How Gordon Ramsay appeals to consumers : effects of self-concept clarity and celebrity meaning on celebrity endorsements

How do celebrities like Gordon Ramsay appeal to consumers? This article examines one explanation. We study how celebrities appeal to consumers in the context of celebrity chefs. We examine how a consumer's self-concept clarity (SCC) interacts with their perception of the meaning that a celebrity endorser possesses. An experiment comparing fictional ads endorsed by different celebrity chefs yields the surprising result that consumers with a clear sense of who they are (high-SCC consumers) are more influenced by an ad featuring a celebrity high in meaning (Ramsay), whereas low-SCC consumers are influenced to slightly higher levels by a celebrity with lower levels of celebrity meaning.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Limitations of a laboratory scale model in predicting optimal pilot scale conditions for dilute acid pretreatment of sugarcane bagasse

Pilot and industrial scale dilute acid pretreatment data can be difficult to obtain due to the significant infrastructure investment required. Consequently, models of dilute acid pretreatment by necessity use laboratory scale data to determine kinetic parameters and make predictions about optimal pretreatment conditions at larger scales. In order for these recommendations to be meaningful, the ability of laboratory scale models to predict pilot and industrial scale yields must be investigated. A mathematical model of the dilute acid pretreatment of sugarcane bagasse has previously been developed by the authors. This model was able to successfully reproduce the experimental yields of xylose and short chain xylooligomers obtained at the laboratory scale. In this paper, the ability of the model to reproduce pilot scale yield and composition data is examined. It was found that in general the model over predicted the pilot scale reactor yields by a significant margin. Models that appear very promising at the laboratory scale may have limitations when predicting yields on a pilot or industrial scale. It is difficult to comment whether there are any consistent trends in optimal operating conditions between reactor scale and laboratory scale hydrolysis due to the limited reactor datasets available. Further investigation is needed to determine whether the model has some efficacy when the kinetic parameters are re-evaluated by parameter fitting to reactor scale data, however, this requires the compilation of larger datasets. Alternatively, laboratory scale mathematical models may have enhanced utility for predicting larger scale reactor performance if bulk mass transport and fluid flow considerations are incorporated into the fibre scale equations. This work reinforces the need for appropriate attention to be paid to pilot scale experimental development when moving from laboratory to pilot and industrial scales for new technologies.

Frequency independence of ultrasound transit time spectroscopy

Recent studies have shown that ultrasound transit time spectroscopy (UTTS) is an alternative method to describe ultrasound wave propagation through complex samples as an array of parallel sonic rays. This technique has the potential to characterize bone properties including volume fraction and may be implemented in clinical systems to predict osteoporotic fracture risk. In contrast to broadband ultrasound attenuation, which is highly frequency dependent, we hypothesise that UTTS is frequency independent. This study measured 1 MHz and 5 MHz broadband ultrasound signals through a set of acrylic step-wedge samples. Digital deconvolution of the signals through water and each sample was applied to derive a transit time spectrum. The resulting spectra at both 1 MHz and 5 MHz were compared to the predicted transit time values. Linear regression analysis yields agreement (R2) of 99.23% and 99.74% at 1 MHz and 5 MHz respectively indicating frequency independence of transit time spectra.

Fast unscented transformation-based transient stability margin estimation incorporating uncertainty of wind generation

Intermittent generation from wind farms leads to fluctuating power system operating conditions pushing the stability margin to its limits. The traditional way of determining the worst case generation dispatch for a system with several semi-scheduled wind generators yields a conservative solution. This paper proposes a fast estimation of the transient stability margin (TSM) incorporating the uncertainty of wind generation. First, the Kalman filter (KF) is used to provide linear estimation of system angle and then unscented transformation (UT) is used to estimate the distribution of the TSM. The proposed method is compared with the traditional Monte Carlo (MC) method and the effectiveness of the proposed approach is verified using Single Machine Infinite Bus (SMIB) and IEEE 14 generator Australian dynamic system. This method will aid grid operators to perform fast online calculations to estimate TSM distribution of a power system with high levels of intermittent wind generation.

Mechanisms of cisplatin resistance: DNA repair and cellular implications

Cisplatin (cis-diamminedichloroplatinum (II)), is a platinum based chemotherapeutic employed in the clinic to treat patients with lung, ovarian, colorectal or head and neck cancers. Cisplatin acts to induce tumor cell death via multiple mechanisms. The best characterized mode of action is through irreversible DNA cross-links which activate DNA damage signals leading to cell death via the intrinsic mitochondrial apoptosis pathway. However, the primary issue with cisplatin is that while patients initially respond favorably, sustained cisplatin therapy often yields chemoresistance resulting in therapeutic failure. In this chapter, we review the DNA damage and repair pathways that contribute to cisplatin resistance. We also examine the cellular implications of cisplatin resistance that may lead to selection of subpopulations of cells within a tumor. In better understanding the mechanisms conferring cisplatin resistance, novel targets may be identified to restore drug sensitivity.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

