An agent-based model of supply chains with dynamic structures

This paper proposes a common agent-based model for the simulation of MTS and MTO supply chains with dynamic structures. Based on the model, scholars can model supply chains easily. Basic characters of supply chains are proposed in the model. Agents, who are used to simulate the members of supply chains, produce appropriate products by intelligent choices. The relationships among agents are connected by their products. Different agents’ attributes are presented by their knowledge and actions of agents are introduced in the paper. Experiments are produced to show the availability of the agent-based model. The model should be available as a toolkit for the studying of dynamic supply chains.     

Polymeric dibromomaleimides as extremely efficient disulfide bridging bioconjugation and pegylation agents

A series of dibromomaleimides have been shown to be very efficacious at insertion into peptidic disulfide bonds. This conjugation proceeds with a stoichiometric balance of reagents in buffered solutions in less than 15 min to give discrete products while maintaining the disulfide bridge and thus peptide conformation. The insertion is initiated by disulfide reduction using a water-soluble phosphine, tris(2-carboxyethyl)phosphine (TCEP) which allows for subsequent substitution of the two maleimide bromides by the generated thiols. Reaction of salmon calcitonin (sCT) with 2,3-dibromomaleimide (1.1 excess) in the presence of TCEP (1.1 equiv) in aqueous solution at pH 6.2 gives complete production of a single conjugate which requires no workup. A linear methoxy poly(ethylene glycol) (PEG) was functionalized via a Mitsunobu reaction and used for the successful site-specific and rapid pegylation of sCT. This reaction occurs in 15 min with a small stoichiometry excess of the pegylating agent to give insertion at the disulfide with HPLC showing a single product and MALDI-ToF confirming conjugation. Attempts to use the group in a functional ATRP polymerization initiator led to polymerization inhibition. Thus, in order to prepare a range of functional polymers an indirect route was chosen via both azide and aniline functional initiators which were converted to 2,3-dibromomaleimides via appropriate reactions. For example, the azide functional polymer was reacted via a Huisgen CuAAC click reaction to an alkyne functional 2,3-dibromomaleimide. This new reagent allowed for the synthesis of conjugates of sCT with comb polymers derived from PEG methacrylic monomers which in addition gave appropriate cloud points. This reaction represents a highly efficient polymer conjugation method which circumvents problems of purification which normally arise from having to use large excesses of the conjugate. In addition, the tertiary structure of the peptide is efficiently maintained.     

Sparse regularized regression identifies behaviorally-relevant stimulus features from psychophysical data

As a prerequisite to quantitative psychophysical models of sensory processing it is necessary to learn to what extent decisions in behavioral tasks depend on specific stimulus features, the perceptual cues. Based on relative linear combination weights, this study demonstrates how stimulus-response data can be analyzed in this regard relying on an L(1)-regularized multiple logistic regression, a modern statistical procedure developed in machine learning. This method prevents complex models from over-fitting to noisy data. In addition, it enforces "sparse" solutions, a computational approximation to the postulate that a good model should contain the minimal set of predictors necessary to explain the data. In simulations, behavioral data from a classical auditory tone-in-noise detection task were generated. The proposed method is shown to precisely identify observer cues from a large set of covarying, interdependent stimulus features--a setting where standard correlational and regression methods fail. The proposed method succeeds for a wide range of signal-to-noise ratios and for deterministic as well as probabilistic observers. Furthermore, the detailed decision rules of the simulated observers were reconstructed from the estimated linear model weights allowing predictions of responses on the basis of individual stimuli.     

Adaptive wavelet methods for the stochastic Poisson equation

We study the Besov regularity as well as linear and nonlinear approximation of random functions on bounded Lipschitz domains in ? ^d . The random functions are given either (i) explicitly in terms of a wavelet expansion or (ii) as the solution of a Poisson equation with a right-hand side in terms of a wavelet expansion. In the case (ii) we derive an adaptive wavelet algorithm that achieves the nonlinear approximation rate at a computational cost that is proportional to the degrees of freedom. These results are matched by computational experiments.     

