Enabling ultra high precision on hard steels using surface defect machining

This paper is an extension to an idea coined during the 13th EUSPEN Conference (P6.23) named "surface defect machining" (SDM). The objective of this work was to demonstrate how a conventional CNC turret lathe can be used to obtain ultra high precision machined surface finish on hard steels without recourse to a sophisticated ultra precision machine tool. An AISI 4340 hard steel (69 HRC) workpiece was machined using a CBN cutting tool with and without SDM. Post-machining measurements by a Form Talysurf and a Scanning Electron Microscope (FEI Quanta 3D) revealed that SDM culminates to several key advantages (i) provides better quality of the machined surface integrity and offers (ii) lowering feed rate to 5μm/rev to obtain a machined surface roughness of 30 nm (optical quality).

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.

High-precision ultra-distal Holocene tephrochronology in North America

Far-travelled volcanic ashes (tephras) from Holocene eruptions in Alaska and the Pacific northwest have been traced to the easternmost extent of North America, providing the basis for a new high-precision geochronological framework throughout the continent through tephrochronology (the dating and correlation of tephra isochrons in sedimentary records). The reported isochrons are geochemically distinct, with seven correlated to documented sources in Alaska and the Cascades, including the Mazama ash from Oregon (w7600 years old) and the eastern lobe of the White River Ash from Alaska (~1150 years old). These findings mark the beginning of a tephrochronological framework of enhanced precision across North America, with applications in palaeoclimate, surface process and archaeological studies. The particle travel distances involved (up tow7000 km) also demonstrate the potential for continent-wide or trans-Atlantic socio-economic disruption from similar future eruptions.

Precision Teaching in IV Fluid Prescribing and Patient Safety

OBJECTIVES: Precision Teaching (PT) has been shown to be an effective intervention to assess teaching method effectiveness and evaluate learning outcomes. SAFMEDS (Say All Fast Minute Every Day Shuffled) are a practice/assessment procedure within the PT framework to assist learning and fluency. We explored the effects of a brief intervention with PT, to impart high frequency performance in safe intravenous fluid prescription in a group of final year undergraduate medical students.
METHODS: 133 final year undergraduate medical students completed a multiple choice question (MCQ) test on safe IV fluid prescription at the beginning and end of the study. The control group (n= 76) of students were taught using a current standardized teaching method. Students allocated to the intervention arm of the study were additionally instructed on PT and the use of SAFMEDS. The study group (n = 57) received 50 SAFMEDS cards containing information on the principles of IV fluid prescription scenarios. These students were trained/tested twice per day for 1 minute.
RESULTS: Interim analysis showed that the study group displayed an improvement in fluency and accuracy as the study progressed. There was a statistically significant improvement in MCQ performance for the PT group compared with the control group between the beginning and end of the study (35% vs 15%).
CONCLUSION: These results suggest PT employing SAFMEDS is an effective method for improving fluency, accuracy and patient safety in intravenous fluid prescribing amongst undergraduate medical students.

Thermometry precision in strongly correlated ultracold lattice gases

The precise knowledge of the temperature of an ultracold lattice gas simulating a strongly correlated
system is a question of both fundamental and technological importance. Here, we address such
question by combining tools from quantum metrology together with the study of the quantum
correlations embedded in the system at finite temperatures. Within this frame we examine the spin-
1 2 XY chain, first estimating, by means of the quantum Fisher information, the lowest attainable
bound on the temperature precision. We then address the estimation of the temperature of the sample
from the analysis of correlations using a quantum non demolishing Faraday spectroscopy method.
Remarkably, our results show that the collective quantum correlations can become optimal
observables to accurately estimate the temperature of our model in a given range of temperatures.

Cellular signalling effects in high precision radiotherapy

Radiotherapy is commonly planned on the basis of physical dose received by the tumour and surrounding normal tissue, with margins added to address the possibility of geometric miss. However, recent experimental evidence suggests that intercellular signalling results in a given cell's survival also depending on the dose received by neighbouring cells. A model of radiation-induced cell killing and signalling was used to analyse how this effect depends on dose and margin choices. Effective Uniform Doses were calculated for model tumours in both idealised cases with no delivery uncertainty and more realistic cases incorporating geometric uncertainty. In highly conformal irradiation, a lack of signalling from outside the target leads to reduced target cell killing, equivalent to under-dosing by up to 10% compared to large uniform fields. This effect is significantly reduced when higher doses per fraction are considered, both increasing the level of cell killing and reducing margin sensitivity. These effects may limit the achievable biological precision of techniques such as stereotactic radiotherapy even in the absence of geometric uncertainties, although it is predicted that larger fraction sizes reduce the relative contribution of cell signalling driven effects. These observations may contribute to understanding the efficacy of hypo-fractionated radiotherapy.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Precision dating of the Palaeolithic: A new radiocarbon chronology for the Abri Pataud (France), a key Aurignacian sequence

