Size Of Outputs Research Articles

Graded control of Purkinje cell outputs by cAMP through opposing actions on axonal action potential and transmitter release.

All-or-none signalling by action potentials (APs) in neuronal axons is pivotal for the precisely timed and identical size of outputs to multiple distant targets. However, technical limitations with respect to measuring the signalling in small intact axons have hindered the evaluation of high-fidelity signal propagation. Here, using direct recordings from axonal trunks and/or terminals of cerebellar Purkinje cells in slice and culture, we demonstrate that the timing and amplitude of axonal outputs are gradually modulated by cAMP depending on the length of axon. During the propagation in long axon, APs were attenuated and slowed in conduction by cAMP via specifically decreasing axonal Na+ currents. Consequently, the Ca2+ influx and transmitter release at distal boutons are reduced by cAMP, counteracting its direct facilitating effect on release machinery as observed at various CNS synapses. Together, our tour de force functional dissection has unveiled the axonal distance-dependent graded control of output timing and strength by intracellular signalling. KEY POINTS: The information processing in the nervous system has been classically thought to rely on the axonal faithful and high-speed conduction of action potentials (APs). We demonstrate that the strength and timing of axonal outputs are weakened and delayed, respectively, by cytoplasmic cAMP depending on the axonal length in cerebellar Purkinje cells (PCs). Direct axonal patch clamp recordings uncovered axon-specific attenuation of APs by cAMP through reduction of axonal Na+ currents. cAMP directly augments transmitter release at PC terminals without changing presynaptic Ca2+ influx or readily releasable pool of vesicles, although the extent is weaker compared to other CNS synapses. Two opposite actions of cAMP on PC axons, AP attenuation and release augmentation, together give rise to graded control of synaptic outputs in a manner dependent on the axonal length.

Greek Public Sector’s Efficient Resource Allocation: Key Findings and Policy Management

The public sector has limited resources, and how these resources are allocated in expenditures and investments is crucial. Our article focuses on controlling this allocation for the Greek economy from 2000 to 2021, which includes the country’s debt crisis. To do so, we utilized data from national accounts, categorized inputs and outputs, and examined their volatility and stability over time using statistical and mathematical methods. Our analysis revealed that the crisis impacted the size and allocation of public inputs and outputs. While some sectors of the Greek economy displayed stability in financing over time, others were more volatile. Using a mathematical accounting approach contributes to the academic discourse on rational resource allocation in the public sector. Our results validate the tax hypothesis for primary revenues and expenditures and advocate that it is necessary to make targeted recruitments in the sector that is needed each time while keeping the total number constant, which leads to the need to redistribute public sector workers. In the same way, public projects should not only focus on infrastructure projects but should also be spread to new areas related to climate change and the agricultural sector.

Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models

Abstract. With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations, and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. It remains, nonetheless, of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The cloud-based analysis framework (i) minimizes the cost of duplicating and storing ghost copies of data and (ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin- to global-scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.

Open Science Expectations for Simulation-Based Research

There is strong agreement across the sciences that replicable workflows are needed for computational modeling. Open and replicable workflows not only strengthen public confidence in the sciences, but also result in more efficient community science. However, the massive size and complexity of geoscience simulation outputs, as well as the large cost to produce and preserve these outputs, present problems related to data storage, preservation, duplication, and replication. The simulation workflows themselves present additional challenges related to usability, understandability, documentation, and citation. These challenges make it difficult for researchers to meet the bewildering variety of data management requirements and recommendations across research funders and scientific journals. This paper introduces initial outcomes and emerging themes from the EarthCube Research Coordination Network project titled “What About Model Data? - Best Practices for Preservation and Replicability,” which is working to develop tools to assist researchers in determining what elements of geoscience modeling research should be preserved and shared to meet evolving community open science expectations.Specifically, the paper offers approaches to address the following key questions:• How should preservation of model software and outputs differ for projects that are oriented toward knowledge production vs. projects oriented toward data production?• What components of dynamical geoscience modeling research should be preserved and shared?• What curation support is needed to enable sharing and preservation for geoscience simulation models and their output?• What cultural barriers impede geoscience modelers from making progress on these topics?

