Interchange Operation Research Articles

Interchangeable modular design and operation of photo-bioreactors for Chlorella vulgaris cultivation towards a zero-waste biorefinery

This study explores diverse cultivation modes for Chlorella vulgaris within a biorefinery at pilot scale that produces both biodiesel by transesterification of waste frying oils and syngas by gasification of organic wood waste. Given microalgae's comparatively modest biofuel yield relative to principal biorefinery products, the microalgae cultivation process is designed on the biofuels production rates. Liquid and gaseous waste streams are recycled inside the biorefinery: crude glycerol is mixed with wood to enhance the quality of syngas, wastewater is fed to microalgae so as flue gas. Also, the oil extracted from microalgae contributes to produce biodiesel and the waste cells are gasified.Considering that the optimal fit for each cultivation mode varies with the shape of the reactor, we propose a modular approach to assemble them in batteries of tubular, bubble flow, and airlift reactors, and present an operating design criterion that can fulfill the mass balance of the plant by adding/transforming the number of units inside the different batteries. Methods to adjust the operating conditions and control the operating parameters are also discussed.The designed configurations were operated recycling nominal waste streams of about 30 L d−1 of wastewater and 90 Nm3 h−1 of flue gas. Results confirm that the most advantageous one, in terms of volume per recycled waste streams, is a battery of 16 airlift reactors, operating in mixotrophic mode, with growing rate of 0.427 d−1, yield of 3.06, glycerol conversion 39 %, CO2 removal 64 % of inlet 6–10 %(mol) concentration. The same nominal waste streams can also be managed by 40 tubular reactors in almost heterotrophic conditions coupled with 12 bubble columns in autotrophic conditions; working respectively at growing rates of 0.395 d−1 and 0.362 d−1 and yields of 2.94 and 2.84. The battery of tubular reactors reached a glycerol conversion of 45 % and the array of bubble columns removed about 51 % of inlet 12–20 %(mol) CO2 concentration. A complete comparison is reported also in terms of dimensionless numbers and pumping/mixing requirements.

New algorithms for structure informed genome rearrangement

We define two new computational problems in the domain of perfect genome rearrangements, and propose three algorithms to solve them. The rearrangement scenarios modeled by the problems consider Reversal and Block Interchange operations, and a PQ-tree is utilized to guide the allowed operations and to compute their weights. In the first problem, ConstrainedTreeToStringDivergence\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathsf {Constrained \\ TreeToString \\ Divergence}$$\\end{document} (CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}{}$$\\end{document}), we define the basic structure-informed rearrangement measure. Here, we assume that the gene order members of the gene cluster from which the PQ-tree is constructed are permutations. The PQ-tree representing the gene cluster is ordered such that the series of gene IDs spelled by its leaves is equivalent to that of the reference gene order. Then, a structure-informed genome rearrangement distance is computed between the ordered PQ-tree and the target gene order. The second problem, TreeToStringDivergence\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathsf {TreeToString \\ Divergence}$$\\end{document} (TTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{TTSD}{}$$\\end{document}), generalizes CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}{}$$\\end{document}, where the gene order members are not necessarily permutations and the structure informed rearrangement measure is extended to also consider up to dS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_S$$\\end{document} and dT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_T$$\\end{document} gene insertion and deletion operations, respectively, when modelling the PQ-tree informed divergence process from the reference gene order to the target gene order. The first algorithm solves CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}{}$$\\end{document} in O(nγ2·(mp·1.381γ+mq))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n \\gamma ^2 \\cdot (m_p \\cdot 1.381^\\gamma + m_q))$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space, where γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document} is the maximum number of children of a node, n is the length of the string and the number of leaves in the tree, and mp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_p$$\\end{document} and mq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_q$$\\end{document} are the number of P-nodes and Q-nodes in the tree, respectively. If one of the penalties of CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}$$\\end{document} is 0, then the algorithm runs in O(nmγ2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n m \\gamma ^2)$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space. The second algorithm solves TTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{TTSD}{}$$\\end{document} in O(n2γ2dT2dS2m2(mp·5γγ+mq))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2 \\gamma ^2 {d_T}^2 {d_S}^2\\,m^2 (m_p \\cdot 5^\\gamma \\gamma + m_q))$$\\end{document} time and O(dTdSm(mn+5γ))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(d_T d_S m (m n + 5^\\gamma ))$$\\end{document} space, where γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document} is the maximum number of children of a node, n is the length of the string, m is the number of leaves in the tree, mp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_p$$\\end{document} and mq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_q$$\\end{document} are the number of P-nodes and Q-nodes in the tree, respectively, and allowing up to dT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_T$$\\end{document} deletions from the tree and up to dS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_S$$\\end{document} deletions from the string. The third algorithm is intended to reduce the space complexity of the second algorithm. It solves a variant of the problem (where one of the penalties of TTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{TTSD}$$\\end{document} is 0) in O(nγ2dT2dS2m2(mp·4γγ2n(dT+dS+m+n)+mq))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n \\gamma ^2 {d_T}^2 {d_S}^2\\,m^2 (m_p \\cdot 4^{\\gamma }\\gamma ^2n(d_T+d_S+m+n) + m_q))$$\\end{document} time and O(γ2nm2dTdS(dT+dS+m+n))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\gamma ^2 n m^2 d_T d_S (d_T+d_S+m+n))$$\\end{document} space. The algorithm is implemented as a software tool, denoted MEM-Rearrange, and applied to the comparative and evolutionary analysis of 59 chromosomal gene clusters extracted from a dataset of 1487 prokaryotic genomes.

