92,734 research outputs found
Comparison of serious inhaler technique errors made by device-naïve patients using three different dry powder inhalers: a randomised, crossover, open-label study
Background: Serious inhaler technique errors can impair drug delivery to the lungs. This randomised, crossover, open-label study evaluated the proportion of patients making predefined serious errors with Pulmojet compared with Diskus and Turbohaler dry powder inhalers. Methods: Patients ≥18 years old with asthma and/or COPD who were current users of an inhaler but naïve to the study devices were assigned to inhaler technique assessment on Pulmojet and either Diskus or Turbohaler in a randomised order. Patients inhaled through empty devices after reading the patient information leaflet. If serious errors potentially affecting dose delivery were recorded, they repeated the inhalations after watching a training video. Inhaler technique was assessed by a trained nurse observer and an electronic inhalation profile recorder. Results: Baseline patient characteristics were similar between randomisation arms for the Pulmojet-Diskus (n = 277) and Pulmojet-Turbohaler (n = 144) comparisons. Non-inferiority in the proportions of patients recording no nurse-observed serious errors was demonstrated for both Pulmojet versus Diskus, and Pulmojet versus Turbohaler; therefore, superiority was tested. Patients were significantly less likely to make ≥1 nurse-observed serious errors using Pulmojet compared with Diskus (odds ratio, 0.31; 95 % CI, 0.19–0.51) or Pulmojet compared with Turbohaler (0.23; 0.12–0.44) after reading the patient information leaflet with additional video instruction, if required. Conclusions These results suggest Pulmojet is easier to learn to use correctly than the Turbohaler or Diskus for current inhaler users switching to a new dry powder inhaler
Conedy: a scientific tool to investigate Complex Network Dynamics
We present Conedy, a performant scientific tool to numerically investigate
dynamics on complex networks. Conedy allows to create networks and provides
automatic code generation and compilation to ensure performant treatment of
arbitrary node dynamics. Conedy can be interfaced via an internal script
interpreter or via a Python module
The NASA Astrophysics Data System: Data Holdings
Since its inception in 1993, the ADS Abstract Service has become an
indispensable research tool for astronomers and astrophysicists worldwide. In
those seven years, much effort has been directed toward improving both the
quantity and the quality of references in the database. From the original
database of approximately 160,000 astronomy abstracts, our dataset has grown
almost tenfold to approximately 1.5 million references covering astronomy,
astrophysics, planetary sciences, physics, optics, and engineering. We collect
and standardize data from approximately 200 journals and present the resulting
information in a uniform, coherent manner. With the cooperation of journal
publishers worldwide, we have been able to place scans of full journal articles
on-line back to the first volumes of many astronomical journals, and we are
able to link to current version of articles, abstracts, and datasets for
essentially all of the current astronomy literature. The trend toward
electronic publishing in the field, the use of electronic submission of
abstracts for journal articles and conference proceedings, and the increasingly
prominent use of the World Wide Web to disseminate information have enabled the
ADS to build a database unparalleled in other disciplines.
The ADS can be accessed at http://adswww.harvard.eduComment: 24 pages, 1 figure, 6 tables, 3 appendice
Out-Of-Place debugging: a debugging architecture to reduce debugging interference
Context. Recent studies show that developers spend most of their programming
time testing, verifying and debugging software. As applications become more and
more complex, developers demand more advanced debugging support to ease the
software development process.
Inquiry. Since the 70's many debugging solutions were introduced. Amongst
them, online debuggers provide a good insight on the conditions that led to a
bug, allowing inspection and interaction with the variables of the program.
However, most of the online debugging solutions introduce \textit{debugging
interference} to the execution of the program, i.e. pauses, latency, and
evaluation of code containing side-effects.
Approach. This paper investigates a novel debugging technique called
\outofplace debugging. The goal is to minimize the debugging interference
characteristic of online debugging while allowing online remote capabilities.
An \outofplace debugger transfers the program execution and application state
from the debugged application to the debugger application, both running in
different processes.
Knowledge. On the one hand, \outofplace debugging allows developers to debug
applications remotely, overcoming the need of physical access to the machine
where the debugged application is running. On the other hand, debugging happens
locally on the remote machine avoiding latency. That makes it suitable to be
deployed on a distributed system and handle the debugging of several processes
running in parallel.
Grounding. We implemented a concrete out-of-place debugger for the Pharo
Smalltalk programming language. We show that our approach is practical by
performing several benchmarks, comparing our approach with a classic remote
online debugger. We show that our prototype debugger outperforms by a 1000
times a traditional remote debugger in several scenarios. Moreover, we show
that the presence of our debugger does not impact the overall performance of an
application.
Importance. This work combines remote debugging with the debugging experience
of a local online debugger. Out-of-place debugging is the first online
debugging technique that can minimize debugging interference while debugging a
remote application. Yet, it still keeps the benefits of online debugging ( e.g.
step-by-step execution). This makes the technique suitable for modern
applications which are increasingly parallel, distributed and reactive to
streams of data from various sources like sensors, UI, network, etc
- …