Determinants of Off-Farm Labour Participation and Time Allocation: Double-Hurdle Results in the Case of Fiji

This study analyses the determinants of off-farm labour participation decisions of the agricultural households in Fiji. The analysis of joint decision to participate and the amount of time allocated to work is undertaken by using the double-hurdle model based on the Household Income and Expenditure Survey 2008–2009. The results show that household head status, age, marital status, ethnicity and education influence off-farm participation decisions of the farm households. On the households’ time allocation decisions towards off-farm income-generating activities, the family size, remittances, income and several types of agricultural outputs influence the labour supply decisions. These households and farm characteristics are important in creating better returns, reducing risks from land constraints and natural disasters, managing income and consumption uncertainties and providing quality farm inputs. The results highlight some important policy implications that would help address low agricultural productivity and render increased support for off-farm income earning activities.

Denotational recurrence extraction for amortized analysis

A typical way of analyzing the time complexity of functional programs is to extract a recurrence expressing the running time of the program in terms of the size of its input, and then to solve the recurrence to obtain a big-O bound. For recurrence extraction to be compositional, it is also necessary to extract recurrences for the size of outputs of helper functions. Previous work has developed techniques for using logical relations to state a formal correctness theorem for a general recurrence extraction translation: a program is bounded by a recurrence when the operational cost is bounded by the extracted cost, and the output value is bounded, according to a value bounding relation defined by induction on types, by the extracted size. This previous work supports higher-order functions by viewing recurrences as programs in a lambda-calculus, or as mathematical entities in a denotational semantics thereof. In this paper, we extend these techniques to support amortized analysis, where costs are rearranged from one portion of a program to another to achieve more precise bounds. We give an intermediate language in which programs can be annotated according to the banker's method of amortized analysis; this language has an affine type system to ensure credits are not spent more than once. We give a recurrence extraction translation of this language into a recurrence language, a simply-typed lambda-calculus with a cost type, and state and prove a bounding logical relation expressing the correctness of this translation. The recurrence language has a denotational semantics in preorders, and we use this semantics to solve recurrences, e.g analyzing binary counters and splay trees.

A two-stage Microgrid cost optimization considering distribution network loss and voltage deviation

This paper proposes a novel two-stage Microgrid (MG) scheduling methodology to decide optimal location and size of MG and outputs of dispatchable and intermittent distributed generators (DGs). In the first stage, a system stability index (SSI) representing the performance of the distribution network is proposed to select the location of the MG. The size of the MG is derived by minimizing of SSI. In the second stage, the group search optimizer (GSO) algorithm is applied to further determine the outputs of each DG in the MG, including photovoltaic (PV) panels, wind turbines (WT), battery energy storage systems (BESS) and diesel generators (DIG). To validate the proposed approach, the IEEE 33-bus radial distribution systems has been applied. The simulation results show that the approach minimizes MG investment costs under the premise of considering loss and voltage deviation of the distribution network.

White matter fiber analysis using kernel dictionary learning and sparsity priors

Diffusion magnetic resonance imaging, a non-invasive tool to infer white matter fiber connections, produces a large number of streamlines containing a wealth of information on structural connectivity. The size of these tractography outputs makes further analyses complex, creating a need for methods to group streamlines into meaningful bundles. In this work, we address this problem by proposing a set of flexible and efficient streamline clustering approaches based on kernel dictionary learning and sparsity priors. Proposed approaches, which include L0 norm, group sparsity, and manifold regularization prior, allow streamlines to be assigned to more than one bundle, making the clustering more robust to overlapping bundles and inter-subject variations. We evaluate the performance of our method on an expert labeled dataset as well as data from the Human Connectome Project. Results highlight the ability of our method to group streamlines into plausible bundles and illustrate the impact of sparsity priors on the performance of the proposed methods. Methods presented in this work are relevant for the neuroscience studies on diffusion tractography analysis, as well as pattern recognition applications requiring the unsupervised clustering of 3D curves.

