Equilibrium models in multimodal container transport systems

Optimizing the performance of multimodal freight transport networks involves adequately balancing the interplay between costs, volumes, times of departure and arrival, and times of travel. In order to study this interplay, we propose an assignment model that is able to efficiently determine flows and costs in a multimodal network. The model is based on a so-called user equilibrium principle, which is at the basis of Dynamic Traffic Assignment problems. This principle takes into account transport demands to be shipped using vehicles that transport single freight units (such as trucks) or multiple freight units (such as trains and barges, where demand should be bundled to reach efficient operations). Given a particular demand, the proposed model provides an assignment of the demand over the available modes of transport. The outcome of the model, i.e., the equilibrium point, minimizes users’ generalized costs, expressed as a function of mode, travel time and related congestion, and waiting time for bundling sufficient demand in order to fill a vehicle. The model deals with these issues across a doubly-dynamic time scale and in an integrated manner. One dynamic involves a learning dynamic converging towards an equilibrium (day-to-day) situation, reflecting the reaction of the players towards the action of the others. Another dynamic considers the possible departure time that results in minimum expected costs, also due to the fact that players mutually influence each other on the choice of departure times, due to congestion effects and costs for early/late arrival of freight units. This is a choice within a given time horizon such as a day or a week. We present a study on the influence and sensitivity of different model parameters, in order to analyse the implications on strategic decisions, fostering a target modal share for freight transportation. We also study under which conditions the different modes can be substitutes for each other.     

Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies

Due to the restrictions that most traditional scheduling strategies only cared about users’ quality of service (QoS) time or cost requirements, lacked the effective analysis of users’ real service demand and could not guarantee scheduling security, this paper added trust into workflow’s QoS target and proposed a novel customizable cloud workflow scheduling model. In order to better analyze different user’s service requirements and provide customizable services, the new model divided workflow scheduling into two stages: the macro multi-workflow scheduling as the unit of cloud user and the micro single workflow scheduling. It introduced trust mechanism into multi-workflow scheduling level. And in single workflow scheduling level, it classified workflows into time-sensitive, cost-sensitive and balance three types according to different workflow’s QoS demand parameters using fuzzy clustering method. Based on it, it customized different service strategies for different type. The simulation experiments show that the new schema has some advantages in shortening workflow’s final completion time, achieving relatively high execution success rate and user satisfaction compared to other kindred solutions.     

BOD: a customizable bioinformatics on demand system accommodating multiple steps and parallel tasks

The integration of bioinformatics resources worldwide is one of the major concerns of the biological community. We herein established the BOD (Bioinformatics on demand) system to use Grid computing technology to set up a virtual workbench via a web-based platform, to assist researchers performing customized comprehensive bioinformatics work. Users will be able to submit entire search queries and computation requests, e.g. from DNA assembly to gene prediction and finally protein folding, from their own office using the BOD end-user web interface. The BOD web portal parses the user's job requests into steps, each of which may contain multiple tasks in parallel. The BOD task scheduler takes an entire task, or splits it into multiple subtasks, and dispatches the task or subtasks proportionally to computation node(s) associated with the BOD portal server. A node may further split and distribute an assigned task to its sub-nodes using a similar strategy. In the end, the BOD portal server receives and collates all results and returns them to the user. BOD uses a pipeline model to describe the user's submitted data and stores the job requests/status/results in a relational database. In addition, an XML criterion is established to capture task computation program details.     

A common mechanism for the ATP-DnaA-dependent formation of open complexes at the replication origin

