Comparison of control charts for Poisson count data in health‐care monitoring

Statistical surveillance is a noteworthy endeavor in many health‐care areas such as epidemiology, hospital quality, infection control, and patient safety. For monitoring hospital adverse events, the Shewhart u‐control chart is the most used methodology. One possible issue of the u‐chart is that in health‐care applications the lower control limit (LCL) is often conventionally set to zero as the adverse events are rare and the sample sizes are not sufficiently large to obtain LCL greater than zero. Consequently, the control chart loses any ability to signal improvements. Furthermore, as the area of opportunity (sample size) is not constant over time, the in‐control and out‐of‐control run length performances of the monitoring scheme are unknown. In this article, on the basis of a real case and through an intensive simulation study, we first investigate the in‐control statistical properties of the u‐chart. Then we set up several alternative monitoring schemes with the same in‐control performances and their out‐of‐control properties are studied and compared. The aim is to identify the most suitable control chart considering jointly: the ability to detect unexpected changes (usually worsening), the ability to test the impact of interventions (usually improvements), and the ease of use and clarity of interpretation. The results indicate that the exponentially weighted moving average control chart derived under the framework of weighted likelihood ratio test has the best overall performance.     

Phase synchronization,extreme multistability and its control with selection of a desired pattern in hybrid coupled neurons via a memristive synapse

Nonlinear Dynamics - The memristor is a nonlinear electronic nanodevice with incredible biomimetic characteristics generally used by neurologists, more specifically in neuromorphics, to design...     

224 nm Deep-UV laser for native fluorescence, a new opportunity for biomolecules detection

A new highly sensitive and compact 224 nm laser-induced native fluorescence (LINF) detector was developed using a new generation of deep-UV laser and an innovating elliptical flow cell. The use of deep-UV excitation at 224 nm allows to achieve fluorescence detection of an important range of molecules containing a single aromatic ring. The LINF detector was first evaluated in liquid chromatography. An improvement of a factor 500 over a conventional fluorimeter is reached with a limit of detection (LOD) of 1.5 pmole for ibuprofen. LODs were in the nanomole range for phenylalanine and in the picomole range for tyrosine and tryptophan. The LINF detector is able to detect the same levels of peptides concentrations as an ESI-ion trap spectrometer used in scan mode. In this application, LINF outperforms the UV detection at 214 or 254 nm and could be used with different additives with no noticeable effect on the detection.     

Composites of Wood and Biodegradable Thermoplastics: A Review

This paper is an overview of current understanding in the areas of composites made from biodegradable thermoplastics and wood fillers. The review finds that the composite properties depend on the type of wood filler, the choice of polymer matrix, the wood filler content, the compatibilization technique used and the processing parameters. The extent of interfacial adhesion and the filler morphology are identified as the underlying factors that control the composite properties. Future research needs are identified, including establishment of fundamental relationships between quantified interfacial adhesion and end-use properties and advanced modelling of biodegradation processes.