Deep Neural Networks with Extreme Learning Machine for Seismic Data Compression

Advances on seismic survey techniques require a large number of geophones. This leads to an exponential growth in the size of data and prohibitive demands on storage and network communication resources. Therefore, it is desirable to compress the seismic data to the minimum possible, without losing important information. In this paper, a stacked auto-encoder extreme learning machine (AE-ELM) for seismic data compression is proposed. First, a deep asymmetric auto-encoder is constructed, in which nonlinear activation functions are used in the encoder hidden layers and linear activation functions are utilized in the decoder layers. Second, the encoder hidden layers are connected in a cascade way, so that outputs of a hidden layer are considered as the inputs to the succeeding hidden layer. Third, the optimal weights of connections between the layers of the decoder are solved analytically. Lastly, the AE-ELMs are stacked to create the complete encoder/decoder. The extreme learning machine (ELM) is selected due to its analytical calculation of weights efficient training that is suitable for practical implementation. In this neural network, data compression is achieved by transforming the original data through the encoder layers where the size of outputs from the last encoder hidden layer is smaller than the original data size. The proposed method exhibits a comparable reconstruction quality on a real dataset but with a much shorter training duration than other deep neural networks methods. This neural network with more than 8000 hidden units achieved $$ 1.28 \times 10^{ - 3} $$ of normalized mean-squared error for 10:1 of compression ratio with only 8.23 s of training time.

Automated Generation of Work Breakdown Structure and Project Network Model for Earthworks Project Planning: A Flow Network-Based Optimization Approach

The present research proposes an analytical methodology to automatically generate a work breakdown structure (WBS) and a project network model based on activity-on-node (AON), which consists of two stages: (1) optimizing earthwork volume allocation, which is intended to define haul jobs by identifying the most economical combinations of cut and fill cells, thus minimizing the total haul effort in rough-grading operations and (2) according to the optimization results from Stage 1, establishing WBS and defining precedence relationships among jobs in WBS analytically to enable automated generation of the AON project network model. To simplify the newly devised methodology, a flow network-based technique is developed to facilitate earthwork allocation optimization and AON project network generation. Simulation trace, internal validation, and comparison with related established methods were performed for evaluating the effectiveness of the proposed methodology. To reveal limitations inherent in established methods and cross validate the proposed methodology, two established methods were selected, which represent the state of art in the problem domain. Further validation of the new methodology against established ones entails elaborate simulation experiment design by randomly adjusting earth volumes in each cell of the site and varying site size and statistical analysis of simulation outputs. The comparison-based validation shows advantages of the proposed methodology in (1) ensuring practical feasibility of resulting earthmoving job plans and (2) improving achievable productivity performance of construction operations.

Technological congruence and the economic complexity of technological change

Technological congruence is an emergent system property defined by the matching between the relative size of outputs’ elasticity with the relative abundance and cost of inputs in local factor markets. With given total costs, output is larger the larger is the output elasticity of the cheapest input. Technological congruence is a powerful tool that helps grasping the economic complexity of technological change with respect to the determinants of the direction of technological change and its effects in terms of growth accounting and specialization, both at the firm and the system level. Its appreciation stems directly from the advances of the economics of innovation in understanding the endogenous determinants of the introduction and diffusion of directed technological changes. Technological congruence is most relevant to influence the actual levels of total factor productivity of new technologies and – consequently – to shape the competitive advance of firms and countries.

Sup-interpretations, a semantic method for static analysis of program resources

The sup-interpretation method is proposed as a new tool to control memory resources of first order functional programs with pattern matching by static analysis. It has been introduced in order to increase the intensionality, that is the number of captured algorithms, of a previous method, the quasi-interpretations. Basically, a sup-interpretation provides an upper bound on the size of function outputs. A criterion, which can be applied to terminating as well as nonterminating programs, is developed in order to bound the stack frame size polynomially. Since this work is related to quasi-interpretation, dependency pairs, and size-change principle methods, we compare these notions obtaining several results. The first result is that, given any program, we have heuristics for finding a sup-interpretation when we consider polynomials of bounded degree. Another result consists in the characterizations of the sets of functions computable in polynomial time and in polynomial space. A last result consists in applications of sup-interpretations to the dependency pair and the size-change principle methods.

A simplified irreversible Otto engine model with fluctuations in the combustion heat

SYNOPSIS In this work we present a simplified irreversible Otto engine model which includes power losses and a chemical combustion reaction at the end of the adiabatic compression. The heat released in the combustion reaction is taken as a fluctuant quantity around its mean value. The fluctuations are simulated by means of a Gaussian stochastic process for a rich-air intake mixture and by two non-linear deterministic noisy maps in the case of poor-air mixtures. The fluctuant combustion heat drives the Otto engine model producing in turn fluctuant power output and fluctuant cycle efficiency. We analyse the size of the fluctuant outputs in terms of the fluctuant input.

