Curtin-Hammett versus non-Curtin-Hammett frameworks is optimizing the enantioselective binolam/titanium(IV)-catalyzed cyanobenzoylation of aldehydes: Part 2

Extensive experimental and computational studies have been carried out on the enantioselective titanium(IV)-catalyzed cyanobenzoylation of aldehydes using 1:n Binolam:Ti(OiPr)4 mixtures as precatalysts, with the purpose of identifying the key mechanistic aspects governing enantioselectivity. HCN and isopropyl benzoate were detected in the reacting mixtures. This, as well as the reaction’s response to the presence of an exogenous base, and the failure to react in the presence of Binol:Ti(OiPr)4 mixtures, led us to propose not a direct cyanobenzoylation but an indirect process involving enantioselective hydrocyanation followed by O-benzoylation. Computational work provided positive evidence for the intervention of both indirect and direct cyanobenzoylation routes, the former being faster. However, the standard Curtin–Hammett-based optimization search ended with unsatisfactory results. Experimental and computational DFT studies (B3LYP/6-31G*) led us to conclude that: (1) the overall cyanobenzoylation of aldehydes catalyzed by 1:n Binolam:Ti(OiPr)4 mixtures involves an enantioselective hydrocyanation followed by an stereochemically inert O-benzoylation; (2) the initial complexes prevailing in a 1:1 Binolam:Ti(OiPr)4 mixture are the solvated mononuclear monomer 5·2(iPrOH) and solvated dinuclear dimer 9·2(iPrOH), whereas 9·2(iPrOH) is the major component in a 1:2 or higher 1:n mixture; (3) since the slowest step is that of benzoylation of ligated iPrOH which yields the actual catalysts 5–9, the catalytic system fits into a non-Curtin–Hammett framework, the final products deriving from a kinetic quench of the competing routes; and (4) accordingly, catalysis by 1:1 Binolam:Ti(OiPr)4 mixtures should involve cyanobenzoylations promoted by mononuclear 5, contaminated with those promoted by some dinuclear open dimer 9, whereas cyanobenzoylations catalyzed by a 1:2 and higher 1:n mixtures should be the result of catalysis promoted by the large amounts of dinuclear open dimer 9.

A model driven framework for geographic knowledge discovery

Geographic knowledge discovery (GKD) is the process of extracting information and knowledge from massive georeferenced databases. Usually the process is accomplished by two different systems, the Geographic Information Systems (GIS) and the data mining engines. However, the development of those systems is a complex task due to it does not follow a systematic, integrated and standard methodology. To overcome these pitfalls, in this paper, we propose a modeling framework that addresses the development of the different parts of a multilayer GKD process. The main advantages of our framework are that: (i) it reduces the design effort, (ii) it improves quality systems obtained, (iii) it is independent of platforms, (iv) it facilitates the use of data mining techniques on geo-referenced data, and finally, (v) it ameliorates the communication between different users.

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.

Pulse phase-coherent timing and spectroscopy of CXOU J164710.2−45521 outbursts

We present a long-term phase-coherent timing analysis and pulse-phase resolved spectroscopy for the two outbursts observed from the transient anomalous X-ray pulsar CXOU J164710.2−455216. For the first outburst we used 11 Chandra and XMM–Newton observations between 2006 September and 2009 August, the longest baseline yet for this source. We obtain a coherent timing solution with P = 10.61065583(4) s, Ṗ = 9.72(1) × 10−13 s s−1 and P̈ = –1.05(5) × 10−20 s s−2. Under the standard assumptions this implies a surface dipolar magnetic field of ∼1014 G, confirming this source as a standard B magnetar. We also study the evolution of the pulse profile (shape, intensity and pulsed fraction) as a function of time and energy. Using the phase-coherent timing solution we perform a phase-resolved spectroscopy analysis, following the spectral evolution of pulse-phase features, which hints at the physical processes taking place on the star. The results are discussed from the perspective of magnetothermal evolution models and the untwisting magnetosphere model. Finally, we present similar analysis for the second, less intense, 2011 outburst. For the timing analysis we used Swift data together with 2 XMM–Newton and Chandra pointings. The results inferred for both outbursts are compared and briefly discussed in a more general framework.

Direct Visual Servoing Framework based on Optimal Control for Redundant Joint Structures

This paper presents a new framework based on optimal control to define new dynamic visual controllers to carry out the guidance of any serial link structure. The proposed general method employs optimal control to obtain the desired behaviour in the joint space based on an indicated cost function which determines how the control effort is distributed over the joints. The proposed approach allows the development of new direct visual controllers for any mechanical joint system with redundancy. Finally, authors show experimental results and verifications on a real robotic system for some derived controllers obtained from the control framework.

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.