Initiation of chromosomal replication and its cell cycle-coordinated regulation bear crucial and fundamental mechanisms in most cellular organisms. Escherichia coli DnaA protein forms a homomultimeric complex with the replication origin (oriC). ATP-DnaA multimers unwind the duplex within the oriC unwinding element (DUE). In this study, structural analyses suggested that several residues exposed in the central pore of the putative structure of DnaA multimers could be important for unwinding. Using mutation analyses, we found that, of these candidate residues, DnaA Val-211 and Arg-245 are prerequisites for initiation in vivo and in vitro. Whereas DnaA V211A and R245A proteins retained normal affinities for ATP/ADP and DNA and activity for the ATP-specific conformational change of the initiation complex in vitro, oriC complexes of these mutant proteins were inactive in DUE unwinding and in binding to the single-stranded DUE. Unlike oriC complexes including ADP-DnaA or the mutant DnaA, ATP-DnaA-oriC complexes specifically bound the upper strand of single-stranded DUE. Specific T-rich sequences within the strand were required for binding. The corresponding conserved residues of the DnaA ortholog in Thermotoga maritima, an ancient eubacterium, were also required for DUE unwinding, consistent with the idea that the mechanism and regulation for DUE unwinding can be evolutionarily conserved. These findings provide novel insights into mechanisms for pore-mediated origin unwinding, ATP/ADP-dependent regulation, and helicase loading of the initiation complex.     

DNA rings with multiple energy minima

Within the context of DNA rings, we analyze the relationship between intrinsic shape and the existence of multiple stable equilibria, either nicked or cyclized with the same link. A simple test, based on a perturbation expansion of symmetry breaking within a continuum elastic rod model, provides good predictions of the occurrence of such multiple equilibria. The reliability of these predictions is verified by direct computation of nicked and cyclized equilibria for several thousand DNA minicircles with lengths of 200 and 900 bp. Furthermore, our computations of equilibria for nicked rings predict properties of the equilibrium distribution of link, as calculated by much more computationally intensive Monte Carlo simulations.     

Advanced computing for systems biology

Systems biology is based on computational modelling and simulation of large networks of interacting components. Models may be intended to capture processes, mechanisms, components and interactions at different levels of fidelity. Input data are often large and geographically disperse, and may require the computation to be moved to the data, not vice versa. In addition, complex system-level problems require collaboration across institutions and disciplines. Grid computing can offer robust, scaleable solutions for distributed data, compute and expertise. We illustrate some of the range of computational and data requirements in systems biology with three case studies: one requiring large computation but small data (orthologue mapping in comparative genomics), a second involving complex terabyte data (the Visible Cell project) and a third that is both computationally and data-intensive (simulations at multiple temporal and spatial scales). Authentication, authorisation and audit systems are currently not well scalable and may present bottlenecks for distributed collaboration particularly where outcomes may be commercialised. Challenges remain in providing lightweight standards to facilitate the penetration of robust, scalable grid-type computing into diverse user communities to meet the evolving demands of systems biology.     

Sustainable sourcing of strategic raw materials by integrating recycled materials

In this paper we investigate a manufacturer’s sustainable sourcing strategy that includes recycled materials. To produce a short life-cycle electronic good, strategic raw materials can be bought from virgin material suppliers in advance of the season and via emergency shipments, as well as from a recycler. Hence, we take into account virgin and recycled materials from different sources simultaneously. Recycling makes it possible to integrate raw materials out of steadily increasing waste streams back into production processes. Considering stochastic prices for recycled materials, stochastic supply quantities from the recycler and stochastic demand as well as their potential dependencies, we develop a single-period inventory model to derive the order quantities for virgin and recycled raw materials to determine the related costs and to evaluate the effectiveness of the sourcing strategy. We provide managerial insights into the benefits of such a green sourcing approach with recycling and compare this strategy to standard sourcing without recycling. We conduct a full factorial design and a detailed numerical sensitivity analysis on the key input parameters to evaluate the cost savings potential. Furthermore, we consider the effects of correlations between the stochastic parameters. Green sourcing is especially beneficial in terms of cost savings for high demand variability, high prices of virgin raw material and low expected recycling prices as well as for increasing standard deviation of the recycling price. Besides these advantages it also contributes to environmental sustainability as, compared to sourcing without recycling, it reduces the total quantity ordered and, hence, emissions are reduced.     

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

Background  

We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness.     

