field that argue for a greater internationalisation to both resist some of the negative aspects of globalisation and to represent countries’ traditions in the professional curriculum. Then, it identifies literature that presents the underlying principles and values of Islamic education that guide curriculum and pedagogy and shape its administration and leadership including the Qur’an and Sunnah and the classical educational literature which focuses on aims, values and goals of education as well as character development upon which a ‘good’ society is built. This is followed by a section on the Islamic administration and leadership traditions that are relevant to education, including the values of educational organisations and how they should be administered, identifying literature on the distinctive Islamic traditions of leadership and administrator education and training as it applies to education from the establishment of Islam and early classical scholars and senior administrators in the medieval period who laid a strong foundation for a highly sophisticated preparation and practice of administration in philosophical writings and the Mirrors of Princes writings, and subsequent authors who have built upon it up to the contemporary period. The final section provides an overview of the chapters in this collection

Image unmosaicing without location information using stacked GAN

Image mosaicing is an image processing technique that is most commonly used to conceal identities of sensitive objects. The authors’ research features recovering the mosaiced parts in an image, especially focusing on facial parts. While recent image completion methods based on deep learning have shown promising results on recovering damaged parts in an image, they have not addressed the problem of image unmosaicing. Moreover, all those methods necessitate the location information of damaged parts to tackle the recovery problem. They formulate unmosaicing as an image‐to‐image translation problem, and propose a two‐stage method using generative adversarial network (GAN): stage‐I GAN generates a coarse prediction followed by stage‐II GAN which produces a final unmosaiced image with finer information. A combination of low‐level l1 loss and high‐level structural similarity loss is used to attain visually plausible and semantically consistent output. They have evaluated their method on the CelebA dataset and achieved better results than state‐of‐the‐art image completion methods without explicitly exploiting the location information of mosaiced parts