F. Di Palma

Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes.

To test in an outpatient setting the safety and efficacy of continuous subcutaneous insulin infusion (CSII) driven by a modular model predictive control (MMPC) algorithm informed by continuous glucose monitoring (CGM) measurement.

Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing

In semiconductor manufacturing, the spatial pattern of failed devices in a wafer can give precious hints on which step of the process is responsible for the failures. In the literature, Kohonens Self Organizing Feature Maps (SOM) and Adaptive Resonance Theory 1 (ART1) architectures have been compared, concluding that the latter are to be preferred. However, both the simulated and the real data sets used for validation and comparison were very limited. In this paper, the use of ART1 and SOM as wafer classifiers is re-assessed on much more extensive simulated and real data sets. We conclude that ART1 is not adequate, whereas SOM provide completely satisfactory results including visually effective representation of spatial failure probability of the pattern classes.

A multi-model structure for model predictive control

Abstract Model Predictive Control (MPC) is a wide popular control technique that can be applied starting from several model structures. In this paper black box models are considered. In particular it is analysed the sets of regressors that it is better to use in order to obtain the best model for multi step prediction. It is observed that for each prediction a different set of real data output and predicted output are available. Based on this observation a multi-model structure is proposed in order to improve the predictions needed in the computation of the MPC control law. A comparison with a classical one-model structure is discussed. A simulation experiment is presented.

Process diagnosis via electrical-wafer-sorting maps classification

The commonality analysis is a proven tool for fault detection in semiconductor manufacturing. This methodology extracts subsets of production lots from all the available data. Then, data mining techniques are used only on the selected data. This approach loses part of the available information and does not discriminate among the lots. The new methodology performance the automatic classification of the electrical wafer test maps in order to identify the classes of failure present in the production lots. Subsequently, the proposed procedure uses the process history of each wafer to create a list of the root cause candidates. This methodology is the core of the software tool ACID which is currently used for process diagnosis at the Agrate site of the ST Microelectronics. A real analysis is presented.

Parental evaluation of a telemonitoring service for children with Type 1 Diabetes

Introduction In the past years, we developed a telemonitoring service for young patients affected by Type 1 Diabetes. The service provides data to the clinical staff and offers an important tool to the parents, that are able to oversee in real time their children. The aim of this work was to analyze the parents’ perceived usefulness of the service. Methods The service was tested by the parents of 31 children enrolled in a seven-day clinical trial during a summer camp. To study the parents’ perception we proposed and analyzed two questionnaires. A baseline questionnaire focused on the daily management and implications of their children’s diabetes, while a post-study one measured the perceived benefits of telemonitoring. Questionnaires also included free text comment spaces. Results Analysis of the baseline questionnaires underlined the parents’ suffering and fatigue: 51% of total responses showed a negative tendency and the mean value of the perceived quality of life was 64.13 in a 0–100 scale. In the post-study questionnaires about half of the parents believed in a possible improvement adopting telemonitoring. Moreover, the foreseen improvement in quality of life was significant, increasing from 64.13 to 78.39 (p-value = 0.0001). The analysis of free text comments highlighted an improvement in mood, and parents’ commitment was also proved by their willingness to pay for the service (median = 200 euro/year). Discussion A high number of parents appreciated the telemonitoring service and were confident that it could improve communication with physicians as well as the family’s own peace of mind.

Reduction of Hyper- and Hypoglycemia during Two Months with a Wearable Artifi cial Pancreas from Dinner to Breakfast in Patients with Type 1 Diabetes

929-P Patient Responses to Interim Data from Cardiovascular Outcomes Trials: Results from an Online Patient Survey MANU V. VENKAT, RICHARD S. WOOD, ADAM S. BROWN, PHIN YOUNGE, LISA S. ROTENSTEIN, KELLY L. CLOSE, San Francisco, CA, Boston, MA The disclosure of interim data from ongoing clinical trials is usually discouraged, as it can alter participant behavior and threaten trial integrity. Current U.S. regulatory guidance requires long-term cardiovascular outcomes trials (CVOTs) for new T2DM drugs, but allows interim data to be disclosed to support approval. The purpose of this study was to examine how such disclosure could influence enrollment dynamics in a CVOT. An online survey from the diabetes market research company dQ&A was distributed to a panel of adult T2DM patients. Of the 1,984 total respondents with T2DM, 1,542 reported a history of CVD and/or being told by their healthcare provider that they are at elevated CVD risk. This represents a patient subgroup that is targeted for enrollment in most diabetes CVOTs. In the survey, respondents were described a hypothetical CVOT. Next, they were randomized to receive scenarios in which evidence of either an increase or decrease in CVD incidence was disclosed during the trial. In both scenarios, the drug was approved. All participants selected one of four choices regarding their subsequent actions (see table below).

Unsupervised algorithms for the automatic classification of EWS maps: a comparison

Recently, it has been shown that the classification of electrical wafer sorting failure maps can be performed by means of unsupervised methods. In this work four different unsupervised methods are compared: SOM, K-means, neural gas, and an expectation maximization. The algorithms are compared using a benchmark based on a probabilistic model. The performance of the classification is assessed by means of an new index, called index-F, based on the knowledge of the real classification. Moreover it is studied the correlation between the proposed index and the following indexes: CH-index, D-index, I-index and average likelihood.

Some Results on the Stability of Multi-Controlled System

This paper considers a particular class of hybrid systems, called multi-controlled systems (MCS), where a single plant is controlled by a number of different controllers according to a prescribed switching strategy. This class includes many practical control schemes, such as those based on gain scheduling or sliding mode controllers. Some new criteria for the stability of the movement of MCS are presented together with an illustrative simulation example

ACID: Automatic Sort-Map Classification for Interactive Process Diagnosis

Early detection of faulty process steps through process diagnosis is critical to the semiconductor industry. The AC-ID methodology can isolate the root causes of yield loss by combining end-of-line tests with process history information. The ACID software tool automates this methodology and is fully operational at several industry production sites.