Aggregation of larvae of the house cricket,Acheta domesticus (L.), by propionic acid present in the excreta

Acetic, propionic, butyric, isobutyric, valeric, and isovaleric acids have been identified in extracts of the excreta of the house cricket,Acheta domesticus (L.), by gas-liquid chromatography. Solutions of propionic acid applied to filter paper aggregated 1- to 2-week-old larvae, while solutions of acetic, butyric, valeric, and isovaleric acids were without effect.     

A comparison of pyrrolidide and picolinyl ester derivatives for the identification of fatty acids in natural samples by gas chromatography-mass spectrometry

The pyrrolidide and picolinyl ester derivatives of the fatty acids in two natural lipid samples rich in unsaturated fatty acids, pig testis lipids and cod liver oil were satisfactorily resolved on capillary columns of fused silica coated with stationary phases of varying polarity. The picolinyl esters, in particular, when subjected to gas chromatography-mass spectrometry on a column containing a cross-linked methyl silicone, gave distinctive mass spectra, which could be interpreted in terms of both the numbers and positions of the double bonds.     

Deep learning for graphics recognition: document understanding and beyond

International Journal on Document Analysis and Recognition (IJDAR) -     

High-temperature Na2SO4 interaction with air plasma sprayed Yb2Si2O7 + Si EBC system: Topcoat behavior

The high-temperature interaction between ~2.5 mg/cm² of Na₂SO₄ and an atmospheric plasma sprayed (APS) Yb₂Si₂O₇ topcoat–Si bond coat system on SiC CMC substrates was studied for times up to 240 h at 1000°C–1316°C in a 0.1% SO₂–O₂ gaseous environment. Yb₂Si₂O₇ reacted with Na₂SO₄ to form Yb₂SiO₅ and an intergranular amorphous Na-silicate phase. Below 1200°C, the reaction was sluggish, needing days to cause morphological changes to the “splat microstructure” associated with APS coatings. The reaction was rapid at 1200°C and above, needing only a few hours for the entire topcoat to transform into a granulated microstructure consisting of Yb₂SiO₅ and Yb₂Si₂O₇ phases. Na₂SO₄ deposits infiltrated the Yb₂Si₂O₇ topcoat and transformed into an amorphous Na-silicate in less than 1 h at all exposure temperatures. Quantitative assessment of the Yb₂SiO₅ area fraction in the topcoat showed a linear decrease over time at 1316°C, attributed to reaction with the SiO₂ thermally grown oxide (TGO) formed on the Si bond coat and rapid transport through the interpenetrating amorphous Na-silicate grain boundary phase. It was predicted that nearly 2 weeks is needed for complete removal of Yb₂SiO₅ from the topcoat at 1316°C for a single applied loading of Na₂SO₄.     

Na2SO4 deposit-induced hot corrosion of SiC fibers relevant for SiC CMCs

Hi Nicalon, Hi Nicalon S, Sylramic, and Sylramic iBN SiC fibers were exposed to ~60 μg/cm² of Na₂SO₄ in a 0.1% SO₂/O₂ gaseous environment for times between 0.75 and 24 h at 1000°C. After exposure, the corrosion products were characterized using SEM, EDS, ICP-OES, TEM, and EFTEM to determine their high-temperature resistance to Na₂SO₄ and key reaction mechanisms. All SiC fiber types tested in this work exhibited little resistance to Na₂SO₄ deposit-induced attack relative to their behavior in dry O₂ environments. It was found that Hi-Nicalon displayed the least resistance to Na₂SO₄ deposit-induced attack due to excess carbon content resulting in the formation of a highly porous crystalline oxide and promotion of basic corrosion conditions. All fiber types formed a crystalline SiO₂ reaction product, either cristobalite or tridymite. Sylramic and Sylramic iBN formed a crystalline SiO₂ reaction layer containing TiO₂ needles due oxidation of TiB₂ particles. Additionally, Na₂SO₄ deposits resulted in pitting of all fiber surfaces.     

Standard goal programming with fuzzy hierarchies: a sequential approach

This paper proposes a pragmatic model for multi-objective decision-making processes involving clusters of objectives which have a decisional meaning for the decision maker (DM). We provide the DMs with a comfortable tool that allows them to express their preferences both by comparing criteria of the same cluster and via the comparison between the different clusters. In standard goal programming the importance of the goals is modeled by the introduction of preferential weights or/and the incorporation of pre-emptive priorities. However, in many cases the DM is not able to establish a precise preference structure. Even in the case of precise weights the solution does not match necessarily the relative weights or, in the case of precise pre-emptive priority, the result could be very restrictive. In order to overcome these drawbacks, in this paper the normalized unwanted deviations are interpreted in terms of achievement degrees of the goals and fuzzy relations are used to model the relative importance of the goals. Thus, we show how several methodologies from the fuzzy goal programming literature can be tailored for solving standard GP problems. We apply this new modeling to problems where there is a “natural” clustering between goals of the same class. We address this situation by solving two phases; in the first one each class is handled separately taking into account the hierarchy of their goals and, in the second phase, we integrate the results of the first phase and the imprecise hierarchy of the different classes. We formulate a new goal programming model called as sequential goal programming with fuzzy hierarchy model. Because many real situations involve decision making in this environment, our proposal can be a useful tool of broad application. A numerical example illustrates the methodology.     

Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Fuzzy clustering enables the simultaneous membership of objects in two or more clusters. This is particularly pertinent where time series are concerned, because very often patterns of time series change over time. Thus, a time series might belong to different clusters over different periods of time, in which case, crisp clustering is unable to capture this multi-cluster membership. In this paper, we adopt a Fuzzy C-Medoids approach to clustering time series based on autoregressive estimates of models fitted to the time series. We illustrate very good performance of this approach in a range of simulation studies. By means of two applications, we also show the usefulness of this clustering approach in the air pollution monitoring, by considering air pollution time series, i.e., CO time series, CO₂ time series and NO time series monitored on world and urban scales. In particular, we show that, by considering in the clustering process, the autoregressive representation of these air pollution time series, we are able to detect possible information redundancy in the monitoring networks and then, decreasing the number of monitoring stations, to reduce the monitoring costs and then to increase the monitoring efficiency of the networks.     

A prototype for cartographic human body analysis

A cartographic-oriented model uses algebraic map operations to perform spatial analysis of medical data relative to the human body. A prototype system uses 3D visualization techniques to deliver analysis results. A prototype implementation suggests the model might provide the basis for a medical application tool that introduces new information insight.