Semi‐labeled unrooted binary tree optimization subject to nonnegativity

Let D n × n denote the distance matrix of n objects, and let T be an unrooted binary tree in which the leaves denote those n objects. We want to find such a tree with the constraint that the edge weights are nonnegative where the distances between the leaves best estimate their corresponding values in D. Accordingly, we have adopted the residual sum of squares (RSS) criterion to minimize the discrepancy between the distance between leaves in the tree and their corresponding distance in D. For this optimization problem, we have designed an iterated local search (ILS) scheme based on the nearest neighbor interchange (NNI) operation to search the neighborhood.

A Review on Terrazyme as Pavement Enhancement Approach

During construction, Engineers often face difficulty related to the stability of soil with or on which the structure is being constructed as the unstable soil cannot not withstand the loads imposed over it. Since the layers of pavements distributes the load evenly over the subgrade, the design of pavement layer is very much dependent on the strength of the subgrade soil over which they are to be laid. So, there is an urgent need to improve the properties of subgrade. An effort to treat the earth with an enzyme that proves very much advantageous for engineering purposes. It is a natural solution which is generated by the enzymatic action on fruits, edibles, saccharine and water by means of fermentation. In this research, a bio-enzyme labelled as Terrazyme is being utilized that significantly improves the properties of soil. Terrazyme being cost effective, efficient, non-toxic, non-inflammable increases the stability by accelerating the enzymatic reactions between the argil and cations(organic) and accelerates the cationic interchange operation to lower down the diffused double layer thickness. The paper deals with all the information about terrazyme including its working mechanism and different properties of soil. It has been investigated that on incrementing the dosage of terrazyme in test sample, notable improvement in the value of UCS and CBR value of soil is observed.

A polynomial‐time algorithm for simple undirected graph isomorphism

A polynomial‐time algorithm for simple undirected graph isomorphism

Cover Feature: The PhotoFlexys: A Multi‐Purpose Reactor Platform to Simplify Process Intensification and Mechanistic Studies in Photochemistry (Chemistry ‐ Methods 6/2021)

The Cover Feature shows the “configurator” for the proposed photoreactor platform. Providing interchangeable emission wavelengths and operation modes, the reactor platform can be efficiently configured to match the diverse reaction conditions in photochemistry. Its adaptability simplifies the implementation of photochemical reactions from first batch attempts to process intensification in continuous flow. Accompanied by process analytical technologies, the reactor platform further paves the way for reliable kinetic and mechanistic studies in photochemistry. More information can be found in the Full Paper by Martin Rößler et al.

Safety Management Strategies for Cross-railway Interchange Widening Project in Highway Reconstruction and Extension

Under the background of rapid social and economic development, the traffic volume of highways has increased, which has brought tremendous pressure to the old highways. In response to this situation, the original highway must be reconstructed and extended. It is necessary to focus on the analysis and exploration of the methods of interchange reconstruction and extension in current highway reconstruction and extension to avoid affecting normal traffic, as it will inevitably have an impact on the traffic operation environment. For this situation, it is necessary to analyze and understand the format and characteristics of traffic organization of the interchange operation area in time and propose corresponding safeguard measures.