Distributed generation, reactive sources and network-configuration planning for power and energy-loss reduction

Taking account of the mutual impacts of distributed generation, reactive power and network-configuration planning, a new and more comprehensive distributed-generation-planning problem including simultaneous distributed generation, reactive sources and network configuration planning is defined. By solving this problem the amount of distributed-generation resources (DGRs) and reactive-power sources (RPSs) in selected buses of a distribution system, are determined taking account of their outputs at different load levels, tap positions of voltage regulators (VRs) and the status of sectionalising switches, in order to minimise the cost of power, energy losses and the total required reactive power subjected to a given total of distributed generation. Since the defined planning problem is a combinatorial one, a solution algorithm based on tabu search, that is an efficient heuristic method to a combinatorial-optimisation problem, is proposed. Various memory structures including short-, intermediate- and long-term memories have been implemented in the algorithm. The method has been tested on 33-bus and 69-bus radial distribution systems to demonstrate the performance of the algorithm and to investigate impact of some parameters such as maximum limits on size of DGRs (RPSs), constant or controllable outputs of these sources and different control variables sets on results of system planning. In addition, a novel technique for determining the candidate buses to install active (reactive) sources based on clustering system buses in view of assigning DGRs (RPSs) is presented.

Short-term effect of a toxicant on scope for change in ascendency in a microcosm community

A need in the field of toxicity testing is the development of parallel tests applicable to all levels of biological organization: single-species tests alone cannot predict effects at ecosystem levels. Scope for change in ascendency (SfCA) is a comparative index applicable to the short-term assessment of stress effects on any biological system. The SfCA of a community, defined by analogy to scope for growth of an organism, is the balance of ascendency (network size and organization) of population inputs and outputs. It takes into account the interrelated processes of growth and development, both of which can be affected by stress factors. To illustrate this approach, the effect of atrazine on SfCA of a simple epilimnetic microcosm community was investigated. Simultaneous, short-term measurements of all carbon flows between populations were made and SfCA was calculated. Compounded effects of stress on populations, measured as relative drop in community SfCA in the presence of atrazine, were observed within 2–3 weeks. This indicates that SfCA may be useful as a comparative index for the short-term assessment of perturbation effects. A refinement of the methodology for measuring resource flows at levels other than the organism would allow full advantage of this assessment tool to be taken.

A New Proof of the Non-Substitution Theorem

THE TITLE OF THIS NOTE explains its content as well as its motivation in that the abundance of proofs of an established theorem is taken to connote a continuing interest in its significance. The theorem asserts that whatever final bill of goods can be produced in a general (Static) Leontief model can also be produced by a technique embedded in it. (The terms are defined below). It follows that this technique is optimal for all compositions of final outputs in the sense that given any such composition, one gets the largest size of final outputs by using only this technique. I shall give a direct proof of this statement by formulating a linear programming (LP) problem to maximise the 'size' of final outputs at a given 'composition' and showing that the optimal basis for this problem (which defines a corresponding optimal technique) remains so when the output-composition is changed arbitrarily. The present proof shares its method with the Dantzig-Gale proof of the same theorem, as reported in [1, (303-306)]. To recall, they consider the LP problem of minimising the primary input requirement for producing a given feasible final output vector and show that the same optimal basis is obtained for this problem, whatever the final output vector specified. The main theorem is an easy consequence of this invariance of the optimal basis for either LP problem (the one considered by Dantzig-Gale or by myself). While the two proofs use the same tools and employ the same broad strategy, they differ rather significantly on a technical point. Technically, both proofs are concerned with the sensitivity analysis of an LP problem, say max cx subject to Ax ? b, x > 0 where A is a matrix and b, c and x are vectors of appropriate dimensions (A, b and c are given). The sensitivity analysis is carried out with respect to certain coefficients in the matrix A in the present proof and with respect to the vector b in the Dantzig-Gale proof. The latter type of sensitivity analysis has a much richer body of general results to draw upon than the former. In particular, the Dantzig-Gale proof utilizes precisely such a general result (Lemma 9.3 in [1]), whereas the present proof is entirely self-contained excepting for its dependence on the standard theorems in LP, in common with Dantzig-Gale. For reference, I shall state the relevant theorems in the form they will be used at appropriate places. Their proofs are available in any standard text on LP theory e.g. [1, chapter 3]. Turning to substantive matters, a general Leontief model produces some n goods by means of m > n linear activities obeying the two special assumptions of no joint production and a single primary input, i.e., each activity (a) produces exactly one good, and (b) uses, besides the produced goods, one common non