Investigation of lignin biosynthesis in sugarcane for improved lignocellulosic ethanol production

As oil use increases at a rate unsustainable for the environment and unmatchable by current levels of oil production, a major shift towards renewable energy is necessary. By expanding the current knowledge of lignin biosynthesis and its manipulation in sugarcane, this PhD contributes to the production of economically viable second generation bioethanol, a fuel produced from plant biomass. The findings of this thesis contribute to the limited knowledge of lignin biosynthesis and deposition in sugarcane, and the application of biotechnology to produce sugarcane, and the resulting bagasse, with a modified cell wall. Reducing or modifying the lignin content in the cell wall of bagasse can reduce production costs and increase yields of bioethanol. This makes bioethanol more economically competitive with oil as an alternative energy source. A move to using bioethanol over fossil based transport fuels will have global economic and environmental benefits.

Frequent (flier) frustration and the dark side of word‐of‐web: exploring online dysfunctional behavior in online feedback forums

Purpose Following the perspective of frustration theory customer frustration incidents lead to frustration behavior such as protest (negative word‐of‐mouth). On the internet customers can express their emotions verbally and non‐verbally in numerous web‐based review platforms. The purpose of this study is to investigate online dysfunctional customer behavior, in particular negative “word‐of‐web” (WOW) in online feedback forums, among customers who participate in frequent‐flier programs in the airline industry. Design/methodology/approach The study employs a variation of the critical incident technique (CIT) referred to as the critical internet feedback technique (CIFT). Qualitative data of customer reviews of 13 different frequent‐flier programs posted on the internet were collected and analyzed with regard to frustration incidents, verbal and non‐verbal emotional effects and types of dysfunctional word‐of‐web customer behavior. The sample includes 141 negative customer reviews based on non‐recommendations and low program ratings. Findings Problems with loyalty programs evoke negative emotions that are expressed in a spectrum of verbal and non‐verbal negative electronic word‐of‐mouth. Online dysfunctional behavior can vary widely from low ratings and non‐recommendations to voicing switching intentions to even stronger forms such as manipulation of others and revenge intentions. Research limitations/implications Results have to be viewed carefully due to methodological challenges with regard to the measurement of emotions, in particular the accuracy of self‐report techniques and the quality of online data. Generalization of the results is limited because the study utilizes data from only one industry. Further research is needed with regard to the exact differentiation of frustration from related constructs. In addition, large‐scale quantitative studies are necessary to specify and test the relationships between frustration incidents and subsequent dysfunctional customer behavior expressed in negative word‐of‐web. Practical implications The study yields important implications for the monitoring of the perceived quality of loyalty programs. Management can obtain valuable information about program‐related and/or relationship‐related frustration incidents that lead to online dysfunctional customer behavior. A proactive response strategy should be developed to deal with severe cases, such as sabotage plans. Originality/value This study contributes to knowledge regarding the limited research of online dysfunctional customer behavior as well as frustration incidents of loyalty programs. Also, the article presents a theoretical “customer frustration‐defection” framework that describes different levels of online dysfunctional behavior in relation to the level of frustration sensation that customers have experienced. The framework extends the existing perspective of the “customer satisfaction‐loyalty” framework developed by Heskett et al.

Characterization of alluvial formation by stochastic modelling of paleo-fluvial processes: The concept and method

Modelling fluvial processes is an effective way to reproduce basin evolution and to recreate riverbed morphology. However, due to the complexity of alluvial environments, deterministic modelling of fluvial processes is often impossible. To address the related uncertainties, we derive a stochastic fluvial process model on the basis of the convective Exner equation that uses the statistics (mean and variance) of river velocity as input parameters. These statistics allow for quantifying the uncertainty in riverbed topography, river discharge and position of the river channel. In order to couple the velocity statistics and the fluvial process model, the perturbation method is employed with a non-stationary spectral approach to develop the Exner equation as two separate equations: the first one is the mean equation, which yields the mean sediment thickness, and the second one is the perturbation equation, which yields the variance of sediment thickness. The resulting solutions offer an effective tool to characterize alluvial aquifers resulting from fluvial processes, which allows incorporating the stochasticity of the paleoflow velocity.

