Similarities and differences of pumping conventional and self-compacting concrete

In Practice, Self-Compacting Concrete (SCC) is Considered as a Simple Extension of Conventional Vibrated Concrete (CVC) When Pumping is Concerned. the Same Equipment, Materials, Pumping Procedures and Guidelines Used for CVC Are Applied When Pumping SCC. on the Other Hand, It Has Been Clearly Shown that the Rheological Properties and the Mix Design of SCC Are Different Than CVC. Can the Same Pumping Principles Employed for CVC Be Applied for SCC? This Paper Compares the Some Published Results of Pumping of CVC with Those for SCC. a First Striking Difference between Pumping of CVC and SCC is the Flow Behaviour in the Pipes. the Flow of CVC is a Plug, Surrounded by a Lubricating Layer, While during the Flow of SCC, Part of the Concrete Volume itself is Sheared Inside the Pipe. as a Result, the Importance of Viscosity Increases in Case of SCC. Due to the Low Yield Stress of SCC, the Behaviour in Bends is Different, But Quite Complex to Study. Due to the Lower Content of Aggregate and Better Stability of SCC, as It is Less Prone to Internal Water Migration, Blocking is Estimated to Occur at Lower Frequency in Case of SCC. © RILEM 2010

Democratization in a passive dendritic tree : an analytical investigation

One way to achieve amplification of distal synaptic inputs on a dendritic tree is to scale the amplitude and/or duration of the synaptic conductance with its distance from the soma. This is an example of what is often referred to as “dendritic democracy”. Although well studied experimentally, to date this phenomenon has not been thoroughly explored from a mathematical perspective. In this paper we adopt a passive model of a dendritic tree with distributed excitatory synaptic conductances and analyze a number of key measures of democracy. In particular, via moment methods we derive laws for the transport, from synapse to soma, of strength, characteristic time, and dispersion. These laws lead immediately to synaptic scalings that overcome attenuation with distance. We follow this with a Neumann approximation of Green’s representation that readily produces the synaptic scaling that democratizes the peak somatic voltage response. Results are obtained for both idealized geometries and for the more realistic geometry of a rat CA1 pyramidal cell. For each measure of democratization we produce and contrast the synaptic scaling associated with treating the synapse as either a conductance change or a current injection. We find that our respective scalings agree up to a critical distance from the soma and we reveal how this critical distance decreases with decreasing branch radius

From cellular attractor selection to adaptive signal control for traffic networks

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains

Bankrupting terrorism: the role of US anti-terrorism litigation in the prevention of terrorism and other hybrid threats: a legal assessment and outlook

Global terrorist networks are dependent on receiving financial support from a variety of sources, including individuals, charities and corporations. Also known as terrorist financing, the potential of terrorism finance to resemble a global threat has been recognised and also its closeness to other international crimes such as money laundering and organized crime. As a result, possible responses have to constitute co-ordinated, multi-lateral and multi faceted actions under the umbrella of a wide range of international stakeholders such as the United Nations Security Council and the Financial Action Task Force. Combating terrorism requires a ‘holistic’ approach which allows for a mix of possible responses. Besides “kinetic” security operations (such as targeted killings) and the adoption of criminal prosecution measures another possible response could be the use of US styled transnational civil litigation by victims of terrorism against both, terrorist groups and their sponsors. Corporations, both profit and non profit, such as banks and other legal entities, as well as individuals, are often complicit in international terrorism in a role of aiders and abettors by providing financial assistance to the perpetrators (cf. UN Al-Qaida Sanctions List: The List established and maintained by the 1267 Committee with respect to individuals, groups, undertakings and other entities associated with Al-Qaida). Such collusion in acts of terrorism gains additional importance against the background of so called “Hybrid Threats”, NATO’s new concept of identifying and countering new threats arising from multi-level threat scenarios. This article discusses the potential impact of US terrorism lawsuits for the global fight against terrorism