Electronic laboratory notebook: the academic point of view

Based on a requirement analysis and alternative design considerations, a platform-independent electronic laboratory notebook (ELN) has been developed that specifically targets academic users. Its intuitive design and numerous productivity features motivate chemical researchers and students to record their data electronically. The data are stored in a highly structured form that offers substantial benefits over laboratory notebooks written on paper with regard to data retrieval, data mining, and exchange of results.     

Optical sensors with molecularly imprinted nanospheres: a promising approach for robust and label-free detection of small molecules

Molecularly imprinted nanospheres obtained by miniemulsion polymerization have been applied as the sensitive layer for label-free direct optical sensing of small molecules. Using these particles as the sensitive layer allowed for improving response times in comparison to sensors using MIP layers. As a model compound, well-characterized nanospheres imprinted against l-Boc-phenylalanine anilide (l-BFA) were chosen. For immobilization, a simple concept based on electrostatic adsorption was used, showing its applicability to different types of surfaces, leading to a good surface coverage. The sensor showed short response times, good selectivity, and high reversibility with a limit of detection down to 60 μM and a limit of quantitation of 94 μM. Furthermore, reproducibility, selectivity, and long-term stability of the sensitive layers were tested. The best results were achieved with an adsorption on aminopropylsilane layers, showing a chip-to-chip reproducibility of 22%. Furthermore, the sensors showed no loss in signal after a storage time of 1 year.     

High‐energy transmission Laue micro‐beam X‐ray diffraction: a probe for intra‐granular lattice orientation and elastic strain in thicker samples

An understanding of the mechanical response of modern engineering alloys to complex loading conditions is essential for the design of load‐bearing components in high‐performance safety‐critical aerospace applications. A detailed knowledge of how material behaviour is modified by fatigue and the ability to predict failure reliably are vital for enhanced component performance. Unlike macroscopic bulk properties (e.g. stiffness, yield stress, etc.) that depend on the average behaviour of many grains, material failure is governed by `weakest link'‐type mechanisms. It is strongly dependent on the anisotropic single‐crystal elastic–plastic behaviour, local morphology and microstructure, and grain‐to‐grain interactions. For the development and validation of models that capture these complex phenomena, the ability to probe deformation behaviour at the micro‐scale is key. The diffraction of highly penetrating synchrotron X‐rays is well suited to this purpose and micro‐beam Laue diffraction is a particularly powerful tool that has emerged in recent years. Typically it uses photon energies of 5–25 keV, limiting penetration into the material, so that only thin samples or near‐surface regions can be studied. In this paper the development of high‐energy transmission Laue (HETL) micro‐beam X‐ray diffraction is described, extending the micro‐beam Laue technique to significantly higher photon energies (50–150 keV). It allows the probing of thicker sample sections, with the potential for grain‐level characterization of real engineering components. The new HETL technique is used to study the deformation behaviour of individual grains in a large‐grained polycrystalline nickel sample during in situ tensile loading. Refinement of the Laue diffraction patterns yields lattice orientations and qualitative information about elastic strains. After deformation, bands of high lattice misorientation can be identified in the sample. Orientation spread within individual scattering volumes is studied using a pattern‐matching approach. The results highlight the inability of a simple Schmid‐factor model to capture the behaviour of individual grains and illustrate the need for complementary mechanical modelling.     

Transport properties of graphene functionalized with molecular switches

We provide a theory of the electronic transport properties of a graphene layer functionalized with molecular switches. Our considerations are motivated by the spiropyran-merocyanine system which is non-polar in its ring-closed spiropyran form and zwitterionic in its ring-open merocyanine form. The reversible switching between these two isomers affects the carriers in graphene through the associated change in the molecular dipole moment, turning the graphene layer into a sensor of the molecular switching state. We present results for both the quasiclassical (Boltzmann) and the quantum coherent regimes of transport. Quite generally, we find a linear sensitivity of the conductance on the molecular dipole moment whenever quantum interference effects play an essential role which contrasts with a quadratic (and typically weaker) dependence when quantum interference is absent.