Catkin liked nano-Co3O4 catalyst built-in organic microreactor by PEMOCVD method for trace CO oxidation at room temperature

In this paper, tricobalt tetraoxide (Co₃O₄) catalyst was coated on the polydimethylsiloxane microchannel by the plasma-enhanced metal-organic chemical vapor deposition technology. The obtained Co₃O₄ film was characterized by SEM, XRD, XPS, and TEM, and the results show that the as-deposited Co₃O₄ film was initially composed of many cauliflowers-shaped microclusters. Also, the microcauliflower was transformed from an amorphous phase to a crystal phase when the Co₃O₄ film was treated by Ar and O₂ plasma for more than 20 min, and the crystal lattice line occurred on the surface of nano-sized-Co₃O₄ particles. Meanwhile, the interface of Co₃O₄ particles with diameter between 3 and 12 nm became obvious and some nano-catkin structures were also formed on the Co₃O₄ film. The ratio of Co³⁺/Co²⁺ in the spinel-type Co₃O₄ was nearly 2, and the nano-particles predominantly expose their {311}, {111}, and {220} planes. These morphologies and structure characteristics were found to be ideal for increasing the catalytic activity efficiency of Co₃O₄ for CO oxidation, and the catalytic stability of Co₃O₄ coated on the organic microreactor lasted nearly 85 h for trace CO oxidation at room temperature.     

Design methodology of piezoelectric energy-harvesting skin using topology optimization

This paper describes a design methodology for piezoelectric energy harvesters that thinly encapsulate the mechanical devices and exploit resonances from higher-order vibrational modes. The direction of polarization determines the sign of the piezoelectric tensor to avoid cancellations of electric fields from opposite polarizations in the same circuit. The resultant modified equations of state are solved by finite element method (FEM). Combining this method with the solid isotropic material with penalization (SIMP) method for piezoelectric material, we have developed an optimization methodology that optimizes the piezoelectric material layout and polarization direction. Updating the density function of the SIMP method is performed based on sensitivity analysis, the sequential linear programming on the early stage of the optimization, and the phase field method on the latter stage of the optimization to obtain clear optimal shapes without intermediate density. Numerical examples are provided that illustrate the validity and utility of the proposed method.     

Addressing universal access in social networks: an inclusive search mechanism

Social network services (SNSs) have brought new possibilities and challenges to the design of software environments that respect people’s cultural differences. These systems may represent an opportunity for social and digital inclusion. However, search mechanisms in these systems impose serious barriers for people in the process of acquiring digital literacy. One of the barriers is the difficulty of using the adequate terms/keywords to perform content searches. This paper presents an approach to allow ordinary, non-technology proficient people to access the content of a network through the use of search parameters that make sense to them. The proposal is grounded on Semantic Web technologies (Web ontology) combined with Organizational Semiotics concepts and methods to identify the users’ profile and language. A case study was conducted with the search mechanism integrated into a SNS, and a preliminary evaluation reveals the advantages and drawbacks of the approach.     

Variability for oil and fatty acid composition in castorbean varieties

Thirty-six castorbean varieties were surveyed for oil and fatty acid composition, in order to determine variability of these seed compounds. A large variability of seed oil percentage was observed, ranging from 39.6 to 59.5%. Concerning the fatty acids, little variability was observed for ricinoleic acid, which was the most abundant in the oil, ranging from 83.65 to 90.00%. The other fatty acids appeared in small concentrations and showed a small range: 0.87 to 2.35, 0.68 to 1.84, 2.96 to 5.64, 3.19 to 5.98, and 0.34 to 0.91%, for palmitic, stearic, oleic, linoleic, and linolenic acid, respectively. Non-significant correlations were observed between fatty acids and seed oil percentage. However, significant correlations were observed among fatty acid concentrations: positive and negative ones. These significant correlations could be associated with the biosynthetic pathways of the fatty acids, which are not fully elucidated. They suggest, however, that selection for a particular fatty acid will tend to increase those positively correlated, and decrease those negative ones. Selection and plant breeding techniques could then be applied to modify the oil content of the castorbean seeds, considering the variability observed. For the fatty acid composition, however, the variability was not large enough to make substantial changes in their concentrations by selection procedures. More varieties should be surveyed to find out if such variability is available.     

