Inspiratory flow shape clustering: an automated method to monitor upper airway performance during sleep

We describe an automated method for monitoring airflow dynamics in the upper airway of a sleeping subject. Its main task is to determine a set of inspiratory flow shape representatives and their relative incidence in a given respiratory airflow material. The flow shape clustering aims at reducing redundant information in the data, and thereby decreases the time needed to score overnight sleep recordings. Compared with previous computer-assisted systems, built on a pre-defined classification of prototype shapes, we require no a priori assumptions of the flow shape clusters to be discovered. The intrinsic flow shape clustering is performed with a modification of the Isodata algorithm, and the K-means clustering is used as a reference in comparison studies. The operation of the method is demonstrated on clinical sleep recordings both from patients with nocturnal breathing disorders and from non-symptomatic individuals. The feasible results obtained in the practical research design suggest that application of clustering algorithms to respiratory airflow measurements could give important insights into the subtle flow shape abnormalities underlying obstructive sleep-disordered breathing.     

Linear-Time Preprocessing in Optimal Numerical Range Partitioning

Only a subset of the boundary points—the segment borders—have to be taken into account in searching for the optimal multisplit of a numerical value range with respect to the most commonly used attribute evaluation functions of classification learning algorithms. Segments and their borders can be found efficiently in a linear-time preprocessing step.In this paper we expand the applicability of segment borders by showing that inspecting them alone suffices in optimizing any convex evaluation function. For strictly convex evaluation functions inspecting all segment borders is also necessary. These results are derived directly from Jensen's inequality.We also study the evaluation function Training Set Error which is not strictly convex. With that function the data can be preprocessed into an even smaller number of cut point candidates, called alternations, when striving for optimal partition. Examining all alternations also seems necessary, since—analogously to strictly convex functions—the placement of neighboring cut points affects the optimality of an alternation. We test empirically the reduction of the number of cut point candidates that can be obtained for Training Set Error on real-world data.     

Monotonicity of approximate entropy during transition from awareness to unresponsiveness due to propofol anesthetic induction

The ability to monitor the physiological effects of sedative medication accurately is of interest in clinical practice. During the anesthetic agent driven transition to unresponsiveness, nonstationary changes such as signal amplitude variations appear in electroencephalography. In this paper, it is studied whether the application of the approximate entropy (ApEn) method to electroencephalographic (EEG) signal produces a monotonic response curve during the transition from awareness to unresponsiveness. Data from fourteen patients, undergoing propofol anesthetic induction were studied. To optimize the ApEn performance, different parameter choices were carefully evaluated. It was assumed with our protocol, that the level of anesthesia changes monotonically with the elapsed induction time. The monotonicity of the ApEn change was assessed with the prediction probability statistic (PK). The monotonicity of the ApEn time-series depends on the parameters employed in the algorithm and the varying signal amplitude. Depending on the parameter values, the median PK value ranged from 0.886 to 0.527. Thus, a good directionality and concordance was observed, but the nonstationarity of the signal affected the results. In conclusion, EEG-based ApEn measure shows a nonlinear response during propofol induction. With a judicious choice of parameters, a monotonic response is confirmed using PK statistic.     

Fully microfabricated and integrated SU-8-based capillary electrophoresis-electrospray ionization microchips for mass spectrometry

We present a fully microfabricated and monolithically integrated capillary electrophoresis (CE)-electrospray ionization (ESI) chip for coupling with high-throughput mass spectrometric (MS) analysis. The chips are fabricated fully of a negative photoresist SU-8 by a standard lithographic process which enables straightforward batch fabrication of multiple chips with precisely controlled dimensions and, thus, reproducible analytical performance from chip to chip. As the coaxial sheath flow interface is patterned as an integral part of the SU-8 chip, the fluidic design is dead-volume-free. No significant peak broadening occurs so that very narrow peak widths (down to 2-3 s) are obtained. The sheath flow interface also enables comprehensive optimization of both the CE and the ESI conditions separately so that the same chip design is adaptable to diverse analytical conditions. Plate numbers of the order of 105 m-1 and good resolution are routinely reached for small molecules and peptides within a 2 cm separation length and a typical cycle time of only 30-90 s per sample. In addition, a limit of detection of 100 nM corresponding to a total amount of only 4.5 amol (per injection volume of 45 pL) and excellent quantitative linearity (R2 = 0.9999; 100 nM to 100 microM) were obtained in small-molecule analysis using verapamil as a test compound. The quantitative repeatability was proven good (8.5-21.4% relative standard deviation, peak area) also for the other drug substances and peptides tested.     