Python as a Federation Tool for GENESIS 3.0

The GENESIS simulation platform was one of the first broad-scale modeling systems in computational biology to encourage modelers to develop and share model features and components. Supported by a large developer community, it participated in innovative simulator technologies such as benchmarking, parallelization, and declarative model specification and was the first neural simulator to define bindings for the Python scripting language. An important feature of the latest version of GENESIS is that it decomposes into self-contained software components complying with the Computational Biology Initiative federated software architecture. This architecture allows separate scripting bindings to be defined for different necessary components of the simulator, e.g., the mathematical solvers and graphical user interface. Python is a scripting language that provides rich sets of freely available open source libraries. With clean dynamic object-oriented designs, they produce highly readable code and are widely employed in specialized areas of software component integration. We employ a simplified wrapper and interface generator to examine an application programming interface and make it available to a given scripting language. This allows independent software components to be ‘glued’ together and connected to external libraries and applications from user-defined Python or Perl scripts. We illustrate our approach with three examples of Python scripting. (1) Generate and run a simple single-compartment model neuron connected to a stand-alone mathematical solver. (2) Interface a mathematical solver with GENESIS 3.0 to explore a neuron morphology from either an interactive command-line or graphical user interface. (3) Apply scripting bindings to connect the GENESIS 3.0 simulator to external graphical libraries and an open source three dimensional content creation suite that supports visualization of models based on electron microscopy and their conversion to computational models. Employed in this way, the stand-alone software components of the GENESIS 3.0 simulator provide a framework for progressive federated software development in computational neuroscience

A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components

Why Are Computational Neuroscience and Systems Biology So Separate?

Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both fields can learn quite a bit from each other. Several examples are given, covering sociological, software technical, and methodological aspects. Systems biology is a better organized community which is very effective at sharing resources, while computational neuroscience has more experience in multiscale modeling and the analysis of information processing by biological systems. Finally, I speculate about how the relationship between the two fields may evolve in the near future

Magnetic resonance imaging assessment of renal flow distribution patterns during ex vivo normothermic machine perfusion in porcine and human kidneys

Acceptance criteria of deceased donor organs have gradually been extended toward suboptimal quality, posing an urgent need for more objective pre-transplant organ assessment. Ex vivo normothermic machine perfusion (NMP) combined with magnetic resonance imaging (MRI) could assist clinicians in deciding whether a donor kidney is suitable for transplantation. Aim of this study was to characterize the regional distribution of perfusate flow during NMP, to better understand how ex vivo kidney assessment protocols should eventually be designed. Nine porcine and 4 human discarded kidneys underwent 3 h of NMP in an MRI-compatible perfusion setup. Arterial spin labeling scans were performed every 15 min, resulting in perfusion-weighted images that visualize intrarenal flow distribution. At the start of NMP, all kidneys were mainly centrally perfused and it took time for the outer cortex to reach its physiological dominant perfusion state. Calculated corticomedullary ratios based on the perfusion maps reached a physiological range comparable to in vivo observations, but only after 1 to 2 h after the start of NMP. Before that, the functionally important renal cortex appeared severely underperfused. Our findings suggest that early functional NMP quality assessment markers may not reflect actual physiology and should therefore be interpreted with caution

Modelling of Short-Term Interactions Between Concrete Support and the Excavated Damage Zone Around Galleries Drilled in Callovo–Oxfordian Claystone

peer reviewedProduction of energy from nuclear power plants generates high-level radioactive nuclear waste, harmful during dozens of thousand years. Deep geological disposal of nuclear waste represents the most reliable solutions for its safe isolation. Confinement of radioactive wastes relies on the multi-barrier concept in which isolation is provided by a series of engineered (canister, backfill) and natural (host rock) barriers. Few underground research laboratories have been built all over the world to test and validate storage solutions. The underground drilling process of disposal drifts may generate cracks, fractures/strain localisation in shear bands within the rock surrounding the gallery especially in argillaceous rocks. These degradations affect the hydro-mechanical properties of the material, such as permeability, e.g. creating a preferential flow path for radionuclide migration. Hydraulic conductivity increase within this zone must remain limited to preserve the natural barrier. In addition galleries are currently reinforced by different types of concrete supports such as shotcrete and/or prefab elements. Their purpose is twofold: avoiding partial collapse of the tunnel during drilling operations and limiting convergence of the surrounding rock. Properties of both concrete and rock mass are time dependent, due to shotcrete hydration and hydromechanical couplings within the host rock. By the use of a hydro-mechanical coupled Finite Element Code with a Second Gradient regularization, this paper aims at investigating and predicting support and rock interactions (convergence, stress field). The effect of shotcrete hydration evolution, spraying time and use of compressible wedges is studied in order to determine their relative influence