Variational stability of infinite-dimensional optimal control problems

In this paper we examine the variational stability of infinite-dimensional optimal control problems governed by nonlinear evolution equations. Our tools are the Kuratowski—Mosco convergence of sets and the corresponding τ-convergence of functions. We prove the τ-convergence of cost functional and the convergence of the values of the problems, and we examine the variational stability of the solution and reachable sets. These results are then applied to a sequence of nonlinear parabolic distributed-parameter optimal control problems     

The DPSIR Approach for an Integrated River Management Framework. A Preliminary Application on a Mediterranean Site (Kalamas River -NW Greece)

The European Water Policy introduced the necessity to apply new methodological approaches for the sustainable management of water resources. In the present paper the Driving-Pressure-State-Impact-Response (DPSIR) framework was developed as a methodological tool for the case study of Kalamas River basin (NW Greece). According to the DPSIR approach, it was revealed that the main driving forces, leading to pressures, were the agriculture, the livestock and the numerous point-pollution sources located at the catchment area. Elevated nutrients concentrations (NO₃-N:0.1–2.6 mg/L, NH₄-N:0.01–1.29 mg/L, SRP: 0.03–5.76 mg/L) along with high chlorophyll-a values (0.54–6.14 mg/m³) highlight river eutrophication. Response actions include elimination of diffuse pollution as well as reduction of the organic load through the optimization of the existed treatment plants. Since several parts of Kalamas River are designated as protected areas, specific measures for protecting biodiversity should be undertaken.     

Prediction of cement-based mortars compressive strength using machine learning techniques

Neural Computing and Applications - The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations....     

Fast approximation of betweenness centrality through sampling

Betweenness centrality is a fundamental measure in social network analysis, expressing the importance or influence of individual vertices (or edges) in a network in terms of the fraction of shortest paths that pass through them. Since exact computation in large networks is prohibitively expensive, we present two efficient randomized algorithms for betweenness estimation. The algorithms are based on random sampling of shortest paths and offer probabilistic guarantees on the quality of the approximation. The first algorithm estimates the betweenness of all vertices (or edges): all approximate values are within an additive factor \(\varepsilon \in (0,1)\) from the real values, with probability at least \(1-\delta \). The second algorithm focuses on the top-K vertices (or edges) with highest betweenness and estimate their betweenness value to within a multiplicative factor \(\varepsilon \), with probability at least \(1-\delta \). This is the first algorithm that can compute such approximation for the top-K vertices (or edges). By proving upper and lower bounds to the VC-dimension of a range set associated with the problem at hand, we can bound the sample size needed to achieve the desired approximations. We obtain sample sizes that are independent from the number of vertices in the network and only depend on a characteristic quantity that we call the vertex-diameter, that is the maximum number of vertices in a shortest path. In some cases, the sample size is completely independent from any quantitative property of the graph. An extensive experimental evaluation on real and artificial networks shows that our algorithms are significantly faster and much more scalable as the number of vertices grows than other algorithms with similar approximation guarantees.     

A Visualization Framework and User Studies for Overloaded Orthogonal Drawings

Overloaded orthogonal drawing (OOD) is a recent graph visualization style specifically conceived for directed graphs. It merges the advantages of some popular drawing conventions like layered drawings and orthogonal drawings, and provides additional support for some common analysis tasks. We present a visualization framework called DAGView, which implements algorithms and graphical features for the OOD style. Besides the algorithm for acyclic digraphs, the DAGView framework implements extensions to visualize both digraphs with cycles and undirected graphs, with the additional possibility of taking into account user preferences and constraints. It also supports an interactive visualization of clustered digraphs, based on the use of strongly connected components. Moreover, we describe an experimental user study, aimed to investigate the usability of OOD within the DAGView framework. The results of our study suggest that OOD can be effectively exploited to perform some basic tasks of analysis in a faster and more accurate way when compared to other drawing styles for directed graphs.     

Photoacoustic Imaging of Embryonic Stem Cell‐Derived Cardiomyocytes in Living Hearts with Ultrasensitive Semiconducting Polymer Nanoparticles

Human embryonic stem cell‐derived cardiomyocytes (hESC‐CMs) have become promising tools to repair injured hearts. To achieve optimal outcomes, advanced molecular imaging methods are essential to accurately track these transplanted cells in the heart. In this study, it is demonstrated for the first time that a class of photoacoustic nanoparticles (PANPs) incorporating semiconducting polymers (SPs) as contrast agents can be used in the photoacoustic imaging (PAI) of transplanted hESC‐CMs in living mouse hearts. This is achieved by virtue of two benefits of PANPs. First, strong photoacoustic (PA) signals and specific spectral features of SPs allow PAI to sensitively detect and distinguish a small number of PANP‐labeled cells (2000) from background tissues. Second, the PANPs show a high efficiency for hESC‐CM labeling without adverse effects on cell structure, function, and gene expression. Assisted by ultrasound imaging, the delivery and engraftment of hESC‐CMs in living mouse hearts can be assessed by PANP‐based PAI with high spatial resolution (≈100 µm). In summary, this study explores and validates a novel application of SPs as a PA contrast agent to track labeled cells with high sensitivity and accuracy in vivo, highlighting the advantages of integrating PAI and PANPs to advance cardiac regenerative therapies.     

Particle Transport and Deposition under High-Frequency Oscillatory Ventilation and Normal Breathing

The fluid mechanics of high-frequency oscillatory ventilation (HFOV) for gas transport in the pulmonary region of the human lungs have been thoroughly studied by different methods. The major concept of HFOV adaptation is to push gas into further generation of the bronchial tree, with adequate gas mixing and small tidal volume. However, particle transport and deposition under HFOV is a rarely studied case where different mechanisms, compared to the mechanisms of gas transport, may associate. The target of this study is to numerically compare the efficiency of particle drug deposition under HFOV to normal breathing (NB) and to further clarify the mechanisms of particle transport and deposition under oscillating flows. A fully Eulerian computational fluid particles dynamic (CFPD) model is used for studying the transport and deposition of several sizes of inertia particles, under different transient flow conditions, inside a single physiologically realistic bifurcation created by generations G3–G4 of the human lung. An insight into the particle dynamics under high-frequency oscillating flow fields is given and the results showed that the highly oscillating field (HFOV) displayed stronger secondary flows, thinner boundary layers, and strong counter flow that accumulate and deposit particles further than a lower frequency oscillatory field (NB).

Copyright 2014 American Association for Aerosol Research