Dealing with residual energy when transmitting data in energy-constrained capacitated networks

This paper addresses several problems relating to the energy available after the transmission of a given amount of data in a capacitated network. The arcs have an associated parameter representing the energy consumed during the transmission along the arc and the nodes have limited power to transmit data. In the first part of the paper, we consider the problem of designing a path which maximizes the minimum of the residual energy remaining at the nodes. After formulating the problem and proving the main theoretical results, a polynomial time algorithm is proposed based on computing maxmin paths in a sequence of non-capacitated networks. In the second part of the paper, the problem of obtaining a quickest path in this context is analyzed. First, the bi-objective variant of this problem is considered in which we aim to minimize the transmission time and to maximize the minimum residual energy. An exact polynomial time algorithm is proposed to find a minimal complete set of efficient solutions which amounts to solving shortest path problems. Second, the problem of computing an energy-constrained quickest path which guarantees at least a given residual energy at the nodes is reformulated as a variant of the energy-constrained quickest path problem. The algorithms are tested on a set of benchmark problems providing the optimal solution or the Pareto front within reasonable computing times

Order processing improvement in military logistics by Value Stream Analysis lean methodology

Military logistics is a complex process where response times, demand uncertainty, high variety of material references and cost effectiveness turn decisive for combat capability. Considered as the bridge between the deployed forces and the industrial base that provides materials and services that the forces needed to accomplish their mission, capacity and efficiency of delivery are required for its processes. The required flexibility could only be achieved by improving the Supply Chain Management (SCM) in order to optimize delivery lead times. To cope with these requirements, lean thinking can be extended to military organizations. This research justifies and proposes the use of Lean Six Sigma (LSS) methodologies from manufacturing to optimize logistics processes in the defense sector. In particular, the article presents the benefits and results obtained using Value Stream Analysis and DMAIC (Define, Measure, Analyze, Improve, Control) problem-solving methodology to improve the order processing lead-time as key performance indicator of a military unit delivery fulfilment

Gradient induced artifacts in simultaneous EEG-fMRI: Effect of synchronization on spiral and EPI k-space trajectories

The nature of the gradient induced electroencephalography (EEG) artifact is analyzed and compared for two functional magnetic resonance imaging (fMRI) pulse sequences with different k-space trajectories: echo planar imaging (EPI) and spiral. Furthermore, the performance of the average artifact subtraction algorithm (AAS) to remove the gradient artifact for both sequences is evaluated. The results show that the EEG gradient artifact for spiral sequences is one order of magnitude higher than for EPI sequences due to the chirping spectrum of the spiral sequence and the dB/dt of its crusher gradients. However, in the presence of accurate synchronization, the use of AAS yields the same artifact suppression efficiency for both pulse sequences below 80 Hz. The quality of EEG signal after AAS is demonstrated for phantom and human data. EEG spectrogram and visual evoked potential (VEP) are compared outside the scanner and use both EPI and spiral pulse sequences. MR related artifact residues affect the spectra over 40 Hz (less than 0.2 μV up to 120 Hz) and modify the amplitude of P1, N2 and P300 in the VEP. These modifications in the EEG signal have to be taken into account when interpreting EEG data acquired in simultaneous EEG-fMRI experiments

Disparate connectivity for structural and functional networks is revealed when physical location of the connected nodes is considered

Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical con- nections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 dif- ferent brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very- large-scale integration circuits analyses, shows that func- tional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrange- ments for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal?ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organi- zations that can only be identified when the physical locations of the nodes are included in the analysis

Changes in anatomical and functional connectivity related to lower hippocampal volume

Anatomical 3D T1 weighted images have been widely used to assess the volume of subcortical structures. It has been demonstrated that volume loss of hippocampi, entorhinal cortex and amygdala are early biomarkers for the diagnosis of cognitive impairments and AD1. In this work, we investigate AD biomarkers based on brain connectivity. SC-FC differences have been reported between controls and patients at risk for AD2. We will try to anticipate to MCI or AD diagnoses by differentiating in a sample of healthy subjects, i.e. with no cognitive impairment, other than related to ageing, using as criterion of separation the normalized hippocampal volume (NHV). The sample is formed by volunteers in the Valleca?s Initiative, a longitudinal study evaluating normal ageing in a cohort of more than 600 healthy elder people (70-85 years). The prevalence of AD in people older than 65 years is 13%3 suggesting that a certain number of those subjects will develop AD in the next years. The subjects with lower NHV are more prone to have AD than subjects with higher NHV. Thus they are more likely to manifest connectivity patterns that can be considered as AD biomarkers

Identification of non-small cell lung cancer biomarkers using mass spectrometry

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Anatomo-functional organization in brain networks

There are several studies focused on comparing rsFC networks with their structural substrate \cite{hagmann2008, honey2010}. However an accurate description of how anatomo-­functional connections are organized, both at physical and topological levels, is still to be defined. Here we present an approach to quantify the anatomo-functional organization and discuss its consistency