The metabolism of 1-acyl-PAF in rabbit platelets and its possible interaction with PAF

The metabolism of 1-acyl-2-acetyl-sn-glycero-3-phosphocholine (1-acyl-PAF), a naturally occurring analogue of platelet activating factor (PAF), was investigated in rabbit platelets. Our studies showed that 1-acyl-[³H]PAF (1-palmitoyl-2-acetyl-sn-glycero-3-phospho[N-methyl-³H]-choline) was converted by platelets into phosphatidyl-[³H]choline ([³H]PC) in a time-dependent fashion. The formation of [³H]PC occurred at a rate similar to that observed when lyso-[³H]PC (palmitoyl-sn-glycero-3-phospho[N-methyl-³H]choline) was used as substrate. In addition, a time-dependent increase in the level of water-soluble radioactivity was observed during the incubation of platelets with either 1-acyl-[³H]PAF or lyso-[³H]PC. This increase was parallel to the formation of [³H]PC and was not observed in the presence of [¹⁴C]PAF (1-octadecyl-2-acetyl-sn-glycerol-3-phospho[N methyl-¹⁴C]choline). Analysis by thin-layer chromatography showed that the soluble radioactivity was mainly associated with glycerophosphocholine (GPC). On the other hand, the preincubation of platelets with phenylmethylsulfonyl fluoride, an inhibitor of the acetylhydrolase, reduced the hydrolysis of 1-acyl-[³H]PAF to [³H]GPC with a concomitant accumulation of radioactivity in 1-acyl-PAF. These findings suggest that 1-acyl-PAF is converted into PC through deacetylation-reacylation with lysoPC as an obligatory intermediate. The findings also indicate that the lysoPC resulting from 1-acyl-PAF is either reacylated to phosphatidylcholine (PC) or hydrolyzed to GPC by lysophospholipase. Finally, we showed that the stimulation of platelets with PAF led to a time- and concentration-dependent increase in the conversion of 1-acyl-[³H]PAF to [³H]PC. The stimulatory effect of PAF was not observed when platelets were lysed before incubation, suggesting that PAF enhances the metabolism of 1-acyl-PAF, probably by accelerating its translocation through the plasma membrane.     

Wearable cognitive assistants in a factory setting: a critical review of a promising way of enhancing cognitive performance and well-being

Cognition, Technology & Work - Rapid technological innovations are constantly influencing the complexification and automatization of the work lines pushing human operators to use diverse...     

Sense representations for Portuguese: experiments with sense embeddings and deep neural language models

Sense representations have gone beyond word representations like Word2Vec, GloVe and FastText and achieved innovative performance on a wide range of natural language processing tasks. Although very useful in many applications, the traditional approaches for generating word embeddings have a strict drawback: they produce a single vector representation for a given word ignoring the fact that ambiguous words can assume different meanings. In this paper, we explore unsupervised sense representations which, different from traditional word embeddings, are able to induce different senses of a word by analyzing its contextual semantics in a text. The unsupervised sense representations investigated in this paper are: sense embeddings and deep neural language models. We present the first experiments carried out for generating sense embeddings for Portuguese. Our experiments show that the sense embedding model (Sense2vec) outperformed traditional word embeddings in syntactic and semantic analogies task, proving that the language resource generated here can improve the performance of NLP tasks in Portuguese. We also evaluated the performance of pre-trained deep neural language models (ELMo and BERT) in two transfer learning approaches: feature based and fine-tuning, in the semantic textual similarity task. Our experiments indicate that the fine tuned Multilingual and Portuguese BERT language models were able to achieve better accuracy than the ELMo model and baselines.

     

Genetic programming for QoS-aware web service composition and selection

Web services, which can be described as functionality modules invoked over a network as part of a larger application are often used in software development. Instead of occasionally incorporating some of these services in an application, they can be thought of as fundamental building blocks that are combined in a process known as Web service composition. Manually creating compositions from a large number of candidate services is very time consuming, and developing techniques for achieving this objective in an automated manner becomes an active research field. One promising group of techniques encompasses evolutionary computing, which can effectively tackle the large search spaces characteristic of the composition problem. Therefore, this paper proposes the use of genetic programming for Web service composition, investigating three variations to ensure the creation of functionally correct solutions that are also optimised according to their quality of service. A variety of comparisons are carried out between these variations and two particle swarm optimisation approaches, with results showing that there is likely a trade-off between execution time and the quality of solutions when employing genetic programming and particle swarm optimisation. Even though genetic programming has a higher execution time for most datasets, the results indicate that it scales better than particle swarm optimisation.     

Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.     

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.