Highly organized DnaA-oriC complexes recruit the single-stranded DNA for replication initiation

In Escherichia coli, the replication origin oriC consists of two functional regions: the duplex unwinding element (DUE) and its flanking DnaA-assembly region (DAR). ATP-DnaA molecules multimerize on DAR, unwinding DUE for DnaB helicase loading. However, DUE-unwinding mechanisms and functional structures in DnaA-oriC complexes supporting those remain unclear. Here, using various in vitro reconstituted systems, we identify functionally distinct DnaA sub-complexes formed on DAR and reveal novel mechanisms in DUE unwinding. The DUE-flanking left-half DAR carrying high-affinity DnaA box R1 and the ATP-DnaA-preferential DnaA box R5, τ1-2 and I1-2 sites formed a DnaA sub-complex competent in DUE unwinding and ssDUE binding, thereby supporting basal DnaB loading activity. This sub-complex is further subdivided into two; the DUE-distal DnaA sub-complex formed on the ATP-DnaA-preferential sites binds ssDUE. Notably, the DUE-flanking, DnaA box R1-DnaA sub-complex recruits DUE to the DUE-distal DnaA sub-complex in concert with a DNA-bending nucleoid protein IHF, thereby promoting DUE unwinding and binding of ssDUE. The right-half DAR-DnaA sub-complex stimulated DnaB loading, consistent with in vivo analyses. Similar features are seen in DUE unwinding of the hyperthermophile, Thermotoga maritima, indicating evolutional conservation of those mechanisms.     

The DNA unwinding element in a yeast replication origin functions independently of easily unwound sequences present elsewhere on a plasmid.

We have previously identified a DNA unwinding element (DUE) in autonomously replicating sequences (ARSs) and demonstrated a correlation between single-strand-specific nuclease hypersensitivity of the DUE and ARS-mediated plasmid replication in yeast. The DUE in the H4 ARS is the most easily unwound sequence in a supercoiled DNA molecule, in the context of the Ylp5 plasmid. To determine whether sequences which are more readily unwound than the ARS can influence replication activity, we have inserted such sequences, called 'torsional sinks', into the plasmids at a site distal to the ARS. We show that the torsional sink sequences effect reduction or elimination of the nuclease hypersensitivity of a variety of H4 ARS derivatives. However, we detect no difference in the in vivo replication activity of an individual ARS plasmid with or without a torsional sink. Thus, the function of the DUE in a yeast replication origin is unaffected by easily unwound sequences present elsewhere on the same plasmid.     

IPad: ID-based public auditing for the outsourced data in the standard model

Cloud storage is an important cloud computing service, it allows data users to store and access their files anytime, from anywhere and with any device. To ensure the security of the outsourced data, it also must allow data user to periodically verify integrity of the data which was outsourced to an untrusted cloud server at a relatively low cost. To solve this problem, most recent auditing protocols are mainly based on the traditional-public key infrastructure. In this infrastructure, the auditor must validate the certificates of data user before auditing data integrity. Thus, it results in a large amount of computation cost and is not suitable to the multi-user setting. To overcome this problem, in this paper, we propose two efficient ID-based public auditing protocols for the outsourced data by combing Water’s signature and public auditing for the outsourced data. And the two protocols are provably secure in the standard security model. Especially, our optimized protocol has constant communication overhead and computation cost. To the best of our knowledge, it is the first ID-based auditing for data integrity in the standard security model. By comparison with Wang et al.’s scheme and Tan et al.’s scheme, our protocols have the large advantages over the other two schemes in terms of communication cost and computation cost. Simulation results show that our proposed ID-based auditing protocols are the most efficient among three schemes in terms of computation cost.     

A Linear City Model with Asymmetric Consumer Distribution

The article analyzes a linear-city model where the consumer distribution can be asymmetric, which is important because in real markets this distribution is often asymmetric. The model yields equilibrium price differences, even though the firms’ costs are equal and their locations are symmetric (at the two endpoints of the city). The equilibrium price difference is proportional to the transportation cost parameter and does not depend on the good''s cost. The firms'' markups are also proportional to the transportation cost. The two firms’ prices will be equal in equilibrium if and only if half of the consumers are located to the left of the city’s midpoint, even if other characteristics of the consumer distribution are highly asymmetric. An extension analyzes what happens when the firms have different costs and how the two sources of asymmetry – the consumer distribution and the cost per unit – interact together. The model can be useful as a tool for further development by other researchers interested in applying this simple yet flexible framework for the analysis of various topics.     

