Deliberately Losing Control of C−H Activation Processes in the Design of Small-Molecule-Fragment Arrays Targeting Peroxisomal Metabolism

Combined photochemical arylation, “nuisance effect” (S_NAr) reaction sequences have been employed in the design of small arrays for immediate deployment in medium-throughput X-ray protein–ligand structure determination. Reactions were deliberately allowed to run “out of control” in terms of selectivity; for example the ortho-arylation of 2-phenylpyridine gave five products resulting from mono- and bisarylations combined with S_NAr processes. As a result, a number of crystallographic hits against NUDT7, a key peroxisomal CoA ester hydrolase, have been identified.     

Glycerolysis of Fatty Acid Methyl Esters: 1. Investigations in a Batch Reactor

The present work focused on the glycerolysis of fatty acid methyl esters. The aim was to develop and test a kinetic model that could be used to reliably simulate different process alternatives for this reaction. A prerequisite was the identification and characterization of the factors that affect the reaction kinetics. Experiments were carried out in a batch reactor with and without forced removal of methanol, which is one of the reaction products. Concentrations of all components in the two-phase system were measured. It was found that the methanol concentration has a strong effect on the reaction rate and equilibrium conversion. Near-complete conversions were obtained by stripping methanol with an inert gas. The glycerol concentration in the ester phase was found to increase as the reaction proceeds, which also accelerates the reaction. Effects of mass transfer on the reaction rate were not found to control the reaction rate under well-agitated conditions. A semi-empirical model was used to simulate the reaction. The results from the semi-empirical model show good agreement with experimental results.     

CD73 Overexpression in Podocytes: A Novel Marker of Podocyte Injury in Human Kidney Disease

The CD73 pathway is an important anti-inflammatory mechanism in various disease settings. Observations in mouse models suggested that CD73 might have a protective role in kidney damage; however, no direct evidence of its role in human kidney disease has been described to date. Here, we hypothesized that podocyte injury in human kidney diseases alters CD73 expression that may facilitate the diagnosis of podocytopathies. We assessed the expression of CD73 and one of its functionally important targets, the C-C chemokine receptor type 2 (CCR2), in podocytes from kidney biopsies of 39 patients with podocytopathy (including focal segmental glomerulosclerosis (FSGS), minimal change disease (MCD), membranous glomerulonephritis (MGN) and amyloidosis) and a control group. Podocyte CD73 expression in each of the disease groups was significantly increased in comparison to controls (p < 0.001–p < 0.0001). Moreover, there was a marked negative correlation between CD73 and CCR2 expression, as confirmed by immunohistochemistry and immunofluorescence (Pearson r = −0.5068, p = 0.0031; Pearson r = −0.4705, p = 0.0313, respectively), thus suggesting a protective role of CD73 in kidney injury. Finally, we identify CD73 as a novel potential diagnostic marker of human podocytopathies, particularly of MCD that has been notorious for the lack of pathological features recognizable by light microscopy and immunohistochemistry.     

Ultrasonic Fabrication of Polymer Plate Reactors with a Surface Coating

Microreactors offer several advantages compared to the industrial scale when developing new chemical processes. Especially the production and investment costs are low if polymer microreactors are generated by ultrasonic processes. In order to observe the chemical reaction and the flow configuration, these microreactors need to be optically transparent, mechanically stable, and chemically inert to several reagents. The manufacturing process of a transparent polymer plate reactor with a chemically inert surface coating by ultrasonic fabrication is described. Experimental characterization of the microreactors showed that they are leak tight up to a pressure difference of at least 300 kPa and the mixing times are in the range of milliseconds.     

Spontaneous polymerization and chain microstructure evolution in high-temperature solution polymerization of n-butyl acrylate

This study concerns understanding of the underlying mechanistic pathways in high temperature solution polymerization of n-butyl acrylate (nBA) in the absence of added thermal initiators. The particular system of interest is the batch polymerization of nBA in xylene at temperatures between 140 and 180 °C with initial monomer content between 20 and 40 wt%. A mechanistic process model is developed to capture the dynamics of the polymerization system. Postulated reaction mechanisms include chain-initiation by monomer (self-initiation), chain-initiation by unknown impurities, chain-propagation by secondary and tertiary radicals, intra-molecular chain-transfer to polymer (back-biting), chain-fragmentation (β-scission), chain-transfer to monomer and solvent, and chain termination by disproportionation and combination. The extent of the reactions is quantified by estimating the reaction rate constants of the initiation and the secondary reactions, based on a set of process measurements. The set of measurements considered in the parameter estimation includes monomer conversion, number- and weight-average molecular weights, and average number of chain-branches per chain (CBC). Effect of temperature on chain microstructures was observed to be most evident when microstructures are expressed in terms of their quantities per chain. The evolution of other microstructural quantities such as average number of terminal double bonds per chain (TDBC) and average number of terminal solvent groups per chain (TSGC) was then also investigated. Microstructural quantities per polymer chain (TDBC, TSGC, CBC) are defined based on combinations of ¹³C, ¹H NMR and chromatographic measurements. This study presents (i) a mechanistic explanation for the competing nature of short-chain-branch and terminal double bond formation (i.e. as temperature increases, number of chain branches per chain decreases and number of terminal double bonds per chain increases), (ii) quantitative insights into dominant modes of chain-initiation and chain-termination reactions, and (iii) mechanistic explanations for the observed spontaneous polymerization. The study also reports estimated Arrhenius parameters for second-order self initiation, tertiary radical propagation, secondary radical backbiting and tertiary radical β-scission reaction rate constants. Validation of the mechanistic process model with the estimated Arrhenius parameters and comparison of estimated parameter values to recently reported estimates are also presented.     

