Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Fitting Physical Models to Spatiotemporal Observations: Discovering Developmental Regulatory Networks of Drosophila

Deep learning continues to solve significant scientific and engineering problems, but the solutions found are neural networks with thousands of parameters that provide no scientific or engineering insights. A solution to this problem, explored in this work, is to learn mathematical models that represent mechanisms that can be interpreted by scientists and engineers. A challenging learning problem is to discover the genetic regulatory mechanisms that drive pattern formation during early biological development. Using known mathematical models of these processes, consisting of coupled ordinary differential and partial differential equations, we aim to identify the model parameters that describe the biological mechanisms at play. To guide learning, we use raw gene expression data sampled from the model organism Drosophila melanogaster, a fruit fly, which is normalized through a series of processing steps before learning. Our learning method applies the powerful techniques of algorithmic differentiation and gradient descent that underlie deep-learning advances. The results of this study reveal multiple genetic regulatory solutions capable of producing genetic expression patterns that match those observed in the fruit fly embryo. Cluster analysis of these solutions identifies a set of discrete genetic regulatory networks that more closely match those that function in the actual embryo

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Computational studies of vascularized tumors

Cancer is a hard problem touching numerous branches of life science. One reason for the complexity of cancer is that tumors act across many different time and length scales ranging from the subcellular to the macroscopic level. Modern sciences still lack an integral understanding of cancer, however in recent years, increasing computational power enabled computational models to accompany and support conventional medical and biological methods bridging the scales from micro to macro. Here I report a multiscale computational model simulating the progression of solid tumors comprising the vasculature mimicked by artificial arterio-venous blood vessel networks. I present a numerical optimization procedure to determine radii of blood vessels in an artificial microcirculation based on physiological stimuli independently of Murray’s law. Comprising the blood vessels, the reported model enables the inspection of blood vessel remodeling dynamics (angiogenesis, vaso-dilation, vessel regression and collapse) during tumor growth. We successfully applied the method to simulated tumor blood vessel networks guided by optical mammography data. In subsequent model development, I included cellular details into the method enabling a computational study of the tumor microenvironment at cellular resolution. I found that small vascularized tumors at the angiogenic switch exhibit a large ecological niche diversity resulting in high evolutionary pressure favoring the colonal selecion hypothesis.Krebs ist ein schwieriges Thema und tritt in zahlreichen Gebieten auf. Ein Grund für die Komplexität des Tumorwachstums sind die unterschiedlichen Zeit- und Längenskalen. In der aktuellen Forschung fehlt immernoch ein ganzheitliches Verständnis von Krebs, obwohl die computergestützten Methoden in den vergangenen Jahren die konventionellen Methoden der Medizin und der Biologie erweitern und unterstützen. Damit wird die Kluft zwischen subzellulären und makroskopischen Prozessen bereits verringert. In der vorliegenden Arbeit dokumentiere ich ein computergestütztes Verfahren, welches das Tumorwachstum auf mehreren Skalen simuliert. Insbesondere wird das Blutgefäßsystem durch künstliche Gefäße nachgeahmt. Es wurde ein numerisches Optimierungsverfahren zur Bestimmung der Gefäßradien eines künstlichen Blutkreislaufes entwickelt, welches auf physiologischen Reizen basiert und unabhängig von Murray‘s Gesetz ist. Da das beschriebene Verfahren zur Simulation von Tumoren Blutgefäße beinhaltet, kann die Umbildung des Gefäßbaumes während des Tumorwachstums untersucht werden. Das Modell wurde erfolgreich mit krankhaften Gefäßsystemen verglichen. In der darauffolgenden Weiterentwicklung des Modells berücksichtigte ich zelluläre Feinheiten, die es mir erlaubten das Mikromilieu in zellulärer Auflösung zu untersuchen. Meine Resultate zeigen, dass bereits kleine Tumore eine hohe ökologische Vielfalt besitzen, was den Selektionsdruck erhöht und damit die Klon-Selektionstheorie begünstigt

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

Battery Systems and Energy Storage beyond 2020

Currently, the transition from using the combustion engine to electrified vehicles is a matter of time and drives the demand for compact, high-energy-density rechargeable lithium ion batteries as well as for large stationary batteries to buffer solar and wind energy. The future challenges, e.g., the decarbonization of the CO2-intensive transportation sector, will push the need for such batteries even more. The cost of lithium ion batteries has become competitive in the last few years, and lithium ion batteries are expected to dominate the battery market in the next decade. However, despite remarkable progress, there is still a strong need for improvements in the performance of lithium ion batteries. Further improvements are not only expected in the field of electrochemistry but can also be readily achieved by improved manufacturing methods, diagnostic algorithms, lifetime prediction methods, the implementation of artificial intelligence, and digital twins. Therefore, this Special Issue addresses the progress in battery and energy storage development by covering areas that have been less focused on, such as digitalization, advanced cell production, modeling, and prediction aspects in concordance with progress in new materials and pack design solutions

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD