Corrosion resistant fibre reinforced polymer (FRP) reinforcement for bridge deck slabs

This paper discusses the beneficial influence of compressive membrane action in fibre reinforced polymer (FRP)reinforced in-plane restrained slabs in bridge deck slabs and the improved service performance when archingaction occurs. Bridge deck slabs that are exposed to extreme environmental conditions can experience severecorrosion damage. Expansive corrosion in steel reinforcement significantly reduces the design life and durabilityof concrete structures; for example, on one short section of the M1 in Northern Ireland, nearly £1 million was spent last year on the maintenance and repair of bridges due to corrosion. Corrosion-resistant compositereinforcement such as basalt fibre reinforced polymer (BFRP) and glass fibre reinforced polymer (GFRP) provides adurable alternative to reinforcing steel. In this research, two BFRP reinforced slabs and two GFRP reinforced slabswere constructed using high-strength concrete with a target cube compressive strength of 65 N/mm2. The slabsrepresented typical full-scale dimensions of a real bridge deck slab 475 mm wide by 1425 mm long and 150 mmdeep. The service and ultimate behaviour of the slabs are discussed and the results are compared with the relevantdesign guidelines.

Quantum annealing of an ising spin-glass by Green's function Monte Carlo

We present an implementation of quantum annealing (QA) via lattice Green's function Monte Carlo (GFMC), focusing on its application to the Ising spin glass in transverse field. In particular, we study whether or not such a method is more effective than the path-integral Monte Carlo- (PIMC) based QA, as well as classical simulated annealing (CA), previously tested on the same optimization problem. We identify the issue of importance sampling, i.e., the necessity of possessing reasonably good (variational) trial wave functions, as the key point of the algorithm. We performed GFMC-QA runs using such a Boltzmann-type trial wave function, finding results for the residual energies that are qualitatively similar to those of CA (but at a much larger computational cost), and definitely worse than PIMC-QA. We conclude that, at present, without a serious effort in constructing reliable importance sampling variational wave functions for a quantum glass, GFMC-QA is not a true competitor of PIMC-QA.

Relative Biological Effectiveness Variation Along Monoenergetic and Modulated Bragg Peaks of a 62-MeV Therapeutic Proton Beam: A Preclinical Assessment

iological optimization of proton therapy critically depends on detailed evaluation of relative biological effectiveness (RBE) variations along the Bragg curve. The clinically accepted RBE value of 1.1 is an oversimplification, which disregards the steep rise of linear energy transfer (LET) at the distal end of the spread-out Bragg peak. We observed significant cell killing RBE variations dependent on beam modulation, intrinsic radiosensitivity, and LET in agreement with the LEM predicted values, indicating dose-averaged LET as a suitable parameter for biological effectiveness. Data have also been used to validate a RBE parameterized model.

Outgassing Behavior of C/2012 S1 (ISON) from 2011 September to 2013 June

We report photometric observations for comet C/2012 S1 (ISON) obtained during the time period immediately after discovery (r = 6.28 AU) until it moved into solar conjunction in mid-2013 June using the UH2.2 m, and Gemini North 8 m telescopes on Mauna Kea, the Lowell 1.8 m in Flagstaff, the Calar Alto 1.2 m telescope in Spain, the VYSOS-5 telescopes on Mauna Loa Hawaii and data from the CARA network. Additional pre-discovery data from the Pan STARRS1 survey extends the light curve back to 2011 September 30 (r = 9.4 AU). The images showed a similar tail morphology due to small micron sized particles throughout 2013. Observations at submillimeter wavelengths using the James Clerk Maxwell Telescope on 15 nights between 2013 March 9 (r = 4.52 AU) and June 16 (r = 3.35 AU) were used to search for CO and HCN rotation lines. No gas was detected, with upper limits for CO ranging between 3.5-4.5 × 1027 molecules s-1. Combined with published water production rate estimates we have generated ice sublimation models consistent with the photometric light curve. The inbound light curve is likely controlled by sublimation of CO2. At these distances water is not a strong contributor to the outgassing. We also infer that there was a long slow outburst of activity beginning in late 2011 peaking in mid-2013 January (r ~ 5 AU) at which point the activity decreased again through 2013 June. We suggest that this outburst was driven by CO injecting large water ice grains into the coma. Observations as the comet came out of solar conjunction seem to confirm our models.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads. © 2013 IEEE.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

CHARACTERIZATION OF SURFACE-PLASMONS ON METAL-OXIDE-SEMICONDUCTOR STRUCTURES

Using the Otto (prism-air gap-sample) configuration p-polarized light of wavelength 632.8 nm has been coupled with greater than 80% efficiency to surface plasmons on the aluminium electrode of silicon-silicon dioxide-aluminium structures. The results show that if the average power per unit area dissipated on the metal film exceeds approximately 1 mW mm-2, then the coupling gap and thus the characteristics of the surface plasmon resonance are noticeably altered. In modelling the optical response of such systems the inclusion of both a non-uniform air coupling gap and a thin cermet layer at the aluminium surface may be necessary.

3D Efflux Velocity Characteristics of Marine Propeller Jets

The determination of the efflux velocity is key to the process of calculating the subsequent value of velocity at any other location within a propeller jet. This paper reports on the findings of an experimental investigation into the magnitude of the efflux velocities within the jets produced by four differing propellers. Measurements of velocity have been made using a 3D LDA system with the test propellers operating at a range of rotational speeds which bound typical operational values. Comparisons are made with existing predictive theories and to aid design engineers, methods are presented by which the 3D efflux velocity components, as well as the resultant efflux value, can be determined.

Post-tensioning glulam timber beams with basalt FRP tendons

Improvements in the structural performance of glulam timber beams by the inclusion of reinforcing materials can increase both the service performance and ultimate capacity. This paper describes a series of four-point bending tests conducted, under service loads and to failure, on unreinforced, reinforced and post-tensioned glulam timber beams, where the reinforcing tendon used is 12 mm dia. basalt fibre-reinforced polymer. The research is designed to evaluate the benefits offered by including an active reinforcement in contrast to the passive reinforcement typically used within timber strengthening works, in addition to establishing the effect that bonding the reinforcing tendon has on the material's performance. Further experimental tests have also been developed to investigate the long-term implications of this research, with emphasis placed upon creep and loss of post-tensioning; however, this is ongoing and is not presented in this paper. The laboratory investigations establish that the flexural strength and stiffness increase for both the unbonded and bonded post-tensioned timbers compared to the unreinforced and reinforced beams. Timber that is post-tensioned with an unbonded basalt fibre-reinforced polymer tendon shows a flexural strength increase of 2ṡ8% and an increase in stiffness of 8ṡ7%. Post-tensioned beams with a bonded basalt fibre-reinforced polymer tendon show increases in flexural strength and stiffness of 15ṡ4% and 11ṡ5% respectively.

Regret minimisation and utility maximisation in a freight transport context

This study is the first to compare random regret minimisation (RRM) and random utility maximisation (RUM) in freight transport application. This paper aims to compare RRM and RUM in a freight transport scenario involving negative shock in the reference alternative. Based on data from two stated choice experiments conducted among Swiss logistics managers, this study contributes to related literature by exploring for the first time the use of mixed logit models in the most recent version of the RRM approach. We further investigate two paradigm choices by computing elasticities and forecasting choice probability. We find that regret is important in describing the managers’ choices. Regret increases in the shock scenario, supporting the idea that a shift in reference point can cause a shift towards regret minimisation. Differences in elasticities and forecast probability are identified and discussed appropriately.