The Pupal Parasitoid Trichopria drosophilae Is Attracted to the Same Yeast Volatiles as Its Adult Host

Journal of Chemical Ecology - There is increasing evidence that microorganisms, particularly fungi and bacteria, emit volatile compounds that mediate the foraging behaviour of insects and therefore...     

The biological leaching of an auriferous pyrite ore

The oxidation of an auriferous pyrite ore sample was evaluated in biological leaching experiments for subsequent gold recovery via cyanidation. In batch cultures, organisms derived from the mine site oxidized pyrite and ferrous iron at pH values as low as pH 0.6. The recovery of gold was variable in shake flask experiments. In stirred tank bioreactor leaching, gold recovery was proportional to the extent of iron dissolution by bioleaching. The leaching of arsenic from the sample was also directly proportional to iron dissolution.     

The Inhibitive Effect of Traces of SO2 on the Oxidation of Iron

The oxidation of Fe was investigated at 500–700°C in the presence of O₂ with 0–1000 ppm SO₂. The exposures were carried out in a thermobalance and lasted for 24 h. The oxidized samples were investigated by grazing-angle XRD, SEM/EDX, GDOES and XPS. The rate of oxidation of pure iron is slowed down by traces of O₂ in O₂ below 600°C while SO₂ has no effect on oxidation rate at higher temperatures. Exposure to SO₂<600°C resulted in the formation of small amounts of sulfate at the gas/oxide interface. In addition, sulfur, probably sulfide, accumulated at the metal/oxide interface. The influence of SO₂ on oxidation rate is attributed to surface sulfate. The sulfur distribution in the scale is rationalized in terms of the thermodynamic stability of compounds in the Fe–O–S system. Exposure to SO₂ caused the formation of hematite whiskers.     

Consequences of customer engagement behavior: when negative Facebook posts have positive effects

Sharing product information has become an integral part of today’s online social networking world. This research study addresses the effects of customer engagement behavior in online social networks on other consumers in order to understand how online social connections impact decision making. We investigate how different variations of a brand-related Facebook post trigger different response reactions. In particular, we analyze under which conditions negative posts can have positive consequences. The results of two online experiments set in a restaurant context suggest a difference when the user knows the restaurant brand. For users who are familiar with the restaurant brand, a positive effect of negative information posted by distant acquaintances is found with regard to the visiting intention of the user. The results of both experiments demonstrate that information posted by a close friend is perceived to be more diagnostic. For users not familiar with the restaurant brand, negative posts from strong ties induce the highest diagnosticity levels.     

A new method for evaluating the subjective image quality of photographs: dynamic reference

The Dynamic Reference (DR) method has been developed for subjective image quality experiments in which original or undistorted images are unavailable. The DR method creates reference image series from test images. Reference images are presented to observers as a slide show prior to evaluating their quality. As the observers view the set of reference images, they determine the overall variation in quality within the set of test images. This study compared the performance of the DR method to that of the standardized absolute category rating (ACR) and paired comparison (PC) methods. We measured the performance of each method in terms of time effort and discriminability. The results showed that the DR method is faster than the PC method and more accurate than the ACR method. The DR method is especially suitable for experiments that require highly accurate results in a short time.     

Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.     

Two CEGAR-based approaches for the safety verification of PLC-controlled plants

In this paper we address the safety analysis of chemical plants controlled by programmable logic controllers (PLCs). We consider a specification of the control program of the PLCs, extended with the specification of the dynamic plant behavior. The resulting hybrid models can be transformed to hybrid automata, for which advanced techniques for reachability analysis exist. However, the hybrid automata models are often too large to be analyzed. We propose two counterexample-guided abstraction refinement (CEGAR) approaches to keep the size of the hybrid models moderate.     

Socially shared regulation of learning in CSCL: understanding and prompting individual- and group-level shared regulatory activities

The field of computer supported collaborative learning (CSCL) is progressing instrumentally and theoretically. Nevertheless, few studies examine the effectiveness and efficiency of CSCL with respect to cognitive, motivational, emotional, and social issues, despite the fact that the role of regulatory processes is critical for the quality of students’ engagement in collaborative learning settings. We review the four earlier lines in developing support in CSCL and show how there has been a lack of work to support individuals in groups to engage in, sustain, and productively regulate their own and the group’s collaborative processes. Our aim is to discuss how our conceptual work in socially shared regulation of learning (SSRL) contributes to effective and efficient CSCL, what tools are presently available, and what the implications of research on these tools are for future tool development.