This paper presents a new series of AMS dates on ultrafiltered bone gelatin extracted from identified cutmarked or humanly-modified bones and teeth from the site of Abri Pataud, in the French Dordogne. The sequence of 32 new determinations provides a coherent and reliable chronology from the site's early Upper Palaeolithic levels 5-14, excavated by Hallam Movius. The results show that there were some problems with the previous series of dates, with many underestimating the real age. The new results, when calibrated and modelled using a Bayesian statistical method, allow detailed understanding of the pace of cultural changes within the Aurignacian I and II levels of the site, something not achievable before. In the future, the sequence of dates will allow wider comparison to similarly dated contexts elsewhere in Europe. High precision dating is only possible by using large suites of AMS dates from humanly-modified material within well understood archaeological sequences modelled using a Bayesian statistical method. © 2011.

Personalised and Precision Medicine in Cancer Clinical Trials: Panacea for Progress or Pandora's Box?

Cancer clinical trials have been one of the key foundations for significant advances in oncology. However, there is a clear recognition within the academic, care delivery and pharmaceutical/biotech communities that our current model of clinical trial discovery and development is no longer fit for purpose. Delivering transformative cancer care should increasingly be our mantra, rather than maintaining the status quo of, at best, the often miniscule incremental benefits that are observed with many current clinical trials. As we enter the era of precision medicine for personalised cancer care (precision and personalised medicine), it is important that we capture and utilise our greater understanding of the biology of disease to drive innovative approaches in clinical trial design and implementation that can lead to a step change in cancer care delivery. A number of advances have been practice changing (e.g. imatinib mesylate in chronic myeloid leukaemia, Herceptin in erb-B2-positive breast cancer), and increasingly we are seeing the promise of a number of newer approaches, particularly in diseases like lung cancer and melanoma. Targeting immune checkpoints has recently yielded some highly promising results. New algorithms that maximise the effectiveness of clinical trials, through for example a multi-stage, multi-arm type design are increasingly gaining traction. However, our enthusiasm for the undoubted advances that have been achieved are being tempered by a realisation that these new approaches may have significant cost implications. This article will address these competing issues, mainly from a European perspective, highlight the problems and challenges to healthcare systems and suggest potential solutions that will ensure that the cost/value rubicon is addressed in a way that allows stakeholders to work together to deliver optimal cost-effective cancer care, the benefits of which can be transferred directly to our patients.

Modelling the fresh properties of self-compacting concrete using support vector machine approach

The main objective of the study presented in this paper was to investigate the feasibility using support vector machines (SVM) for the prediction of the fresh properties of self-compacting concrete. The radial basis function (RBF) and polynomial kernels were used to predict these properties as a function of the content of mix components. The fresh properties were assessed with the slump flow, T50, T60, V-funnel time, Orimet time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 min after adding the first water. The water dosage varied from 188 to 208 L/m3, the dosage of superplasticiser (SP) from 3.8 to 5.8 kg/m3, and the volume of coarse aggregates from 220 to 360 L/m3. In total, twenty mixes were used to measure the fresh state properties with different mixture compositions. RBF kernel was more accurate compared to polynomial kernel based support vector machines with a root mean square error (RMSE) of 26.9 (correlation coefficient of R2 = 0.974) for slump flow prediction, a RMSE of 0.55 (R2 = 0.910) for T50 (s) prediction, a RMSE of 1.71 (R2 = 0.812) for T60 (s) prediction, a RMSE of 0.1517 (R2 = 0.990) for V-funnel time prediction, a RMSE of 3.99 (R2 = 0.976) for Orimet time prediction, and a RMSE of 0.042 (R2 = 0.988) for L-box ratio prediction, respectively. A sensitivity analysis was performed to evaluate the effects of the dosage of cement and limestone powder, the water content, the volumes of coarse aggregate and sand, the dosage of SP and the testing time on the predicted test responses. The analysis indicates that the proposed SVM RBF model can gain a high precision, which provides an alternative method for predicting the fresh properties of SCC.