Rock Salt Oxide Hollow Spheres Achieving Durable Performance in Bifunctional Oxygen Energy Cells

The interchangeable operation of alkaline oxygen evolution and reduction using bifunctional electrocatalysts in devices consolidates the commercialization milestone of energy storage in hydrogen, a...

Sign-restricted matrices of 0's, 1's, and −1's

We study sign-restricted matrices (SRMs), a class of rectangular (0,±1)-matrices generalizing the alternating sign matrices (ASMs). In an SRM each partial column sum, starting from row 1, equals 0 or 1, and each partial row sum, starting from column 1, is nonnegative. We determine the maximum number of nonzeros in SRMs and characterize the possible row and column sum vectors. Moreover, a number of results on interchange operations are shown, both for SRMs and, more generally, for (0,±1)-matrices. The Bruhat order on ASMs can be extended to SRMs with the result a distributive lattice. Also, we study polytopes associated with SRMs and some relates decompositions.

Computing nearest neighbour interchange distances between ranked phylogenetic trees

Many popular algorithms for searching the space of leaf-labelled (phylogenetic) trees are based on tree rearrangement operations. Under any such operation, the problem is reduced to searching a graph where vertices are trees and (undirected) edges are given by pairs of trees connected by one rearrangement operation (sometimes called a move). Most popular are the classical nearest neighbour interchange, subtree prune and regraft, and tree bisection and reconnection moves. The problem of computing distances, however, is {mathbf {N}}{mathbf {P}}-hard in each of these graphs, making tree inference and comparison algorithms challenging to design in practice. Although ranked phylogenetic trees are one of the central objects of interest in applications such as cancer research, immunology, and epidemiology, the computational complexity of the shortest path problem for these trees remained unsolved for decades. In this paper, we settle this problem for the ranked nearest neighbour interchange operation by establishing that the complexity depends on the weight difference between the two types of tree rearrangements (rank moves and edge moves), and varies from quadratic, which is the lowest possible complexity for this problem, to {mathbf {N}}{mathbf {P}}-hard, which is the highest. In particular, our result provides the first example of a phylogenetic tree rearrangement operation for which shortest paths, and hence the distance, can be computed efficiently. Specifically, our algorithm scales to trees with tens of thousands of leaves (and likely hundreds of thousands if implemented efficiently).

How tree-based is my network? Proximity measures for unrooted phylogenetic networks

Tree-based networks are a class of phylogenetic networks that attempt to formally capture what is meant by “tree-like” evolution. A given non-tree-based phylogenetic network, however, might appear to be very close to being tree-based, or very far. In this paper, we formalize the notion of proximity to tree-based for unrooted phylogenetic networks, with a range of proximity measures. These measures also provide characterizations of tree-based networks. One measure in particular, related to the nearest neighbour interchange operation, allows us to define the notion of “tree-based rank”. This provides a subclassification within the tree-based networks themselves, identifying those networks that are “very” tree-based. Finally, we prove results relating tree-based networks in the settings of rooted and unrooted phylogenetic networks, showing effectively that an unrooted network is tree-based if and only if it can be made a rooted tree-based network by rooting it and orienting the edges appropriately. This leads to a clarification of the contrasting decision problems for tree-based networks, which are polynomial in the rooted case but NP complete in the unrooted.

Solving Overstay and Stochasticity in PEV Charging Station Planning With Real Data

This article studies optimal plug-in electric vehicle (PEV) charging station planning, with consideration for the “overstay” problem. Today, public PEV charging station utilization is typically around 15%. When un-utilized, the chargers are either idle or occupied by a fully charged PEV that has not departed. We call this “overstay.” This motivates a strategy for increasing utilization by interchanging fully charged PEVs with those waiting for service-an issue which is not well addressed in the existing literature. Thus, this article studies the PEV charging station planning problem taking strategic interchange into account. To our best understanding, this has not been studied in the literature. With interchange, the objective is to enhance the charger utilization rate and, thus, reduce the number of chargers. This potentially reduces the capital investment and operational cost. A novel power/energy aggregation model is proposed, and a chance-constrained stochastic programming planning model with interchange is developed for a public charging station to incorporate customer demand uncertainties. Numerical experiments are conducted to illustrate the performance of the proposed method. Simulation results show that incorporating strategic interchange operation can significantly decrease the number of chargers, enhance utilization and economic efficiency.

