Search advertising placement strategy: Exploring the efficacy of the conventional wisdom

Search advertising is one of the most important forms of electronic commerce. While click-through rates are considered a key measure of search advertising effectiveness by search providers, brand attention can also be a valuable objective. Our paper reports on an experiment that investigated how search advertisement placement affected search users’ brand recall and recognition. The results showed that semantically associated search ads generated significantly higher levels of brand attention than contextually associated ones. Significant interaction effects were found among search ad position, keyword association, and search result quality. Implications for decision makers are discussed.     

Effect of La2CuO4 electrode area on potentiometric NOx sensor response and its implications on sensing mechanism

The intent of this work is to look at the effects of varying the La₂CuO₄ electrode area and the asymmetry between the sensing and counter electrode in a solid state potentiometric sensor with respect to NO_x sensitivity. NO₂ sensitivity was observed at 500-600 °C with a maximum sensitivity of ∼22 mV/decade [NO₂] observed at 500 °C for the sensor with a La₂CuO₄ electrode area of ∼30 mm². The relationship between NO₂ sensitivity and area is nearly parabolic at 500 °C, decreases linearly with increasing electrode area at 600 °C, and was a mixture of parabolic and linear behavior 550 °C. NO sensitivity varied non-linearly with electrode area with a minima (maximum sensitivity) of ∼−22 mV/decade [NO] at 450 °C for the sensor with a La₂CuO₄ electrode area of 16 mm². The behavior at 400 °C was similar to that of 450 °C, but with smaller sensitivities due to a saturation effect. At 500 °C, NO sensitivity decreases linearly with area.We also used electrochemical impedance spectroscopy (EIS) to investigate the electrochemical processes that are affected when the sensing electrode area is changed. Changes in impedance with exposure to NO_x were attributed to either changes in La₂CuO₄ conductivity due to gas adsorption (high frequency impedance) or electrocatalysis occurring at the electrode/electrolyte interface (total electrode impedance). NO₂ caused a decrease in high frequency impedance while NO caused an increase. In contrast, NO₂ and NO both caused a decrease in the total electrode impedance. The effect of area on both the potentiometric and impedance responses show relationships that can be explained through the mechanistic contributions included in differential electrode equilibria.     

A photoluminescence-based quantum semiconductor biosensor for rapid in situ detection of Escherichia coli

This work describes a novel method of detecting Escherichia coli using photoluminescence (PL) emission from III–V quantum semiconductor (QS) devices functionalized with two different antibody-based architectures. The first approach employed self-assembled monolayers of biotinylated polyethylene glycol thiols to immobilize biotinylated antibody via neutravidin. In the second approach, we used QS microstructures coated with a thin layer of Si₃N₄ allowing direct functionalization with E. coli antibodies through hydrofluoric acid etching and glutaraldehyde-based reticulation. Atomic force, optical and fluorescence microscopy measurements were used to assess the immobilization process. Depending on the biosensing architecture, density of the immobilized bacteria was observed in the range of 0.5–0.7 bacteria/100 μm². The detection of E. coli at 10⁴ CFU/ml was achieved within less than 120 min of the bacteria exposure. It is expected that an even better sensitivity threshold could be achieved following further optimization of the method.     

Robot-Assisted Bridge Inspection

The Center for Robot-Assisted Search and Rescue (CRASAR®) deployed a customized AEOS man-portable unmanned surface vehicle and two commercially available underwater vehicles (the autonomous YSI EcoMapper and the tethered VideoRay) for inspection of the Rollover Pass bridge in the Bolivar peninsula of Texas in the aftermath of Hurricane Ike. A preliminary domain analysis with the vehicles identified key tasks in subsurface bridge inspection (mapping of the debris field and inspecting the bridge footings for scour), control challenges (navigation under loss of GPS, underwater obstacle avoidance, and stable positioning in high currents without GPS), possible improvements to human-robot interaction (having additional display units so that mission specialists can view and operate on imagery independently of the operator control unit, incorporating 2-way audio to allow operator and field personnel to communicate while launching or recovering the vehicle, and increased state sensing for reliability), and discussed the cooperative use of surface, underwater, and aerial vehicles. The article posits seven milestones in the development of a fully functional UMV for bridge inspection: standardize mission payloads, add health monitoring, improve teleoperation through better human-robot interaction, add 3D obstacle avoidance, improve station-keeping, handle large data sets, and support cooperative sensing.     

Setpoint regulation for stochastically interacting robots

We present an integral feedback controller that regulates the average copy number of an assembly in a system of stochastically interacting robots. The mathematical model for these robots is a tunable reaction network, which makes this approach applicable to a large class of other systems, including ones that exhibit stochastic self-assembly at various length scales. We prove that this controller works for a range of setpoints and how to compute this range both analytically and experimentally. Finally, we demonstrate these ideas on a physical testbed.     

Portfolios of buyer–supplier exchange relationships in an online marketplace for IT services

Most studies on the role of IT for economic exchange predicted that under a given set of exchange attributes buyers would choose a certain mode of relationship with suppliers. Our study of an online IT services marketplace revealed that buyers do not have a single, uniformly preferred type of relationship, but rather maintain a portfolio of relationships. Furthermore, different buyers arrange their portfolios of exchange relationships in different ways. We found four clusters of buyers' portfolios of relationships labeled Transactional buyers, Recurrent buyers, Small diversifiers and Large diversifiers, that differ in their usage of auction or negotiation mechanism, their supplier relations as well as their usage of preferred suppliers. Our results thus paint a richer picture of how buyers organize their supplier networks online.     

Discovery of sound sources by an autonomous mobile robot

In this work, we describe an autonomous mobile robotic system for finding, investigating, and modeling ambient noise sources in the environment. The system has been fully implemented in two different environments, using two different robotic platforms and a variety of sound source types. Making use of a two-step approach to autonomous exploration of the auditory scene, the robot first quickly moves through the environment to find and roughly localize unknown sound sources using the auditory evidence grid algorithm. Then, using the knowledge gained from the initial exploration, the robot investigates each source in more depth, improving upon the initial localization accuracy, identifying volume and directivity, and, finally, building a classification vector useful for detecting the sound source in the future.     

Evidential data mining: precise support and confidence

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.