Free vibrations of thin isotropic oblate spheroidal shells

Free vibrations of thin isotropic oblate spheroidal shell

Dynamic stability of space vehicles. Volume 15 - Shell dynamics with special applications to control problems

Dynamic stability of thin walled elastic shells and applications to space vehicle control problem

Rapid Estradiol Modulation of Neuronal Connectivity and Its Implications for Disease

Estrogens have multiple actions in the brain including modulating synaptic plasticity, connectivity, and cognitive behaviors. While the classical view of estrogens are as endocrine signals, whose effects manifest via the regulation of gene transcription, mounting evidence has been presented demonstrating that estrogens have rapid effects within specific areas of the brain. The emergence that 17 β-estradiol can be produced locally in the brain which can elicit rapid (within minutes) cellular responses has led to its classification as a neurosteroid. Moreover, recent studies have also begun to detail the molecular and cellular underpinnings of how 17 β-estradiol can rapidly modulate spiny synapses (dendritic spines). Remodeling of dendritic spines is a key step in the rewiring of neuronal circuitry thought to underlie the processing and storage of information in the forebrain. Conversely, abnormal remodeling of dendritic spines is thought to contribute to a number of psychiatric and neurodevelopmental disorders. Here we review recent molecular and cellular work that offers a potential mechanism of how 17 β-estradiol may modulate synapse structure and function of cortical neurons. This mechanism allows cortical neurons to respond to activity-dependent stimuli with greater efficacy. In turn this form of plasticity may provide an insight into how 17 β-estradiol can modulate the rewiring of neuronal circuits, underlying its ability to influencing cortically based behaviors. We will then go on to discuss the potential role of 17 β-estradiol modulation of neural circuits and its potential relevance for the treatment of psychiatric and neurodevelopmental disorders

ET^2: A Metric For Time and Energy Efficiency of Computation

We investigate an efficiency metric for VLSI computation that includes energy, E, and time, t, in the form E t^2. We apply the metric to CMOS circuits operating outside velocity saturation when energy and delay can be exchanged by adjusting the supply voltage; we prove that under these assumptions, optimal Et^2 implies optimal energy and delay. We give experimental and simulation evidences of the range and limits of the assumptions. We derive several results about sequential, parallel, and pipelined computations optimized for E t^2, including a result about the optimal length of a pipeline. We discuss transistor sizing for optimal Et^2 and show that, for fixed, nonzero execution rates, the optimum is achieved when the sum of the transistor-gate capacitances is twice the sum of the parasitic capacitances-not for minimum transistor sizes. We derive an approximation for E t^n (for arbitrary n) of an optimally sized system that can be computed without actually sizing the transistors; we show that this approximation is accurate. We prove that when multiple, adjustable supply voltages are allowed, the optimal Et^2 for the sequential composition of components is achieved when the supply voltages are adjusted so that the components consume equal power. Finally, we give rules for computing the Et^2 of the sequential and parallel compositions of systems, when the Et^2 of the components are known

Speed and Energy Performance of an Asynchronous MIPS R3000 Microprocessor

This paper presents the speed and energy figures for an asynchronous implementation of a MIPS R3000 microprocessor. The design is almost entirely QDI and introduces a new fine-grained pipeline. The performance figures show that this design is four times as efficient as equivalent clocked designs and that its cycle time in FO4 units compares to that of high-performance dynamic pipelines

