Leveraging RFID in hospitals: patient life cycle and mobility perspectives

The application of Radio Frequency Identification (RFID) to patient care in hospitals and healthcare facilities has only just begun to be accepted. This article develops a set of frameworks based on patient life cycle and time-and-motion perspectives for how RFID can be leveraged atop existing information systems to offer many benefits for patient care and hospital operations. It examines how patients are processed from admission to discharge, and considers where RFID can be applied. From a time-and-motion perspective, it shows how hospitals can apply RFID in three ways: fixed RFID readers interrogate mobile objects; mobile, handheld readers interrogate fixed objects; and mobile, handheld readers interrogate mobile objects. Implemented properly, RFID can significantly aid the medical staff in performing their duties. It can greatly reduce the need for manual entry of records, increase security for both patient and hospital, and reduce errors in administering medication. Hospitals are likely to encounter challenges, however, when integrating the technology into their day-to-day operations. What we present here can help hospital administrators determine where RFID can be deployed to add the most value

Recent Trends in Software Testing Education: A Systematic Literature Review

Testing is a critical aspect of software development. Far too often software is released with critical faults. However, testing is often considered tedious and boring. Unfortunately, many graduates might join the work force without having had any education in software testing, which exacerbates the problem even further. Therefore, teaching software testing as part of a university degree in software engineering and is very important. But it is an open challenge how to teach software testing in an effective way that can successfully motivate students. In this paper, we have carried out a systematic literature review on the topic of teaching software testing. We analysed and reviewed 30 papers that were published between 2013 and 2017. The review points out to a few different trends, like the use of gamification to make the teaching of software testing less tedious

Accurate molecular polarizabilities with coupled-cluster theory and machine learning

The molecular polarizability describes the tendency of a molecule to deform or polarize in response to an applied electric field. As such, this quantity governs key intra- and inter-molecular interactions such as induction and dispersion, plays a key role in determining the spectroscopic signatures of molecules, and is an essential ingredient in polarizable force fields and other empirical models for collective interactions. Compared to other ground-state properties, an accurate and reliable prediction of the molecular polarizability is considerably more difficult as this response quantity is quite sensitive to the description of the underlying molecular electronic structure. In this work, we present state-of-the-art quantum mechanical calculations of the static dipole polarizability tensors of 7,211 small organic molecules computed using linear-response coupled-cluster singles and doubles theory (LR-CCSD). Using a symmetry-adapted machine-learning based approach, we demonstrate that it is possible to predict the molecular polarizability with LR-CCSD accuracy at a negligible computational cost. The employed model is quite robust and transferable, yielding molecular polarizabilities for a diverse set of 52 larger molecules (which includes challenging conjugated systems, carbohydrates, small drugs, amino acids, nucleobases, and hydrocarbon isomers) at an accuracy that exceeds that of hybrid density functional theory (DFT). The atom-centered decomposition implicit in our machine-learning approach offers some insight into the shortcomings of DFT in the prediction of this fundamental quantity of interest

The Arches Cluster: Extended Structure and Tidal Radius

At a projected distance of ~26 pc from Sgr A*, the Arches cluster provides insight to star formation in the extreme Galactic Center (GC) environment. Despite its importance, many key properties such as the cluster's internal structure and orbital history are not well known. We present an astrometric and photometric study of the outer region of the Arches cluster (R > 6.25") using HST WFC3IR. Using proper motions we calculate membership probabilities for stars down to F153M = 20 mag (~2.5 M_sun) over a 120" x 120" field of view, an area 144 times larger than previous astrometric studies of the cluster. We construct the radial profile of the Arches to a radius of 75" (~3 pc at 8 kpc), which can be well described by a single power law. From this profile we place a 3-sigma lower limit of 2.8 pc on the observed tidal radius, which is larger than the predicted tidal radius (1 - 2.5 pc). Evidence of mass segregation is observed throughout the cluster and no tidal tail structures are apparent along the orbital path. The absence of breaks in the profile suggests that the Arches has not likely experienced its closest approach to the GC between ~0.2 - 1 Myr ago. If accurate, this constraint indicates that the cluster is on a prograde orbit and is located front of the sky plane that intersects Sgr A*. However, further simulations of clusters in the GC potential are required to interpret the observed profile with more confidence.Comment: 24 pages (17-page main text, 7-page appendix), 24 figures, accepted to Ap

Data-driven Abstractions for Verification of Deterministic Systems

A common technique to verify complex logic specifications for dynamical systems is the construction of symbolic abstractions: simpler, finite-state models whose behaviour mimics the one of the systems of interest. Typically, abstractions are constructed exploiting an accurate knowledge of the underlying model: in real-life applications, this may be a costly assumption. By sampling random $\ell$-step trajectories of an unknown system, we build an abstraction based on the notion of $\ell$-completeness. We newly define the notion of probabilistic behavioural inclusion, and provide probably approximately correct (PAC) guarantees that this abstraction includes all behaviours of the concrete system, for finite and infinite time horizon, leveraging the scenario theory for non convex problems. Our method is then tested on several numerical benchmarks