On the usage of sorting networks to control greenhouse climatic factors

The goal of this article is the application of a non-adaptive classification algorithm to support the variable management process for internal climate control. The protected agriculture has given many advantages for the care and improvement in the production of almost any food. This work is focused on improving a control system for climate variables. The decision for activating an actuator for the correct care of the crop is very important. A sorting network technique with predefined compare–interchange operations and designed to order data very efficiently is proposed. This approach has been applied to the process of handling actuators within a control system. An advantage of using the sorting networks is that it has an inflexibility when processing a data list; it always takes the same units of time and is executed in the same number of operations for an input size of n. Sorting network–based applications are scarce in the state-of-the-art because there are not many techniques that are effective for sorting very small data sizes. The use of sorting networks in the process of assessing the determination of actuators is proposed. This operation is scheduled in the design of control and performed at each reading back data.

Bounds for phylogenetic network space metrics

Phylogenetic networks are a generalization of phylogenetic trees that allow for representation of reticulate evolution. Recently, a space of unrooted phylogenetic networks was introduced, where such a network is a connected graph in which every vertex has degree 1 or 3 and whose leaf-set is a fixed set X of taxa. This space, denoted mathcal {N}(X), is defined in terms of two operations on networks—the nearest neighbor interchange and triangle operations—which can be used to transform any network with leaf set X into any other network with that leaf set. In particular, it gives rise to a metric d on {mathcal {N}}(X) which is given by the smallest number of operations required to transform one network in {mathcal {N}}(X) into another in {mathcal {N}}(X). The metric generalizes the well-known NNI-metric on phylogenetic trees which has been intensively studied in the literature. In this paper, we derive a bound for the metric d as well as a related metric d_{N!N!I} which arises when restricting d to the subset of mathcal {N}(X) consisting of all networks with 2(|X|-1+i) vertices, i ge 1. We also introduce two new metrics on networks—the SPR and TBR metrics—which generalize the metrics on phylogenetic trees with the same name and give bounds for these new metrics. We expect our results to eventually have applications to the development and understanding of network search algorithms.

Optimal operations management of residential energy supply networks with power and heat interchanges

An optimal operations management system of residential energy supply networks employing power and heat interchanges among cogeneration units and storage tanks was developed. This system integrated energy demand prediction, operation scheduling to predicted energy demand using mixed-integer linear programming, and real-time control for the cogeneration units and the heat interchange hierarchically. The energy demand prediction and operation scheduling were updated using a receding horizon approach. The novelty of the study is characterized by developing an operations management framework for heat interchange among storage tanks and by proposing an event-driven receding horizon approach. The developed operations management system was applied to annual operating simulation of a residential energy supply network, consisting of four fuel cell-based cogeneration units and four storage tanks. The results showed that employing the power and heat interchanges increases a reduction rate of annual primary energy consumption by 3.24 and 5.63 percentage points relative to the power interchange operation and separate operation of the cogeneration units, respectively. Moreover, the event-driven receding horizon approach based on heat interchange schedule maintained an energy-saving performance subequal to the conventional receding horizon approach and reduced the daily receding number by 46.7% of the conventional receding horizon approach.

Partial evaluation in Rank Aggregation Problems

Solving a problem by using metaheuristic algorithms requires the evaluation of a large number of potential solutions. This paper presents a theoretical and experimental study of the application of partial evaluation in the Rank Aggregation Problem (RAP). Partial evaluation just computes the part of the objective function that is affected by the modifications introduced by certain operators. In particular, we study some of the most common mutation/neighboring operators that are used in problems defined in the space of permutations, namely insertion, interchange and inversion. The theoretical study shows that the complexity of evaluating the original objective function can be reduced from quadratic to linear by using partial evaluation after applying insertion and interchange. Regarding the experimental study, it shows that for the insertion and interchange operators, the running time for the evaluation of the objective function may be reduced by a factor of 10 (30) for problems with more than 50 (100) items to be ranked. In the case of the inversion operator the amount of time saved time is not so large, although it is still significant.

Doubly stochastic matrices and the Bruhat order

The Bruhat order is defined in terms of an interchange operation on the set of permutation matrices of order n which corresponds to the transposition of a pair of elements in a permutation. We introduce an extension of this partial order, which we call the stochastic Bruhat order, for the larger class Ω n of doubly stochastic matrices (convex hull of n×n permutation matrices). An alternative description of this partial order is given. We define a class of special faces of Ω n induced by permutation matrices, which we call Bruhat faces. Several examples of Bruhat faces are given and several results are presented.

