MILP-based approaches for medium-term planning of single-stage continuous multiproduct plants with parallel units

In this paper, we address the problem of medium-term planning of single-stage continuous multiproduct plants with multiple processing units in parallel. Sequence-dependent changeover times and costs occur when switching from one type of product to another. A traveling salesman problem (TSP)-based mixed-integer linear programming (MILP) model is proposed based on a hybrid discrete/continuous time representation. We develop additional constraints and variables to ensure that subtours do not occur in the solution. The model is successfully applied to an example of a polymer processing plant to illustrate its applicability. In order to solve larger model instances and planning horizons, a rolling horizon approach is developed to reduce the computational expense. Finally, the proposed model is compared to a recently published approach through literature examples, and the results show that the computational performance of the proposed model is superior.     

Fused dihydrofurans from the one-pot,three-component reaction of 1,3-cyclohexanedione,iodobenzene diacetate and alkenes

The one-pot, three-component reactions of substituted 1,3-cyclohexanediones, iodobenzene diacetate and alkenes, under photochemical activation, yields fused dihydrofuran derivatives in good yield via the in situ formation of iodonium ylides.     

The reaction of phenyliodonium bis-(phenylsulfonyl)methylide with some thiophenes

The title iodonium ylide gives with thiophenes C-H insertion products of the C(SO₂Ph)₂ group and with benzothiophenes transylidation products.     

Absence of kinetic effects in reaction-diffusion processes in scale-free networks

We show that the chemical reactions of the model systems of A+A-->0 and A+B-->0 when performed on scale-free networks exhibit drastically different behavior as compared to the same reactions in normal spaces. The exponents characterizing the density evolution as a function of time are considerably higher than 1, implying that both reactions occur at a much faster rate. This is due to the fact that the discerning effects of the generation of a depletion zone (A+A) and the segregation of the reactants (A+B) do not occur at all as in normal spaces. Instead we observe the formation of clusters of A (A+A reaction) and of mixed A and B (A+B reaction) around the hubs of the network. Only at the limit of very sparse networks is the usual behavior recovered.     

A review of fractality and self-similarity in complex networks

We review recent findings of self-similarity in complex networks. Using the box-covering technique, it was shown that many networks present a fractal behavior, which is seemingly in contrast to their small-world property. Moreover, even non-fractal networks have been shown to present a self-similar picture under renormalization of the length scale. These results have an important effect in our understanding of the evolution and behavior of such systems. A large number of network properties can now be described through a set of simple scaling exponents, in analogy with traditional fractal theory.     

Scaling of degree correlations and its influence on diffusion in scale-free networks

Connectivity correlations play an important role in the structure of scale-free networks. While several empirical studies exist, there is no general theoretical analysis that can explain the largely varying behavior of real networks. Here, we use scaling theory to quantify the degree of correlations in the particular case of networks with a power-law degree distribution. These networks are classified in terms of their correlation properties, revealing additional information on their structure. For instance, the studied social networks and the Internet at the router level are clustered around the line of random networks, implying a strongly connected core of hubs. On the contrary, some biological networks and the WWW exhibit strong anticorrelations. The present approach can be used to study robustness or diffusion, where we find that anticorrelations tend to accelerate the diffusion process.     

A simple 1H NMR method for determining the σ-donor properties of N-heterocyclic carbenes

The σ-donor properties of NHC ligands (NHC?=?N-heterocyclic carbene) are crucial in controlling their interaction with transition metals, and as a consequence, to determine the selectivity and reactivity of NHCs in transition-metal-catalysis. Herein, we report a simple NMR method for estimating the σ-donor properties of NHC ligands based on a straightforward ¹H NMR measurement of ligand precursors. We present evaluation of σ-donating properties for a range of NHC ligands varied by structure and electronics that are relevant to transition-metal-catalysis. We expect that the simple measurement of σ-donating properties of NHCs, together with the known methods for evaluating sterics and π-backbonding, will enhance the understanding of the properties of NHCs in transition-metal-catalysis.     

Dielectric properties and molecular mobility of organic/inorganic polymer composites

Microstructure-dielectric properties relationship and molecular mobility of organic/inorganic polymer composites (OIPCs), consisting of polyurethane (PU) and sodium silicate (NaSi), were investigated in this work. Broadband dielectric relaxation spectroscopy (DRS) and thermally stimulated depolarization current (TSDC) techniques were employed. Our interest was focused on the study of the glass transition mechanism and conductivity relaxation. The influence of the molecular weight of PU and inorganic phase content on the dielectric properties of the composites was of particular interest. Glass transition temperature shifts to higher temperatures with the addition of NaSi. The overall molecular mobility was found to increase in the composites, compared to the pure PU matrix. The results are more intense for the composites based on the PU with low molecular weight.     

Electrical properties of carbon black‐filled polymer composites

This work deals with the dielectric properties of conductive composite materials, which consist of thermoplastic polypropylene (PP) matrix filled with carbon black (CB). The CB concentration was systematically varied in a wide range. Our main interest is focused on the investigation of electrical conductivity mechanism and related percolation phenomena in these materials. To study the electrical and dielectric properties of composites we used broadband ac dielectric relaxation spectroscopy (DRS) techniques in a wide temperature range. By measurements of complex dielectric permittivity, ϵ*, the dependence of ac conductivity, σ_ac, and dc conductivity, σ_dc, on the frequency, the temperature and the concentration of the conductive filler was investigated. The behavior of this system is described by means of percolation theory. The percolation threshold, P_C, value was calculated to be 6.2 wt.% CB. Both, dielectric constant and dc conductivity follow power‐law behavior, yielding values for the critical exponents, which are in good agreement with the theoretical ones. Indications for tunneling effect in the charge carriers transport through the composites are presented. The temperature dependence of dc conductivity gives evidence for the presence of positive temperature coefficient (PTC) effect.     

Detecting drug promiscuity using gaussian ensemble screening

Polypharmacology describes the binding of a ligand to multiple protein targets (a promiscuous ligand) or multiple diverse ligands binding to a given target (a promiscuous target). Pharmaceutical companies are discovering increasing numbers of both promiscuous drugs and drug targets. Hence, polypharmacology is now recognized as an important aspect of drug design. Here, we describe a new and fast way to predict polypharmacological relationships between drug classes quantitatively, which we call Gaussian Ensemble Screening (GES). This approach represents a cluster of molecules with similar spherical harmonic surface shapes as a Gaussian distribution with respect to a selected center molecule. Calculating the Gaussian overlap between pairs of such clusters allows the similarity between drug classes to be calculated analytically without requiring thousands of bootstrap comparisons, as in current promiscuity prediction approaches. We find that such cluster similarity scores also follow a Gaussian distribution. Hence, a cluster similarity score may be transformed into a probability value, or "p-value", in order to quantify the relationships between drug classes. We present results obtained when using the GES approach to predict relationships between drug classes in a subset of the MDL Drug Data Report (MDDR) database. Our results indicate that GES is a useful way to study polypharmacology relationships, and it could provide a novel way to propose new targets for drug repositioning.