电力市场环境下发电公司风险管理框架

在电力市场环境下,一方面,市场价格波动剧烈,发电公司在追求利润最大化的同时必须考虑相应的交易风险;另一方面,市场为发电公司提供了多种交易途径,以便其进行风险管理.因此,充分利用多种交易途径,为发电公司建立一个合适的风险管理框架是必需的,也是可能的.借鉴金融界广泛采用的风险管理方法,针对现有电力市场中各种交易途径及相应的风险,提出了一个用于发电公司电力交易的分层的风险管理框架.该框架包括目标及条件确认、风险控制和风险评估3个方面.其中,风险控制应用了风险规避及现代投资组合理论,风险评估采用VaR(value at risk)方法.该风险管理框架有助于发电公司明确交易目标,并制定计及风险因素的电能交易计划,从而在最大限度上降低整体交易风险,或者在发电公司可以承受的风险范围内使其利润最大化,即达到风险管理的目的--利润最大化和风险最小化.     

Evaluation of the mechanical behavior of W7-X central support connections by means of semi-automated FE analysis

The stellarator Wendelstein 7-X is under construction at the Max-Planck-Institut für Plasmaphysik in Greifswald. Its superconducting coil system is fixed by a massive structure. During machine operation the coils exert high forces and moments against each other and the central support structure (CSS). Therefore, the detailed analysis of the coil to CSS connections, the so-called central support elements (CSE), is a critical item. The major details of the design have been frozen; nevertheless, there is still need for detailed analysis of the CSEs due to assembly issues, and later on for exploring operational limits of the machine. These analyses have to be performed quickly, reliably, and shall provide results in a standardized form to enable timely responses to the assembly team. Special numerical tools – finite element (FE) parametric models of CSEs – have been developed for the purpose of such analyses. In the models, the geometry, material properties, contact conditions, loads as well as results presentation are defined in a parametric way. The use of the developed models for the definition of the final weld parameters, bolt preloads, assessment of acceptable tolerances, and optimal positions of the CSE-wedges before welding is also discussed.     

New concepts for lithium minerals processing

A new concept for the enrichment of lithium minerals is offering higher purity and higher yield by applying new processing technologies addressing the specific properties of the pegmatitic host rock. Additional value is created from high quality by-products which will improve the sustainability of mining activity and the overall cost base. Within the new concept, flotation may even be avoided where wet separation techniques are not applicable.Two innovative processing technologies have been integrated in the new concept: electrodynamic fragmentation and optical sorting. Detailed results of lab-technical tests show that the new process design can achieve highly selective liberation and separation processes. Optical and mineralogical analyses of the test products indicate a high benefit for commercial applications.     

Modelling time-varying higher moments with maximum entropy density

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model of Engle [R. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica 50 (1982) 987–1007], the literature of modelling the conditional second moment has become increasingly popular in the last two decades. Many extensions and alternate models of the original ARCH have been proposed in the literature aiming to capture the dynamics of volatility more accurately. Interestingly, the Quasi Maximum Likelihood Estimator (QMLE) with normal density is typically used to estimate the parameters in these models. As such, the higher moments of the underlying distribution are assumed to be the same as those of the normal distribution. However, various studies reveal that the higher moments, such as skewness and kurtosis of the distribution of financial returns are not likely to be the same as the normal distribution, and in some cases, they are not even constant over time. These have significant implications in risk management, especially in the calculation of Value-at-Risk (VaR) which focuses on the negative quantile of the return distribution. Failed to accurately capture the shape of the negative quantile would produce inaccurate measure of risk, and subsequently lead to misleading decision in risk management. This paper proposes a solution to model the distribution of financial returns more accurately by introducing a general framework to model the distribution of financial returns using maximum entropy density (MED). The main advantage of MED is that it provides a general framework to estimate the distribution function directly based on a given set of data, and it provides a convenient framework to model higher order moments up to any arbitrary finite order k. However this flexibility comes with a high cost in computational time as k increases, therefore this paper proposes an alternative model that would reduce computation time substantially. Moreover, the sensitivity of the parameters in the MED with respect to the dynamic changes of moments is derived analytically. This result is important as it relates the dynamic structure of the moments to the parameters in the MED. The usefulness of this approach will be demonstrated using 5 min intra-daily returns of the Euro/USD exchange rate.     

Multicriterion genetic optimization for due date assigned distribution network problems

This paper focuses on the demand due date factor in multiechelon distribution network problems and its impact on the production scheduling in manufacturing plants. A reliable demand due date is critical in winning of customer orders. However, this may usually require high collaboration among entities in the network. Mismatching of one single schedule may seriously influence the reliability. In this connection, holistically optimizing the schedule of each entity among the network is essential. In addition, on time delivery may induce high operating cost. A trade-off between earliness, on time, and tardiness should also be considered. Hence, a multicriterion genetic optimization methodology is developed to holistically optimize them. It determines the optimized schedule to collaborate each entity to fulfill the demands. For enabling multicriterion decision-making, the proposed algorithm combines analytic hierarchy process with genetic algorithms (GAs). The problem is divided into two parts—(i) demand allocation and transportation problem, and (ii) production scheduling problem. The optimization approach is applied to iteratively optimize part (i), and then part (ii). Three experiments have been carried out, and the computation results show that the effect of due date is critical, and the ability of the proposed algorithms in taking trade-off between earliness and tardiness.