Comparing the effects of two school workstations on spine positions and mobility, and opinions on the workstations – A 2-year controlled intervention

A wide variety of conventional design workstations are used in schools and workstations have often been described as inappropriate for children's body dimensions. The present study aimed to investigate the effects of saddle chairs and desks with comfort curve for the body on schoolchildren's spine positions and mobility and opinions of workstations compared to conventional workstations. During the first 12-month study period, the number of participants was 98, 48 in the intervention and 50 in the control group. Since the older control group (9th graders, n = 29) decided against participation after the first 12 months, the number of participants in the entire follow-up of 24 months, was 47 (27 intervention and 20 control participants). During the follow-up, the intervention group used new chairs and desks while the controls used conventional workstations. Spine positions and mobility were measured and both groups completed a questionnaire, concerning opinions of workstations. The results indicated that no difference between groups was found when comparing workstation effects on spine positions and mobility during the whole follow-up. Schoolchildren preferred the ergonomic workstations to the conventional workstations during the first year (p < 0.001), but no difference was seen during the second year.

Relevance to industry

: The present results indicated that new design workstations had no effects on spine positions compared to conventional workstations. However, some intra-group positive effects were observed among intervention group. Therefore, the authors believe further development of both school workstations and school environment should be encouraged to ensure better long-term back health in children.     

Infinite Photography: New Mathematical Model for High-Resolution Images

A new mathematical image model is introduced using the photographic process as the starting point. Images are represented as infinite sequences of photons allowing analysis at arbitrarily high resolution and leading to novel computational methods for processing, representation, transmission and storage of images. The resulting space of infinite photographs is proved to have a metric structure and to be intimately connected with bounded Borel measures. Theorems are proved indicating that the imaging power of the photographic process exceeds function spaces in the high resolution limit; this implies in particular that natural photographic images need to be modelled by generalized functions. Furthermore, computational results are presented to illustrate the novel algorithms based on photon sequences. The algorithms include stochastic halftoning, representation of cartoon images with outlines, anti-aliasing, blurring and singularity extraction.     

Memory-based scheduling of scientific computing clusters

This study looks at how increased memory utilisation affects throughput and energy consumption in scientific computing, especially in high-energy physics. Our aim is to minimise energy consumed by a set of jobs without increasing the processing time. The earlier tests indicated that, especially in data analysis, throughput can increase over 100% and energy consumption decrease 50% by processing multiple jobs in parallel per CPU core. Since jobs are heterogeneous, it is not possible to find an optimum value for the number of parallel jobs. A better solution is based on memory utilisation, but finding an optimum memory threshold is not straightforward. Therefore, a fuzzy logic-based algorithm was developed that can dynamically adapt the memory threshold based on the overall load. In this way, it is possible to keep memory consumption stable with different workloads while achieving significantly higher throughput and energy-efficiency than using a traditional fixed number of jobs or fixed memory threshold approaches.     

Contextual usage patterns in smartphone communication services

The mobile end user context has received a lot of attention from the mobile services industry lately. The location-based and context-sensitive information that are characteristic for smartphones can be utilized to study the use context of mobile end users. Accordingly, this article utilizes handset-based data in analyzing how the context of use affects the usage of smartphone communication services. The context is identified with an algorithm utilizing mobile network cell ID and WLAN data and resulting in five place-related contexts, namely Home, Office, Other meaningful, Elsewhere and Abroad. According to our analysis, voice calls are used least intensively in the Home context where the length of the voice calls is the longest, however. Email and SMS are used most intensively in the Office context, where the voice calls are the shortest in duration. Finally, mobile IM/VoIP and social media services are more free-time oriented as they are used most intensively in Elsewhere and Other meaningful contexts. The findings imply that people use smartphone communication services differently depending on the use context. However, context can be defined and identified in a number of ways, and this article presents only one solution that is highly dependent on the type of data collected.     

Energy efficiency of dynamic management of virtual cluster with heterogeneous hardware

Cloud computing is an essential part of today’s computing world. Continuously increasing amount of computation with varying resource requirements is placed in large data centers. The variation among computing tasks, both in their resource requirements and time of processing, makes it possible to optimize the usage of physical hardware by applying cloud technologies. In this work, we develop a prototype system for load-based management of virtual machines in an OpenStack computing cluster. Our prototype is based on an idea of ‘packing’ idle virtual machines into special park servers optimized for this purpose. We evaluate the method by running real high-energy physics analysis software in an OpenStack test cluster and by simulating the same principle using the Cloudsim simulator software. The results show a clear improvement, 9–48 % , in the total energy efficiency when using our method together with resource overbooking and heterogeneous hardware.