A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice

This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers’ route choice behavior.     

Sequence-specific interactions of Rep proteins with ssDNA in the AT-rich region of the plasmid replication origin

The DNA unwinding element (DUE) is a sequence rich in adenine and thymine residues present within the origin region of both prokaryotic and eukaryotic replicons. Recently, it has been shown that this is the site where bacterial DnaA proteins, the chromosomal replication initiators, form a specific nucleoprotein filament. DnaA proteins contain a DNA binding domain (DBD) and belong to the family of origin binding proteins (OBPs). To date there has been no data on whether OBPs structurally different from DnaA can form nucleoprotein complexes within the DUE. In this work we demonstrate that plasmid Rep proteins, composed of two Winged Helix domains, distinct from the DBD, specifically bind to one of the strands of ssDNA within the DUE. We observed nucleoprotein complexes formed by these Rep proteins, involving both dsDNA containing the Rep-binding sites (iterons) and the strand-specific ssDNA of the DUE. Formation of these complexes required the presence of all repeated sequence elements located within the DUE. Any changes in these repeated sequences resulted in the disturbance in Rep-ssDNA DUE complex formation and the lack of origin replication activity in vivo or in vitro.     

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.     

A Partition Function Approximation Using Elementary Symmetric Functions

In statistical mechanics, the canonical partition function can be used to compute equilibrium properties of a physical system. Calculating however, is in general computationally intractable, since the computation scales exponentially with the number of particles in the system. A commonly used method for approximating equilibrium properties, is the Monte Carlo (MC) method. For some problems the MC method converges slowly, requiring a very large number of MC steps. For such problems the computational cost of the Monte Carlo method can be prohibitive. Presented here is a deterministic algorithm – the direct interaction algorithm (DIA) – for approximating the canonical partition function in operations. The DIA approximates the partition function as a combinatorial sum of products known as elementary symmetric functions (ESFs), which can be computed in operations. The DIA was used to compute equilibrium properties for the isotropic 2D Ising model, and the accuracy of the DIA was compared to that of the basic Metropolis Monte Carlo method. Our results show that the DIA may be a practical alternative for some problems where the Monte Carlo method converge slowly, and computational speed is a critical constraint, such as for very large systems or web-based applications.     

Simulated annealing for the vehicle routing problem with two-dimensional loading constraints

This paper addresses the capacitated vehicle routing problem with two-dimensional loading constraints (2L-CVRP). The 2L-CVRP is a combination of the two most important problems in distribution logistics, which are loading of freight into vehicles, and the successive routing of the vehicles to satisfy customer demand. The objective is to minimize the transportation cost. All vehicles must start and terminate at a central depot, and the transported items carried by the vehicles must be feasibly packed into the loading surfaces of the vehicles. A simulated annealing algorithm to solve the problem is presented, in which the loading component of the problem is solved through a collection of packing heuristics. A novel approach to plan packing is employed. An efficient data structure (Trie) is used to accelerate the algorithm. The extensive computational results prove the effectiveness of the algorithm.     

Technoecological analysis of energy carriers for long‐haul transportation

Long‐haul transportation demand is predicted to increase in the future, resulting in higher carbon dioxide emissions. Different drivetrain technologies, such as hybrid or battery electric vehicles, electrified roads, liquefied natural gas and hydrogen, might offer solutions to this problem. To assess their ecological and economic impact, these concepts were simulated including a weight and cost model to estimate the total cost of ownership. An evolutionary algorithm optimizes each vehicle to find a concept specific optimal solution. A model calculates the minimum investment in infrastructure required to meet the energy demand for each concept. A well‐to‐wheel analysis takes into account upstream and on‐road carbon dioxide emissions, to compare fully electric vehicles with conventional combustion engines. Investment in new infrastructure is the biggest drawback of electrified road concepts, although they offer low CO₂ emissions. The diesel hybrid is the best compromise between carbon reduction and costs.