Transforming phylogenetic networks: Moving beyond tree space

Phylogenetic networks are a generalization of phylogenetic trees that are used to represent reticulate evolution. Unrooted phylogenetic networks form a special class of such networks, which naturally generalize unrooted phylogenetic trees. In this paper we define two operations on unrooted phylogenetic networks, one of which is a generalization of the well-known nearest-neighbor interchange (NNI) operation on phylogenetic trees. We show that any unrooted phylogenetic network can be transformed into any other such network using only these operations. This generalizes the well-known fact that any phylogenetic tree can be transformed into any other such tree using only NNI operations. It also allows us to define a generalization of tree space and to define some new metrics on unrooted phylogenetic networks. To prove our main results, we employ some fascinating new connections between phylogenetic networks and cubic graphs that we have recently discovered. Our results should be useful in developing new strategies to search for optimal phylogenetic networks, a topic that has recently generated some interest in the literature, as well as for providing new ways to compare networks.

Characterizing Local Optima for Maximum Parsimony.

Finding the best phylogenetic tree under the maximum parsimony optimality criterion is computationally difficult. We quantify the occurrence of such optima for well-behaved sets of data. When nearest neighbor interchange operations are used, multiple local optima can occur even for "perfect" sequence data, which results in hill-climbing searches that never reach a global optimum. In contrast, we show that when neighbors are defined via the subtree prune and regraft metric, there is a single local optimum for perfect sequence data, and thus, every such search finds a global optimum quickly. We further characterize conditions for which sequences simulated under the Cavender-Farris-Neyman and Jukes-Cantor models of evolution yield well-behaved search spaces.

New Tools and Strategies for Design and Operation of Urban Transport Interchanges

NODES “New tOols for Design and OpEration of Urban Transport InterchangeS” is a collaborative project co-funded by the Seventh Framework Programme focused on building and testing a Toolbox to support European cities in the design and operation of new or upgraded urban transport interchanges. NODES thereby provides greater support, services and satisfaction to travellers and users, to interchange operators as well as those societal and economic actors depending on the efficiency of the interchange. NODES provides a helping hand for European cities striving for inclusive, multimodal and sustainable urban transport. NODES is able to supply solutions and methods for Local Authorities, Public Transport operators as well as interchange promoters to better and more effectively plan, design and manage multimodal transport interchanges in European cities and beyond. NODES uses an integrated approach balancing and integrating land use and transport planning, interchange design, intermodality and Information Communication Technologies (ICT), interchange management and business models, energy and environment issues.The developed Toolbox is a catalogue of integrated planning, design and management tools for new or upgraded efficient urban interchange enabling every interchange promoter to create a more efficient, effective and inclusive urban transport system, bringing together all elements of a clean, energy-efficient, safe and intelligent transport. The Toolbox consists in a set of tools including technical, organizational, legislative, financial, energy and environment tools. NODES successfully developed and tested tools and integrated methodologies based on the most innovative technique, methods, models, software and regulations aimed at boosting performance of urban transport interchange in the different lifecycle stages such as newly build, refurbishment, governance and management changes, etc.NODES has assessed the effects of the Toolbox in the areas of land use planning, transport, society, economy, energy and environment. Nine different European cities have successfully tested the tools and integrated methodologies included in the Toolbox covering different situations, transport patterns and local peculiarities. These cities are: Toulouse (France), Reading (UK), Budapest (Hungary), Rotterdam (Netherlands), Rouen (France), Rome (Italy), Thessaloniki (Greece), Osnabrück (Germany), Birmingham (UK). Despite of significant differences among the above cities and relevant interchanges involved in the demonstrationś activities (e.g. small city interchange, central interchange in large city, etc.), all the involved sites achieved tangible results and benefits from the Toolbox's implementation, enabling to transfer results in other cities beyond the project.The main benefits can be measured in terms of increasing multimodal integration and integrated information provision, reducing time of transfer among different modes, enhancing liveability and experience from travellers’ prospective, safety and security, enhancing energy efficiency, etc. The achieved results are the basis for a future further exploitation of the Toolbox towards a more technological and modelling-oriented solution based on multi-criteria, object oriented and agent-based approaches, enabling to deliver most effective and efficient combination of integrated tools and techniques, through an incremental and modular approach.