Synthesis, characterization and crystal structure of a novel thiocyanate–ruthenium(II) complex

The cis-[Ru(dppb)(Me-bipy)(NCS)₂], dppb = 1,4-bis (diphenylphosphino)butane, Me-bipy = 4,4′-dimethyl-2,2′-bipyridine, and NCS = thiocyanate, was synthesized and characterized by spectroscopic and electrochemical techniques and its structure was determined by crystal X-ray analysis. The crystal structure reveals that the coordination geometry around the Ru(II) center is distorted octahedron where two molecules of thiocyanate are bonded to the ruthenium through nitrogen atom in cis orientation. The half-wave formal potential value E_1/2 = 0.8 V (versus Ag/AgCl) observed is considerable higher than that for the cis-[RuCl₂(dppb)(Me-bipy)] complex, E_1/2 = 0.6 V (versus Ag/AgCl), well illustrating the strong π-acceptor effect the NCS ligand toward the backbonding interaction with the Ru(II) metal center. The MLCT absorption bands of the thiocyanate complex present a higher molar absorptivity (about 12%) compared with the cis-[RuCl₂(dppb)(Me-bipy)] complex, in the same experimental conditions. These properties make the complex potentially promising for the photosensitization process.     

Two new aluminophosphates, IST-1 and IST-2: First examples of a dual templating role of water and methylamine in generating microporous structures

This study is aimed at exploring the ability of very small sized N-bearing molecules to generate and stabilize microporous aluminophosphates. Two new AlPO₄-n materials, called IST-1 and IST-2, have been obtained in aqueous media using, as main template, methylamine (MA), directly added, or generated in situ from methylformamide (MF) degradation. While IST-1 topology proved to be novel, IST-2 appears structurally related to AlPO₄-53(A). The obtained materials were characterized by powder XRD, TG/DSC, SEM and solid-state NMR. Tetraalkylammonium (TEA) cations were used as potential co-templates but only MA and water were found incorporated in the pore volumes of both structures, which argues for their true templating role. In IST-1, ¹³C solid-state NMR studies showed that half of MA species, presumably protonated, is H-bonded to framework oxygens while the other half surprisingly bonds directly to framework Al atoms. ¹³C NMR showed that only protonated MA occurs in IST-2 channels. TEA⁺ cations definitely do not play any specific template role. They indirectly favor the crystallization of IST-1 or IST-2 devoid from other crystalline or amorphous side phases, by interacting with part of the Al and P in solution and forming soluble [AlPO₄(OH)]–[TEA,HMA] complexes, substantially modifying the compositions of gels precursors to each phase during nucleation and/or growth steps. While both IST-1 and IST-2 crystallize from gels of similar initial compositions, it was demonstrated that the new MA/T ratio (T = Al or P) obtained after in situ complexation was the key parameter that specifically governs the crystallization of each phase.     

OpenCOPI: middleware integration for Ubiquitous Computing

In this paper, we present OpenCOPI (Open COntext Platform Integration), a Service-Oriented Architecture-based middleware platform that supports the integration of services provided by distinct sources, ranging from services offered by simple systems to more complex services provided by context-provision middleware. OpenCOPI offers selection and composition mechanisms to, respectively, select and compose services provided by different sources, considering applications of both Quality of Service and Quality of Context requirements. It also offers an adaptation mechanism that enables to adapt the application execution due to service failures, service quality fluctuation and user mobility. OpenCOPI allows the definition of applications in a higher abstraction level by the specification of a semantic workflow that contains abstract activities. This paper illustrates the use of OpenCOPI in an application from the Gas & Oil Industry and it also shows the evaluation of the main mechanisms of OpenCOPI: the service selection, composition, adaptation and workflow execution.     

Master batch approach for developing PVDF/EVA/CNT nanocomposites with co-continuous morphology and improved electrical conductivity

Conducting polymer composites constituted by co-continuous poly (vinylidene fluoride) (PVDF)/ ethylene- vinyl acetate copolymer (EVA) blends with multiwalled carbon nanotube (CNT) were prepared by melt mixing using different procedures. The effect of the master batch approach on the conductivity, morphology, mechanical, thermal and rheological properties of PVDF/EVA/CNT nanocomposites was compared with that based on one step mixing strategy. The selective extraction experiments revealed that CNT was preferentially localized in the EVA phase in all situations, even when PVDF@CNT master batch was employed. Nanocomposites prepared with EVA@CNT master batch displayed higher conductivity, whose value reached around 10⁻¹ S m⁻¹ with the addition of 0.56 vol% of CNT. The better electrical performance was attributed to the better distribution of the filler, as indicated by transmission electron microscopy and rheological behavior. The electrical and rheological behavior were also investigated as a function of the CNT content.     

A Closed-Loop Automatic Data-Mining Approach for Preventive Network Monitoring

As telecommunication networks grow in size and complexity, monitoring systems need to scale up accordingly. Alarm data generated in a large network are often highly correlated. These correlations can be explored to simplify the process of network fault management, by reducing the number of alarms presented to the network-monitoring operator. This makes it easier to react to network failures. But in some scenarios, it is highly desired to prevent the occurrence of these failures by predicting the occurrence of alarms before hand. This work investigates the usage of data mining methods to generate knowledge from historical alarm data, and using such knowledge to train a machine learning system, in order to predict the occurrence of the most relevant alarms in the network. The learning system was designed to be retrained periodically in order to keep an updated knowledge base.     

Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: Potential applications to cardiovascular disease and diabetes

Type 2 diabetes mellitus (T2DM) is an important risk factor for cardiovascular disease (CVD)—the leading cause of death in the United States. Yet not all subjects with T2DM are at equal risk for CVD complications; the challenge lies in identifying those at greatest risk. Therapies directed toward treating conventional risk factors have failed to significantly reduce this residual risk in T2DM patients. Thus newer targets and markers are needed for the development and testing of novel therapies. Herein we review two complementary MS-based approaches—mass spectrometric immunoassay (MSIA) and MS/MS as MRM—for the analysis of plasma proteins and PTMs of relevance to T2DM and CVD. Together, these complementary approaches allow for high-throughput monitoring of many PTMs and the absolute quantification of proteins near the low picomolar range. In this review article, we discuss the clinical relevance of the high density lipoprotein (HDL) proteome and Apolipoprotein A-I PTMs to T2DM and CVD as well as provide illustrative MSIA and MRM data on HDL proteins from T2DM patients to provide examples of how these MS approaches can be applied to gain new insight regarding cardiovascular risk factors. Also discussed are the reproducibility, interpretation, and limitations of each technique with an emphasis on their capacities to facilitate the translation of new biomarkers into clinical practice.     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

Influence of green creativity on organizations: A case study from the perspectives of leaders and subordinates

The sustainability framework has a strong influence on the policies, practices and procedures of companies. Green creativity plays a pivotal role in the development of initiatives and innovations for the environmental pillar of sustainability. Based on a survey applied to a Portuguese company with an accredited environmental management system (N = 146), the findings reveal that green creativity is influenced in different ways when analysed from distinct perspectives. The leader's green creativity is positively influenced by the subordinate's green self-efficacy and personal identification with the leader, whereas the subordinate's green creativity is positively influenced by their green passion and green self-efficacy, and the leaders' green creativity. This study shows that green creativity develops differentially according to distinct job positions and expectations that in practice must be complementary and synergistic for its effective development.     

Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease

Magnetic Resonance Materials in Physics, Biology and Medicine - Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small...