Energy-Delay Complexity of Asynchronous Circuits

In this thesis, a circuit-level theory of energy-delay complexity is developed for asynchronous circuits. The energy-delay efficiency of a circuit is characterized using the metric Et^n, where E is the energy consumed by the computation, t is the delay of the computation, and n is a positive number that reflects a chosen trade-off between energy and delay. Based on theoretical and experimental evidence, it is argued that for a circuit optimized for minimal Et^n, the consumed energy is independent, in first approximation, of the types of gates (NAND, NOR, etc.) used by the circuit and is solely dependent on n and the total amount of wiring capacitance switched during computation. Conversely, the circuit speed is independent, in first approximation, of the wiring capacitance and depends only on n and the types of gates used. The complexity model allows us to compare the energy-delay efficiency of two circuits implementing the same computation. On the other hand, the complexity model itself does not say much about the actual transistor sizes that achieve the optimum. For this reason, the problem of transistor sizing of circuits optimize d for Et^n is investigated, as well. A set of analytical formulas that closely approximate the optimal transistor sizes are explored. An efficient iteration procedure that can further improve the original analytical solution is then studied. Based on these results, a novel transistor-sizing algorithm for energy-delay efficiency is introduced. It is shown that the Et^n metric for the energy-delay efficiency index n ≥ 0 characterizes any optimal trade-off between the energy and the delay of a computation. For example, any problem of minimizing the energy of a system for a given target delay can be restated as minimizing Et^n for a certain n. The notion of it minimum-energy function is developed and applied to the parallel and sequential composition of circuits in general and, in particular, to circuits optimized through transistor sizing and voltage scaling. Bounds on the energy and delay of the optimized circuits are computed, and necessary and sufficient conditions are given under which these bounds are reached. Necessary and sufficient conditions are also given under which components of a design can be optimized independently so as to yield a global optimum when composed. Through these applications, the utility of the minimum-energy function is demonstrated. The use of this minimum-energy function yields practical insight into ways of improving the overall energy-delay efficiency of circuits

Dendritic spinule-mediated structural synaptic plasticity: Implications for development, aging, and psychiatric disease

Dendritic spines are highly dynamic and changes in their density, size, and shape underlie structural synaptic plasticity in cognition and memory. Fine membranous protrusions of spines, termed dendritic spinules, can contact neighboring neurons or glial cells and are positively regulated by neuronal activity. Spinules are thinner than filopodia, variable in length, and often emerge from large mushroom spines. Due to their nanoscale, spinules have frequently been overlooked in diffraction-limited microscopy datasets. Until recently, our knowledge of spinules has been interpreted largely from single snapshots in time captured by electron microscopy. We summarize herein the current knowledge about the molecular mechanisms of spinule formation. Additionally, we discuss possible spinule functions in structural synaptic plasticity in the context of development, adulthood, aging, and psychiatric disorders. The literature collectively implicates spinules as a mode of structural synaptic plasticity and suggests the existence of morphologically and functionally distinct spinule subsets. A recent time-lapse, enhanced resolution imaging study demonstrated that the majority of spinules are small, short-lived, and dynamic, potentially exploring their environment or mediating retrograde signaling and membrane remodeling via trans-endocytosis. A subset of activity-enhanced, elongated, long-lived spinules is associated with complex PSDs, and preferentially contacts adjacent axonal boutons not presynaptic to the spine head. Hence, long-lived spinules can form secondary synapses with the potential to alter synaptic connectivity. Published studies further suggest that decreased spinules are associated with impaired synaptic plasticity and intellectual disability, while increased spinules are linked to hyperexcitability and neurodegenerative diseases. In summary, the literature indicates that spinules mediate structural synaptic plasticity and perturbations in spinules can contribute to synaptic dysfunction and psychiatric disease. Additional studies would be beneficial to further delineate the molecular mechanisms of spinule formation and determine the exact role of spinules in development, adulthood, aging, and psychiatric disorders

Innovation and Open Access to Public Sector Information

Most speakers at this summit have been looking at open access from the supply side, presenting the points of view of custodians of government information. What might we lob over the fence to whoever is on the other side? So far we have not paid much attention to this demand side - the potential beneficiaries of changed information policies. So I see it as my task to address what I believe is the core rationale for this policy initiative, which is the promotion of innovation and creativity. My perspective on the topic brings together my deep interest in the whole matter of innovation, and my long involvement with the digital content industries. Why do we need to act on this possible policy initiative? I will try to put the question in the context of some conceptual frameworks and models of innovation, and of business models for information and content production. My premise is that data and information – content – is the currency of creativity and innovation. Information is what energises our national innovation system. Governments produce and hold a wealth of information and data