An ontology-based nurse call management system (oNCS) with probabilistic priority assessment

<p>Abstract</p> <p>Background</p> <p>The current, place-oriented nurse call systems are very static. A patient can only make calls with a button which is fixed to a wall of a room. Moreover, the system does not take into account various factors specific to a situation. In the future, there will be an evolution to a mobile button for each patient so that they can walk around freely and still make calls. The system would become person-oriented and the available context information should be taken into account to assign the correct nurse to a call.</p> <p>The aim of this research is (1) the design of a software platform that supports the transition to mobile and wireless nurse call buttons in hospitals and residential care and (2) the design of a sophisticated nurse call algorithm. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff members and patients into account. Additionally, the priority of a call probabilistically depends on the risk factors, assigned to a patient.</p> <p>Methods</p> <p>The <it>ontology-based Nurse Call System (oNCS) </it>was developed as an extension of a <it>Context-Aware Service Platform</it>. An ontology is used to manage the profile information. Rules implement the novel nurse call algorithm that takes all this information into account. Probabilistic reasoning algorithms are designed to determine the priority of a call based on the risk factors of the patient.</p> <p>Results</p> <p>The <it>oNCS </it>system is evaluated through a prototype implementation and simulations, based on a detailed dataset obtained from Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls amongst nurses and the assignment of priorities to calls are compared for the <it>oNCS </it><it>system </it>and the current, place-oriented nurse call system. Additionally, the performance of the system is discussed.</p> <p>Conclusions</p> <p>The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the <it>oNCS system </it>significantly improves the assignment of nurses to calls. Calls generally have a nurse present faster and the workload-distribution amongst the nurses improves.</p

The Eighteenth Data Release of the Sloan Digital Sky Surveys: Targeting and First Spectra from SDSS-V

The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs, or "Mappers": Milky Way Mapper (MWM), Black Hole Mapper (BHM), and Local Volume Mapper (LVM). This data release contains extensive targeting information for the two multi-object spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration- and scientifically-focused components. DR18 also includes ~25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field.Comment: Accepted to ApJ

The eighteenth data release of the Sloan Digital Sky Surveys : targeting and first spectra from SDSS-V

The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs, or "Mappers": Milky Way Mapper (MWM), Black Hole Mapper (BHM), and Local Volume Mapper (LVM). This data release contains extensive targeting information for the two multi-object spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration- and scientifically-focused components. DR18 also includes ~25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field.Publisher PDFPeer reviewe

Evolution of green plants and their relationship with other photosynthetic eukaryotes as deduced from 5S ribosomal RNA sequences

The nucleotide sequence of cytoplasmic 5S ribosomal RNAs from three gymnosperms, Pinus contorta, Taxus baccata and Juniperus media and from one fern, Pteridium aquilinum, have been determined. These sequences were aligned with all hitherto known cytoplasmic 5S ribosomal RNA sequences of photosynthetic eukaryotes. A dendrogram based on that set of sequences was constructed by a distance matrix method and the resulting tree compared with established views concerning plant and algal evolution. The following monophyletic groups of photosynthetic eukaryotes are recognizable: the Rhodophyta, a group consisting of Phaeophyta, Bacillariophyta and Chrysophyta, and the green plants, the latter comprising green algae, Bryophyta, Pteridophyta and Spermatophyta. According to our 5S ribosomal RNA tree, green plants may have originated from some type of a green flagellated organism such as Chlamydomonas. The land plants seem to have originated from some form of charophyte such as Nitella. 5S ribosomal RNA seems to be less appropriate to estimate dissimilarities between species which have diverged relatively recently, like the angiosperms. Therefore, a precise evolutionary process is difficult to reconstruct for members of this group