Groundwater balance research to improve the accuracy of drawdown predictions in the Surat Basin, Queensland

Groundwater modelling studies rely on an accurate determination of inputs and outputs that make up the water balance. Often there is large uncertainty associated with estimates of recharge and unmetered groundwater use. This can translate to equivalent uncertainty in the forecasting of sustainable yields, impacts of extraction, and susceptibility of groundwater dependent ecosystems. In the case of Coal Seam Gas, it is important to characterise the temporal and special distribution of depressurisation in the reservoir and how this may or may not extend to the adjacent aquifers. A regional groundwater flow model has been developed by the Queensland Government to predict drawdown impacts due to Coal Seam Gas activities in the Surat basin. This groundwater model is undergoing continued refinement and there is currently scope to address some of the key areas of uncertainty including better quantification of groundwater recharge and unmetered groundwater extractions. Research is currently underway to improve the accuracy of estimates of both of these components of the groundwater balance in order to reduce uncertainty in predicted groundwater drawdowns due to CSG activities.

Design guidance for blast-resistant glazing

This paper reviews current design standards and test methods for blast-resistant glazing design and compares a typical design outcome with that from comprehensive finite-element (FE) analysis. Design standards are conservative and are limited to the design of relatively small glazed panels. Standard test methods are expensive, create environmental pollution, and can classify the hazard ratings of only smaller glazed panels. Here the design of a laminated glass (LG) panel is carried out according to an existing design standard, and then its performance is examined using comprehensive FE modeling and analysis. Finite-element results indicate that both glass panes crack, the interlayer yields with little damage, and the sealant joints do not fail for the designed blast load. This failure pattern satisfies some of the requirements for minimal hazard rating in the design standard. It is evident that interlayer thickness and material properties are important during the post-crack stage of an LG panel, but they are not accounted for in the design standards. The new information generated in this paper will contribute toward an enhanced blast design of LG panels.

Delay tolerant routing protocols for energy-neutral animal tracking

This paper investigates communication protocols for relaying sensor data from animal tracking applications back to base stations. While Delay Tolerant Networks (DTNs) are well suited to such challenging environments, most existing protocols do not consider the available energy that is particularly important when tracking devices can harvest energy. This limits both the network lifetime and delivery probability in energy-constrained applications to the point when routing performance becomes worse than using no routing at all. Our work shows that substantial improvement in data yields can be achieved through simple yet efficient energy-aware strategies. Conceptually, there is need for balancing the energy spent on sensing, data mulling, and delivery of direct packets to destination. We use empirical traces collected in a flying fox (fruit bat) tracking project and show that simple threshold-based energy-aware strategies yield up to 20% higher delivery rates. Furthermore, these results generalize well for a wide range of operating conditions.

Visible light-driven cross-coupling reactions at lower temperatures using a photocatalyst of palladium and gold alloy nanoparticles

Palladium (Pd)-catalyzed cross-coupling reactions are among the most important methods in organic synthesis. We report the discovery of highly efficient and green photocatalytic processes by which cross-coupling reactions, including Sonogashira, Stille, Hiyama, Ullmann, and Buchwald–Hartwig reactions, can be driven with visible light at temperatures slightly above room temperature using alloy nanoparticles of gold and Pd on zirconium oxide, thus achieving high yields. The alloy nanoparticles absorb visible light, and their conduction electrons gain energy, which is available at the surface Pd sites. Results of the density functional theory calculations indicate that transfer of the light excited electrons from the nanoparticle surface to the reactant molecules adsorbed on the nanoparticle surface activates the reactants. When the light intensity was increased, a higher reaction rate was observed, because of the increased population of photoexcited electrons. The irradiation wavelength also has an important impact on the reaction rates. Ultraviolet irradiation can drive some reactions with the chlorobenzene substrate, while visible light irradiation failed to, and substantially improve the yields of the reactions with the bromobenzene substrate. The discovery reveals the possibility of using low-energy and -density sources such as sunlight to drive chemical transformations.

Synthesis and luminescent properties of star-burst D-Pi-A compounds based on 1,3,5-triazine core and carbazole end-capped phenylene ethynylene arms

Two new star-burst compounds based on 1,3,5-triazine core and carbazole end-capped phenylene ethynylene arms (1a and 1b) were synthesized and characterized. Their photophysical properties were investigated systematically via spectroscopic and theoretical methods. Both compounds exhibit strong 1π–π⁎ transitions in the UV region and intense 1π–π⁎/intramolecular charge transfer (1ICT) absorption bands in the UV–vis region. Introducing the carbazole end-capped phenylene ethynylene arm on the 1,3,5-triazine core causes a slight bathochromic shift and enhanced molar extinction coefficient of the 1π–π⁎/1ICT transition band. Both compounds are emissive in solution at room temperature and 77 K, which exhibit pronounced positive solvatochromic effect. The emitting state could be ascribed to 1ICT state in more polar solvent, and 1π–π⁎ state in low-polarity solvent. The high emission quantum yields (Φem=0.90~1.0) of 1a and 1b (in hexane and toluene) make them potential candidates as efficient light-emitting materials. The spectroscopic studies and theoretical calculations indicate that the photophysical properties of these compounds can be tuned by the carbazole end-capped phenylene ethynylene arm, which would also be useful for rational design of photofunctional materials.