Confinement of discrete breathers in inhomogeneously profiled nonlinear chains

We investigate numerically the scattering of a moving discrete breather on a pair of junctions in a Fermi-Pasta-Ulam chain. These junctions delimit an extended region with different masses of the particles. We consider (i) a rectangular trap, (ii) a wedge shaped trap, and (iii) a smoothly varying convex or concave mass profile. All three cases lead to DB confinement, with the ease of trapping depending on the profile of the trap. We also study the collision and trapping of two DBs within the profile as a function of trap width, shape, and approach time at the two junctions. The latter controls whether one or both DBs are trapped.

Stochastic processes induced by dichotomous markov noise: Some exact dynamical results

Stochastic processes defined by a general Langevin equation of motion where the noise is the non-Gaussian dichotomous Markov noise are studied. A non-FokkerPlanck master differential equation is deduced for the probability density of these processes. Two different models are exactly solved. In the second one, a nonequilibrium bimodal distribution induced by the noise is observed for a critical value of its correlation time. Critical slowing down does not appear in this point but in another one.

Variational localized-site cluster expansions. X. Dimerization in linear Heisenberg chains

Ground-state instability to bond alternation in long linear chains is considered from the point of view of valence-bond (VB) theory. This instability is viewed as the consequence of a long-range order (LRO) which is expected if the ground state is reasonably described in terms of the Kekulé states (with nearest-neighbor singlet pairing). It is argued that the bond alternation and associated LRO predicted by this simple, VB picture is retained for certain linear Heisenberg models; many-body VB calculations on spin s=1 / 2 and s=1 chains are carried out in a test of this argument.

Dissociation from BiP and Retrotranslocation of Unassembled Immunoglobulin Light Chains Are Tightly Coupled to Proteasome Activity

Unassembled immunoglobulin light chains expressed by the mouse plasmacytoma cell line NS1 (KNS1) are degraded in vivo with a half-life of 50-60 min in a way that closely resembles endoplasmic reticulum (ER)-associated degradation (Knittler et al., 1995). Here we show that the peptide aldehydes MG132 and PS1 and the specific proteasome inhibitor lactacystin effectively increased the half-life of KNS1, arguing for a proteasome-mediated degradation pathway. Subcellular fractionation and protease protection assays have indicated an ER localization of KNS1 upon proteasome inhibition. This was independently confirmed by the analysis of the folding state of KNS1and size fractionation experiments showing that the immunoglobulin light chain remained bound to the ER chaperone BiP when the activity of the proteasome was blocked. Moreover, kinetic studies performed in lactacystin-treated cells revealed a time-dependent increase in the physical stability of the BiP-KNS1complex, suggesting that additional proteins are present in the older complex. Together, our data support a model for ER-associated degradation in which both the release of a soluble nonglycosylated protein from BiP and its retrotranslocation out of the ER are tightly coupled with proteasome activity.

Análise da transição entre dias secos e chuvosos por meio da cadeia de Markov de terceira ordem

O objetivo deste trabalho foi verificar se as ocorrências de dias secos e chuvosos são condicionalmente dependentes da seqüência dos três dias secos e chuvosos anteriores, numa zona pluviometricamente homogênea, por meio da cadeia não-homogênea de Markov de terceira ordem. Os resultados mostraram que as probabilidades diárias de transição podem ser adequadamente estimadas, com base em dados agregados bimestralmente, seguidas de interpolação por meio de funções sinusoidais. Além disso, evidenciou-se que, naquela zona, as ocorrências diárias de chuva são condicionalmente dependentes da seqüência de dias secos e chuvosos nos três dias anteriores. A cadeia não-homogênea de Markov de terceira ordem é um importante instrumento para a análise da dependência entre as seqüências de dias secos e chuvosos em determinadas regiões.

Bayesian Markov-chain-Monte-Carlo inversion of time-lapse cross hole ground-penetrating radar data to characterize the vadose zone at the Arrenaes field site, Denmark

The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten-Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov-chain-Monte-Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time-lapse GPR data for characterizing vadose zone dynamics.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

In this paper, the theory of hidden Markov models (HMM) isapplied to the problem of blind (without training sequences) channel estimationand data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedureassumes the model (i.e., the channel response) to be static throughoutthe observation sequence. By means of introducing a parametric model fortime-varying channel responses, a version of the algorithm, which is moreappropriate for mobile channels [time-dependent Baum-Welch (TDBW)] isderived. Aiming to compare algorithm behavior, a set of computer simulationsfor a GSM scenario is provided. Results indicate that, in comparisonto other Baum–Welch (BW) versions of the algorithm, the TDBW approachattains a remarkable enhancement in performance. For that purpose, onlya moderate increase in computational complexity is needed.

