Bridging crack model for fracture of spruce

An analysis is presented of how cracking in wood can be predicted using fracture mechanics. In situ, real-time, scanning electron microscopy was used as a tool to estimate the physical mechanism of fracture in softwoods using end-tapered double cantilever beam specimens. It was found that bridging behind the crack tip is the main toughening mechanism, which contributes to non-linear wood behavior in the presence of stress concentrations. A new bridging crack model is presented that mimics the observed cracking mechanism. Intrinsic flaw size is found to correspond to the fiber length of spruce.     

Using Hashtags to Capture Fine Emotion Categories from Tweets

Detecting emotions in microblogs and social media posts has applications for industry, health, and security. Statistical, supervised automatic methods for emotion detection rely on text that is labeled for emotions, but such data are rare and available for only a handful of basic emotions. In this article, we show that emotion‐word hashtags are good manual labels of emotions in tweets. We also propose a method to generate a large lexicon of word–emotion associations from this emotion‐labeled tweet corpus. This is the first lexicon with real‐valued word–emotion association scores. We begin with experiments for six basic emotions and show that the hashtag annotations are consistent and match with the annotations of trained judges. We also show how the extracted tweet corpus and word–emotion associations can be used to improve emotion classification accuracy in a different nontweet domain. Eminent psychologist Robert Plutchik had proposed that emotions have a relationship with personality traits. However, empirical experiments to establish this relationship have been stymied by the lack of comprehensive emotion resources. Because personality may be associated with any of the hundreds of emotions and because our hashtag approach scales easily to a large number of emotions, we extend our corpus by collecting tweets with hashtags pertaining to 585 fine emotions. Then, for the first time, we present experiments to show that fine emotion categories such as those of excitement, guilt, yearning, and admiration are useful in automatically detecting personality from text. Stream‐of‐consciousness essays and collections of Facebook posts marked with personality traits of the author are used as test sets.     

Observations of asymmetric turbulent stirring in inner and marginal seas using satellite imagery

For the open ocean, it was already reported on the cyclonic (anticyclonic) asymmetry of appearance of eddies of a certain spatial scale. In this article, we scrutinize the ratios of mostly mesoscale cyclonic and anticyclonic eddies in a number of inner and marginal marine basins, namely in the Baltic, Black, North, and Western Mediterranean Seas. As research material, over 9700 eddy manifestations in the thermal infrared, visible-range, and radar satellite imagery were used. The analysis performed showed that in all the seas the typical and average values of the diameter of anticyclonic eddies were greater than those of cyclonic eddies. The main factor that defines the ratio between anticyclonic and cyclonic eddies in the basins under consideration was discovered to be the intensity of their surface currents. Thus, in the presence of a strong jet flow at the scales of about 2–4 baroclinic Rossby radii, the cyclonic eddy dominance typical for smaller eddies was replaced by the anticyclonic one. If strong jet streams were missing, as that typical of the Baltic Sea, cyclonic eddies were prevailing over the entire spectrum of eddy diameters.     

Determination of the allowable graphite amount of a composite electrode for an electric arc furnace

Distribution of heat loads along the length and perimeter of electrodes in terms of the nominal power of transformer, optical characteristics of surfaces taking part in heat exchange and geometric dimensions of the furnace and electrodes has been obtained on the basis of mathematical simulation of processes of heat transfer in working volume of an electric arc furnace, the screening of radiation by electrodes being taken into account. The results obtained give an opportunity of determining the minimal permissible length of the composite electrode graphite part for furnaces with various nominal power transformers. The minimal permissible length of electrode-consumed graphite part is in the range of 80–100 cm for an 100-t electric arc furnace with the transformer of nominal power equal to 75 or 90 MVA. Decrease of the length of electrode graphite part will lead to damage of cooled metal part due to water burn-out.     

Lanthanide NIR luminescence for telecommunications, bioanalyses and solar energy conversion

Present-day advanced technologies heavily rely on the exciting magnetic and spectroscopic properties of lanthanide ions. In particular, their ability to generate well-characterized and intense near-infrared (NIR) luminescence is exploited in any modern fiber-optic telecommunication network. In this feature article, we first summarize the whereabouts underlying the design of highly luminescent NIR molecular edifices and materials. We then focus on describing the main trends in three applications related to this spectral range: telecommunications, biosciences, and solar energy conversion. In telecommunications, efforts concentrate presently on getting easily processable polymer-based waveguide amplifiers. Upconversion nanophosphors emitting in the visible after NIR excitation are now ubiquitous in many bioanalyses while their application to bio-imaging is still in its early stages; however, highly sensitive NIR-NIR systems start to be at hand for both in vitro and in vivo imaging, as well as dual probes combining magnetic resonance and optical imaging. Finally, both silicon-based and dye-sensitized solar cells benefit from the downconversion and upconversion capabilities of lanthanide ions to harvest UV and NIR solar light and to boost the overall quantum efficiency of these next-generation devices.     

Modelling material flow using the Milk run and Kanban systems in the automotive industry

Material flow management refers to the analysis and specific optimization of the inventory‐production system. Material flow can be characterized as the organized flow of material in a production process with the required sequence determined by a technological procedure. The Milk run system assures the transportation of materials at the right time and in an optimal manner. It should be combined with the Kanban system to highlight when something is required in the production process. This paper presents biological swarm intelligence, in general, and a particular model, particle swarm optimization (PSO), for modelling material flow using a Milk run system supported by a Kanban system in the automotive industry. The aim of this study is to create a new model for the optimal number of trailers for one train and optimal number of containers in a tugger train system when the route time period has been defined. A new modified PSO approach for integrating inventory‐production in a unique optimization model is used. The major modification to the original PSO is using the capacity of a container instead of a velocity component. Each new Kanban trigger is checked, and the total timing for the Milk run delivery solution is calculated for the necessary raw material capacity for each shop floor.     

Bond-quality characterization of silicon-glass anodic bonding

A simple testing method is presented that allows the comparison of the bond quality for anodically bonded wafers. An array of parallel metal lines of predetermined thickness is formed on a glass wafer. The estimation of the bond quality can be performed by visual inspection after the bonding. This method enables comparison of the anodic-bonding process performance for different glasses, for intermediate layers and various bonding conditions. The optimization of silicon-glass anodic bonding with an intermediate phosphosilicate glass (PSG) layer is shown using this technique.     

Projective Visual Hulls

This article presents a novel method for computing the visual hull of a solid bounded by a smooth surface and observed by a finite set of cameras. The visual hull is the intersection of the visual cones formed by back-projecting the silhouettes found in the corresponding images. We characterize its surface as a generalized polyhedron whose faces are visual cone patches; edges are intersection curves between two viewing cones; and vertices are frontier points where the intersection of two cones is singular, or intersection points where triples of cones meet. We use the mathematical framework of oriented projective differential geometry to develop an image-based algorithm for computing the visual hull. This algorithm works in a weakly calibrated setting–-that is, it only requires projective camera matrices or, equivalently, fundamental matrices for each pair of cameras. The promise of the proposed algorithm is demonstrated with experiments on several challenging data sets and a comparison to another state-of-the-art method.