Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

/pdf/the-galaxy-platform-for-accessible-reproducible-and-onagm5kjs7.pdf

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

Stochastic chemical kinetics describes the time evolution of a wellstirred chemically reacting system in a way that takes into account the fact that molecules come in whole numbers and exhibit some degree of randomness in their dynamical behavior. Researchers are increasingly using this approach to chemical kinetics in the analysis of cellular systems in biology, where the small molecular populations of only a few reactant species can lead to deviations from the predictions of the deterministic differential equations of classical chemical kinetics. After reviewing the supporting theory of stochastic chemical kinetics, I discuss some recent advances in methods for using that theory to make numerical simulations. These include improvements to the exact stochastic simulation algorithm (SSA) and the approximate explicit tau-leaping procedure, as well as the development of two approximate strategies for simulating systems that are dynamically stiff: implicit tau-leaping and the slow-scale SSA.

/pdf/stochastic-simulation-of-chemical-kinetics-53h3kqhk7k.pdf

Stochastic Simulation of Chemical Kinetics

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

https://www.mdpi.com/1424-8220/16/5/707/pdf

Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances.

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading molecular biology of the gene is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.

Molecular Biology of the Gene.

This paper investigates a method to address the present and future flight control system analog IC requirements. The explosive growth in the development of VHDL-AMS (Analog Mixed Signal) has initiated the need for commercial CAD products based on mixed-signal design methodologies. Several design methodologies exist for developing analog-digital interface circuits. These methodologies range from the use of commercial off the shelf parts (COTS) to custom analog Integrated Circuit (IC) components that meet performance specifications. The current VHDL-AMS language has potential problems in the design of custom analog IC components in terms of scaling the internal device dimensions and maintaining robust design performance. As an example, a digital controller is being used in flight control systems. The digital controller is an embedded processor, which, through a D/A, controls the actuators of the aircraft. Sensors from the actuators are fed back through an A/D, a low-pass filter, and then to the embedded processor, completing the loop. In this example, the VHDL-AMS language is used to describe the electronic elements of the flight control system, and also, uniquely, the digital compensator. The digital compensator and filter are designed with the use of quantitative feedback theory (QFT) that emphasizes the use of feedback to achieve the desired system performance tolerances despite plant uncertainty and disturbances.

VHDL-AMS design for flight control systems

The development of large, complex systems, the training of personnel, and the refinement of concepts of operations all depend on high-performance simulation technologies. For each of the above application areas, among others, there is a chronic need for ever-higher performance. This paper addresses the application of three simulation acceleration approaches, used independently or in conjunction with one another: parallel simulation, mixed-abstraction/multiresolution simulation, and hardware acceleration via reconfigurable computing elements. After discussing the merits of each approach, the paper presents analytic techniques for determining the most effective approach to use for a given simulation problem.

Evaluating simulation acceleration techniques

Effective Execution Time Estimation for Heterogeneous Parallel Computing.

Fielding and managing the dynamic, complex information systems infrastructure necessary for defense operations presents significant opportunities for revolutionary improvements in capabilities. An example of this technology trend is the creation and validation of the Joint Battlespace Infosphere (JBI) being developed by the Air Force Research Lab. The JBI is a system of systems that integrates, aggregates, and distributes information to users at all echelons, from the command center to the battlefield. The JBI is a key enabler of meeting the Air Force's Joint Vision 2010 core competencies such as Information Superiority, by providing increased situational awareness, planning capabilities, and dynamic execution. At the same time, creating this new operational environment introduces significant risk due to an increased dependency on computational and communications infrastructure combined with more sophisticated and frequent threats. Hence, the challenge facing the nation is the most effective means to exploit new computational and communications technologies while mitigating the impact of attacks, faults, and unanticipated usage patterns.

Designing and validating the Joint Battlespace Infosphere

Dedicated hardware implementations of artificial neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, custom hardware implementations do not support intrinsic training of these networks on-chip. Training is typically done using software simulations and the obtained network is synthesized and targeted to hardware offline. The ANN FPGA design presented here facilitates dynamic network structure and parameter changes required for intrinsic training of artificial neural networks, without reliance on runtime FPGA reconfigurations. This is an important feature for an online trainable system as typical reconfiguration cycle times on the order of a few milliseconds pose to be a major performance bottleneck for iterative training algorithms. The designed platform implements block-based neural networks and can be evolved and adapted in-field.

Gregory D. Peterson

Papers

VHDL-AMS design for flight control systems

Evaluating simulation acceleration techniques

Effective Execution Time Estimation for Heterogeneous Parallel Computing.

Designing and validating the Joint Battlespace Infosphere

An evolvable artificial neural network platform for dynamic environments