Blind channel estimation and data detection using hidden Markov models theory

In this correspondence, we propose applying the hiddenMarkov models (HMM) theory to the problem of blind channel estimationand data detection. The Baum–Welch (BW) algorithm, which is able toestimate all the parameters of the model, is enriched by introducingsome linear constraints emerging from a linear FIR hypothesis on thechannel. Additionally, a version of the algorithm that is suitable for timevaryingchannels is also presented. Performance is analyzed in a GSMenvironment using standard test channels and is found to be close to thatobtained with a nonblind receiver.

Value added logistics in supply and demand chains. Smile. Part 1. eBusiness between global company and itslocal sme supplier network

VALOSADE (Value Added Logistics in Supply and Demand Chains) is the research project of Anita Lukka's VALORE (Value Added Logistics Research) research team inLappeenranta University of Technology. VALOSADE is included in ELO (Ebusiness logistics) technology program of Tekes (Finnish Technology Agency). SMILE (SME-sector, Internet applications and Logistical Efficiency) is one of four subprojects of VALOSADE. SMILE research focuses on case network that is composed of small and medium sized mechanical maintenance service providers and global wood processing customers. Basic principle of SMILE study is communication and ebusiness insupply and demand network. This first phase of research concentrates on creating backgrounds for SMILE study and for ebusiness solutions of maintenance case network. The focus is on general trends of ebusiness in supply chains and networksof different industries; total ebusiness system architecture of company networks; ebusiness strategy of company network; information value chain; different factors, which influence on ebusiness solution of company network; and the correlation between ebusiness and competitive advantage. Literature, interviews and benchmarking were used as research methods in this qualitative case study. Networks and end-to-end supply chains are the organizational structures, which can add value for end customer. Information is one of the key factors in these decentralized structures. Because of decentralization of business, information is produced and used in different companies and in different information systems. Information refinement services are needed to manage information flows in company networksbetween different systems. Furthermore, some new solutions like network information systems are utilised in optimising network performance and in standardizingnetwork common processes. Some cases have however indicated, that utilization of ebusiness in decentralized business model is not always a necessity, but value-add of ICT must be defined case-specifically. In the theory part of report, different ebusiness and architecture models are introduced. These models are compared to empirical case data in research results. The biggest difference between theory and empirical data is that models are mainly developed for large-scale companies - not for SMEs. This is due to that implemented network ebusiness solutions are mainly large company centered. Genuine SME network centred ebusiness models are quite rare, and the study in that area has been few in number. Business relationships between customer and their SME suppliers are nowadays concentrated more on collaborative tactical and strategic initiatives besides transaction based operational initiatives. However, ebusiness systems are further mainly based on exchange of operational transactional data. Collaborative ebusiness solutions are in planning or pilot phase in most case companies. Furthermore, many ebusiness solutions are nowadays between two participants, but network and end-to-end supply chain transparency and information systems are quite rare. Transaction volumes, data formats, the types of exchanged information, information criticality,type and duration of business relationship, internal information systems of partners, processes and operation models (e.g. different ordering models) differ among network companies, and furthermore companies are at different stages on networking and ebusiness readiness. Because of former factors, different customer-supplier combinations in network must utilise totally different ebusiness architectures, technologies, systems and standards.

Content value chains modelling using a copyright ontology

Existing digital rights management (DRM) systems, initiatives like Creative Commons or research works as some digital rights ontologies provide limited support for content value chains modelling and management. This is becoming a critical issue as content markets start to profit from the possibilities of digital networks and the World Wide Web. The objective is to support the whole copyrighted content value chain across enterprise or business niches boundaries. Our proposal provides a framework that accommodates copyright law and a rich creation model in order to cope with all the creation life cycle stages. The dynamic aspects of value chains are modelled using a hybrid approach that combines ontology-based and rule-based mechanisms. The ontology implementation is based on Web Ontology Language and Description Logic (OWL-DL) reasoners, are directly used for license checking. On the other hand, for more complex aspects of the dynamics of content value chains, rule languages are the choice.

Easy introduction of maleimides at different positions of oligonucleotide chains for conjugation purposes

[2,5-Dimethylfuran]-protected maleimides were placed at both internal positions and the 3'-end of oligonucleotides making use of solid-phase synthesis procedures. A new phosphoramidite derivative and a new solid support incorporating the protected maleimide moiety were prepared for this purpose. In all cases maleimide deprotection (retro-Diels-Alder reaction) followed by reaction with thiol-containing compounds afforded the target conjugate.