Investigation of factors affecting the adsorption of functional molecules onto gel silicas. 1. Flow microcalorimetry and infrared spectroscopy

Flow microcalorimetry and infrared spectroscopy were used to study the surface structure and adsorptive properties of a series of calcined and uncalcined porous silicas. The adsorbates DL-menthol, (R)-(+)-limonene, (+/-)-citronellal and carvone were selected for their functionality, that included carbonyl, vinylic and hydroxyl groups. The amounts of probe retained by the silicas together with the energy exchange involved in the adsorption/desorption process were determined by flow microcalorimetry. The functional groups involved in these interactions were studied by means of infrared spectroscopy. It was observed that the strongest interactions with the silica surface took place through hydrogen bonding onto the surface silanol. The most retentive probes were found to be those with a carbonyl group in their structure. Adsorption onto calcined silicas was found to be less energetic than onto the equivalent calcined samples. The adsorption densities were compared with theoretical predictions based on molecular models. In all cases apart from citronellal monolayer coverage were not observed due to steric effects.     

Measuring the efficiency of decision making units

A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.     

Multitude of core-localized shear Alfvén waves in a high-temperature fusion plasma

Evidence is presented for a multitude of discrete frequency Alfvén waves in the core of magnetically confined high-temperature fusion plasmas. Multiple diagnostic instruments confirm wave excitation over a wide spatial range from the device size at the longest wavelengths down to the thermal ion Larmor radius. At the shortest scales, the poloidal wavelengths are comparable to the scale length of electrostatic drift wave turbulence. Theoretical analysis confirms a dominant interaction of the modes with particles in the thermal ion distribution traveling well below the Alfvén velocity.     

Critical Homogenization of SDEs Driven by a Levy Process in Random Medium

We are concerned with homogenization of stochastic differential equations (SDE) with stationary coefficients driven by Poisson random measures and Brownian motions in the critical case, that is, when the limiting equation admits both a Brownian part as well as a pure jump part. We state an annealed convergence theorem. This problem is deeply connected with homogenization of integral partial differential equations.     

Observation of reduced electron-temperature fluctuations in the core of H-mode plasmas

Core electron-temperature fluctuations [0.5%< or =T[over ]_(e)/T_(e)< or =2%, k_(theta)rho_(s)< or =0.3 in neutral-beam-heated low confinement-mode (L-mode) plasmas] are observed to decrease by at least a factor of 4 in standard and quiescent high-confinement-mode (H-mode and QH-mode) regimes in the DIII-D tokamak (r/a=0.7). These fluctuations are attributed to ion temperature gradient (ITG) modes stabilized by rotational shear at the H-mode transition. The simultaneous reduction in electron heat diffusivity (chi_(e)(QH)/chi_(e)(L)<0.25) suggests that T[over ]_(e) fluctuations can contribute significantly to L-mode electron heat transport.     

New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.