Rattling modes and the intrinsic vibrational spectrum of beetle-type scanning tunneling microscopes

It is known that the vibrational spectra of beetle-type scanning tunneling microscopes with a total mass of ≈3–4 g contain extrinsic ‘rattling’ modes in the frequency range extending from 500 to 1700 Hz that interfere with image acquisition. These modes lie below the lowest calculated eigenfrequency of the beetle and it has been suggested that they arise from the inertial sliding of the beetle between surface asperities on the raceway. In this paper we describe some cross-coupling measurements that were performed on three home-built beetle-type STMs of two different designs. We provide evidence that suggests that for beetles with total masses of 12–15 g all the modes in the rattling range are intrinsic. This provides additional support for the notion that the vibrational properties of beetle-type scanning tunneling microscopes can be improved by increasing the contact pressure between the feet of the beetle and the raceway.

Kinetic analysis of nitroxide radical coupling reactions mediated by CuBr

In a previous paper, we described the room temperature rapid, selective, reversible, and near quantitative Cu-activated nitroxide radical coupling (NRC) technique to prepare 3-arm polystyrene stars. In this work, we evaluated the Cu-activation mechanism, either conventional atom transfer or single electron transfer (SET), through kinetic simulations. Simulation data showed that one can describe the system by either activation mechanism. We also found through simulations that bimolecular radical termination, regardless of activation mechanism, was extremely low and could be considered negligible in an NRC reaction. Experiments were carried out to form 2- and 3-arm PSTY stars using two ligands, PMDETA and Me6TREN, in a range of solvent conditions by varying the ratio of DMSO to toluene, and over a wide temperature range. The rate of 2- or 3-arm star formation was governed by the choice of solvent and ligand. The combination of Me6TREN and toluene/DMSO showed a relatively temperature independent rate, and remarkably reached near quantitative yields for 2-arm star formation after only 1 min at 25 °C.

Ultrafast and reversible multiblock formation by the SET-Nitroxide radical coupling reaction

The single electron transfer-nitroxide radical coupling (SET-NRC) reaction has been used to produce multiblock polymers with high molecular weights in under 3 min at 50◦C by coupling a difunctional telechelic polystyrene (Br-PSTY-Br)with a dinitroxide. The well known combination of dimethyl sulfoxide as solvent and Me6TREN as ligand facilitated the in situ disproportionation of CuIBr to the highly active nascent Cu0 species. This SET reaction allowed polymeric radicals to be rapidly formed from their corresponding halide end-groups. Trapping of these carbon-centred radicals at close to diffusion controlled rates by dinitroxides resulted in high-molecular-weight multiblock polymers. Our results showed that the disproportionation of CuI was critical in obtaining these ultrafast reactions, and confirmed that activation was primarily through Cu0. We took advantage of the reversibility of the NRC reaction at elevated temperatures to decouple the multiblock back to the original PSTY building block through capping the chain-ends with mono-functional nitroxides. These alkoxyamine end-groups were further exchanged with an alkyne mono-functional nitroxide (TEMPO–≡) and ‘clicked’ by a CuI-catalyzed azide/alkyne cycloaddition (CuAAC) reaction with N3–PSTY–N3 to reform the multiblocks. This final ‘click’ reaction, even after the consecutive decoupling and nitroxide-exchange reactions, still produced high molecular-weight multiblocks efficiently. These SET-NRC reactions would have ideal applications in re-usable plastics and possibly as self-healing materials.

Rapid, selective, and reversible Nitroxide Radical Coupling (NRC) reactions at amibient temperature

High activation of polystyrene with bromine end groups (PSTY-Br) to their incipient radicals occurred in the presence of Cu(I)Br, Me6TREN, and DMSO solvent. These radicals were then trapped by nitroxide species leading to coupling reactions between PSTY-Br and nitroxides that were ultrafast and selective in the presence of a diverse range of functional groups. The nitroxide radical coupling (NRC) reactions have the attributes of a “click” reaction with near quantitative yields of product formed, but through the reversibility of this reaction, it has the added advantage of permitting the exchange of chemical functionality on macromolecules. Conditions were chosen to facilitate the disproportionation of Cu(I)Br to the highly activating nascent Cu(0) and deactivating Cu(II)Br2 in the presence of DMSO solvent and Me6TREN ligand. NRC at room temperature gave near quantitative yields of macromolecular coupling of low molecular weight polystyrene with bromine chain-ends (PSTY-Br) and nitroxides in under 7 min even in the presence of functional groups (e.g., −≡, −OH, −COOH, −NH2, =O). Utilization of the reversibility of the NRC reaction at elevated temperatures allowed the exchange of chain-end groups with a variety of functional nitroxide derivatives. The robustness and orthogonality of this NRC reaction were further demonstrated using the Cu-catalyzed azide/alkyne “click” (CuAAC) reactions, in which yields greater than 95% were observed for coupling between PSTY-N3 and a PSTY chain first trapped with an alkyne functional TEMPO (PSTY-TEMPO-≡).

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

I know what you did this summer: Users' behavior on internet

Human age is surrounded by assumed set of rules and behaviors imposed by local culture and the society they live in. This paper introduces software that counts the presence of a person on the Internet and examines the activities he/she conducts online. The paper answers questions such as how "old" are you on the Internet? How soon will a newbie be exposed to adult websites? How long will it take for a new Internet user to know about social networking sites? And how many years a user has to surf online to celebrate his/her first "birthday" of Internet presence? Paper findings from a database of 105 school and university students containing their every click of first 24 hours of Internet usage are presented. The findings provide valuable insights for Internet Marketing, ethics, Internet business and the mapping of Internet life with real life. Privacy and ethical issues related to the study have been discussed at the end. © Springer Science+Business Media B.V. 2010.

Prey of the silver-headed antechinus (Antechinus argentus), a new species of Australian dasyurid marsupial

The silver-headed antechinus (Antechinus argentus) is one of Australia’s most recently described mammals, and the single known population at Kroombit Tops in south-east Queensland is threatened. Nothing is known of the species’ ecology, so during 2014 we collected faecal pellets each month (March–September) from a population at the type locality to gather baseline data on diet composition. A total of 38 faecal pellets were collected from 12 individuals (eight females, four males) and microscopic analysis of pellets identified seven invertebrate orders, with 70% combined mean composition of beetles (Coleoptera: 38%) and cockroaches (Blattodea: 32%). Other orders that featured as prey were ants, crickets/grasshoppers, butterflies/moths, spiders, and true bugs. Given that faecal pellets could only be collected from a single habitat type (Eucalyptus montivaga high-altitude open forest) and location, this is best described as a generalist insectivorous diet that is characteristic of other previously studied congeners.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.